Cardiac CT (CCT) is of vital importance in heart disease diagnosis but is conventionally limited by its complex workflow that requires dedicated phase and bolus monitoring devices [e.g., electrocardiogram (ECG) gating]. Our previous work has demonstrated the possibility of replacing ECG devices with deep learning (DL)-based monitoring of continuously acquired pulsed mode projections (PMPs, i.e., only a few sparsely sampled projections per gantry rotation). In this work, we report the development of a new projection domain DL-based cardiac phase estimation method that uses ensemble learning [i.e., training multiple convolution neural network (CNN) in parallel] to estimate and reduce DL uncertainty. The estimated DL uncertainty information was then used drive an analytical regularizer in a principled time-dependent manner (i.e., stronger regularization when DL uncertainty is higher). Combined with our previous work on PMP-based bolus curve estimation, the proposed method could potentially be used to achieve autonomous cardiac scanning in a robust (i.e., reduced uncertainty) manner without ECG and bolus timing devices.
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