Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography-Reference-Cited by-同舟云学术

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

Published:2015-03-06 Issue:1 Volume:2 Page:014003
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:en
Short-container-title:J. Med. Imag

Author:

Kang Dongwoo¹,Dey Damini²,Slomka Piotr J.³,Arsanjani Reza³,Nakazato Ryo³,Ko Hyunsuk¹,Berman Daniel S.³,Li Debiao²,Kuo C.-C. Jay¹

Affiliation:

1. University of Southern California, Department of Electrical Engineering, Los Angeles, California 90089, United States

2. Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Department of Biomedical Sciences, Los Angeles, California 90048, United States

3. Cedars-Sinai Medical Center, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Los Angeles, California 90048, United States

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

Reference55 articles.

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3. Diagnostic Performance of 64-Multidetector Row Coronary Computed Tomographic Angiography for Evaluation of Coronary Artery Stenosis in Individuals Without Known Coronary Artery Disease

4. Non-invasive coronary computed tomographic angiography for patients with suspected coronary artery disease: the Coronary Angiography by Computed Tomography with the Use of a Submillimeter resolution (CACTUS) trial

5. 64-Slice Computed Tomography Coronary Angiography in Patients With High, Intermediate, or Low Pretest Probability of Significant Coronary Artery Disease

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