Estimation of contrast agent concentration from pulsed-mode projections to time contrast-enhanced CT scans

Cardiac CT exams are some of the most complex CT exams due to the need to carefully time the scan to capture the heart during a quiescent cardiac phase and when the intravenous contrast bolus is at its peak concentration in the left and/or right heart. We are interested in developing a robust and autonomous cardiac CT exam, using deep learning approaches to extract contrast and cardiac phase timing directly from projections. In this paper, we present a new approach to estimate contrast bolus timing directly from a sparse set of CT projections. We present a deep learning approach to estimate contrast agent concentration in left and right sides of the heart directly from a set of projections. We use a virtual imaging framework to generate training and test data, derived from real patient datasets. We finally combine this with a simple analytical approach to decide on the start of the cardiac CT exam.