Noise modelling of perfusion CT images for robust hemodynamic parameter estimations

Abstract

Abstract Objective. The radiation dose of cerebral perfusion computed tomography (CPCT) imaging can be reduced by lowering the milliampere-second or kilovoltage peak. However, dose reduction can decrease image quality due to excessive x-ray quanta fluctuation and reduced detector signal relative to system electronic noise, thereby influencing the accuracy of hemodynamic parameters for patients with acute stroke. Existing low-dose CPCT denoising methods, which mainly focus on specific temporal and spatial prior knowledge in low-dose CPCT images, not take the noise distribution characteristics of low-dose CPCT images into consideration. In practice, the noise of low-dose CPCT images can be much more complicated. This study first investigates the noise properties in low-dose CPCT images and proposes a perfusion deconvolution model based on the noise properties. Approach. To characterize the noise distribution in CPCT images properly, we analyze noise properties in low-dose CPCT images and find that the intra-frame noise distribution may vary in the different areas and the inter-frame noise also may vary in low-dose CPCT images. Thus, we attempt the first-ever effort to model CPCT noise with a non-independent and identical distribution (i.i.d.) mixture-of-Gaussians (MoG) model for noise assumption. Furthermore, we integrate the noise modeling strategy into a perfusion deconvolution model and present a novel perfusion deconvolution method by using self-relative structural similarity information and MoG model (named as SR-MoG) to estimate the hemodynamic parameters accurately. In the presented SR-MoG method, the self-relative structural similarity information is obtained from preprocessed low-dose CPCT images. Main results. The results show that the presented SR-MoG method can achieve promising gains over the existing deconvolution approaches. In particular, the average root-mean-square error (RMSE) of cerebral blood flow (CBF), cerebral blood volume, and mean transit time was improved by 40.3%, 69.1%, and 40.8% in the digital phantom study, and the average RMSE of CBF can be improved by 81.0% in the clinical data study, compared with tensor total variation regularization deconvolution method. Significance. The presented SR-MoG method can estimate high-accuracy hemodynamic parameters andachieve promising gains over the existing deconvolution approaches.