DOI QR코드

DOI QR Code

Dynamic Chest X-Ray Using a Flat-Panel Detector System: Technique and Applications

  • Akinori Hata (Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School) ;
  • Yoshitake Yamada (Department of Diagnostic Radiology, Keio University School of Medicine) ;
  • Rie Tanaka (Department of Radiological Technology, School of Health Sciences, College of Medical, Pharmaceutical and Health Sciences, Kanazawa University) ;
  • Mizuki Nishino (Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School) ;
  • Tomoyuki Hida (Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University) ;
  • Takuya Hino (Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School) ;
  • Masako Ueyama (Department of Health Care, Fukujuji Hospital, Japan Anti-Tuberculosis Association) ;
  • Masahiro Yanagawa (Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine) ;
  • Takeshi Kamitani (Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University) ;
  • Atsuko Kurosaki (Department of Diagnostic Radiology, Fukujuji Hospital, Japan Anti-Tuberculosis Association) ;
  • Shigeru Sanada (Clinical Engineering, Komatsu University) ;
  • Masahiro Jinzaki (Department of Diagnostic Radiology, Keio University School of Medicine) ;
  • Kousei Ishigami (Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University) ;
  • Noriyuki Tomiyama (Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine) ;
  • Hiroshi Honda (Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University) ;
  • Shoji Kudoh (Japan Anti-Tuberculosis Association) ;
  • Hiroto Hatabu (Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School)
  • 투고 : 2020.09.17
  • 심사 : 2020.10.26
  • 발행 : 2021.04.01

초록

Dynamic X-ray (DXR) is a functional imaging technique that uses sequential images obtained by a flat-panel detector (FPD). This article aims to describe the mechanism of DXR and the analysis methods used as well as review the clinical evidence for its use. DXR analyzes dynamic changes on the basis of X-ray translucency and can be used for analysis of diaphragmatic kinetics, ventilation, and lung perfusion. It offers many advantages such as a high temporal resolution and flexibility in body positioning. Many clinical studies have reported the feasibility of DXR and its characteristic findings in pulmonary diseases. DXR may serve as an alternative to pulmonary function tests in patients requiring contact inhibition, including patients with suspected or confirmed coronavirus disease 2019 or other infectious diseases. Thus, DXR has a great potential to play an important role in the clinical setting. Further investigations are needed to utilize DXR more effectively and to establish it as a valuable diagnostic tool.

키워드

과제정보

The investigator, HH, is supported by R01CA203636 and U01CA209414 (NCI).

참고문헌

  1. Yamada Y, Ueyama M, Abe T, Araki T, Nishino M, Jinzaki M, et al. Dynamic chest radiography using flat panel detector system: technique and applications. Dps2016.rsna.org Web site. https://dps2016.rsna.org/exhibit/?exhibit=CH112-ED-X. Published November 2016. Accessed November 11, 2020 
  2. Tanaka R. Dynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging. Radiol Phys Technol 2016;9:139-153  https://doi.org/10.1007/s12194-016-0361-6
  3. Mortensen J, Berg RMG. Lung scintigraphy in COPD. Semin Nucl Med 2019;49:16-21  https://doi.org/10.1053/j.semnuclmed.2018.10.010
  4. Win T, Laroche CM, Groves AM, White C, Wells FC, Ritchie AJ, et al. Use of quantitative lung scintigraphy to predict postoperative pulmonary function in lung cancer patients undergoing lobectomy. Ann Thorac Surg 2004;78:1215-1218  https://doi.org/10.1016/j.athoracsur.2004.04.010
  5. Kim NH, Delcroix M, Jais X, Madani MM, Matsubara H, Mayer E, et al. Chronic thromboembolic pulmonary hypertension. Eur Respir J 2019;53:1801915 
  6. Johns CS, Swift AJ, Rajaram S, Hughes PJC, Capener DJ, Kiely DG, et al. Lung perfusion: MRI vs. SPECT for screening in suspected chronic thromboembolic pulmonary hypertension. J Magn Reson Imaging 2017;46:1693-1697  https://doi.org/10.1002/jmri.25714
  7. Ter-Karapetyan A, Triphan SMF, Jobst BJ, Anjorin AF, Ley-Zaporozhan J, Ley S, et al. Towards quantitative perfusion MRI of the lung in COPD: the problem of short-term repeatability. PLoS One 2018;13:e0208587 
  8. Weidman EK, Plodkowski AJ, Halpenny DF, Hayes SA, Perez-Johnston R, Zheng J, et al. Dual-energy CT angiography for detection of pulmonary emboli: incremental benefit of iodine maps. Radiology 2018;289:546-553  https://doi.org/10.1148/radiol.2018180594
  9. Masy M, Giordano J, Petyt G, Hossein-Foucher C, Duhamel A, Kyheng M, et al. Dual-energy CT (DECT) lung perfusion in pulmonary hypertension: concordance rate with V/Q scintigraphy in diagnosing chronic thromboembolic pulmonary hypertension (CTEPH). Eur Radiol 2018;28:5100-5110  https://doi.org/10.1007/s00330-018-5467-2
  10. Zhang G, Dilling TJ, Stevens CW, Forster KM. Functional lung imaging in thoracic cancer radiotherapy. Cancer Control 2008;15:112-119  https://doi.org/10.1177/107327480801500203
  11. Doganay O, Matin T, Chen M, Kim M, McIntyre A, McGowan DR, et al. Time-series hyperpolarized xenon-129 MRI of lobar lung ventilation of COPD in comparison to V/Q-SPECT/CT and CT. Eur Radiol 2019;29:4058-4067  https://doi.org/10.1007/s00330-018-5888-y
  12. Yamasaki Y, Abe K, Hosokawa K, Kamitani T. A novel pulmonary circulation imaging using dynamic digital radiography for chronic thromboembolic pulmonary hypertension. Eur Heart J 2020;41:2506 
  13. Verschakelen JA, Deschepper K, Jiang TX, Demedts M. Diaphragmatic displacement measured by fluoroscopy and derived by Respitrace. J Appl Physiol (1985) 1989;67:694-698  https://doi.org/10.1152/jappl.1989.67.2.694
  14. Kleinman BS, Frey K, VanDrunen M, Sheikh T, DiPinto D, Mason R, et al. Motion of the diaphragm in patients with chronic obstructive pulmonary disease while spontaneously breathing versus during positive pressure breathing after anesthesia and neuromuscular blockade. Anesthesiology 2002;97:298-305  https://doi.org/10.1097/00000542-200208000-00003
  15. Fujita H, Doi K, MacMahon H, Kume Y, Giger ML, Hoffmann KR, et al. Basic imaging properties of a large image intensifier-TV digital chest radiographic system. Invest Radiol 1987;22:328-335  https://doi.org/10.1097/00004424-198704000-00009
  16. Desprechins B, Luypaert R, Delree M, Freson M, Malfroot A, Dab I, et al. Evaluation of time interval difference digital subtraction fluoroscopy in patients with cystic fibrosis. Scand J Gastroenterol Suppl 1988;143:86-92  https://doi.org/10.3109/00365528809090224
  17. Lam KL, Chan HP, MacMahon H, Oravecz WT, Doi K. Dynamic digital subtraction evaluation of regional pulmonary ventilation with nonradioactive xenon. Invest Radiol 1990;25:728-735  https://doi.org/10.1097/00004424-199006000-00021
  18. Hoffmann KR, Doi K, Fencil LE. Determination of instantaneous and average blood flow rates from digital angiograms of vessel phantoms using distance-density curves. Invest Radiol 1991;26:207-212  https://doi.org/10.1097/00004424-199103000-00001
  19. Kiuru A, Svedstrom E, Kuuluvainen I. Dynamic imaging of pulmonary ventilation. Description of a novel digital fluoroscopic system. Acta Radiol 1991;32:114-119  https://doi.org/10.1177/028418519103200205
  20. Kiuru A, Svedstrom E, Korvenranta H, Kuuluvainen I. Dynamic pulmonary imaging: performance properties of a digital fluoroscopy system. Med Phys 1992;19:467-473  https://doi.org/10.1118/1.596835
  21. Srinivas Y, Wilson DL. Image quality evaluation of flat panel and image intensifier digital magnification in x-ray fluoroscopy. Med Phys 2002;29:1611-1621  https://doi.org/10.1118/1.1487858
  22. Vano E, Geiger B, Schreiner A, Back C, Beissel J. Dynamic flat panel detector versus image intensifier in cardiac imaging: dose and image quality. Phys Med Biol 2005;50:5731-5742  https://doi.org/10.1088/0031-9155/50/23/022
  23. Yamada Y, Ueyama M, Abe T, Araki T, Abe T, Nishino M, et al. 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 2017;87:76-82  https://doi.org/10.1016/j.ejrad.2016.12.014
  24. Tanaka R, Sanada S, Suzuki M, Kobayashi T, Matsui T, Inoue H, et al. Breathing chest radiography using a dynamic flat-panel detector combined with computer analysis. Med Phys 2004;3:2254-2262  https://doi.org/10.1118/1.1769351
  25. Tanaka R, Sanada S, Okazaki N, Kobayashi T, Fujimura M, Yasui M, et al. Evaluation of pulmonary function using breathing chest radiography with a dynamic flat panel detector: primary results in pulmonary diseases. Invest Radiol 2006;41:735-745  https://doi.org/10.1097/01.rli.0000236904.79265.68
  26. Tanaka R, Sanada S, Okazaki N, Kobayashi T, Suzuki M, Matsui T, et al. Detectability of regional lung ventilation with flat-panel detector-based dynamic radiography. J Digit Imaging 2008;21:109-120  https://doi.org/10.1007/s10278-007-9017-8
  27. Tanaka R, Sanada S, Fujimura M, Yasui M, Tsuji S, Hayashi N, et al. Pulmonary blood flow evaluation using a dynamic flat-panel detector: feasibility study with pulmonary diseases. Int J Comput Assist Radiol Surg 2009;4:449-455  https://doi.org/10.1007/s11548-009-0364-4
  28. Tanaka R, Samei E, Segars P, Abadi E, Roth H, Oda H, et al. Dynamic chest radiography for pulmonary function diagnosis: a validation study using 4D extended cardiac-torso (XCAT) phantom. Proceedings of SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109483I-1-8; 2019 Feb 17-20; San Diego, CA, USA: Medical Imaging 
  29. Hiasa Y, Otake Y, Tanaka R, Sanada S, Sato Y. Recovery of 3D rib motion from dynamic chest radiography and CT data using local contrast normalization and articular motion model. Med Image Anal 2019;51:144-156  https://doi.org/10.1016/j.media.2018.10.002
  30. Tanaka R, Sanada S, Fujimura M, Yasui M, Tsuji S, Hayashi N, et al. 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 2011;6:103-110  https://doi.org/10.1007/s11548-010-0491-y
  31. Tanaka R, Tani T, Nitta N, Tabata T, Matsutani N, Muraoka S, et al. Detection of pulmonary embolism based on reduced changes in radiographic lung density during cardiac beating using dynamic flat-panel detector: an animal-based study. Acad Radiol 2019;26:1301-1308  https://doi.org/10.1016/j.acra.2018.12.012
  32. Tanaka R, Tani T, Nitta N, Tabata T, Matsutani N, Muraoka S, et al. Pulmonary function diagnosis based on respiratory changes in lung density with dynamic flat-panel detector imaging: an animal-based study. Invest Radiol 2018;53:417-423  https://doi.org/10.1097/RLI.0000000000000457
  33. Tamura M, Matsumoto I, Saito D, Yoshida S, Takata M, Tanaka R, et al. Dynamic chest radiography: novel and less-invasive imaging approach for preoperative assessments of pleural invasion and adhesion. Radiol Case Rep 2020;15:702-704  https://doi.org/10.1016/j.radcr.2020.02.019
  34. Ohkura N, Kasahara K, Watanabe S, Hara J, Abo M, Sone T, et al. Dynamic-ventilatory digital radiography in air flow limitation: a change in lung area reflects air trapping. Respiration 2020;99:382-388  https://doi.org/10.1159/000506881
  35. Yamada Y, Ueyama M, Abe T, Araki T, Abe T, Nishino M, et al. 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 2017;24:393-400  https://doi.org/10.1016/j.acra.2016.11.014
  36. Yamada Y, Ueyama M, Abe T, Araki T, Abe T, Nishino M, et al. 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 2017;92:37-44  https://doi.org/10.1016/j.ejrad.2017.04.016
  37. Hida T, Yamada Y, Ueyama M, Araki T, Nishino M, Kurosaki A, et al. 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 2019;112:28-36  https://doi.org/10.1016/j.ejrad.2018.12.023
  38. Hida T, Yamada Y, Ueyama M, Araki T, Nishino M, Kurosaki A, et al. 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 2019;113:59-65  https://doi.org/10.1016/j.ejrad.2019.01.034
  39. Korner M, Weber CH, Wirth S, Pfeifer KJ, Reiser MF, Treitl M. Advances in digital radiography: physical principles and system overview. Radiographics 2007;27:675-686  https://doi.org/10.1148/rg.273065075
  40. Kawashima H, Tanaka R, Matsubara K, Ichikawa K, Sakuta K, Minami S, et al. Temporal-spatial characteristic evaluation in a dynamic flat-panel detector system. Proceedings of SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 76224T-1-8; 2019 Feb 17-20; San Diego, CA, USA: Medical Imaging 
  41. Tanaka R, Sanada S, Tsujioka K, Matsui T, Takata T, Matsui O. 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 2008;1:27-32  https://doi.org/10.1007/s12194-007-0003-0
  42. International Atomic Energy Agency. International basic safety standards for protection against ionizing radiation and for the safety of radiation sources. Gnssn.iaea.org Web site. https://gnssn.iaea.org/Superseded%20Safety%20Standards/Safety_Series_115_1996_Pub996_EN.pdf. Published 1996. Accessed November 11, 2020 
  43. Kano A, Doi K, MacMahon H, Hassell DD, Giger ML. Digital image subtraction of temporally sequential chest images for detection of interval change. Med Phys 1994;21:453-461  https://doi.org/10.1118/1.597308
  44. Ishida T, Ashizawa K, Engelmann R, Katsuragawa S, MacMahon H, Doi K. Application of temporal subtraction for detection of interval changes on chest radiographs: improvement of subtraction images using automated initial image matching. J Digit Imaging 1999;12:77-86  https://doi.org/10.1007/BF03168846
  45. Suzuki K, Abe H, MacMahon H, Doi K. Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Med Imaging 2006;25:406-416  https://doi.org/10.1109/TMI.2006.871549
  46. Oda S, Awai K, Suzuki K, Yanaga Y, Funama Y, MacMahon H, et al. Performance of radiologists in detection of small pulmonary nodules on chest radiographs: effect of rib suppression with a massive-training artificial neural network. AJR Am J Roentgenol 2009;193:W397-W402  https://doi.org/10.2214/AJR.09.2431
  47. Freedman MT, Lo SC, Seibel JC, Bromley CM. Lung nodules: improved detection with software that suppresses the rib and clavicle on chest radiographs. Radiology 2011;260:265-273  https://doi.org/10.1148/radiol.11100153
  48. Schalekamp S, van Ginneken B, Meiss L, Peters-Bax L, Quekel LG, Snoeren MM, et al. Bone suppressed images improve radiologists' detection performance for pulmonary nodules in chest radiographs. Eur J Radiol 2013;82:2399-2405  https://doi.org/10.1016/j.ejrad.2013.09.016
  49. Miyoshi T, Yoshida J, Aramaki N, Matsumura Y, Aokage K, Hishida T, et al. Effectiveness of bone suppression imaging in the detection of lung nodules on chest radiographs: relevance to anatomic location and observer's experience. J Thorac Imaging 2017;32:398-405  https://doi.org/10.1097/RTI.0000000000000299
  50. Xu T, Ducote JL, Wong JT, Molloi S. Dynamic dual-energy chest radiography: a potential tool for lung tissue motion monitoring and kinetic study. Phys Med Biol 2011;56:1191-1205  https://doi.org/10.1088/0031-9155/56/4/019
  51. Xu XW, Doi K. Image feature analysis for computer-aided diagnosis: accurate determination of ribcage boundary in chest radiographs. Med Phys 1995;22:617-626  https://doi.org/10.1118/1.597549
  52. Candemir S, Antani S. A review on lung boundary detection in chest X-rays. Int J Comput Assist Radiol Surg 2019;14:563-576  https://doi.org/10.1007/s11548-019-01917-1
  53. Hoffmann KR, Doi K, Chen SH, Chan HP. Automated tracking and computer reproduction of vessels in DSA images. Invest Radiol 1990;25:1069-1075  https://doi.org/10.1097/00004424-199010000-00001
  54. Shirato H, Shimizu S, Kunieda T, Kitamura K, van Herk M, Kagei K, et al. Physical aspects of a real-time tumor-tracking system for gated radiotherapy. Int J Radiat Oncol Biol Phys 2000;48:1187-1195  https://doi.org/10.1016/S0360-3016(00)00748-3
  55. Chen QS, Weinhous MS, Deibel FC, Ciezki JP, Macklis RM. Fluoroscopic study of tumor motion due to breathing: facilitating precise radiation therapy for lung cancer patients. Med Phys 2001;28:1850-1856  https://doi.org/10.1118/1.1398037
  56. Terunuma T, Tokui A, Sakae T. Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy. Radiol Phys Technol 2018;11:43-53  https://doi.org/10.1007/s12194-017-0435-0
  57. Tanaka R, Sanada S, Sakuta K, Kawashima H. Improved accuracy of markerless motion tracking on bone suppression images: preliminary study for image-guided radiation therapy (IGRT). Phys Med Biol 2015;60:N209-N218  https://doi.org/10.1088/0031-9155/60/10/N209
  58. Tanaka R, Sanada S, Sakuta K, Kawashima H. Quantitative analysis of rib kinematics based on dynamic chest bone images: preliminary results. J Med Imaging (Bellingham) 2015;2:024002 
  59. Nason LK, Walker CM, McNeeley MF, Burivong W, Fligner CL, Godwin JD. Imaging of the diaphragm: anatomy and function. Radiographics 2012;32:E51-70  https://doi.org/10.1148/rg.322115127
  60. Ricoy J, Rodriguez-Nunez N, Alvarez-Dobano JM, Toubes ME, Riveiro V, Valdes L. Diaphragmatic dysfunction. Pulmonology 2019;25:223-235  https://doi.org/10.1016/j.pulmoe.2018.10.008
  61. Suga K, Tsukuda T, Awaya H, Takano K, Koike S, Matsunaga N, et al. Impaired respiratory mechanics in pulmonary emphysema: evaluation with dynamic breathing MRI. J Magn Reson Imaging 1999;10:510-520  https://doi.org/10.1002/(SICI)1522-2586(199910)10:4<510::AID-JMRI3>3.0.CO;2-G
  62. Unal O, Arslan H, Uzun K, Ozbay B, Sakarya ME. Evaluation of diaphragmatic movement with MR fluoroscopy in chronic obstructive pulmonary disease. Clin Imaging 2000;24:347-350  https://doi.org/10.1016/S0899-7071(00)00245-X
  63. Womack CJ, Hyman BA, Gardner AW. Prediction of peak oxygen consumption in patients with intermittent claudication. Angiology 1998;49:591-598  https://doi.org/10.1177/000331979804900801
  64. Davies A, Moores C. Structure of the respiratory system, related to function. The Respiratory System 2010:11-28 
  65. Ugander M, Jense E, Arheden H. Pulmonary intravascular blood volume changes through the cardiac cycle in healthy volunteers studied by cardiovascular magnetic resonance measurements of arterial and venous flow. J Cardiovasc Magn Reson 2009;11:42 
  66. Tanaka R, Sanada S, Fujimura M, Yasui M, Tsuji S, Hayashi N, et al. Dynamic chest radiography with a flat-panel detector (FPD): ventilation-perfusion study. Proceedings of SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 76224T-1-7; 2011 Feb 13-16; Lake Buena Vista, FL, USA: Medical Imaging 
  67. Tanaka R, Matsumoto I, Tamura M, Takata M, Kasahara K, Ohkura N, et al. 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 2020 Jul [Epub]. https://doi.org/10.1002/nbm.3665 
  68. Yamasaki Y, Hosokawa K, Tsutsui H, Ishigami K. Pulmonary ventilation-perfusion mismatch demonstrated by dynamic chest radiography in giant cell arteritis. Eur Heart J 2020 Jun [Epub]. https://doi.org/10.1093/eurheartj/ehaa443 
  69. Petersson J, Glenny RW. Gas exchange and ventilation-perfusion relationships in the lung. Eur Respir J 2014;44:1023-1041  https://doi.org/10.1183/09031936.00037014
  70. Thomas KS, Mann A, Williams J. Pulmonary (V/Q) imaging. J Nucl Med Technol 2018;46:87-88  https://doi.org/10.2967/jnmt.118.222190
  71. Sakamoto A, Sakamoto I, Nagayama H, Koike H, Sueyoshi E, Uetani M. Quantification of lung perfusion blood volume with dual-energy CT: assessment of the severity of acute pulmonary thromboembolism. AJR Am J Roentgenol 2014;203:287-291  https://doi.org/10.2214/AJR.13.11586
  72. Park EA, Goo JM, Park SJ, Lee HJ, Lee CH, Park CM, et al. Chronic obstructive pulmonary disease: quantitative and visual ventilation pattern analysis at xenon ventilation CT performed by using a dual-energy technique. Radiology 2010;256:985-997  https://doi.org/10.1148/radiol.10091502
  73. Kong X, Sheng HX, Lu GM, Meinel FG, Dyer KT, Schoepf UJ, et al. Xenon-enhanced dual-energy CT lung ventilation imaging: techniques and clinical applications. AJR Am J Roentgenol 2014;202:309-317  https://doi.org/10.2214/AJR.13.11191
  74. Perez JS, Meinhardt-Llopis E, Facciolo G. TV-L1 optical flow estimation. Image Processing On Line 2013;3:137-150  https://doi.org/10.5201/ipol.2013.26
  75. Cicero M, Bilbily A, Colak E, Dowdell T, Gray B, Perampaladas K, et al. Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Invest Radiol 2017;52:281-287  https://doi.org/10.1097/RLI.0000000000000341
  76. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017;284:574-582  https://doi.org/10.1148/radiol.2017162326
  77. Majkowska A, Mittal S, Steiner DF, Reicher JJ, McKinney SM, Duggan GE, et al. Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation. Radiology 2020;294:421-431  https://doi.org/10.1148/radiol.2019191293
  78. Nasr-Esfahani E, Samavi S, Karimi N, Soroushmehr SM, Ward K, Jafari MH, et al. Vessel extraction in X-ray angiograms using deep learning. Annu Int Conf IEEE Eng Med Biol Soc 2016:643-646 
  79. Yang S, Kweon J, Roh JH, Lee JH, Kang H, Park LJ, et al. Deep learning segmentation of major vessels in X-ray coronary angiography. Sci Rep 2019;9:16897 
  80. Hino T, Hata A, Hida T, Yamada Y, Ueyama M, Araki T, et al. Projected lung areas using dynamic X-ray (DXR). Eur J Radiol Open 2020;7:100263 
  81. George PM, Wells AU, Jenkins RG. Pulmonary fibrosis and COVID-19: the potential role for antifibrotic therapy. Lancet Respir Med 2020;8:807-815  https://doi.org/10.1016/S2213-2600(20)30225-3
  82. Spagnolo P, Balestro E, Aliberti S, Cocconcelli E, Biondini D, Casa GD, et al. Pulmonary fibrosis secondary to COVID-19: a call to arms? Lancet Respir Med 2020;8:750-752 https://doi.org/10.1016/S2213-2600(20)30222-8