| Coronary atherosclerotic precursors of acute coronary syndromes HJ Chang, FY Lin, SE Lee, D Andreini, J Bax, F Cademartiri, ... Journal of the American College of Cardiology 71 (22), 2511-2522, 2018 | 383 | 2018 |
| Society of Cardiovascular Computed Tomography/North American Society of Cardiovascular Imaging–expert consensus document on coronary CT imaging of atherosclerotic plaque LJ Shaw, R Blankstein, JJ Bax, M Ferencik, MS Bittencourt, JK Min, ... Journal of Cardiovascular Computed Tomography 15 (2), 93-109, 2021 | 126 | 2021 |
| Association of high-density calcified 1K plaque with risk of acute coronary syndrome AR Van Rosendael, J Narula, FY Lin, IJ Van Den Hoogen, U Gianni, ... JAMA cardiology 5 (3), 282-290, 2020 | 107 | 2020 |
| Relationship of hypertension to coronary atherosclerosis and cardiac events in patients with coronary computed tomographic angiography R Nakanishi, L Baskaran, H Gransar, MJ Budoff, S Achenbach, ... Hypertension 70 (2), 293-299, 2017 | 87 | 2017 |
| Identification and quantification of cardiovascular structures from CCTA: an end-to-end, rapid, pixel-wise, deep-learning method L Baskaran, G Maliakal, SJ Al’Aref, G Singh, Z Xu, K Michalak, K Dolan, ... Cardiovascular Imaging 13 (5), 1163-1171, 2020 | 65 | 2020 |
| Machine learning framework to identify individuals at risk of rapid progression of coronary atherosclerosis: from the PARADIGM registry D Han, KK Kolli, SJ Al'Aref, L Baskaran, AR van Rosendael, H Gransar, ... Journal of the American Heart Association 9 (5), e013958, 2020 | 63 | 2020 |
| Relationship between endothelial wall shear stress and high‐risk atherosclerotic plaque characteristics for identification of coronary lesions that cause ischemia: a direct … D Han, A Starikov, B ó Hartaigh, H Gransar, KK Kolli, JH Lee, A Rizvi, ... Journal of the American Heart Association 5 (12), e004186, 2016 | 61 | 2016 |
| Relationship between endothelial wall shear stress and high‐risk atherosclerotic plaque characteristics for identification of coronary lesions that cause ischemia: a direct … D Han, A Starikov, B ó Hartaigh, H Gransar, KK Kolli, JH Lee, A Rizvi, ... Journal of the American Heart Association 5 (12), e004186, 2016 | 61 | 2016 |
| Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: a machine learning approach D Han, JH Lee, A Rizvi, H Gransar, L Baskaran, J Schulman-Marcus, ... Journal of Nuclear Cardiology 25 (1), 223-233, 2018 | 57 | 2018 |
| A comparison of the updated Diamond-Forrester, CAD Consortium, and CONFIRM history-based risk scores for predicting obstructive coronary artery disease in patients with stable … L Baskaran, I Danad, H Gransar, B Ó Hartaigh, J Schulman-Marcus, ... JACC: Cardiovascular Imaging 12 (7 Part 2), 1392-1400, 2019 | 54 | 2019 |
| Patient-specific design of a soft occluder for the left atrial appendage SS Robinson, S Alaie, H Sidoti, J Auge, L Baskaran, K Avilés-Fernández, ... Nature biomedical engineering 2 (1), 8-16, 2018 | 53 | 2018 |
| Incremental prognostic value of coronary computed tomography angiography over coronary calcium scoring for major adverse cardiac events in elderly asymptomatic individuals D Han, BÓ Hartaigh, H Gransar, JH Lee, A Rizvi, L Baskaran, ... European Heart Journal-Cardiovascular Imaging 19 (6), 675-683, 2018 | 48 | 2018 |
| A boosted ensemble algorithm for determination of plaque stability in high-risk patients on coronary CTA SJ Al’Aref, G Singh, JW Choi, Z Xu, G Maliakal, AR van Rosendael, ... Cardiovascular Imaging 13 (10), 2162-2173, 2020 | 40 | 2020 |
| Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation G Singh, Y Hussain, Z Xu, E Sholle, K Michalak, K Dolan, BC Lee, ... PLoS One 15 (9), e0239934, 2020 | 37 | 2020 |
| Non-obstructive high-risk plaques increase the risk of future culprit lesions comparable to obstructive plaques without high-risk features: the ICONIC study RA Ferraro, AR van Rosendael, Y Lu, D Andreini, MH Al-Mallah, ... European Heart Journal-Cardiovascular Imaging 21 (9), 973-980, 2020 | 33 | 2020 |
| Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning L Baskaran, SJ Al’Aref, G Maliakal, BC Lee, Z Xu, JW Choi, SE Lee, ... PloS one 15 (5), e0232573, 2020 | 33 | 2020 |
| Quantitative measurement of lipid rich plaque by coronary computed tomography angiography: a correlation of histology in sudden cardiac death D Han, S Torii, K Yahagi, FY Lin, JH Lee, A Rizvi, H Gransar, MW Park, ... Atherosclerosis 275, 426-433, 2018 | 29 | 2018 |
| Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction W Huang, TW Ying, WLC Chin, L Baskaran, OEH Marcus, KK Yeo, ... Scientific Reports 12 (1), 1033, 2022 | 26 | 2022 |
| Age-and sex-related features of atherosclerosis from coronary computed tomography angiography in patients prior to acute coronary syndrome: results from the ICONIC study E Conte, A Dwivedi, S Mushtaq, G Pontone, FY Lin, EJ Hollenberg, ... European Heart Journal-Cardiovascular Imaging 22 (1), 24-33, 2021 | 26 | 2021 |
| Cardiac CT blooming artifacts: clinical significance, root causes and potential solutions JD Pack, M Xu, G Wang, L Baskaran, J Min, B De Man Visual computing for industry, biomedicine, and art 5 (1), 29, 2022 | 23 | 2022 |
