systematic review of chest x ray

A Systematic Review: Classification of Lung Diseases from Chest X-Ray Images Using Deep Learning Algorithms

• Review Article
• Published: 06 April 2024
• Volume 5 , article number  405 , ( 2024 )

Cite this article

• Aya Hage Chehade   ORCID: orcid.org/0000-0001-9642-565X 1 ,
• Nassib Abdallah 1 , 2 ,
• Jean-Marie Marion 1 ,
• Mathieu Hatt 3 ,
• Mohamad Oueidat 4 &
• Pierre Chauvet 1  

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The purpose of this survey is to provide a comprehensive review of the most recent publications on lung disease classification from chest X-ray images using deep learning algorithms. Methods: This research aims to present several common chest radiography datasets and to introduce briefly the general image preprocessing procedures that are applied to chest X-ray images. Then, the classification of specific and multiple lung diseases is described, focusing on the method and dataset used in the selected studies, the evaluation measures and the results. In addition, the problems and future direction of lung diseases classification are discussed to provide an important research base for researchers in the future. As the most common examination tool, Chest X-ray (CXR) is crucial in the medical field for disease diagnosis. Thus, the classification of chest diseases based on chest X-ray has gained significant attention from researchers. In recent years, deep learning methods have been used and have emerged as powerful techniques in medical imaging fields. One hundred ten articles published from 2016 to 2023 were reviewed and summarized, confirming that this particular research area is very important and has great potential for future research.

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Forum of International Respiratory Societies. The Global Impact of Respiratory Disease – Second Edition. Sheffield, European Respiratory Society, (2017) (Accessed on 14 Jan 2023)

World Health Organization. Coronavirus Disease 2019 (COVID-19) Situation Report, Technical Report March, World Health Organization: Geneva, Switzerland, (2020) (Accessed on 20 Jan 2023)

Welte T. Imaging in the diagnosis of lung disease: more sophisticated methods require greater interdisciplinary collaboration. Dtsch Arztebl Int. 2014;111(11):179. https://doi.org/10.3238/arztebl.2014.0179 .

Article   Google Scholar  

Wang H, Xia Y. Chestnet: a deep neural network for classification of thoracic diseases on chest radiography. arXiv preprint. 2018. https://doi.org/10.48550/arXiv.1807.03058 .

Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K, et al. Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint. 2017. https://doi.org/10.48550/arXiv.1711.05225 .

Rajpurkar P, et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the chexnext algorithm to practicing radiologists. PLoS Med. 2018;15(11):1002686. https://doi.org/10.1371/journal.pmed.1002686 .

Kieu STH, Bade A, Hijazi MHA, Kolivand H. A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions. J Imaging. 2020;6(12):131. https://doi.org/10.3390/jimaging6120131 .

Jaeger S, Candemir S, Antani S, Wáng Y-XJ, Lu P-X, Thoma G. Two public chest x-ray dataset for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg. 2014;4(6):475–7. https://doi.org/10.3978/j.issn.2223-4292.2014.11.20 .

Chest x-ray images (pneumonia), (2019). [Online] Available: https://www.kaggle.com/datasets/paultimothymooney/chest-xraypneumonia (2019)

Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu K-I, Matsui M, Fujita H, Kodera Y, Doi K. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am J Roentgenol. 2000;174(1):71–4. https://doi.org/10.2214/ajr.174.1.1740071 .

Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) Chestxray8: Hospital-scale chest x-ray database and benchmarks on weaklysupervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097–2106. https://doi.org/10.1109/CVPR.2017.369

Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, Marklund H, Haghgoo B, Ball R, Shpanskaya K, et al. Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proceed AAAI Conf Artif Intell. 2019;33:590–7. https://doi.org/10.1609/aaai.v33i01.3301590 .

Johnson AEW, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng C-Y, Mark RG, Horng S. Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data. 2019. https://doi.org/10.1038/s41597-019-0322-0 .

Ma Y, Zhou Q, Chen X, Lu H, Zhao Y (2019) Multi-attention network for thoracic disease classification and localization. In: ICASSP 20192019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1378–1382. 10.1109/ ICASSP.2019.8682952. IEEE

Guendel S, Grbic S, Georgescu B, Liu S, Maier A, Comaniciu D. Learning to recognize abnormalities in chest x-rays with locationaware dense networks. Iberoam Congr Pattern Recogn. 2018. https://doi.org/10.1007/978-3-030-13469-3_88 .

Xu J, Li H, Li X. Ms-anet: deep learning for automated multi-label thoracic disease detection and classification. PeerJ Comput Sci. 2021;7:541. https://doi.org/10.7717/peerj-cs.541/supp-1 .

Chaudhary A, Hazra A, Chaudhary P (2019) Diagnosis of chest diseases in x-ray images using deep convolutional neural network. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6. 10.1109/ ICCCNT45670.2019.8944762

Al Mamlook RE, Chen S, Bzizi HF (2020) Investigation of the performance of machine learning classifiers for pneumonia detection in chest x-ray images. In: 2020 IEEE International Conference on Electro Information Technology (EIT), pp. 098–104. https://doi.org/10.1109/EIT48999.2020.9208232

Xu X, Wang Y, Yang G, Hu Y (2016) Image enhancement method based on fractional wavelet transform. In: 2016 IEEE International Conference on Signal and Image Processing (ICSIP), pp. 194–197. https://doi.org/10.1109/SIPROCESS.2016.7888251

Savitha S, Naveen N (2016) Algorithm for pre-processing chest-x-ray using multi-level enhancement operation. In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 2182–2186. https://doi.org/10.1109/WiSPNET.2016 . 7566529

Rajaraman S, Candemir S, Xue Z, Alderson PO, Kohli M, Abuya J, Thoma GR, Antani S (2018) A novel stacked generalization of models for improved tb detection in chest radiographs. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 718–721. https://doi.org/10.1109/EMBC . 2018.8512337

Hijazi MHA, Hwa SKT, Bade A, Yaakob R, Jeffree MS. Ensemble deep learning for tuberculosis detection using chest x-ray and canny edge detected images. IAES Int J Artif Intell. 2019;8(4):429–35. https://doi.org/10.11591/ijai.v8.i4.pp429-435 .

Veluchamy M, Subramani B. Image contrast and color enhancement using adaptive gamma correction and histogram equalization. Optik. 2019;183:329–37. https://doi.org/10.1016/j.ijleo.2019.02.054 .

Munadi K, Muchtar K, Maulina N, Pradhan B. Image enhancement for tuberculosis detection using deep learning. IEEE Access. 2020;8:217897–907. https://doi.org/10.1109/ACCESS.2020.3041867 .

Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Kashem SBA, Islam MT, Al Maadeed S, Zughaier SM, Khan MS, et al. Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images. Comput Biol Med. 2021;132:104319. https://doi.org/10.1016/j.compbiomed.2021.104319 .

El Asnaoui K, Chawki Y. Using x-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn. 2021;39(10):3615–26. https://doi.org/10.1080/07391102.2020.1767212 .

Iqbal A, Usman M, Ahmed Z. An efficient deep learning-based framework for tuberculosis detection using chest x-ray images. Tuberculosis. 2022;136: 102234. https://doi.org/10.1016/j.tube.2022.102234 .

AbdElhamid AA, AbdElhalim E, Mohamed MA, Khalifa F. Multi-classification of chest x-rays for covid-19 diagnosis using deep learning algorithms. Appl Sci. 2022;12(4):2080. https://doi.org/10.3390/app12042080 .

Al-Waisy AS, Al-Fahdawi S, Mohammed MA, Abdulkareem KH, Mostafa SA, Maashi MS, Arif M, Garcia-Zapirain B. Covidchexnet: hybrid deep learning framework for identifying covid-19 virus in chest x-rays images. Soft Comput. 2023;27(5):2657–72. https://doi.org/10.1007/s00500-020-05424-3 .

Shamrat FJM, Azam S, Karim A, Ahmed K, Bui FM, De Boer F. High-precision multiclass classification of lung disease through customized mobilenetv2 from chest x-ray images. Comput Biol Med. 2023;155: 106646. https://doi.org/10.1016/j.compbiomed.2023.106646 .

Hage Chehade A, Abdallah N, Marion J-M, Oueidat M, Chauvet P. Lung and colon cancer classification using medical imaging: a feature engineering approach. Phys Eng Sci Med. 2022;45(3):729–46. https://doi.org/10.1007/s13246-022-01139-x .

Candemir S, Jaeger S, Lin W, Xue Z, Antani S, Thoma G. Automatic heart localization and radiographic index computation in chest x-rays. Med Imaging Comput-Aided Diagn. 2016;9785:302–9. https://doi.org/10.1117/12.2217209 .

Wang C, Elazab A, Wu J, Hu Q. Lung nodule classification using deep feature fusion in chest radiography. Comput Med Imaging Graph. 2017;57:10–8. https://doi.org/10.1016/j.compmedimag.2016.11.004 .

Novikov AA, Lenis D, Major D, Hladvka J, Wimmer M, Bühler K. Fully convolutional architectures for multiclass segmentation in chest radiographs. IEEE Trans Med Imaging. 2018;37(8):1865–76. https://doi.org/10.1109/TMI.2018.2806086 .

Dai W, Dong N, Wang Z, Liang X, Zhang H, Xing E. Structure correcting adversarial network for organ segmentation in chest x-rays. Deep Learn Med Image Analy Multimod Learn Clin Decis Support. 2018. https://doi.org/10.1007/978-3-030-00889-5_30 .

Liu H, Wang L, Nan Y, Jin F, Wang Q, Pu J. Sdfn: segmentation-based deep fusion network for thoracic disease classification in chest x-ray images. Comput Med Imaging Graph. 2019;75:66–73. https://doi.org/10.1016/j.compmedimag.2019.05.005 .

Chen S, Han Y, Lin J, Zhao X, Kong P. Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection. Artif Intell Med. 2020;107: 101881. https://doi.org/10.1016/j.artmed.2020.101881 .

Chen B, Zhang Z, Lin J, Chen Y, Lu G. Two-stream collaborative network for multi-label chest x-ray image classification with lung segmentation. Pattern Recogn Lett. 2020;135:221–7. https://doi.org/10.1016/j.patrec.2020.04.016 .

Mendoza J, Pedrini H. Detection and classification of lung nodules in chest x-ray images using deep convolutional neural networks. Comput Intell. 2020;36(2):370–401. https://doi.org/10.1111/coin.12241 .

Article   MathSciNet   Google Scholar  

Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R, Hamid T, Islam MT, Kashem S, Mahbub ZB, et al. Reliable tuberculosis detection using chest x-ray with deep learning, segmentation and visualization. IEEE Access. 2020;8:191586–601. https://doi.org/10.1109/ACCESS.2020.3031384 .

Xu Y, Lam H-K, Jia G. Manet: A two-stage deep learning method for classification of covid-19 from chest x-ray images. Neurocomputing. 2021;443:96–105. https://doi.org/10.1016/j.neucom.2021.03.034 .

Iqbal A, Usman M, Ahmed Z. Tuberculosis chest x-ray detection using cnn-based hybrid segmentation and classification approach. Biomed Signal Process Control. 2023;84:104667. https://doi.org/10.1016/j.bspc.2023.104667 .

Sharma V, Gupta SK, Shukla KK, et al. Deep learning models for tuberculosis detection and infected region visualization in chest xray images. Intell Med. 2023. https://doi.org/10.1016/j.imed.2023.06.001 .

Li F, Engelmann R, Pesce L, Armato SG, MacMahon H. Improved detection of focal pneumonia by chest radiography with bone suppression imaging. Eur Radiol. 2012;22(12):2729–35. https://doi.org/10.1007/s00330-012-2550-y .

Yang W, Chen Y, Liu Y, Zhong L, Qin G, Lu Z, Feng Q, Chen W. Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Med Image Anal. 2017;35:421–33. https://doi.org/10.1016/j.media.2016.08.004 .

Gordienko Y, Gang P, Hui J, Zeng W, Kochura Y, Alienin O, Rokovyi O, Stirenko S (2018) Deep learning with lung segmentation and bone shadow exclusion techniques for chest x-ray analysis of lung cancer. In: Advances in Computer Science for Engineering and Education, International Conference on Computer Science, Engineering and Education Applications (ICCSEEA), pp. 638–647. 10. 1007/978–3–319–91008–6_63

Mamalakis M, Swift AJ, Vorselaars B, Ray S, Weeks S, Ding W, Clayton RH, Mackenzie LS, Banerjee A. Denrescov-19: A deep transfer learning network for robust automatic classification of covid19, pneumonia, and tuberculosis from x-rays. Comput Med Imaging Graph. 2021;94: 102008. https://doi.org/10.1016/j.compmedimag.2021.102008 .

Joshi RC, Yadav S, Pathak VK, Malhotra HS, Khokhar HVS, Parihar A, Kohli N, Himanshu D, Garg RK, Bhatt MLB, et al. A deep learning-based covid-19 automatic diagnostic framework using chest x-ray images. Biocybern Biomed Eng. 2021;41(1):239–54. https://doi.org/10.1016/j.bbe.2021.01.002 .

Rahimzadeh M, Attar A. A modified deep convolutional neural network for detecting covid-19 and pneumonia from chest x-ray images based on the concatenation of xception and resnet50v2. Inform Med Unlocked. 2020;19: 100360. https://doi.org/10.1016/j.imu.2020.100360 .

Baltruschat IM, Nickisch H, Grass M, Knopp T, Saalbach A. Comparison of deep learning approaches for multi-label chest x-ray classification. Sci Rep. 2019;9(1):1–10. https://doi.org/10.1038/s41598-019-42294-8 .

Sharma H, Jain JS, Bansal P, Gupta S (2020) Feature extraction and classification of chest x-ray images using cnn to detect pneumonia. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 227–231. 10. 1109/Confluence47617.2020.9057809

Kumar N, Gupta M, Gupta D, Tiwari S. Novel deep transfer learning model for covid-19 patient detection using x-ray chest images. J Ambient Intell Humaniz Comput. 2023;14(1):469–78. https://doi.org/10.1007/s12652-021-03306-6 .

Sirazitdinov I, Kholiavchenko M, Mustafaev T, Yixuan Y, Kuleev R, Ibragimov B. Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Comput Electr Eng. 2019;78:388–99. https://doi.org/10.1016/j.compeleceng.2019.08.004

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. Adv Neural Inform Process Syst. 2014;27:2672–80.

Google Scholar  

Salehinejad H, Valaee S, Dowdell T, Colak E, Barfett J (2018) Generalization of deep neural networks for chest pathology classification in xrays using generative adversarial networks. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 990–994. https://doi.org/10.1109/ICASSP.2018.8461430

Madani A, Moradi M, Karargyris A, Syeda-Mahmood T. Chest xray generation and data augmentation for cardiovascular abnormality classification. Med Imaging: Image Process. 2018;10574:415–20. https://doi.org/10.1117/12.2293971 .

Kora Venu S, Ravula S. Evaluation of deep convolutional generative adversarial networks for data augmentation of chest x-ray images. Future Internet. 2020;13(1):8. https://doi.org/10.3390/fi13010008 .

Albahli S, Yar GNAH. Ai-driven deep convolutional neural networks for chest x-ray pathology identification. J X-Ray Sci Technol. 2022;30(2):365–76. https://doi.org/10.3233/XST-211082 .

Islam MT, Aowal MA, Minhaz AT, Ashraf K. Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv Preprint. 2017. https://doi.org/10.48550/arXiv.1705.09850 .

Hooda R, Mittal A, Sofat S. Automated tb classification using ensemble of deep architectures. Multimed Tools Appl. 2019;78(22):31515–32. https://doi.org/10.1007/s11042-019-07984-5 .

Abbas A, Abdelsamea MM, Gaber MM. Detrac: Transfer learning of class decomposed medical images in convolutional neural networks. IEEE Access. 2020;8:74901–13. https://doi.org/10.1109/ACCESS.2020.2989273 .

Pham HH, Le TT, Tran DQ, Ngo DT, Nguyen HQ. Interpreting chest x-rays via cnns that exploit hierarchical disease dependencies and uncertainty labels. Neurocomputing. 2021;437:186–94. https://doi.org/10.1016/j.neucom.2020.03.127 .

Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–82. https://doi.org/10.1148/radiol.2017162326 .

Abbas A, Abdelsamea MM, Gaber MM. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. Appl Intell. 2021;51:854–64. https://doi.org/10.1007/s10489-020-01829-7 .

Rohilla A, Hooda R, Mittal A (2017) Tb detection in chest radiograph using deep learning architecture. ICETETSM-17, 136–147

Shibly KH, Dey SK, Islam MT-U, Rahman MM. Covid faster r– cnn: A novel framework to diagnose novel coronavirus disease (covid-19) in x-ray images. Inform Med Unlocked. 2020;20: 100405. https://doi.org/10.1016/j.imu.2020.100405 .

Panwar H, Gupta P, Siddiqui MK, Morales-Menendez R, Singh V. Application of deep learning for fast detection of covid-19 in x-rays using ncovnet. Chaos Solitons Fractals. 2020;138:109944. https://doi.org/10.1016/j.chaos.2020.109944 .

Militante SV, Dionisio NV, Sibbaluca BG (2020) Pneumonia and covid19 detection using convolutional neural networks. In: 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE), pp. 1–6. https://doi.org/10.1109/ICVEE50212.2020 . 9243290

Ibrahim DM, Elshennawy NM, Sarhan AM. Deep-chest: Multiclassification deep learning model for diagnosing covid-19, pneumonia, and lung cancer chest diseases. Comput Biol Med. 2021;132: 104348. https://doi.org/10.1016/j.compbiomed.2021.104348 .

Ibrahim AU, Ozsoz M, Serte S, Al-Turjman F, Yakoi PS. Pneumonia classification using deep learning from chest x-ray images during covid-19. Cogn Comput. 2021. https://doi.org/10.1007/s12559-020-09787-5 .

Cicero M, Bilbily A, Colak E, Dowdell T, Gray B, Perampaladas K, Barfett J. Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Invest Radiol. 2017;52(5):281–7. https://doi.org/10.1097/RLI.0000000000000341 .

Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43(2):635–40. https://doi.org/10.1007/s13246-020-00865-4 .

Sethi R, Mehrotra M, Sethi D (2020) Deep learning based diagnosis recommendation for covid-19 using chest x-rays images. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 1–4. https://doi.org/10.1109/ICIRCA48905 . 2020.9183278

Souid A, Sakli N, Sakli H. Classification and predictions of lung diseases from chest x-rays using mobilenet v2. Appl Sci. 2021;11(6):2751. https://doi.org/10.3390/app11062751 .

Elshennawy NM, Ibrahim DM. Deep-pneumonia framework using deep learning models based on chest x-ray images. Diagnostics. 2020;10(9):649. https://doi.org/10.3390/diagnostics10090649 .

Bush I (2016) Lung nodule detection and classification. Rep Stanford Comput Sci

Tang Y, Wang X, Harrison AP, Lu L, Xiao J, Summers RM. Attention-guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs. Int Workshop Mach Learn Med Imaging. 2018. https://doi.org/10.1007/978-3-030-00919-9_29 .

Li Z, Wang C, Han M, Xue Y, Wei W, Li L-J, Fei-Fei L (2018) Thoracic disease identification and localization with limited supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8290–8299

Rahman T, Chowdhury ME, Khandakar A, Islam KR, Islam KF, Mahbub ZB, Kadir MA, Kashem S. Transfer learning with deep convolutional neural network (cnn) for pneumonia detection using chest x-ray. Appl Sci. 2020;10(9):3233. https://doi.org/10.3390/app10093233 .

Ausawalaithong W, Thirach A, Marukatat S, Wilaiprasitporn T (2018) Automatic lung cancer prediction from chest x-ray images using the deep learning approach. In: 2018 11th Biomedical Engineering International Conference (BMEiCON), pp. 1–5 (2018). 10. 1109/BMEiCON.2018.8609997

Ge Z, Mahapatra D, Sedai S, Garnavi R, Chakravorty R. Chest x-rays classification: a multi-label and fine-grained problem. arXiv preprint. 2018. https://doi.org/10.48550/arXiv.1807.07247 .

Mo S, Cai M. Deep learning based multi-label chest x-ray classification with entropy weighting loss. Int Symp Comput Intell Design (ISCID). 2019;2:124–7. https://doi.org/10.1109/ISCID.2019.10111 .

Chen B, Li J, Lu G, Zhang D. Lesion location attention guided network for multi-label thoracic disease classification in chest x-rays. IEEE J Biomed Health Inform. 2019;24(7):2016–27. https://doi.org/10.1109/JBHI.2019.2952597 .

Ge Z, Mahapatra D, Chang X, Chen Z, Chi L, Lu H. Improving multi-label chest x-ray disease diagnosis by exploiting disease and health labels dependencies. Multimed Tools Appl. 2020;79(21):14889–902. https://doi.org/10.1007/s11042-019-08260-2 .

Guan Q, Huang Y, Zhong Z, Zheng Z, Zheng L, Yang Y. Thorax disease classification with attention guided convolutional neural network. Pattern Recogn Lett. 2020;131:38–45. https://doi.org/10.1016/j.patrec.2019.11.040 .

Guan Q, et al. Multi-label chest x-ray image classification via categorywise residual attention learning. Pattern Recogn Lett. 2020;130:259–66. https://doi.org/10.1016/j.patrec.2018.10.027 .

Hwang S, Kim H-E, Jeong J, Kim H-J. A novel approach for tuberculosis screening based on deep convolutional neural networks. Med Imaging Comput-aided Diagn. 2016;9785:750–7. https://doi.org/10.1117/12.2216198 .

Andika LA, Pratiwi H, Handajani SS. Convolutional neural network modeling for classification of pulmonary tuberculosis disease. J Phys Conf Ser. 2020;1490:012020. https://doi.org/10.1088/1742-6596/1490/1/012020 .

Huy VTQ, Lin C-M. An improved densenet deep neural network model for tuberculosis detection using chest x-ray images. IEEE Access. 2023. https://doi.org/10.1109/ACCESS.2023.3270774 .

Acharya AK, Satapathy R. A deep learning based approach towards the automatic diagnosis of pneumonia from chest radio-graphs. Biomed Pharmacol J. 2020;13(1):449–55. https://doi.org/10.13005/bpj/1905 .

Yang Y, Mei G, Piccialli F. A deep learning approach considering image background for pneumonia identification using explainable ai (xai). IEEE/ACM Trans Comput Biol Bioinf. 2022. https://doi.org/10.1109/TCBB.2022.3190265 .

Sharma S, Guleria K. A deep learning based model for the detection of pneumonia from chest x-ray images using vgg-16 and neural networks. Proced Comput Sci. 2023;218:357–66. https://doi.org/10.1016/j.procs.2023.01.018 .

Takemiya R, Kido S, Hirano Y, Mabu S. Detection of pulmonary nodules on chest x-ray images using r-cnn. Int Forum Med Imaging Asia. 2019;11050:147–52. https://doi.org/10.1117/12.2521652 .

Salman FM, Abu-Naser SS, Alajrami E, Abu-Nasser BS, Alashqar BA. Covid-19 detection using artificial intelligence. Int J Acad Eng Res (IJAER). 2020;4:18–25.

Das D, Santosh K, Pal U. Truncated inception net: Covid-19 outbreak screening using chest x-rays. Phys Eng Sci Med. 2020;43(3):915–25. https://doi.org/10.1007/s13246-020-00888-x .

Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A. Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. Pattern Recogn Lett. 2020;138:638–43. https://doi.org/10.1016/j.patrec.2020.09.010 .

Khan AI, Shah JL, Bhat MM. Coronet: A deep neural network for detection and diagnosis of covid-19 from chest x-ray images. Comput Methods Programs Biomed. 2020;196:105581. https://doi.org/10.1016/j.cmpb.2020.105581 .

Hussain E, Hasan M, Rahman MA, Lee I, Tamanna T, Parvez MZ. Corodet: A deep learning based classification for covid-19 detection using chest x-ray images. Chaos Solitons Fractals. 2021;142: 110495. https://doi.org/10.1016/j.chaos.2020.110495 .

Sahin ME. Deep learning-based approach for detecting covid-19 in chest x-rays. Biomed Signal Process Control. 2022;78: 103977. https://doi.org/10.1016/j.bspc.2022.103977 .

Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of covid-19 cases using deep neural networks with x-ray images. Comput Biol Med. 2020;121: 103792. https://doi.org/10.1016/j.compbiomed.2020.103792 .

Khan E, Rehman MZU, Ahmed F, Alfouzan FA, Alzahrani NM, Ahmad J. Chest x-ray classification for the detection of covid19 using deep learning techniques. Sensors. 2022;22(3):1211. https://doi.org/10.3390/s22031211 .

Alshmrani GMM, Ni Q, Jiang R, Pervaiz H, Elshennawy NM. A deep learning architecture for multi-class lung diseases classification using chest x-ray (cxr) images. Alex Eng J. 2023;64:923–35. https://doi.org/10.1016/j.aej.2022.10.053 .

Rubin J, Sanghavi D, Zhao C, Lee K, Qadir A, Xu-Wilson M. Large scale automated reading of frontal and lateral chest x-rays using dual convolutional neural networks. arXiv preprint. 2018. https://doi.org/10.48550/arXiv.1804.07839 .

Allaouzi I, Ahmed MB. A novel approach for multi-label chest xray classification of common thorax diseases. IEEE Access. 2019;7:64279–88. https://doi.org/10.1109/ACCESS.2019.2916849 .

Yao L, Poblenz E, Dagunts D, Covington B, Bernard D, Lyman K. Learning to diagnose from scratch by exploiting dependencies among labels. ArXiv preprint. 2017. https://doi.org/10.48550/arXiv.1710.10501 .

Kumar P, Grewal M, Srivastava MM (2018) Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs. In: International Conference Image Analysis and Recognition, ICIAR 2018, Lecture Notes in Computer Science, pp. 546–552. https://doi.org/10.1007/978-3-319-93000-8_62

Yan C, Yao J, Li R, Xu Z, Huang J (2018) Weakly supervised deep learning for thoracic disease classification and localization on chest x-rays. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 103–110. https://doi.org/10.1145/3233547.3233573

Shen Y, Gao M (2018) Dynamic routing on deep neural network for thoracic disease classification and sensitive area localization. In: International Workshop on Machine Learning in Medical Imaging, pp. 389–397. https://doi.org/10.1007/978-3-030-00919-9_45

Ho TKK, Gwak J. Multiple feature integration for classification of thoracic disease in chest radiography. Appl Sci. 2019;9(19):4130. https://doi.org/10.3390/app9194130 .

Chen B, Li J, Guo X, Lu G. Dualchexnet dual asymmetric feature learning for thoracic disease classification in chest x-rays. Biomed Signal Process Control. 2019;53:101554. https://doi.org/10.1016/j.bspc.2019.04.031 .

Ma C, Wang H, Hoi SC (2019) Multi-label thoracic disease image classification with cross-attention networks. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 730–738. https://doi.org/10.1007/978-3-030-32226 –7_ 81

Teixeira V, Braz L, Pedrini H, Dias Z 2020) Dualanet: dual lesion attention network for thoracic disease classification in chest x-rays. In: 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 69–74 ( https://doi.org/10.1109/IWSSIP48289 . 2020.9145037

Albahli S, Rauf HT, Algosaibi A, Balas VE. Ai-driven deep cnn approach for multi-label pathology classification using chest xrays. PeerJ Comput Sci. 2021;7:495. https://doi.org/10.7717/peerj-cs.495 .

Zhao J, Li M, Shi W, Miao Y, Jiang Z, Ji B. A deep learning method for classification of chest x-ray images. J Phys Conf Ser. 2021;1848:012030. https://doi.org/10.1088/1742-6596/1848/1/012030 .

Yang M, Tanaka H, Ishida T. Performance improvement in multilabel thoracic abnormality classification of chest x-rays with noisy labels. Int J Comput Assist Radiol Surg. 2022. https://doi.org/10.1007/s11548-022-02684-2 .

Jin Y, Lu H, Zhu W, Huo W. Deep learning based classification of multi-label chest x-ray images via dual-weighted metric loss. Comput Biol Med. 2023;157: 106683. https://doi.org/10.1016/j.compbiomed.2023.106683 .

Mann M, Badoni RP, Soni H, Al-Shehri M, Kaushik AC, Wei D-Q. Utilization of deep convolutional neural networks for accurate chest x-ray diagnosis and disease detection. Interdiscip Sci Comput Life Sci. 2023. https://doi.org/10.1007/s12539-023-00562-2 .

World Health Organization. Tuberculosis - 14 October 2021. (Accessed on 14 Jan 2023)

World Health Organization. Pneumonia in children - 11 November 2022. (Accessed on 10 Jan 2023)

World Health Organization. Cancer - 3 February 2022. (accessed on 23 january 2023)

Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A. Rusboost: A hybrid approach to alleviating class imbalance. IEEE Trans Syst, Man, and cybern-Part A: Syst Hum. 2009;40(1):185–97. https://doi.org/10.1109/TSMCA.2009.2029559 .

World Health Organization. Naming the coronavirus disease (COVID19) and the virus that causes it, (2020). (Accessed on 20 jan 2023)

World Health Organization. WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 march 2020. (Accessed on 20 Jan 2023)

Phung H, Dao Q, Tran A (2023) Wavelet diffusion models are fast and scalable image generators. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10199–10208

Domingos P. A few useful things to know about machine learning. Commun ACM. 2012;55(10):78–87. https://doi.org/10.1145/2347736.2347755 .

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Hage Chehade, A., Abdallah, N., Marion, JM. et al. A Systematic Review: Classification of Lung Diseases from Chest X-Ray Images Using Deep Learning Algorithms. SN COMPUT. SCI. 5 , 405 (2024). https://doi.org/10.1007/s42979-024-02751-2

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Chest X-ray Systematic Approach Tutorial introduction

Checklist for a systematic approach.

• Patient identity
• Image quality
• The obvious abnormality - description/location
• Systematic check of anatomy
• Review areas
• Consider the clinical question

Related tutorials

• Chest X-ray - Anatomy
• Chest X-ray - Abnormalities
• Chest X-ray - Quality
• Chest X-ray - Tubes

A systematic approach for viewing chest X-rays ensures no important structures are ignored, but a flexible approach is required to suit each clinical setting.

When formally presenting a chest X-ray, it is necessary to demonstrate a logical system. Although there is no agreed order of observation, you may find the sequence described in the chest X-ray anatomy tutorial helpful.

Here it is in brief.

Anatomical structures to check

• 1. Trachea and bronchi
• 2. Hilar structures
• 3. Lung zones
• 5. Lung lobes and fissures
• 6. Costophrenic angles
• 7. Diaphragm
• 9. Mediastinum
• 1 0. Soft tissues

This tutorial will help you develop your own system, starting with patient and image data, and image quality. You will go on to learn how abnormalities can be described and located. The tutorial also discusses the review of areas where abnormalities are easily missed.

Your course assessment certificate

A certificated course completion assessment is available which is based on the material in this tutorial and the related sections.

All the certificated online course completion assessments provided by Radiology Masterclass award CPD/CME credits in accordance with the CPD Scheme of the Royal College of Radiologists, London, UK.

Page author: Dr Graham Lloyd-Jones BA MBBS MRCP FRCR - Consultant Radiologist - Salisbury NHS Foundation Trust UK ( Read bio )

Last reviewed: July 2019

Chest X-Ray - Basic Interpretation

Robin smithuis and otto van delden, radiology department of the alrijne hospital, leiderdorp and the academical medical centre, amsterdam, the netherlands.

Publicationdate 2013-02-18 [update 2022-04-04]

The chest x-ray is the most frequently requested radiologic examination. In fact every radiologst should be an expert in chest film reading. The interpretation of a chest film requires the understanding of basic principles.

In this article we will focus on:

• Normal anatomy and variants.
• Systematic approach to the chest film using an inside-out approach.
• Pathology of the heart, mediastinum, lungs and pleura, chest wall and abdomen.

Normal and Variants

On the PA chest-film it is important to examine all the areas where the lung borders the diaphragm, the heart and other mediastinal structures.

At these borders lung-soft tissue interfaces are seen resulting in a:

• Line or stripe - for instance the right para tracheal stripe.
• Silhouette - for instance the normal silhouette of the aortic knob or left ventricle

These lines and silhouettes are useful localizers of disease, because they can be displaced or obscured with loss of the normal silhouette. This is called the silhouette sign, which we will discuss later.

The paraspinal line may be displaced by a paravertebral abscess, hemorrhage due to a fracture or extravertebral extension of a neoplasm.

Widening of the paratracheal line (> 2-3mm) may be due to lymphadenopathy, pleural thickening, hemorrhage or fluid overload and heart failure.

Displacement of the para-aortic line can be due to elongation of the aorta, aneurysm, dissection and rupture.

The anterior and posterior junction lines are formed where the upper lobes join anteriorly and posteriorly. These are usely not well seen and we will not discuss them.

An important mediastinal-lung interface to look for is the azygoesophageal line or recess (arrow).

Azygo-esophageal recess

The azygo-esophageal recess is the region inferior to the level of the azygos vein arch in which the right lung forms an interface with the mediastinum between the heart anteriorly and vertebral column posteriorly. It is bordered on the left side by the esophagus.

Deviation of the azygoesophageal line is caused by:

• Hiatal hernia
• Esophageal disease
• Left atrial enlargement
• Subcarinal lymphadenopathy
• Bronchogenic cyst

Notice the deviation of the azygoesophageal line on the PA-film.

It is caused by a hiatal hernia. The arrow point to the barium contrast within the hiatal hernia.

Vena azygos lobe

A common normal variant is the azygos lobe.

The azygos lobe is created when a laterally displaced azygos vein makes a deep fissure in the upper part of the lung.

On a chest film it is seen as a fine line that crosses the apex of the right lung.

Here another patient with an azygos lobe. The azygos vein is seen as a thick structure within the azygos fissure.

In some patients an extra joint is seen in the anterior part of the first rib at the point where the bone meets the calcified cartilageneous part (arrow).

This may simulate a lung mass.

Pectus excavatum

In patients with a pectus excavatum the right heart border can be ill-defined, but this is normal. It produces a silhouette sign and thus simulating a consolidation or atelectasis of the right middle lobe.

The lateral view is helpful in such cases.

Pectus excavatum is a congenital deformity of the ribs and the sternum producing a concave appearance of the anterior chest wall.

Lateral view

On a normal lateral view the contours of the heart are visible and the IVC is seen entering the right atrium.

The retrosternal space contains air and should be radiolucent down to the level where the right ventricle borders the sternum (small black arrow). Any radiopacity in this upper retrosternal area is suspective of a process in the anterior mediastinum or upper lobes of the lungs.

As you go from superior to inferior over the vertebral bodies they should get darker, because usually there will be less soft tissue and more radiolucent lung tissue (white arrow). If this area becomes more dense, look carefully for pathology in the lower lobes.

The contours of the left and right diaphragm should be visible.

The right diaphragm should be visible all the way to the anterior chest wall (red arrow). Actually we see the interface between the air in the lungs and the soft tissue structures in the abdomen.

The left diaphragm can only be seen to a point where it borders the heart (blue arrow). At that point the interface is lost, since the heart has the same density as the structures below the diaphragm.

Pulmonary vessels

The left main pulmonary artery (in purple) passes over the left main bronchus and is higher than the right pulmonary artery (in blue) which passes in front of the right main bronchus.

Once you know how the normal hilar structures look like on a lateral view, it is easier to detect abnormalities.

In this case on the PA-view there is hilar enlargement. On the PA-view it is not clear whether this is due to dilated vessels or enlarged lymph nodes. On the lateral view there are round structures in areas where you don't expect any vessels. So we can conclude that we are dealing with enlarged lymph nodes.

This patient has sarcoidosis. Notice also the widening of the paratracheal line (or stripe) as a result of enlarged lymph nodes.

On the lateral view spondylosis may mimick a lung mass.

Any density in the area of the vertebral bodies should lead you to the PA-film to look for spondylosis, which is usually located on the right side (arrows). On the left side the formation of osteophytes is hampered by the pulsations of the aorta.

On the PA-view the superior mediastinum is widened. The lateral view is helpful in this case because it demonstrates a density in the upper retrosternal space. Now the differential diagnosis is limited to a mass in the anterior mediastinum (4 T's).

This was a Hodgkins lymphoma.

A common incidental finding in adults is a Bochdalek hernia, which is due to a congenital defect in the posterior diaphragm (arrows). In most cases it only contains retroperitoneal fat and is asymptomatic, but occasionally it may contain abdominal organs.

Large hernias are sometimes seen in neonates and can be complicated by pulmonary hypoplasia.

A hernia of Morgagni is also a congenital diaphragmatic hernia, but is less common. It is located anteriorly.

Systematic Approach

Whenever you review a chest x-ray, always use a systematic approach. We use an inside-out approach from central to peripheral. First the heart figure is evaluated, followed by mediastinum and hili. Subsequently the lungs, lungborders and finally the chest wall and abdomen are examined.

You have to know the normal anatomy and variants. Find subtle abnormalities by using the sihouette sign and mediastinal lines. Once you see an abnormality use a pattern approach to come up with the most likely diagnosis and differential diagnosis.

It is extremely important to always compare with old films, as we will demonstrate in this case. Actually someone said that the most important radiograph is the old film, since it gives you so much information. For instance a lung mass, which hasn't changed in many years is not a lung cancer.

First study the chest films. Based on these films, you could make the diagnosis of congestive heart failure, but the findings are subtle.

Continue with the old film...

Scroll back and forth to the old film. Once you compare the chest film to the old one, things become more obvious and you will be much more confident in your diagnosis of congestive heart failure:

• The size of the heart is slightly increased compared to the old film, but was already large on the old film.
• The pulmonary vessels are slightly increased in diameter indicating increased pulmonary pressure.
• There are maybe some subtle interstitial markings as a result of interstitial edema.
• There is pleural fluid bilaterally. Notice that the infero-posterior border of the lower lobes has changed in position.

Silhouette sign

The loss of the normal silhouette of a structure is called  t he silhouette sign . This is an important sign, because it enables us to find subtle pathology and to locate it within the chest.

Here an illustration to explain the silhouette sign:

• No silhouette sign The heart is located anteriorly in the chest and it is bordered by the lingula of the left lung. The difference in density between the heart and the air in the lingula enables us to see the silhouette of the left ventricle. When there is a pneumonia in the left lower lobe, which is located more posteriorly in the chest compared to the heart, the left ventricle will still be bordered by air in the lingula and we will still see the silhouette of the heart (blue arrow).
• Silhouette sign When there is a consolidation in the lingula with the same 'water density' as the heart, the normal silhouette of the left ventreicle will be lost (yellow arrow). This silhouette sign tells us that the pathology is located anteriorly in the chest.

Silhouette sign (2)

The PA-film shows a silhouette sign of the left heart border. Even without looking at the lateral film, we know, that the pathology must be located anteriorly in the left lung. This was a consolidation due to a pneumonia caused by Streptococcus pneumoniae.

Silhouette sign (3)

Here a consolidation which is located in the left lower lobe (yellow arrow). Notice that there is a normal silhouette of the left heart border (blue arrow).

The absence of a silhouette sign tells us that the pathology is located in the left lower lobe and not in the lingula.

Silhouette sign (4)

On this lateral film there is too much density over the lower part of the spine. First study the lateral film and decide on which side the pathology is located.

Then click on the image to enlarge and scroll through the images.

By only looking at the interfaces of the left and right diaphragm on the lateral film, it is possible to tell on which side the pathology is located.

In this case we cannot follow the contour of the right diaphragm all the way to posterior, which indicates that there is something of water-density in the right lower lobe.

Continue with the PA-film of the same patient...

On the PA-film there is a normal silhouette of the right heart border, so the pathology is not in the anterior part of the chest, which we already had decided by studying the lateral view.

Question: Why do we still see the silhouette of the right diaphragm on the PA-film?

Answer: What we see is actually the highest point of the right diaphragm, which is anterior to the pneumonia in the right lower lobe. The pneumonia does not border the highest point of the right diaphragm and there will be no silhouette sign.

Hidden areas

There are some areas that need special attention, because pathology in these areas can easily be overlooked.

These areas are also known as  the hidden areas:

• Apical zones
• Hilar zones
• Retrocardial zone
• Zone below the dome of diaphragm

Notice that there is quite some lung volume below the dome of the diaphragm, which will need your attention (blue area).

Hidden areas (2)

Here an example of a large lesion in the right lower lobe, which is difficult to detect on the PA-film, unless when you give special attention to the hidden areas.

Click on the image for an enlarged view.

Hidden areas (3)

Here a pneumonia which was hidden in the right lower lobe mainly below the level of the dome of the diaphragm (yellow arrow).

Notice the increase in density on the lateral film in the lower vertebral region.

You may have to enlarge the image to get a better view.

Hidden areas (4)

First study the CXR. Then scroll through the images.

Notice the subtle increased density in the area behind the heart that needs special attention (blue area). This was a left lower lobe pneumonia.

First study the CXR.

We know that in some cases there is an extra joint in the anterior part of the first rib which may simulate a mass. However this is also a hidden area where it can be difficult to detect a mass.

In this case a small lung cancer is seen behind the left first rib. Notice that is is also seen on the lateral view in the retrosternal area.

Continue with the PET-CT.

The PET-CT demonstrates the tumor (arrow) which has already spread to the bone and liver. The diagnosis was made by a biopsy of an osteeolytic metastasis in the iliac bone.

First study the CXRs.

There is a subtle consolidation in the left lower lobe in the hidden area behind the heart. Again there is increased density over the lower vertrebral region.

Heart and Pericardium

On a chest film only the outer contours of the heart are seen. In many cases we can only tell whether the heart figure is normal or enlarged and it will be difficult to say anything about the different heart compartments. However it can be helpful to know where the different compartments are situated.

Left Atrium

• Most posterior structure.
• Receives blood from the pulmonary veins that run almost horizontally towards the left atrium.
• Left atrial appendage (in purple) can sometimes be seen as a small outpouching just below the pulmonary trunk.
• Enlargement of the left atrium results on the PA-view in outpouching of the upper heart contour on the right and an obtuse angle between the right and left main bronchus. On the lateral view bulging of the upper posterior contour will be seen.

Right Atrium

• Receives blood from the inferior and superior vena cava.
• Enlargement will cause an outpouching of the right heart contour.

Left Ventricle

• Situated to the left and posteriorly to the right ventricle.
• Enlargement will result on the PA-view in an increase of the heart size to the left and on the lateral view in bulging of the lower posterior contour.

Right Ventricle

• Most anterior structure and is situated behind the sternum.
• Enlargement will result on the PA-view in an increase of the heart size to the left and can finally result in the left heart border being formed by the right ventricle.

• The upper posterior border of the heart is formed by the left atrium.
• Enlargement will result in bulging of the upper posterior contour
• Forms the lower posterior border.
• Enlargement will displace the contour more posteriorly.
• The lower retrosternal space is filled by the right ventricle.
• Enlargement of the right ventricle will result in more superior filling of this retrosternal space.

Left Atrium enlargement This is a patient with longstanding mitral valve disease and mitral valve replacement.

Extreme dilatation of the left atrium has resulted in bulging of the contours (blue and black arrows).

Right ventricle enlargement First study the PA and lateral chest film and then continue reading.

On these chest films the heart is extremely dilated. Notice that it is especially the right ventricle that is dilated. This is well seen on the lateral film (yellow arrow).

There is a small aortic knob (blue arrow), while the pulmonary trunk and the right lower pulmonary artery are dilated. All these findings are probably the result of a left-to-right shunt with subsequent development of pulmonary hypertension.

The location of the cardiac valves is best determined on the lateral radiograph. A line is drawn on the lateral radiograph from the carina to the cardiac apex. The pulmonic and aortic valves generally sit above this line and the tricuspid and mitral valves sit below this line (4).

On this lateral view you can get a good impression of the enlargement of the left atrium.

Cardiac incisura

Click image to enlarge.

On the right side of the chest the lung will lie against the anterior chest wall. On the left however the inferior part of the lung may not reach the anterior chest wall, since the heart or pericardial fat or effusion is situated there.

This causes a density on the anteroinferior side on the lateral view which can have many forms. It is a normal finding, which can be seen on many chest x-rays and should not be mistaken for pathology in the lingula or middle lobe.

The explanation for the cardiac incisura is seen on this CT-image. At the level of the inferior part of the heart we can appreciate that the lower lobe of the right lung is seen more anteriorly compared to the left lower lobe.

Pacemaker There are different types of cardiac pacemakers. Here we see a pacemaker with one lead in the right atrium and another in the right ventricle.

A third lead is seen, which is guided through the coronary sinus towards the left ventricle. This is done in patients with asynchrone ventricular contractions. Pacing both ventricles at the same time will lead to synchrone contractions and a better cardiac output.

More on cardiac pacemakers...

Pericardial effusion

Whenever we encounter a large heart figure, we should always be aware of the possibility of pericardial effusion simulating a large heart.

On the chest x-ray it looks as if this patient has a dilated heart while on the CT it is clear, that it is the pericardial effusion that is responsible for the enlarged heart figure.

Especially in patients who had recent cardiac surgery an enlargement of the heart figure can indicate pericardial bleeding.

This patient had a change in the heart configuration and pericardial bleeding was suspected. Ultrasound demonstrated only a minimal pericardial effusion. Continue with the CT.

There is a large pericardial effusion, which is located posteriorly to the left ventricle (blue arrow). The left ventricle id filled with contrast and is compressed (red arrow). At surgery a large hematoma in the posterior part of the pericardium was found.

Notice that on the anterior side there is only a minimal collection of pericardial fluid, which explains why the ultrasound examination underestimated the amount of pericardial fluid.

Here another patient who had valve-replacement.

Notice the large heart size. There is redistribution of the pulmonary vessels which indicates heart failure.

Continue with the CT.

The CT-image shows a large pericardial effusion.

Always compare these post-operative chest films with the pre-operative ones.

Calcifications

Detection of calcifications within the heart is quite common. The most common are coronary artery calcifications and valve calcifications.

Here we see pericardial calcifications which can be associated with constrictive pericarditis.

In this case there are calcifications that look like pericardial calcifications, but these are myocardial calcifications in an infarcted area of the left ventricle.

Notice that they follow the contour of the left ventricle.

Pericardial fatpad

Pericardial fat depositions are common. Sometimes a large fat pad can be seen (figure).

Necrosis of the fat pad has pathologic features similar to fat necrosis in epiploic appendagitis . It is an uncommon benign condition, that manifests as acute pleuritic chest pain in previously healthy persons (10).

Pericardial cyst

Pericardial cysts are connected to the pericardium and usually contain clear fluid. The majority of pericardial cysts arise in the anterior cardiophrenic angle, more frequently on the rightside, but they can be seen as high as the pericardial recesses at the level of the proximal aorta and pulmonary arteries (11). Most patients are asymptomatic.

On the chest x-ray it seems as if there is a elevated left hemidiaphragm.

On CT however there is a cyst connected to the pericardium.

The normal hilar shadow is for 99% composed of vessels - pulmonary arteries and to a lesser extent veins (1). The vessel margins are smooth and the vessels have branches.

The left pulmonary artery runs over the left main bronchus, while the right pulmonary artery runs in front of the right main bronchus, which is usually lower in position than the left main bronchus.

Hence the left hilum is higher than the right. Only in a minority of cases the right hilus is at the same level as the left, but never higher.

In this illustration the lower lobe arteries are coloured blue because they contain oxygen-poor blood.

They have a more vertical orientation, while the pulmonary veins run more horizontally towards the left atrium, which is located below the level of the main pulmonary arteries.

Both pulmonary arteries and veins can be identified on a lateral view and should not be mistaken for lymphadenopathy.

Sometimes the pulmonary veins can be very prominent.

The left main pulmonary artery passes over the left main bronchus and is higher than the right pulmonary artery which passes in front of the right main bronchus.

These images are thick slab sagittal reconstructions of a chest-ct to get a better view of the hilar structures.

The lower lobe pulmonary arteries extend inferiorly from the hilum. They are described as little fingers, because each has the size of a little finger (1).

On the right side the little finger will be visible in 94% of normal CXRs and on the left side in 62% of normals (1).

Study the CXR of a 70-year old male who fell from the stairs and has severe pain on the right flank..

Notice on the PA-film the absence of the little finger on the right and on the lateral view the increased density over the lower vertebral column.

What is your diagnosis?

There is a right lower lobe atelectasis.

Notice the abnormal right border of the heart. The right interlobar artery is not visible, because it is not surrounded by aerated lung but by the collapsed lower lobe, which is adjacent to the right atrium.

On a follow-up chest film the atelectasis has resolved. We assume that the atelectasis was a result of post-traumatic poor ventilation with mucus plugging.

Notice the reappearance of the right little finger (red arrow) and the normal right heart border (blue arrow).

Hilar enlargement

The table summarizes the causes of hilar enlargement.

Normal hili are:

• Normal in position - left higher than right
• Equal density
• Normal branching vessels

Enlargement of the hili is usually due to lymphadenopathy or enlarged vessels.

In this case there is an enlarged hilar shadow on both sides. This could be the result of enlarged vessels or enlarged lymph nodes. A very helpful finding in this case is the mass on the right of the trachea.

This is known as the 1-2-3 sign in sarcoidosis, i.e. enlargement of left hilum, right hilum and paratracheal.

Here some more examples of sarcoidosis. Click to enlarge.

• Lymphadenopathy and groundglass appearance of the lungs
• Lymphadenopathy, 1-2-3 sign
• Bulky lymphadenopathy
• Nodular lung pattern, no lymphadenopathy
• Hilar and paratracheal lymphadenopathy

Mediastinum

Mediastinal masses are discussed in more detail in Mediastinal masses .

Here is just a brief overview.

The mediastinum can be divided into an anterior, middle and posterior compartment, each with it's own pathology.

Mediastinal lines

Mediastinal lines or stripes are interfaces between the soft tissue of mediastinal structures and the lung. Displacement of these lines is helpful in finding mediastinal pathology, as we have discussed above.

Azygoesophageal recess

The most important mediastinal line to look for is the azygoesophageal line, which borders the azygoesophageal recess.

This line is visible on most frontal CXRs.

The causes of displacement of this line are summarized in the table.

A hiatal hernia is the most common cause of displacement of the azygoesophageal line.

Notice the air within the hernia on the lateral view.

Another common cause of displacement of the azygoesophageal line is subcarinal lymphadenopathy.

Notice the displacement of the upper part of the azygoesophageal line on the chest x-ray in the area below the carina. This is the result of massive lymphadenopathy in the subcarinal region (station 7).

There are also nodes on the right of the trachea displacing the right paratracheal line.

On the PET we can appreciate the massive lymphadenopathy far better than on the CXR.

There are also lymphomas in the neck. this is an important finding, since these nodes are accessible for biopsy.

Continue with images of CT and ultrasound.

Here we see a CT-image. The azygoesophageal recess is displaced by lymph nodes that compress the left atrium.

The final diagnosis of small cel lungcancer was made through a biopsy of a lymphnode in the neck.

First study the chest x-ray. Then continue reading.

Notice the following:

• There is displacement of the azygoesophageal line both superiorly an inferiorly.
• There is an air-fluid level (arrow). Combined with the above this must be a dilated esophagus with residual fluid. The final diagnosis was achalasia.
• The density on the left in the region of the lingula is the result from prior aspiration pneumonia.

Here we have a prior CXR of this patient.

The AP-film shows a right paratracheal mass. The azygoesophageal recess is not identified, because it is displaced and parallels the border of the right atrium. The large round density in the left lung is the result of aspiration.

Notice the massive dilatation of the esophagus on the CT.

Aortopulmonary window

The aortopulmonary window is the interface below the aorta and above the pulmonary trunk and is concave or straight laterally.

Here the AP-window is convex laterally due to a mass that fills the retrosternal space on the lateral view.

On the CT-images a mass in the anterior mediastinum is seen.

Final diagnosis: Hodgkins lymphoma.

Here another case. On the PA-film a mass is seen that fills the aortopulmonary window.

The PET better demonstrates the extent of the lymphnode metastases in this patient.

Final diagnosis: small cell lungcarcinoma.

Lung abnormalities mostly present as areas of increased density, which can be divided into the following patterns:

Consolidation

Atelectasis

• Nodule or mass - solitary or multiple
• Interstitial

Less frequently areas of decreased density are seen as in emphysema or lungcysts.

These lungpatterns will discussed in more detail in an article that will be published soon: Chest X-Ray - Lung disease.

Tap on image to enlarge

Nodule - Masses

Tap on image to enlarge.

Solitary pulmonary node - SPN is discussed here .

Interstitial pattern

Interstitial lung diseases are discussed here .

Pleural fluid

It takes about 200-300 ml of fluid before it comes visible on an CXR (figure). About 5 liters of pleural fluid are present when there is total opacification of the hemithorax.

Total opacification of the right hemithorax in a patient with pleuritis carcinomatosa on both sides.

On the right there is only some air visible in the major bronchi creating an air bronchogram within the compressed lung.

Pleural fluid may become encysted.

Here we see fluid entrapped within the fissure. This can sometimes give the impression of a mass and is called 'vanishing tumor'.

Pneumothorax

The table lists the most common causes of a pneumothorax.

The other cystic lungdisease which causes pneumothorax is Langerhans cell histiocytosis (LCH) which is seen in smokers.

Study the CXR.

There are two important findings.

The retracted visceral pleura is seen (blue arrow) which indicates that there is a pneumothorax.

There is a horizontal line visible (yellow arrow). Normally there are no straight lines in the human body unless when there is an air-fluid level. This means that there is a hydro-pneumothorax.

When a pneumothorax is small, this air-fluid level can be the only key to the diagnosis of a pneumothorax.

There are 3 important findings.

Notice that the mediastinum is slightly displaced to the left. Does this mean that there is a tension pneumothorax?

Do you have an idea about the cause of the pneumothorax?

There is a hydropneumothorax. Notice the air-fluid level (blue arrow).

The upper lobe is still attached to the chest wall by adhesions. Maybe this patient was treated for a prior pneumothorax.

There is a lung cyst in the upper lobe (red arrow). So we can assume that the pneumothorax has something to do with a cystic lung disease.

Since this patient is a woman, lymphangioleiomyomatosis (LAM) is a possible diagnosis.

LAM is a rare lung disease that results in a proliferation of smooth muscle throughout the lungs resulting in the obstruction of small airways leading to pulmonary cyst formation and pneumothorax. LAM also occurs in patients who have tuberous sclerosis.

This is not a pneumothorax but a skin fold.

The radiography was performed supine with a CR cassette inserted underneath the patient, which resulted in a skinfold.

Notice that there are lung markings beyond the apparent pneumothorax.

Here two CXRs of another patient with obvious skinfolds.

Recognition of a pneumothorax depends on the volume of air in the pleural space and the position of the body. On a supine radiograph a pneumothorax can be subtle and approximately 30% of pneumothoraces are undetected.

A sign to look for is the 'deep sulcus sign'. It represents lucency of the lateral costophrenic angle extending toward the hypochondrium (Figure).

The image is of a patient in the ICU who is on mechanical ventilation. There was an acute exacerbation of the dyspnoe. There is a deep sulcus sign on the left.

Notice that the left hemidiaphragm is depressed. This is an important finding since it indicates a tension pneumothorax.

The image on the right is after insertion of an intercostal drain.

Notice that the diaphragm has regained its normal appearance.

Pleural opacities

The table lists the most common causes of pleural opacities.

Pleural plaques The CXR shows multiple opacities. They have irregular shapes and do not look like a lung masses or consolidations.

Some of these opacities are clearly bordering the chest wall (red arrows).

All these findings indicate that we are dealing asbestos related pleural plaques.

Asbestos related pleural plaques are usually:

• bilateral and extensive.
• covering the dome of the diaphragm.

Unilateral pleural calcifications are usually due to:

• infection (TB)
• hemorrhagic

Pleural hematoma These images are of a patient, who had a pleural opacity after a chest trauma.

It was believed to be a hematoma and resolved spontaneously.

Ribfractures The most common identified chest wall abnormalities are old ribfractures.

The CXR shows many rib deformities due to old fracturees.

When a rib fracture heals, the callus formation may create a mass-like appearance (blue arrow).

Sometimes a CT is necessary to differentiate a healing fracture from a lung mass.

Notice the large lung volume and the enlarged pulmonary vessels. Probably we are dealing with pulmonary arterial hypertension in a patient with COPD.

The second most common chest wall abnormalities that we see on a CXR are metastases in vertebral bodies and ribs.

Notice the expansile mass in the posterior rib on the right.

The most obvious finding on this CXR is free air under the diaphragm.

This finding indicates a bowel perforation, unless when the patient had recent abdominal surgery and there is still some air left in the abdomen, which can stay there for several days.

There is another subtle finding in the left upper lobe. A subtle density projecting over the first rib - hidden area - proved to be a lungcarcinoma.

Here another patient with free abdominal air.

Notice the very thin regular line which is the diaphragm (arrow).

At first impression one might think that this is just some plate-like atelectasis due to poor inspiration.

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Chest X-ray - systematic approach

Peer reviewed by Dr Hayley Willacy, FRCGP Last updated by Dr Laurence Knott Last updated 24 Aug 2020

Meets Patient’s editorial guidelines

Medical Professionals

Professional Reference articles are designed for health professionals to use. They are written by UK doctors and based on research evidence, UK and European Guidelines. You may find one of our  health articles  more useful.

In this article :

The 'right film for the right patient', technical details, systematic search for pathology.

• Lateral films

Abnormal opacities

• Chest X-ray in children

Reading a chest X-ray (CXR) requires a systematic approach. It is tempting to leap to the obvious but failure to be systematic can lead to missing 'barn door' pathology, overlooking more subtle lesions, drawing false conclusions based on a film that is technically poor and, hence, misleading, or even basing management on an inaccurate interpretation. There is not just one way to examine a CXR but every doctor should develop their own technique. This article is not a tablet of stone but should be a good starting point to develop one's own routine.

Traditionally, GPs rarely see X-rays but CXRs remain an important diagnostic tool for GPs and appear to be a cost-effective diagnostic test 1 . Hence, learning to interpret X-rays is a skill learned as a junior hospital doctor that should not be lost. There may be occasions when a GP has to make decisions based on an unreported film.

Continue reading below

This may sound pedantic but it is very important 2 . Check that the film bears the patient's name. However, as names can be shared, check other features such as date of birth or hospital number too. The label may also tell of unusual but important features such as anteroposterior (AP) projection or supine position.

Having checked that it is the correct patient, check the date of the film to ascertain which one you are viewing.

Technical aspects should be considered briefly:

Check the position of the side marker (left or right) against features such as the apex of the heart and air bubble in the stomach. A misplaced marker is more common than dextrocardia or situs inversus.

Most films are a posteroanterior (PA) projection . The usual indication for AP is a patient who is confined to bed. It may be noted on the radiograph. If there is doubt, look at the relationship of the scapulae to the lung margins. A PA view shows the scapulae clear of the lungs whilst in AP projection they always overlap. Vertebral endplates are more clearly visible in AP and laminae in PA. This is important because the heart looks bigger on an AP view. The distance from the tube to the patient is also usually reduced in portable films and this also enlarges the shadow of the heart. X-rays are not so much like pictures as like shadows.

The normal posture for films is erect. Supine is usually for patients confined to bed. It should be clear from the label. In an erect film, the gastric air bubble is clearly in the fundus with a clear fluid level but, if supine, in the antrum. In a supine film, blood will flow more to the apices of the lungs than when erect. Failure to appreciate this will lead to a misdiagnosis of pulmonary congestion.

Rotation should be minimal. It can be assessed by comparing the medial ends of the clavicles to the margins of the vertebral body at the same level. Oblique chest films are requested to look for achalasia of the cardia or fractured ribs.

CXR should be taken with the patient in full inspiration but some people have difficulty holding full inspiration. The major exception is when seeking a small pneumothorax as this will show best on full expiration. A CXR in full inspiration should have the diaphragm at the level of the 6th rib anteriorly and the liver pushes it up a little higher in the right than on the left. Do not be unduly concerned about the exact degree of inflation.

Penetration is affected by both the duration of exposure and the power of the beam. More kV gives a more penetrating beam. A poorly penetrated film looks diffusely light (an X-ray is a negative) and soft tissue structures are readily obscured, especially those behind the heart. An over-penetrated film looks diffusely dark and features such as lung markings are poorly seen.

Note breast shadows in adult women.

So far you have checked that it is the right film for the right patient and that it is technically adequate.

Just as palpation of the abdomen and auscultation of the heart are the last parts of that examination, so must the search for pathology be deferred until the preliminaries have been completed 3 .

Have a brief look for obvious unusual opacities such a chest drain, a pacemaker or a foreign body. This is a two-dimensional picture and so a central opacity may not be something that was swallowed and is now impacted in the oesophagus. It might be a metal clip from a bra strap or a hair band on a plait.

Look at the mediastinal contours, first to the left and then to the right. The trachea should be central. The aortic arch is the first structure on the left, followed by the left pulmonary artery. The branches of the pulmonary artery fan out through the lung.

Check the cardio-thoracic ratio (CTR). The width of the heart should be no more than half the width of the chest. About a third of the heart should be to the right and two thirds to the left of centre. NB : the heart looks larger on an AP film and thus you cannot comment on the presence or absence of cardiomegaly on an AP film.

The left border of the heart consists of the left atrium above the left ventricle. The right border is only the right atrium alone and above it is the border of the superior vena cava. The right ventricle is anterior and so does not have a border on the PA CXR film. It may be visible on a lateral view.

The pulmonary arteries and main bronchi arise at the left and right hila. Enlarged lymph nodes or primary tumours make the hilum seem bulky. Know what is normal. Abnormality may be caused by lung cancer or enlarged nodes from causes including sarcoidosis (bilateral hilar lymphadenopathy) and lymphoma.

Now look at the lungs. The pulmonary arteries and veins are lighter and air is black, as it is radiolucent. Check both lungs, starting at the apices and working down, comparing left with right at the same level. The lungs extend behind the heart, so try to look there too. Note the periphery of the lungs - there should be few lung markings here. Disease of the air spaces or interstitium increases opacity. Look for a pneumothorax which shows as a sharp line of the edge of the lung.

Ascertain that the surface of the hemidiaphragms curves downwards and that the costophrenic and cardiophrenic angles are not blunted. Blunting suggests an effusion. Extensive effusion or collapse causes an upward curve. Check for free air under the hemidiaphragm - this occurs with perforation of the bowel but also after laparotomy or laparoscopy.

Finally look at the soft tissues and bones. Are both breast shadows present? Is there a fractured rib? If so, check again for a pneumothorax. Are the bones destroyed or sclerotic?

There are some areas where it is very easy to miss pathology and so it is worth repeating examination. Attention may be merited to apices, periphery of the lungs, under and behind the hemidiaphragms and behind the heart. The diaphragm slopes backwards and so some lung tissue is below the level of the highest part of the diaphragm on the film.

Lateral films 4

A lateral view may have been requested or performed on the initiative of the radiographer or radiologist. As an X-ray is a two-dimensional shadow, a lateral film helps to identify a lesion in three dimensions. The usual indication is to confirm a lesion seen on a PA film.

The heart lies in the antero-inferior field. Look at the area anterior and superior to the heart; this should be black because it contains aerated lung. Similarly, the area posterior to the heart should be black right down to the hemidiaphragms. The degree of blackness in these two areas should be similar, so compare one with the other. If the area anterior and superior to the heart is opacified, it suggests disease in the anterior mediastinum or upper lobes. If the area posterior to the heart is opacified there is probably collapse or consolidation in the lower lobes.

The following diagrams help to understand the interpretation of the CXR.

CHEST X-RAY - INTERPRETATION

When observing an abnormal opacity, note:

Size and shape.

Number and location.

Clarity of structures and their margins.

Homogeneity.

If available, compare with an earlier film.

The common patterns of opacity are:

Collapse and consolidation

Collapse - also called atelectasis - and consolidation are caused by the presence of fluid instead of air in areas of the lung. In an air bronchogram the airway is highlighted against denser consolidation and vascular patterns become obscured.

Confluent opacification of the hemithorax may be caused by consolidation, pleural effusion, complete lobar collapse and after a pneumonectomy. Consolidation is usually interpreted as meaning infection but it is impossible to differentiate between infection and infarction on X-ray. The diagnosis of pulmonary embolism requires a high index of suspicion.

To find consolidation, look for absence or blurring of the border of the heart or hemidiaphragm. The lung volume of the affected segment is usually unaffected.

Collapse of a lobe (atelectasis) may be difficult to see. Look for a shift of the fissures, crowding of vessels and airways and possible shadowing caused by a proximal obstruction like a foreign body or carcinoma.

A small pleural effusion will cause blunting of the costophrenic or cardiophrenic angles. A larger one will produce an angle that is concave upwards. A very large one will displace the heart and mediastinum away from it, whilst collapse draws those structures towards it. Collapse may also raise the hemidiaphragm.

Heart and mediastinum

The heart and mediastinum are deviated away from a pleural effusion or a pneumothorax, especially if it is a tension pneumothorax and towards collapse.

If the heart is enlarged, look for signs of heart failure with an unusually marked vascular pattern in the upper lobes, wide pulmonary veins and possible Kerley B lines. These are tiny horizontal lines from the pleural edge and are typical of fluid overload with fluid collecting in the interstitial space.

If the hilum is enlarged, look for structures at the hilum such as pulmonary artery, main bronchus and enlarged lymph nodes.

Chest X-ray in children 5

Children are not just small adults and this is important when interpreting a child's X-ray. Such matters as identification of the patient are still important. A child, especially if small, is more likely to be unable to comply with instructions such as keeping still, not rotating and holding deep inspiration. Technical considerations such as rotation and under- or over-penetration of the film still merit attention and they are more likely to be unsatisfactory. A child is more likely to be laid down and have an AP film with the radiographer trying to catch the picture at full inspiration. This is even more difficult with tachypnoea 6 .

Assess lung volume

Count down the anterior rib ends to the one that meets the middle of the hemidiaphragm. A good inspiratory film should have the anterior end of the 5th or 6th rib meeting the middle of the diaphragm. More than six anterior ribs shows hyperinflation. Fewer than five indicates an expiratory film or under-inflation.

Tachypnoea in infants causes trapping of air. Expiration compresses the airways, increasing resistance and, especially under 18 months, air enters more easily than it leaves and is trapped, causing hyperinflation. Bronchiolitis, heart failure and fluid overload are all causes.

With under-inflation, the 3rd or 4th anterior rib crosses the diaphragm. This makes normal lungs appear opaque and a normal heart appears enlarged.

Positioning

Sick children, especially if small, may not be cooperative with being positioned. Check if the anterior ends of the ribs are equal distances from the spine. Rotation to the right makes the heart appear central and rotation to the left makes the heart look large and can make the right heart border disappear.

Lung density

Divide the lungs into upper, middle and lower zones and compare the two sides. Infection can cause consolidation, as in an adult. Collapse implies loss of volume and has various causes. The lung is dense because the air has been lost. In children, the cause is usually in the airway, such as an intraluminal foreign body or a mucous plug. Complete obstruction of the airway results in reabsorption of air in the affected lobe or segment. Collapse can also be due to extrinsic compression such as a mediastinal mass or a pneumothorax.

Differentiating between collapse and consolidation can be difficult or impossible, as both are denser. Collapse may pull across the mediastinum and deviate the trachea. This is important, as pneumonia is treated with antibiotics but collapse may require bronchoscopy to find and remove an obstruction.

Pleural effusion

The features of effusion have already been noted for adults. In children, unilateral effusion usually indicates infection whilst bilateral effusion occurs with hypoalbuminaemia as in nephrotic syndrome.

Bronchial wall thickening is a common finding on children's X-rays. Look for 'tram track' parallel lines around the hila. The usual causes are viral infection or asthma but this is a common finding with cystic fibrosis.

The anterior mediastinum, in front of the heart, contains the thymus gland. It appears largest at about 2 years of age but it continues to grow into adolescence. It grows less fast than the rest of the body and so becomes relatively smaller. The right lobe of the lung can rest on the horizontal fissure, which is often called the sail sign.

Assessment of the heart includes assessment of size, shape, position and pulmonary circulation. The cardiothoracic ratio is usually about 50% but can be more in the first year of life and a large thymus can make assessment difficult, as will a film in poor inspiration. As with adults, one third should be to the left of centre and two thirds to the right. Assessment of pulmonary circulation can be important in congenital heart disease but can be very difficult in practice.

Further reading and references

• Candemir S, Antani S ; A review on lung boundary detection in chest X-rays. Int J Comput Assist Radiol Surg. 2019 Apr;14(4):563-576. doi: 10.1007/s11548-019-01917-1. Epub 2019 Feb 7.
• Bouck Z, Mecredy G, Ivers NM, et al ; Routine use of chest x-ray for low-risk patients undergoing a periodic health examination: a retrospective cohort study. CMAJ Open. 2018 Aug 13;6(3):E322-E329. doi: 10.9778/cmajo.20170138. Print 2018 Jul-Sep.
• Speets AM, van der Graaf Y, Hoes AW, et al ; Chest radiography in general practice: indications, diagnostic yield and consequences for patient management. Br J Gen Pract. 2006 Aug;56(529):574-8.
• Brady A, Laoide RO, McCarthy P, et al ; Discrepancy and error in radiology: concepts, causes and consequences. Ulster Med J. 2012 Jan;81(1):3-9.
• Raoof S, Feigin D, Sung A, et al ; Interpretation of plain chest roentgenogram. Chest. 2012 Feb;141(2):545-58. doi: 10.1378/chest.10-1302.
• Feigin DS ; Lateral chest radiograph a systematic approach. Acad Radiol. 2010 Dec;17(12):1560-6. doi: 10.1016/j.acra.2010.07.004.
• Shi J et al ; Chest radiograph (paediatric), Radiopaedia, 2018.
• Bramson RT, Griscom NT, Cleveland RH ; Interpretation of chest radiographs in infants with cough and fever. Radiology. 2005 Jul;236(1):22-9. Epub 2005 Jun 27.

Article History

The information on this page is written and peer reviewed by qualified clinicians.

Next review due: 23 Aug 2025

24 aug 2020 | latest version.

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Chest radiograph assessment using ABCDEFGHI

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At the time the article was created Yoshiharu Ryu had no recorded disclosures.

At the time the article was last revised Daniel J Bell had no recorded disclosures.

• ABCDEFGHI: The way to interpret chest X-Rays

ABCDEFGHI can be used to guide a systematic interpretation of chest x-rays.

On this page:

Assessment of quality / airway, bones and soft tissues, effusions / extrathoracic soft tissue, fields, fissures and foreign bodies, great vessels / gastric bubble, hila and mediastinum.

• Related articles

The quality of the image can be assessed using the mnemonic PIER:

• position: is this a supine AP file? PA? Lateral?
• inspiration: count the posterior ribs. You should see 10 to 11 ribs with a good inspiratory effect
• exposure: well-exposed films have good lung detail and an outline of the spinal column
• rotation: the space between the medial clavicle and the margin of the adjacent vertebrae should be roughly equal to each other; look for indwelling lines or objects

Scan the bones for symmetry, fractures, osteoporosis, and lesions. Evaluate the soft tissues for foreign bodies, swelling, and subcutaneous air.

Evaluate the heart size: the heart should be <50% of the chest diameter on PA films and <60% on AP films. Check for the heart shape, calcifications, and prosthetic valves.

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Check the hemidiaphragms for position (the right is commonly slightly higher than the left due to the liver) and shape (may be flattened bilaterally in chronic asthma or emphysema , or unilaterally in case of tension pneumothorax or foreign body aspiration ). Look below the diaphragm for free gas .

Pleural effusions may be large and obvious or small and subtle. Always check the costophrenic angles for sharpness ( blunted angles may indicate small effusions). Check the lateral film for small posterior effusions (more sensitive for small effusions).

Check lungs for infiltrates (interstitial vs. alveolar), masses, consolidation (+/-  air bronchograms ),  pneumothoraces , and vascular markings. Vessels should taper and should be almost invisible at the lung periphery.

Evaluate the major and minor fissures for thickening, fluid or change in position.

Check the position of foreign bodies e.g. ETT , NGT , pacemaker leads , central venous lines etc. Comment on previous surgery e.g. cholecystectomy clips, sternotomy wires.

Check aortic size and shape and the outlines of pulmonary vessels. The aortic knob should be clearly seen. The gastric bubble  should be seen clearly and not displaced.

Evaluate the hila for lymphadenopathy , calcifications, and masses. The left hilum is normally higher than the right. Check for widening of the mediastinum (which may indicate aortic dissection  in the appropriate clinical setting) and tracheal deviation (which may indicate a mass effect, e.g. from large goiter , or tension pneumothorax ). In children, be careful not to mistake the thymus for a mass!

In most cases, an impression is worthwhile as it not only forces you to synthesize all the findings together but acts as a double check.

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A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques

Affiliation.

• 1 Department of Computer Science, Kamarajar Government Arts College, Tirunelveli, Surandai 627859, India.
• PMID: 36420865
• DOI: 10.1080/0954898X.2022.2147231

COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.

Keywords: AI tools deep learning approaches; COVID-19; chest radiography images; machine learning approaches.

Publication types

• Systematic Review
• Artificial Intelligence*
• COVID-19 Testing
• COVID-19* / diagnostic imaging
• Radiography

• Critical Review
• Open access
• Published: 19 June 2023

Dynamic chest radiography: a state-of-the-art review

• Fred Fyles 1 , 2   na1 ,
• Thomas S. FitzMaurice   ORCID: orcid.org/0000-0002-9334-486X 3 , 4   na1 ,
• Ryan E. Robinson 1 , 2 ,
• Ram Bedi 5 ,
• Hassan Burhan 1 , 2 &
• Martin J. Walshaw 3 , 6  

Insights into Imaging volume  14 , Article number:  107 ( 2023 ) Cite this article

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Dynamic chest radiography (DCR) is a real-time sequential high-resolution digital X-ray imaging system of the thorax in motion over the respiratory cycle, utilising pulsed image exposure and a larger field of view than fluoroscopy coupled with a low radiation dose, where post-acquisition image processing by computer algorithm automatically characterises the motion of thoracic structures. We conducted a systematic review of the literature and found 29 relevant publications describing its use in humans including the assessment of diaphragm and chest wall motion, measurement of pulmonary ventilation and perfusion, and the assessment of airway narrowing. Work is ongoing in several other areas including assessment of diaphragmatic paralysis. We assess the findings, methodology and limitations of DCR, and we discuss the current and future roles of this promising medical imaging technology.

Critical relevance statement Dynamic chest radiography provides a wealth of clinical information, but further research is required to identify its clinical niche.

Graphical abstract

Dynamic chest radiography (DCR) captures high-resolution moving images of the thorax.

The ionising radiation dose of DCR is low.

DCR can image the diaphragm, chest wall, ventilation and perfusion.

Most papers on DCR are small, with heterogeneity in study design or outcome.

Large, multicentre studies with similar outcomes and healthy controls are desirable.

Introduction

First described in the early 2000s [ 1 , 2 , 3 ], dynamic chest radiography (DCR) is a real-time X-ray imaging system that takes sequential images of the thorax in motion usually over 10 to 20 s. A high-resolution flat panel detector (FPD) produces temporal and spatial digital images with a wide field of view (FOV) that captures the entire thorax. The temporal resolution of DCR can be set as high as 15 frames per second (fps), above the minimum necessary to capture rapid movement. Recent hardware advances and segmentation-based proprietary image processing software automatically identify moving structures, such as the diaphragm and visible posteroanterior (PA) lung area, or to assess the change in pixel intensity of lung tissue over the breathing cycle [ 4 ]. When segmentation of lung parenchyma and vasculature is applied, changes in pixel density can infer ventilation and perfusion respectively without the need for intravenous contrast agents or inhaled tracers [ 5 ]. Depending on exposure settings, a single DCR image can be adequate for diagnostic purposes and is visually similar to a standard (‘plain’) PA chest radiograph. A visual description of a DCR image series and its interpretation is shown in Fig.  1 , and a technical description of the image processing methodology can be found as a Additional file 1 .

Dynamic chest radiograph interpretation. Visualisation of a dynamic chest radiograph for the purpose of ( a ) identification of phase of breathing via average pixel density change over time and associated projected lung area (PLA) measurements, ( b ) Automated hemidiaphragm midpoint tracking. c Shows example images of lung area identification (top) and hemidiaphragm tracking (bottom) in a person with cystic fibrosis bronchiectasis. PLA projected lung area, PLA insp PLA at full inspiration, PLA exp PLA at maximum expiration, PLA ti PLA at point of tidal breath in, PLA te PLA at point of tidal breath out

DCR has several advantages as an imaging technology: the equipment has an identical footprint to a standard chest radiography unit and image acquisition confers a lower radiation dose than CT or traditional fluoroscopy. DCR is typically performed in an upright position (although it may be carried out supine), without the necessity for the forced manoeuvres or use of mouthpieces that are required during air flow-based lung function measurement techniques such as spirometry or plethysmography, which interfere with normal respiratory or cardiovascular physiology [ 6 , 7 ]. The image capture process is quick, taking little more time than a standard chest radiograph [ 8 ]. A full list of the exposure settings used by our group can be found as a Additional file 1 . Examples of DCR applications are shown in Figs.  2 and 3 . A variety of potentially useful clinical information can be derived from a single DCR image series, including diaphragm motion, chest wall motion, large airways diameter and ventilation/perfusion. Information on these parameters is important in a variety of different health conditions including diaphragm palsy, restrictive and obstructive lung diseases, and pulmonary vascular disease. For this reason, DCR may be a technology of interest for research into various respiratory conditions or symptoms.

Demonstration of use of dynamic chest radiography in lung parenchymal and diaphragmatic disorders. Still frame at full inspiration shown on left, and still frame at expiration on the right. a DCR post-left lower lobectomy complicated by pneumonia, in a female in her 70 s, demonstrating left mid- and lower-zone consolidation, with normal diaphragmatic movement. b DCR in a male in his 50 s, demonstrating an elevated right hemidiaphragm; paradoxical right diaphragm movement on sniffing consistent with phrenic nerve palsy

Demonstration of use of dynamic chest radiography to detect pulmonary blood flow. DCR in a male in his 50 s following left pneumonectomy. Standard DCR images is shown in lef-thand panels, DCR perfusion mapping in right. Still images prior to ( a ), during ( b ) and after ( c ) right ventricular contraction

Since the first description of DCR a steady stream of papers has entered the literature, however to our knowledge no systematic review of the clinical applications and findings of DCR has been carried out. Our objective was to summarise existing DCR knowledge, produce a narrative description of its current application in medical practice, and explore directions for future research.

Materials and methods

A literature review was conducted using the PRISMA statement for systematic reviews to guide the process; the PRISMA checklist can be found as an Additional file 1 . The study is registered in the PROSPERO database, registration number CRD42022328181.

Search strategy

A comprehensive MEDLINE (1990–January 2022) and EMBASE (1990–January 2022) protocol-driven search was performed. Search terms included ‘dynamic chest x-ray’, ‘DCR’ and ‘dynamic thoracic imaging’. The full list is detailed as a Additional file 1 . References from relevant publications and abstracts from major respiratory conferences were reviewed, and clinical trial registers searched. Any additional publications identified during the hand-search were included if they met all inclusion criteria below.

Study inclusion, data extraction and quality assessment

The following inclusion criteria were used: (1) applies to dynamic chest imaging, (2) English language, (3) performed in humans. Publications were excluded if no full text could be retrieved (e.g. inaccessible despite use of interlibrary loan services). Case reports and narrative reviews were excluded. As the scope of this review was a narrative description of the state of the art, quality was not used as an exclusion criterion. The full texts of studies meeting the inclusion criteria based on their titles and abstracts were obtained and screened by two independent reviewers with several years’ experience of DCR (TSF, 5 years’ experience and RER, 5 years’ experience). A third author (FF, 4 years’ experience) reviewed the list and adjudicated discussions about study inclusion. Data extraction was performed by three independent reviewers (RER, TSF, FF). The study design, sample population, study objectives, statistical analysis and outcomes measured were collated and presented in a table. The Newcastle–Ottawa scale was applied for non-randomised studies and the Joanna Briggs Institute (JBI) Checklist for cross-sectional studies, in order to guide inclusion or exclusion. None of the studies identified were randomised. A narrative synthesis was then created from these papers.

The initial search identified 826 papers published January 2006 to January 2022; 791 papers were excluded as they were either duplicates, or not relevant to the aim of the review after the titles and abstracts had been screened for eligibility. A further 72 articles were identified by a combination of citation searches and website searches. A total of 106 full-text articles were then assessed, 29 of which met the inclusion criteria (see Table 1 ).

Exposure settings and ionising radiation dose

Device exposure settings and estimated ionising radiation dose are described in Table 2 . Image acquisition time ranged from 4 to 37 s, with a median of 10 s; 3 studies did not describe imaging time. Frame rate ranged from 3 to 30 fps, with a median and mode of 15 fps. Earlier studies tended to use a lower frame rate (as low as 3 fps), with later studies almost all using 15 fps. Ionising radiation dose of DCR was not recorded by 3 studies. In those studies where it was reported, the median entrance surface dose (ESD, the measure of radiation absorbed by the skin) was 0.65 mGy, with the highest reported dose being 1.5 mGy. For lateral DCR image series, a dose of 4 mGy was reported. The dose in the lateral view is unsurprisingly higher, due to the need for the X-ray penetration of a greater depth of tissue than in the PA projection. In 3 studies, the radiation exposure was reported in terms of effective dose (ED) and ranged from 0.2 to 0.25 mSv. For comparison, the average ED for a plain PA chest radiograph in the UK is 0.014 mSv [ 35 ], an ultra-low-dose CT 0.08 mSv [ 36 ] and a high-resolution helical computed tomography (CT) chest scan typically around 9.7 mSv [ 37 ]. The imaging protocol varied significantly between studies. However, most studies acquired erect images, with tidal and/or deep breathing manoeuvres overseen by a clinician for diaphragm/chest wall assessment, and constant inspiratory breath holding for pulmonary perfusion analysis [ 5 ]. Instructions were spoken [ 18 , 29 ] or pre-recorded [ 33 ].

Diaphragm and thoracic structure motion

Several studies have used DCR to assess diaphragm motion in healthy volunteers [ 16 , 20 ] and in respiratory conditions including COPD [ 21 ] and cystic fibrosis [ 29 , 34 ] (see Table 3 ). DCR-recorded diaphragm motion over the course of a forced breathing cycle appears similar to a spirogram (see Fig.  1 ), and this motion, from which speed and excursion can be calculated, has been used standalone or to derive further clinical parameters [ 4 ].

Some studies have explored the role of DCR to quantify motion of the diaphragm as a marker of pulmonary function, exploring its relationship to traditional lung function tests such as spirometry [ 16 , 20 , 21 ]. In healthy individuals, average (mean ± standard deviation) diaphragm excursion during forced deep breathing was found to be 49 ± 17 mm on the right-hand side and 52 ± 16 mm on the left [ 20 ]. During tidal breathing the excursion was found to be smaller, at 11 ± 4 mm on the right-hand side and 15 ± 5 mm on the left [ 16 ]. The range of hemidiaphragm excursion observed using DCR is similar to that observed using M-mode ultrasound [ 38 ]. Clear differences in diaphragm motion have been observed using DCR between healthy volunteers and those with COPD [ 18 ], between differing severities of COPD [ 21 ] and before and after treatment of pulmonary exacerbations of cystic fibrosis bronchiectasis [ 34 ]. DCR-measured diaphragm position at rest has been used as a marker of air trapping [ 29 ], with a higher resting diaphragm position associated with improvement in respiratory function after cystic fibrosis transmembrane conductance regulator (CFTR) modulator treatment for adults with cystic fibrosis bronchiectasis [ 29 ]. In individuals with COPD, deep breathing diaphragm excursion has been demonstrated to be significantly less than that in healthy individuals [ 21 ].

The real-time visual properties of DCR have been used to diagnose diaphragm dysfunction [ 30 ], as a form of low-dose fluoroscopy, in which paradoxical diaphragm motion can not only be observed but also quantified by computer assisted tracking of the diaphragm midpoint. Paradoxical diaphragm motion was clearly identified and agreed with the findings of either standard fluoroscopy or ultrasound. Work is ongoing to further explore the use of DCR in cases of suspected diaphragm palsy, with comparison to other reference techniques [ 39 ]. To our knowledge, only two studies have compared healthy individuals to those with respiratory disease [ 16 , 20 ], although no detailed case matching was undertaken. One study tracked rib motion during respiratory manoeuvres [ 15 ]. To our knowledge, this has not been applied to a large number of normal individuals, or in the identification of specific pathology with reference to other thoracic cage motion imaging techniques such as structured light plethysmography [ 40 ]. DCR has also been applied to lung nodule motion analysis in a single small case series [ 13 ].

Lung area change

Lung area, the visible area of lung tissue visible in either the PA or lateral plane, is variously described as ‘projected lung area’ (PLA) or ‘lung field area’ [ 24 , 25 , 29 , 34 ]. An example of a calculated lung field area is shown in Fig.  1 . Inspiratory PLA (PLA insp ) has been found to correlate well with forced vital capacity (FVC) in healthy individuals [ 24 ] and those with respiratory disease [ 29 , 31 , 33 , 34 ]. These findings are detailed in Table 4 . This mirrors well-established associations between FVC and PLA measured on traditional, static chest radiographs [ 41 ]. Further correlations between DCR-derived parameters and pulmonary function test (PFT) results have been explored, highlighting the role DCR may plan as an adjunct in patients whose physiological baseline precludes traditional spirometric testing. The relationship between PLA and FEV 1 is variable. PLA insp demonstrates correlation with FEV 1 in both healthy individuals[ 24 ] and those with restrictive lung diseases [ 31 , 33 ]. However, this relationship was not demonstrated among patients with COPD, where FEV 1 is a key marker of disease state [ 31 ]. Similarly, whilst change in PLA (ΔPLA) showed moderate correlation with FEV 1 within a group of individuals with cystic fibrosis bronchiectasis [ 34 ], there was only a weak correlation between in those with COPD [ 25 ]. These findings may be related to underlying pathophysiology (e.g. presence of emphysema or air trapping) or the low number of COPD patients enrolled in said studies. In restrictive disease, PLA insp has been shown to be significantly reduced [ 31 ]. Similarly, patients with severe obstructive lung disease have significantly increased PLA insp and reduced ΔPLA compared to healthy controls, likely reflecting underlying air trapping [ 25 , 31 ]. DCR has only been used as a marker of response to treatment in individuals with cystic fibrosis bronchiectasis [ 29 , 34 ]. In these studies, reduced expiratory PLA (PLA exp ) following treatment for pulmonary exacerbations was postulated to reflect reduced air trapping.

Ventilation and perfusion imaging

DCR has been proposed as a non-contrast imaging modality to assess ventilation and perfusion (V/Q), potentially addressing the time-consuming and expensive limitations of traditional nuclear medicine V/Q scanning, or the high ionising radiation doses and contrast agent exposure of CT angiography [ 8 ]. Preliminary studies have established that DCR can detect ventilation as a change in pixel density and related signal characteristics over time, and can be applied to detect regional differences in ventilation [ 4 , 8 , 12 , 14 , 42 ].

Few large-scale studies of DCR-derived measures of ventilation have been carried out [ 14 , 17 , 19 , 32 ]. Cranio-caudal gradient of mean pixel contrast change rate (the average maximum rate of pixel contrast change in relation to distance from lung apex) has been studied as a marker of obstructive lung disease. This gradient has been shown to be significantly reduced in those with COPD compared to controls, and those with severe (GOLD COPD severity score 3–4) compared to mild (GOLD 1–2) COPD [ 19 ]. This finding may be explained by obstruction more markedly affecting lower airways and suggests that DCR may be used in evaluation of severe obstruction, particularly where spirometry may not be possible. However, a lack of significant difference between those with mild COPD and controls limits its use as a diagnostic aid.

Several studies have explored the potential of DCR to assess pulmonary circulation with changes in pixel contrast and associated signal characteristics during the circulatory cycle being used to generate blood distribution maps [ 5 , 12 , 26 , 28 ]. While DCR has been able to demonstrate asymmetric circulation in a number of cases [ 5 ], cross-correlation between DCR and perfusion scanning only demonstrated strong correlation in 21% of cases [ 12 ], suggesting it may be used as an adjunct to, rather than replacement of, established diagnostic techniques.

While multiple case reports have explored the utility of DCR to demonstrate V/Q mismatch, the lack of large-scale studies comparing it to gold-standard techniques limits its current use [ 43 , 44 , 45 , 46 ]. Among patients with pulmonary disease, DCR was found to have an accuracy of 62.3% in detecting V/Q mismatch compared to nuclear medicine scanning [ 26 ]. Similarly, in patients with lung cancer, DCR-derived ventilation and perfusion measurements have demonstrated moderate correlation with those derived from nuclear medicine scanning; however, this correlation was markedly affected by overlying soft tissue, and reduced in pathological lungs [ 32 ].

Airways imaging

The role of tracheal narrowing in tracheomalacia and excessive dynamic airway collapse (EDAC) is well-recognised [ 47 ]. The most commonly utilised diagnostic imaging method is dynamic CT [ 48 ] with inspiratory/expiratory slices. DCR, with its ability to assess lung structures throughout the breathing cycle, could theoretically be used to assess tracheal narrowing. Watase et al. [ 27 ] have used DCR to identify significant tracheal narrowing during expiration in those with obstructive disease compared to controls. However, measurements were made manually by three observers, without assessment of inter-observer agreement. Similarly, Okhura et al. [ 25 ] found tracheal narrowing was significantly higher in those with obstructive disease but failed to demonstrate its utility in diagnosing airflow obstruction.

Methodological quality

No standardisation exists in the definition of anatomical landmarks used for lung segmentation (for example, inclusion or omission of the left heart border), breathing protocol, or device exposure settings in acquiring DCR images. As is to be expected in a nascent field, few DCR studies have addressed the same hypothesis with the same methodology in the same population. Most DCR studies are small, non-controlled and observational, concerned with the exploration or development of specific radiological techniques related to DCR, and only a handful [ 22 , 24 , 31 ] have recruited larger numbers of subjects, although these too are relatively small. Reproducibility and reliability are therefore difficult to quantify.

This literature review has identified a number of studies exploring the use of DCR in multiple conditions across different populations, highlighting its potential role in clinical practice and demonstrating its ability to capture thoracic motion and the mechanics of pulmonary ventilation and perfusion despite a low radiation dose. However, studies are small and heterogeneous, and few have addressed the same clinical question using the same methodology.

While DCR can demonstrate differences in diaphragm movement between those with respiratory pathology and healthy controls, as well as pre- and post-clinical intervention, the lack of ‘normal’ ranges of diaphragm motion, and the lack of paired, established comparator techniques such as M-mode ultrasound (which can measure diaphragm motion and dysfunction), limits its use in clinical practice. Its use in assessment of other relevant pathologies such as neuromuscular disease remains unexplored. The use of DCR as a screening tool for diaphragm dysfunction appears to be a promising avenue for research.

A shared aim across several studies has been the exploration of the relationship between DCR-calculated lung area or volume and plethysmographic lung volumes, with the implicit goal being to establish the use of DCR as an alternative to PFTs, for example among those whose poor physiological baseline precludes spirometric testing. There is a strong relationship between FVC and lung area, but other clinically useful parameters such FEV 1 lack a consistent relationship with DCR-derived measurements. These findings likely result from heterogeneity in study design and relatively small sample sizes. The need for specific breathing manoeuvres may prove challenging in some patient cohorts, and studies exploring use of DCR among patients with severe respiratory disease tend to be small in size. There is not enough evidence available for DCR to replace established methods of diagnosis or assessment of lung function in respiratory disease.

Much work has been done to improve the technical ability of DCR to evaluate pulmonary ventilation and perfusion; however its clinical use remains equivocal. Software can map changes in pulmonary blood flow, but automated analysis has not been able to identify perfusion abnormalities reliably, meaning manual (visual) analysis—and the inherent limitations this entails—is still required. This reduces the likelihood that DCR will be able to supplant traditional nuclear medicine techniques in detection of pulmonary or ventilation pathology, at least until improved computation and analysis techniques are developed. While changes in ventilation have been detected between different disease states using DCR, the lack of established ‘normal’ values means further research is required.

There are several barriers to the widespread clinical uptake of DCR. Patients must be able to hold an adequate static position for long enough for image acquisition, which may be challenging for acutely unwell patients. Comparisons of standing and sitting DCR have not yet been performed. Patients with a raised BMI require a higher ionising radiation dose to achieve adequate exposure, and the impact of this on measures such as diaphragmatic excursion and pulmonary perfusion is not known.

Most likely as a result of the novel and investigative nature of the technology, few studies have addressed the same research question in similar populations using similar methods. Likewise, there are no large, multicentre studies amalgamating results or replicating study methods. Studies display significant heterogeneity in their methodology, patient populations, patient positioning, breathing manoeuvres used, and DCR exposure settings and outcomes measured. This, in turn, markedly reduced our ability to numerically synthesise results, limiting the conclusions drawn. It is possible that the heterogeneity of research conducted in DCR research may highlight the technology’s potential strengths. With DCR, analysis of ventilation, perfusion, thoracic structure motion and airways imaging is possible. These data are possible using CT or MRI, but often require a higher-cost and more time- and effort-intensive investigation with the use of contrast dye or inhaled gases. However, no direct comparisons on cost or workflow yet exist to make formal comparisons between DCR and other imaging modalities such as CT, MRI or ultrasound, although work is ongoing [ 39 ].

Conclusions

With the advent of low radiation dose CT systems such as photon counting detector (PCD-) CT [ 49 ] and ultra-low-dose thoracic CT [ 50 ], DCR requires a clinical niche within dynamic imaging, not reliant solely on its low ionising radiation dose, if it is to be widely utilised. Alongside its ability for straightforward image acquisition and small equipment footprint, the appeal of DCR may lie in its ability to combine diagnostic imaging with physiological motion analysis—a function perhaps best demonstrated in the assessment of diaphragm palsy—alongside ventilation/perfusion information. Given the wealth of information generated from a single DCR series, it may be able to provide a ‘one-stop shop’ to screen for a range of respiratory pathologies—a characteristic that might be of particular use in clinical settings where patients may present with a broad range of differential diagnoses. In such cases, DCR might provide information to allow diagnosis, or at the least act as a rule-out tool prior to more detailed or well-established diagnostic imaging such as CT. However, attaining this goal, along with other exploratory uses for DCR, will require larger trials with comparable methodology to allow for synthesis of results, as well as the inclusion of healthy controls. Such studies may allow DCR to find a unique role in combining traditional static lung imaging and spirometric measures of pulmonary physiology.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

Chronic obstructive pulmonary disease

Computed tomography

Dynamic chest radiography

Effective Dose

Entrance surface dose

Forced expiratory volume of air in 1 s

Field of view

Flat panel detector

Frames per second

Forced vital capacity

Global Initiative for Obstructive Lung Disease

Interstitial lung disease

Interquartile range

Millisieverts

Photon-counting detector

Pulmonary function tests

Projected lung area

PLA at maximum expiration

PLA at full inspiration

PLA at point of tidal breath out

PLA at point of tidal breath in

Pulmonary perfusion scintigraphy

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

The International Prospective Register of Systematic Reviews

Radiological Society of North America

Ventilation/perfusion

Change in PLA between full inspiration and full expiration

Tanaka R, Sanada S, Suzuki M, Matsui T, Uoyama Y (2002) New method of screening chest radiography with computer analysis of respiratory kinetics. Nihon Hoshasen Gijutsu Gakkai Zasshi 58(5):665–669

Article   PubMed   Google Scholar  

Sanada S, Tanaka R, Kobayashi T, Suzuki M, Inoue H (2003) Screening chest X-ray examination with kinetic analysis using flat-panel detector. Nippon Igaku Hoshasen Gakkai Zasshi 63(9):579–581

PubMed   Google Scholar  

Tanaka R, Sanada S, Kobayashi T, Suzuki M, Matsui T, Inoue H (2003) Development of breathing chest radiography: study of exposure timing. Nihon Hoshasen Gijutsu Gakkai Zasshi 59(8):984–992

Tanaka R, Sanada S, Kobayashi T, Suzuki M, Matsui T, Matsui O (2006) Computerized methods for determining respiratory phase on dynamic chest radiographs obtained by a dynamic flat-panel detector (FPD) system. J Digit Imaging 19(1):41–51

Article   PubMed   PubMed Central   Google Scholar  

Tanaka R, Sanada S, Fujimura M et al (2009) Pulmonary blood flow evaluation using a dynamic flat-panel detector: feasibility study with pulmonary diseases. Int J Comput Assist Radiol Surg 4(5):449–455

Weissman C, Askanazi J, Milic-Emili J, Kinney JM (1984) Effect of respiratory apparatus on respiration. J Appl Physiol Respir Environ Exerc Physiol 57(2):475–480

CAS   PubMed   Google Scholar  

Han JN, Stegen K, Cauberghs M, Van de Woestijne KP (1997) Influence of awareness of the recording of breathing on respiratory pattern in healthy humans. Eur Respir J 10(1):161–166

Article   CAS   PubMed   Google Scholar  

Tanaka R, Sanada S, Okazaki N et al (2006) Evaluation of pulmonary function using breathing chest radiography with a dynamic flat panel detector: primary results in pulmonary diseases. Invest Radiol 41(10):735–745

Tanaka R, Sanada S, Okazaki N et al (2008) Detectability of regional lung ventilation with flat-panel detector-based dynamic radiography. J Digit Imaging 21(1):109–120

Tanaka R, Sanada S, Fujimura M et al (2008) Development of functional chest imaging with a dynamic flat-panel detector (FPD). Radiol Phys Technol 1(2):137–143

Kawashima H, Tanaka R, Sanada S (2009) Reproducibility of dynamic chest radiography with a flat-panel detector-respiratory changes in pixel value. Nihon Hoshasen Gijutsu Gakkai Zasshi 65(6):738–744

Tanaka R, Sanada S, Fujimura M et al (2010) Development of pulmonary blood flow evaluation method with a dynamic flat-panel detector: quantitative correlation analysis with findings on perfusion scan. Radiol Phys Technol 3(1):40–45

Tsuchiya Y, Kodera Y, Tanaka R, Sanada S (2009) Quantitative kinetic analysis of lung nodules using the temporal subtraction technique in dynamic chest radiographies performed with a flat panel detector. J Digit Imaging 22(2):126–135

Tanaka R, Sanada S, Fujimura M et al (2011) Ventilatory impairment detection based on distribution of respiratory-induced changes in pixel values in dynamic chest radiography: a feasibility study. Int J Comput Assist Radiol Surg 6(1):103–110

Tanaka R, Sanada S, Sakuta K, Kawashima H (2015) Quantitative analysis of rib kinematics based on dynamic chest bone images: preliminary results. J Med Imaging (Bellingham) 2(2):024002

Yamada Y, Ueyama M, Abe T et al (2017) Time-resolved quantitative analysis of the diaphragms during tidal breathing in a standing position using dynamic chest radiography with a flat panel detector system (“dynamic X-ray phrenicography”): initial experience in 172 volunteers. Acad Radiol 24(4):393–400

Tanaka R, Matsuda H, Sanada S (2017) Time-series analysis of lung texture on bone-suppressed dynamic chest radiograph for the evaluation of pulmonary function: a preliminary study: SPIE

Yamada Y, Ueyama M, Abe T et al (2017) Difference in diaphragmatic motion during tidal breathing in a standing position between COPD patients and normal subjects: time-resolved quantitative evaluation using dynamic chest radiography with flat panel detector system (“dynamic X-ray phrenicography”). Eur J Radiol 87:76–82

Yamada Y, Ueyama M, Abe T et al (2017) Difference in the craniocaudal gradient of the maximum pixel value change rate between chronic obstructive pulmonary disease patients and normal subjects using sub-mGy dynamic chest radiography with a flat panel detector system. Eur J Radiol 92:37–44

Hida T, Yamada Y, Ueyama M et al (2019) Time-resolved quantitative evaluation of diaphragmatic motion during forced breathing in a health screening cohort in a standing position: dynamic chest phrenicography. Eur J Radiol 113:59–65

Hida T, Yamada Y, Ueyama M et al (2019) Decreased and slower diaphragmatic motion during forced breathing in severe COPD patients: time-resolved quantitative analysis using dynamic chest radiography with a flat panel detector system. Eur J Radiol 112:28–36

Kitahara Y, Tanaka R, Roth HR, Oda H, Mori K, Kasahara K et al (eds) (2019) Lung segmentation based on a deep learning approach for dynamic chest radiography. Medical Imaging 2019: Computer-Aided Diagnosis; 01 Mar 2019

Hanaoka J, Yoden M, Hayashi K et al (2021) Dynamic perfusion digital radiography for predicting pulmonary function after lung cancer resection. World J Surg Oncol 19(1):43

Hino T, Hata A, Hida T et al (2020) Projected lung areas using dynamic X-ray (DXR). Eur J Radiol Open 7:100263

Ohkura N, Kasahara K, Watanabe S et al (2020) Dynamic-ventilatory digital radiography in air flow limitation: a change in lung area reflects air trapping. Respiration 99(5):382–388

Tanaka R, Matsumoto I, Tamura M et al (2020) Comparison of dynamic flat-panel detector-based chest radiography with nuclear medicine ventilation–perfusion imaging for the evaluation of pulmonary function: a clinical validation study. Med Phys 47(10):4800–4809

Watase S, Sonoda A, Matsutani N et al (2020) Evaluation of intrathoracic tracheal narrowing in patients with obstructive ventilatory impairment using dynamic chest radiography: a preliminary study. Eur J Radiol 129:109141

Yamamoto S, Hasebe T, Tomita K et al (2020) Pulmonary perfusion by chest digital dynamic radiography: comparison between breath-holding and deep-breathing acquisition. J Appl Clin Med Phys 21(11):247–255

FitzMaurice TS, McCann C, Nazareth D, Shaw M, McNamara PS, Walshaw MJ (2022) Measuring the effect of elexacaftor/tezacaftor/ivacaftor combination therapy on the respiratory pump in people with CF using dynamic chest radiography. J Cyst Fibros 21:1036–1041

FitzMaurice TS, McCann C, Nazareth DS, Walshaw MJ (2022) Characterisation of hemidiaphragm dysfunction using dynamic chest radiography: a pilot study. ERJ Open Res 8(1):00343–02021

Ohkura N, Tanaka R, Watanabe S et al (2021) Chest dynamic-ventilatory digital radiography in chronic obstructive or restrictive lung disease. Int J Chron Obstruct Pulmon Dis 16:1393–1399

Tanaka R, Matsumoto I, Tamura M et al (2021) Dynamic chest radiography: clinical validation of ventilation and perfusion metrics derived from changes in radiographic lung density compared to nuclear medicine imaging. Quant Imaging Med Surg 11(9):4016–4027

Ueyama M, Hashimoto S, Takeda A et al (2021) Prediction of forced vital capacity with dynamic chest radiography in interstitial lung disease. Eur J Radiol 142:109866

FitzMaurice TS, McCann C, Nazareth DS, McNamara PS, Walshaw MJ (2022) Use of dynamic chest radiography to assess treatment of pulmonary exacerbations in cystic fibrosis. Radiology 212641

Hart D, Hillier MC, Shrimpton PC (2012) Doses to patients from radiographic and fluoroscopic X-ray imaging procedures in the UK: 2010 review (HPA-CRCE-034)

Moloney F, Kavanagh RG, Ronan NJ et al (2021) Ultra-low-dose thoracic CT with model-based iterative reconstruction (MBIR) in cystic fibrosis patients undergoing treatment with cystic fibrosis transmembrane conductance regulators (CFTR). Clin Radiol 76(5):393

Article   Google Scholar  

Wall BF, Haylock R, Jansen JTM, Hillier MC, Hart D, Shrimpton PC (2011) Radiation risks from medical X-ray examinations as a function of the age and sex of the patient (HPA-CRCE-028)

Boussuges A, Finance J, Chaumet G, Brégeon F (2021) Diaphragmatic motion recorded by M-mode ultrasonography: limits of normality. ERJ Open Res 7(1):00714–02020

Fitzmaurice T (2021) A study comparing ultrasound versus X-rays to diagnose paralysed diaphragm muscles ISRCTN Registry: ISRCTN Registry. Available from: https://www.isrctn.com/ISRCTN31111675

Motamedi-Fakhr S, Wilson RC, Iles R (2016) Tidal breathing patterns derived from structured light plethysmography in COPD patients compared with healthy subjects. Med Devices (Auckl) 10:1–9

PubMed   PubMed Central   Google Scholar  

Pratt PC, Klugh GA (1967) A method for the determination of total lung capacity from posteroanterior and lateral chest roentgenograms. Am Rev Respir Dis 96(3):548–552

Tanaka R, Sanada S, Tsujioka K, Matsui T, Takata T, Matsui O (2008) Development of a cardiac evaluation method using a dynamic flat-panel detector (FPD) system: a feasibility study using a cardiac motion phantom. Radiol Phys Technol 1(1):27–32

Miyatake H, Asada K, Tabata T, Eguchi Y, Matsutani N, Nakagawa Y (2021) Novel pulmonary circulation imaging using dynamic chest radiography for acute pulmonary embolism. Circ J 85(4):400

Hoshino S, Miyatake H, Maruo Y (2021) Using dynamic digital radiography to assess pulmonary circulation imaging in a patient with congenital heart disease. Int J Cardiovasc Imaging. https://doi.org/10.1007/s10554-021-02517-4

Yamasaki Y, Moriyama S, Tatsumoto R, Abe K, Ishigami K (2022) Chronic thromboembolic pulmonary hypertension after acute pulmonary thromboembolism revealed by dynamic chest radiography. Eur Heart J Cardiovasc Imaging 23(6):e264–e265

Yamasaki Y, Ishigami K (2021) Dynamic chest radiography of pulmonary arteriovenous malformation. Radiology 300(2):285

Leong P, Bardin PG, Lau KK (2013) What’s in a name? Expiratory tracheal narrowing in adults explained. Clin Radiol 68(12):1268–1275

Mitropoulos A, Song W-J, Almaghlouth F, Kemp S, Polkey M, Hull JH (2021) Detection and diagnosis of large airway collapse: a systematic review. ERJ Open Res 7(3):00055–02021

Rajendran K, Petersilka M, Henning A et al (2021) First clinical photon-counting detector CT system: technical evaluation. Radiology 303(1):130–138

Kim Y, Kim YK, Lee BE et al (2015) Ultra-low-dose CT of the thorax using iterative reconstruction: evaluation of image quality and radiation dose reduction. AJR Am J Roentgenol 204(6):1197–1202

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Acknowledgements

The authors are grateful to Dr David Green of the Liverpool Heart and Chest Hospital for the provision of clinical images used in this work.

The authors state that this work has not received any funding.

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Fred Fyles and Thomas S. Fitzmaurice: Joint first authorship

Authors and Affiliations

Respiratory Research Group, Liverpool University Hospitals Foundation Trust, Liverpool, UK

Fred Fyles, Ryan E. Robinson & Hassan Burhan

Clinical Sciences Department, Liverpool School of Tropical Medicine, Liverpool, UK

Department of Respiratory Medicine, Liverpool Heart and Chest Hospital NHS Trust, Liverpool, UK

Thomas S. FitzMaurice & Martin J. Walshaw

Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK

Thomas S. FitzMaurice

Department of Bioengineering, University of Washington, Seattle, WA, USA

Institute of Infection and Global Health, University of Liverpool, Liverpool, UK

Martin J. Walshaw

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Contributions

Conceptualisation: T.S.F., R.E.R.. Methodology: R.E.R., T.S.F. Visualisation: T.S.F. Formal analysis: T.S.F., F.F. Writing—original draft: T.S.F., F.F., R.E.R. Writing—review and editing: R.B. Supervision: R.B., H.B., M.J.W.

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Correspondence to Thomas S. FitzMaurice .

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Competing interests: MJW, TSF and FF report support for educational conference travel from Konica Minolta, Inc. RB reports personal fees from Konica Minolta Inc., outside the submitted work. TSF reports honoraria for the presentation of educational material from Konica Minolta Healthcare, Inc., outside the submitted work. None of the other authors declare any competing interests.

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Fyles, F., FitzMaurice, T.S., Robinson, R.E. et al. Dynamic chest radiography: a state-of-the-art review. Insights Imaging 14 , 107 (2023). https://doi.org/10.1186/s13244-023-01451-4

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Received : 15 February 2023

Accepted : 14 May 2023

Published : 19 June 2023

DOI : https://doi.org/10.1186/s13244-023-01451-4

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