Low does calcium scoring in cardiac computer tomography using deep learning

Vanika Singhal; Sidharth Abrol; Daphne Mulot; Amy Deubig; Sandeep Dutta; Bipul Das

doi:10.1117/12.3005114

3 April 2024 Low does calcium scoring in cardiac computer tomography using deep learning

Vanika Singhal, Sidharth Abrol, Daphne Mulot, Amy Deubig, Sandeep Dutta, Bipul Das

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292726 (2024) https://doi.org/10.1117/12.3005114
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Coronary Artery Calcium Scoring (CACS) is used for cardiac risk assessment caused by atherosclerotic plaque or other coronary artery diseases. Images from Non-Contrast (NC) cardiac Computed Tomography (CT) scans acquired at 120kVp are used in computing Agatston scoring for CACS. These scans, if done at lower peak voltage can reduce X-Ray radiation exposure. This, however, changes CT attenuation values for all tissues, as well as calcification compared to 120kVp scan, thus making it unusable for Agatston scoring. We propose a learning-based method to translate a CT image acquired at lower kVp to a 120kVp equivalent image, such that the same calcium scoring protocol can be used on these scans. We establish that the proposed method enables appropriate translation of CT values in calcification regions, thereby allowing similar calcium score (error < 6%) for a patient at reduced dose. Our proposed learning-based approach shows robust performance across datasets.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Vanika Singhal, Sidharth Abrol, Daphne Mulot, Amy Deubig, Sandeep Dutta, and Bipul Das "Low does calcium scoring in cardiac computer tomography using deep learning", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292726 (3 April 2024); https://doi.org/10.1117/12.3005114

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