High Resolution Cerebral Perfusion Deconvolution via Mixture of Gaussian Model based on Noise Properties

Cerebral perfusion computed tomography (CPCT) imaging provides a rapid and accurate noninvasive measurements of the acute stroke by generating hemodynamic parameter maps with a qualitative and quantitative way. However, due to it performs a multiple consecutive scanning protocol at one area of the head, the radiation exposure is relatively higher than a routine protocol. And lowering radiation dose in CPCT protocol would increase the amount of noise and hence influence hemodynamic parameters for patients with acute stroke. Some advanced methods have been proposed and show a great potential in noise suppression for low-dose CPCT imaging. And most of them assume that the embedded noise obeys an independent and identically distribution (i.i.d), but the noise may be more complicated in practical scenarios. In this work, we first analysze the noise properties in low-dose CPCT images. And then present a novel perfusion deconvolution method with a self-relative structure similarity information and a mixture of Gaussians (MoG) noise model (named SR-MoG) to accurately estimate the hemodynamic parameters directly at the low radiation exposure. Experiments implemented on digital brain perfusion phantom verify that the presented SR-MoG method can achieve promising gains over the existing deconvolution approaches.