과제정보
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR20C0026020021); an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2018-0-00861, Intelligent SW Technology Development for Medical Data Analysis); and a grant (2017-7036) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.
참고문헌
- Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 1990;15:827-832 https://doi.org/10.1016/0735-1097(90)90282-T
- Greenland P, Blaha MJ, Budoff MJ, Erbel R, Watson KE. Coronary calcium score and cardiovascular risk. J Am Coll Cardiol 2018;72:434-447 https://doi.org/10.1016/j.jacc.2018.05.027
- Greenland P, Bonow RO, Brundage BH, Budoff MJ, Eisenberg MJ, Grundy SM, et al. ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography) developed in collaboration with the Society of Atherosclerosis Imaging and Prevention and the Society of Cardiovascular Computed Tomography. J Am Coll Cardiol 2007;49:378-402 https://doi.org/10.1016/j.jacc.2006.10.001
- Blaha MJ, Mortensen MB, Kianoush S, Tota-Maharaj R, Cainzos-Achirica M. Coronary artery calcium scoring: is it time for a change in methodology? JACC Cardiovasc Imaging 2017;10:923-937 https://doi.org/10.1016/j.jcmg.2017.05.007
- Hecht HS, Cronin P, Blaha MJ, Budoff MJ, Kazerooni EA, Narula J, et al. 2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: a report of the Society of Cardiovascular Computed Tomography and Society of Thoracic Radiology. J Cardiovasc Comput Tomogr 2017;11:74-84 https://doi.org/10.1016/j.jcct.2016.11.003
- Isgum I, Rutten A, Prokop M, van Ginneken B. Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease. Med Phys 2007;34:1450-1461 https://doi.org/10.1118/1.2710548
- Kurkure U, Chittajallu DR, Brunner G, Le YH, Kakadiaris IA. A supervised classification-based method for coronary calcium detection in non-contrast CT. Int J Cardiovasc Imaging 2010;26:817-828 https://doi.org/10.1007/s10554-010-9607-2
- Brunner G, Chittajallu DR, Kurkure U, Kakadiaris IA. Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data. Int J Cardiovasc Imaging 2010;26:829-838 https://doi.org/10.1007/s10554-010-9608-1
- Shahzad R, van Walsum T, Schaap M, Rossi A, Klein S, Weustink AC, et al. Vessel specific coronary artery calcium scoring: an automatic system. Acad Radiol 2013;20:1-9 https://doi.org/10.1016/j.acra.2012.07.018
- Wolterink JM, Leiner T, Takx RA, Viergever MA, Isgum I. Automatic coronary calcium scoring in non-contrast-enhanced ECG-triggered cardiac CT with ambiguity detection. IEEE Trans Med Imaging 2015;34:1867-1878 https://doi.org/10.1109/TMI.2015.2412651
- van Velzen SGM, Lessmann N, Velthuis BK, Bank IEM, van den Bongard DHJG, Leiner T, et al. Deep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT protocols. Radiology 2020;295:66-79 https://doi.org/10.1148/radiol.2020191621
- Martin SS, van Assen M, Rapaka S, Hudson HT Jr, Fischer AM, Varga-Szemes A, et al. Evaluation of a deep learning-based automated CT coronary artery calcium scoring algorithm. JACC Cardiovasc Imaging 2020;13:524-526 https://doi.org/10.1016/j.jcmg.2019.09.015
- Lessmann N, van Ginneken B, Zreik M, de Jong PA, de Vos BD, Viergever MA, et al. Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions. IEEE Trans Med Imaging 2018;37:615-625 https://doi.org/10.1109/TMI.2017.2769839
- Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Isgum I. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal 2016;34:123-136 https://doi.org/10.1016/j.media.2016.04.004
- Zhang N, Yang G, Zhang W, Wang W, Zhou Z, Zhang H, et al. Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: total and vessel-specific quantifications. Eur J Radiol 2021;134:109420
- Kang SJ, Kim YH, Lee JG, Kang DY, Lee PH, Ahn JM, et al. Impact of subtended myocardial mass assessed by coronary computed tomographic angiography-based myocardial segmentation. Am J Cardiol 2019;123:757-763 https://doi.org/10.1016/j.amjcard.2018.11.042
- Klein S, Staring M, Murphy K, Viergever MA, Pluim JP. elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 2010;29:196-205 https://doi.org/10.1109/TMI.2009.2035616
- Shamonin DP, Bron EE, Lelieveldt BP, Smits M, Klein S, Staring M; Alzheimer's Disease Neuroimaging Initiative. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease. Front Neuroinform 2013;7:50
- Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint 2015;arXiv:1409.1556
- Kim H, Jung J, Kim J, Cho B, Kwak J, Jang JY, et al. Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network. Sci Rep 2020;10:6204
- Yang DH, Kang JW, Kim HK, Choe J, Baek S, Kim SH, et al. Association between C-reactive protein and type of coronary arterial plaque in asymptomatic patients: assessment with coronary CT angiography. Radiology 2014;272:665-673 https://doi.org/10.1148/radiol.14130772
- Yang DH, Kang SJ, Koo HJ, Kweon J, Kang JW, Lim TH, et al. Incremental value of subtended myocardial mass for identifying FFR-verified ischemia using quantitative CT angiography: comparison with quantitative coronary angiography and CT-FFR. JACC Cardiovasc Imaging 2019;12:707-717 https://doi.org/10.1016/j.jcmg.2017.10.027
- Koo HJ, Kang JW, Oh SY, Kim DH, Song JM, Kang DH, et al. Cardiac computed tomography for the localization of mitral valve prolapse: scallop-by-scallop comparisons with echocardiography and intraoperative findings. Eur Heart J Cardiovasc Imaging 2019;20:550-557 https://doi.org/10.1093/ehjci/jey139
- Cano-Espinosa C, Gonzalez G, Washko GR, Cazorla M, Estepar RSJ. Automated Agatston score computation in non-ECG gated CT scans using deep learning. Proc SPIE Int Soc Opt Eng 2018;10574:105742K
- Lee JG, Gumus S, Moon CH, Kwoh CK, Bae KT. Fully automated segmentation of cartilage from the MR images of knee using a multi-atlas and local structural analysis method. Med Phys 2014;41:092303
- Mehta S, Bastero-Caballero RF, Sun Y, Zhu R, Murphy DK, Hardas B, et al. Performance of intraclass correlation coefficient (ICC) as a reliability index under various distributions in scale reliability studies. Stat Med 2018;37:2734-2752 https://doi.org/10.1002/sim.7679
- Kang E, Koo HJ, Yang DH, Seo JB, Ye JC. Cycle-consistent adversarial denoising network for multiphase coronary CT angiography. Med Phys 2019;46:550-562 https://doi.org/10.1002/mp.13284
- Eleid MF, Foley TA, Said SM, Pislaru SV, Rihal CS. Severe mitral annular calcification: multimodality imaging for therapeutic strategies and interventions. JACC Cardiovasc Imaging 2016;9:1318-1337 https://doi.org/10.1016/j.jcmg.2016.09.001