Cardiac CT reconstruction for vendor-neutral virtual imaging trials
Cardiac CT imaging is invaluable in a variety of applications including non-invasive assessment of coronary artery calcifications, the ruling out of acute coronary artery syndrome, and the planning of valve replacement procedures. Newer applications include CT-derived fractional flow reserve and cardiac perfusion with new research opportunities through photon counting CT and deep learning. To facilitate advancements in research and clinical practice, we developed a vendor-neutral pipeline for virtual imaging trials involving cardiac CT simulation and reconstruction. Specifically, we developed a reconstruction method to be combined with dynamic virtual patients (XCAT) and a CT simulator (DukeSim) to generate realistic, retrospectively gated, helical cardiac CT projection data sets. Reconstruction was performed using a multi-channel GPU-based reconstruction toolkit. In this study, CT simulation experiments were performed using a standard adult male XCAT phantom (50th percentile in height and weight) with heart rates of 60, 90, and 120 bpm. The simulated acquisitions were done with a tube voltage of 120 kV, a pitch of 0.14, and a rotation time of 280 msec. Reconstruction was performed analytically, with rebinning and weighted filtered backprojection, and iteratively, with the split Bregman optimization method and low rank and gradient sparsity regularizers. Image quality (SSIM, RMSE) and temporal resolution were evaluated and compared with the ground truth phantoms. Iterative reconstruction was shown to reduce noise power by one order of magnitude without significantly impacting image resolution (NPS, MTF). Analytical reconstructions lead to less than 8% error in the measurement of standard cardiac functional metrics relative to ground truth values.
