PROJECT SUMMARY/ABSTRACT Coronary heart disease (CHD) is the leading cause of death worldwide. An estimated 3.8 million men and 3.4 million women die each year from CHD. Cardiac CT is a safe, accurate, non-invasive imaging modality used for diagnosing CHD and for planning therapeutic interventions. Cardiac CT exams are still challenging to perform due to the beating heart and the need to carefully time the scan based on cardiac phase and based on when the peak iodine contrast enhancement is reached. The overall exam duration and the complexity of performing these exams (contrasted with limited reimbursement levels) have limited patient access to cardiac CT to academic hospitals and specialized cardiac imaging centers. As compared to other CT exams, cardiac CT exams require additional patient preparation time, additional CT scans to track the bolus, and additional contrast agent to avoid missing the peak enhancement. The goal of this project is to develop a smart cardiac CT scanner that autonomously determines the optimal scan time interval without ECG, traditional bolus tracking or timing bolus. Initial results show that it is possible to extract cardiac gating information from a few CT projection measurements prior to the diagnostic CT scan, without reconstruction. This is made possible by an innovative combination of fast X-ray tube pulsing and deep learning raw data analysis. This project builds on GE Research's experience with cardiac CT technologies, deep learning algorithms and X-ray tube physics, as well as the strong clinical cardiac CT expertise at the University of California San Diego. The outcome of this project will be a clinical feasibility study of the autonomous triggering approach, which has the potential to simplify and increase patient access to cardiac CT, while reducing exam time, reducing con- trast agent volume, and ensuring robust image quality.