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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.