Abstract
Dynamic computed tomography (CT) perfusion is a clinically-established imaging method for estimating cerebral perfusion in conditions such as stroke. Low-dose CT perfusion (CTP) imaging suffers from inherent low signal-to-noise ratio (SNR) that affects the quality and accuracy of the derived perfusion maps. We propose a framework to jointly estimate the structural CT images and the functional CBF map using a generalized sparsity prior suitable for low-dose acquisition schemes. We hypothesize that the joint estimation would improve image quality of both CT images and the CBF maps in comparison to image quality of CBF maps obtained through (i) independent two-stage process and (ii) the direct deconvolution methods with prior information. Through empirical analysis on two different in vivo datasets, we demonstrate the efficacy of our method over the state-of-the-art methods on multiple low-dose settings.
Published Version
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