Radiology Commons^™

Sparsity Promoting Regularization For Effective Noise Suppression In Spect Image Reconstruction, Wei Zheng, Si Li, Andrzej Krol, C. Ross Schmidtlein, Xueying Zeng, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The purpose of this research is to develop an advanced reconstruction method for low-count, hence high-noise, Single-Photon Emission Computed Tomography (SPECT) image reconstruction. It consists of a novel reconstruction model to suppress noise while conducting reconstruction and an efficient algorithm to solve the model. A novel regularizer is introduced as the nonconvex denoising term based on the approximate sparsity of the image under a geometric tight frame transform domain. The deblurring term is based on the negative log-likelihood of the SPECT data model. To solve the resulting nonconvex optimization problem a Preconditioned Fixed-point Proximity Algorithm (PFPA) is introduced. We prove …