Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study

Dynamic myocardial perfusion computed tomography (MPCT) is a promising technique for quick diagnosis and risk stratification of coronary artery disease. However, one major drawback of dynamic MPCT imaging is the heavy radiation dose to patients due to its dynamic images acquisition protocol. In this work, to address this issue, we present a robust dynamic MPCT deconvolution algorithm via adaptive-weighted tensor total variation (AwTTV) regularization for accurate residue function estimation with low-mAs data acquisitions. For simplicity, the presented method is termed as “MPD-AwTTV”. More specifically, the gains of the AwTTV regularization are from the anisotropic edge property of the sequential MPCT images over the original tensor total variation regularization. To minimize the associative objective function we propose an efficient iterative optimization strategy with fast convergence rate under the framework of iterative shrinkage/thresholding algorithm. We validate and evaluate the presented algorithm using both digital XCAT phantom and preclinical porcine data. The preliminary experimental results have demonstrated that the presented MPD-AwTTV deconvolution algorithm can achieve remarkable gains in noise-induced artifacts suppression, edge details preservation and accurate flow-scaled residue function and MPHM estimation as compared with the other existing deconvolution algorithms in the digital phantom studies, and the similar gains can be obtained in the porcine data experiment.