Noise suppression for cerebral perfusion CT via intrinsic tensor sparsity regularization: Initial study

Cerebral perfusion computed tomography (CPCT) provides functional information about capillary-level hemody-namic of brain parenchyma, which is an important tool for evaluating acute stroke, vasospasm and other neurovascular disorders. Meanwhile, the side effect of accumulated radiation, relating to genetic or cancerous diseases due to repeated dynamic scans at the same site, has caused great public concerns. One interesting general approach to counter the effect of overdose of radiation is to develop an iterative image restoration algorithm. In this paper, we focus on presenting a tensor-based restoration approach via fully adopting the intrinsic tensor sparsity (ITS) characteristics of CPCT sequential images. For simplicity, the present approach is termed as “CP-ITS”. More specifically, the gains of ITS measure are to adopt the nonlocal self-similarity across space and global correlation along frames, therefore ITS measure can encode both sparsity insights delivered by the most Tucker and CANDECOMP/PAREFAC (CP) low-rank decomposition for a general tensor. Then the ITS measure is introduced into the CPCT images restoration model through characterizing the tensors formed by nonlocal similar patches with in CPCT images. To minimize the associative objective function, we proposed an efficient alternating direction method of multipliers (ADMM) based algorithm. Extensive evaluations on the digital phantom data demonstrate the performance of the present CP-ITS approach for low-dose CPCT imaging.