Automated identification of myocardial perfusion defects in dynamic cardiac computed tomography using deep learning

Assen, 2020, Computed tomography for myocardial characterization in ischemic heart disease: a state-of-the-art review, Eur Radiol Exp, 4, 36, 10.1186/s41747-020-00158-1 Rossi, 2014, Stress myocardial perfusion: imaging with multidetector CT, Radiology, 270, 25, 10.1148/radiol.13112739 Min, 2012, Diagnostic accuracy of fractional flow reserve from anatomic CT angiography, JAMA, 308, 1237, 10.1001/2012.jama.11274 Coenen, 2015, Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm, Radiology, 274, 674, 10.1148/radiol.14140992 van Hamersvelt, 2019, Diagnostic performance of on-site coronary CT angiography-derived fractional flow reserve based on patient-specific lumped parameter models, Radiol Cardiothorac Imaging, 1, e190036, 10.1148/ryct.2019190036 Varga-Szemes, 2015, CT myocardial perfusion imaging, Am J Roentgenol, 204, 487, 10.2214/AJR.14.13546 Avanzo, 2021, Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy, Phys Med, 83, 221, 10.1016/j.ejmp.2021.04.010 Litjens, 2019, State-of-the-Art deep learning in cardiovascular image analysis, JACC Cardiovasc Imaging, 12, 1549, 10.1016/j.jcmg.2019.06.009 Sermesant, 2021, Applications of artificial intelligence in cardiovascular imaging, Nat Rev Cardiol, 18, 600, 10.1038/s41569-021-00527-2 Zreik, 2018, Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis, Med Image Anal, 44, 72, 10.1016/j.media.2017.11.008 Wolterink, 2016, Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks, Med Image Anal, 34, 123, 10.1016/j.media.2016.04.004 de Vos, 2019, Direct automatic coronary calcium scoring in cardiac and chest CT, IEEE Trans Med Imaging, 38, 2127, 10.1109/TMI.2019.2899534 Wolterink, 2017, Generative adversarial networks for noise reduction in low-dose CT, IEEE Trans Med Imaging, 36, 2536, 10.1109/TMI.2017.2708987 Lossau, 2019, Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks, Med Image Anal, 52, 68, 10.1016/j.media.2018.11.003 Lossau (née Elss), 2019, Motion estimation and correction in cardiac CT angiography images using convolutional neural networks, Comput Med Imaging Graph, 76, 101640, 10.1016/j.compmedimag.2019.06.001 Ebersberger, 2014, Dynamic CT myocardial perfusion imaging: performance of 3D semi-automated evaluation software, Eur Radiol, 24, 191, 10.1007/s00330-013-2997-5 Betancur, 2018, Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: A multicenter study, JACC Cardiovasc Imaging, 11, 1654, 10.1016/j.jcmg.2018.01.020 Juarez-Orozco, 2019, Machine learning in the evaluation of myocardial ischemia through nuclear cardiology, Curr Cardiovasc Imaging Reports, 12, 5, 10.1007/s12410-019-9480-x Apostolopoulos, 2021, Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data, Phys Med, 84, 168, 10.1016/j.ejmp.2021.04.011 Monti, 2020, Machine learning and deep neural networks applications in computed tomography for coronary artery disease and myocardial perfusion, J Thorac Imaging, 35, S58, 10.1097/RTI.0000000000000490 Lara Hernandez, 2021, Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability, Comput Biol Med, 130, 104200, 10.1016/j.compbiomed.2020.104200 Ho, 2021, Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification, Sci Rep, 11, 1839, 10.1038/s41598-021-81525-9 Kim, 2020, Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network, Comput Methods Programs Biomed, 185, 105150, 10.1016/j.cmpb.2019.105150 Lewis, 2016, Selecting a CT scanner for cardiac imaging: the heart of the matter, Br J Radiol, 89, 20160376, 10.1259/bjr.20160376 Takafuji, 2020, Myocardial coverage and radiation dose in dynamic myocardial perfusion imaging using third-generation dual-source CT, Korean J Radiol, 21, 58, 10.3348/kjr.2019.0323 Yaniv, 2018, SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research, J Digit Imaging, 31, 290, 10.1007/s10278-017-0037-8 Ronneberger, 2015, Convolutional networks for biomedical image segmentation, 234 Payer, 2019, Integrating spatial configuration into heatmap regression based CNNs for landmark localization, Med Image Anal, 54, 207, 10.1016/j.media.2019.03.007 Ishida, 2016, Underestimation of myocardial blood flow by dynamic perfusion CT: Explanations by two-compartment model analysis and limited temporal sampling of dynamic CT, J Cardiovasc Comput Tomogr, 10, 207, 10.1016/j.jcct.2016.01.008 Bamberg, 2010, Dynamic myocardial stress perfusion imaging using fast dual-source CT with alternating table positions: initial experience, Eur Radiol, 20, 1168, 10.1007/s00330-010-1715-9 van Assen, 2019, Intermodel disagreement of myocardial blood flow estimation from dynamic CT perfusion imaging, Eur J Radiol, 110, 175, 10.1016/j.ejrad.2018.11.029 Kikuchi, 2014, Quantification of myocardial blood flow using dynamic 320-row multi-detector CT as compared with (1)(5)O-H(2)O PET, Eur Radiol, 24, 1547, 10.1007/s00330-014-3164-3 Pedregosa, 2011, Scikit-learn: Machine learning in Python, J Mach Learn Res, 12, 2825 Wichmann, 2015, Absolute versus relative myocardial blood flow by dynamic CT myocardial perfusion imaging in patients with anatomic coronary artery disease, Am J Roentgenol, 205, W67, 10.2214/AJR.14.14087