Julian Betancur
Overview
Explore the profile of Julian Betancur including associated specialties, affiliations and a list of published articles.
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Articles
17
Citations
662
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0
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Recent Articles
1.
Eisenberg E, Miller R, Hu L, Rios R, Betancur J, Azadani P, et al.
J Nucl Cardiol
. 2021 Jul;
29(5):2295-2307.
PMID: 34228341
Background: Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for...
2.
Nakanishi R, Slomka P, Rios R, Betancur J, Blaha M, Nasir K, et al.
JACC Cardiovasc Imaging
. 2020 Nov;
14(3):615-625.
PMID: 33129741
Objectives: The aim of this study was to evaluate whether machine learning (ML) of noncontrast computed tomographic (CT) and clinical variables improves the prediction of atherosclerotic cardiovascular disease (ASCVD) and...
3.
Hu L, Betancur J, Sharir T, Einstein A, Bokhari S, Fish M, et al.
Eur Heart J Cardiovasc Imaging
. 2019 Jul;
21(5):549-559.
PMID: 31317178
Aims: To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce...
4.
Miller R, Hu L, Gransar H, Betancur J, Eisenberg E, Otaki Y, et al.
Eur Heart J Cardiovasc Imaging
. 2019 Jul;
21(5):567-575.
PMID: 31302679
Aims: Ischaemia on single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is strongly associated with cardiovascular risk. Transient ischaemic dilation (TID) and post-stress wall motion abnormalities (WMA) are non-perfusion...
5.
Otaki Y, Betancur J, Sharir T, Hu L, Gransar H, Liang J, et al.
JACC Cardiovasc Imaging
. 2019 Jun;
13(3):774-785.
PMID: 31202740
Objectives: This study compared the ability of automated myocardial perfusion imaging analysis to predict major adverse cardiac events (MACE) to that of visual analysis. Background: Quantitative analysis has not been...
6.
Hu L, Sharir T, Miller R, Einstein A, Fish M, Ruddy T, et al.
J Nucl Cardiol
. 2019 May;
27(4):1180-1189.
PMID: 31087268
Background: Upper reference limits for transient ischemic dilation (TID) have not been rigorously established for cadmium-zinc-telluride (CZT) camera systems. We aimed to derive TID limits for common myocardial perfusion imaging...
7.
Betancur J, Hu L, Commandeur F, Sharir T, Einstein A, Fish M, et al.
J Nucl Med
. 2018 Sep;
60(5):664-670.
PMID: 30262516
Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the...
8.
Commandeur F, Goeller M, Betancur J, Cadet S, Doris M, Chen X, et al.
IEEE Trans Med Imaging
. 2018 Jul;
37(8):1835-1846.
PMID: 29994362
Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine could be a timesaving and reliable tool...
9.
Slomka P, Betancur J, Liang J, Otaki Y, Hu L, Sharir T, et al.
J Nucl Cardiol
. 2018 Jun;
27(3):1010-1021.
PMID: 29923104
Background: We aim to establish a multicenter registry collecting clinical, imaging, and follow-up data for patients who undergo myocardial perfusion imaging (MPI) with the latest generation SPECT scanners. Methods: REFINE...
10.
Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein A, Bokhari S, et al.
JACC Cardiovasc Imaging
. 2018 Mar;
11(11):1654-1663.
PMID: 29550305
Objectives: The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD). Background: Deep convolutional neural networks...