Prior Image Constrained Total Variation-Stokes for Cerebral Perfusion CT Imaging

Cerebral perfusion computed tomography (CPCT) can non-invasively and rapidly assess blood flow circulation in the brain, making it widely adopted in clinical settings. However, the dynamic scanning protocol associated with CPCT entails substantial ionizing radiation exposure, leading to elevated radiation risks. Lowering the tube current can efficiently reduce radiation dose, but leads to significant image quality deterioration for standard filtered back-projection (FBP) algorithm due to increased quantum noise in measured projection data. In this study, we present an iterative image reconstruction method to improve the low-dose CPCT image quality, which uses the prior image constrained total variation-stokes (PICTVS) based on the penalized weighted least squares (PWLS) criterion. This method leverages information from the prior image to enhance the image quality of low-dose CPCT. Specifically, PICTVS utilizes high-quality geometric structural information from the prior image and fuses it into low-dose CPCT image reconstruction while preserving the main features of the target image. An effective alternating minimization method was developed to solve the objective function associated with the PWLS-PICTVS reconstruction. The novelty of the PWLS-PICTVS algorithm is listed as follows: (1) The PICTVS regularization incorporates the structural information of prior image into the target image where the gradients of both images align; (2) In image areas where the gradients differ, the PICTVS regularization employs total variation (TV) instead; and (3) The PICTVS regularization facilitates the integration of shared edge structure information from a high-quality prior image into the low-dose image while avoiding introducing mismatched anatomy information. Qualitative and quantitative analyses were conducted to assess the efficacy of the PWLS-PICTVS image reconstruction algorithm using a digital brain perfusion phantom and simulated low-dose clinical patient data. The experimental results show that the PWLS-PICTVS algorithm significantly improves noise suppression, streak artifact reduction, and edge preservation when compared with the other reconstruction methods. Importantly, the CPCT images reconstructed using the PWLS-PICTVS method yield more accurate hemodynamic parameter maps, enhancing their potential for clinical diagnosis.