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Cardiac CT plays an important role in diagnosing heart diseases but is conventionally limited by its complex workflow that requires dedicated phase and bolus tracking [e.g., electrocardiogram (ECG) gating]. This work reports initial progress towards robust and autonomous cardiac CT exams through deep learning (DL) analysis of pulsed-mode projections (PMPs). To this end, cardiac phase and its uncertainty were simultaneously estimated using a novel projection domain cardiac phase estimation network (PhaseNet), which utilizes a sliding-window multi-channel feature extraction approach and a long short-term memory (LSTM) block to extract temporal correlation between time-distributed PMPs. Monte-Carlo dropout layers were utilized to predict the uncertainty of deep learning-based cardiac phase prediction. The performance of the proposed phase estimation pipeline was evaluated using accurate physics-based emulated data.
PhaseNet demonstrated improved phase estimation accuracy compared to more standard methods in terms of RMSE (~43% improvement vs. a standard CNN-LSTM; ~17% improvement vs. a multi-channel residual network [ResNet]), achieving accurate phase estimation with <8% RMSE in cardiac phase (phase ranges from 0-100%). These findings suggest that the cardiac phase can be accurately estimated with the proposed projection domain approach. Combined with our previous work on PMP-based bolus curve estimation, the proposed method could potentially be used to achieve autonomous cardiac CT scanning without ECG device or expert-in-the-loop bolus timing.
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