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 that under appropriate assumptions, PFPA converges to a local solution of the optimization problem at a global O (1/k) convergence rate. Substantial numerical results for simulation data are presented to demonstrate the superiority of the proposed method in denoising, suppressing artifacts and reconstruction accuracy. We simulate noisy 2D SPECT data from two phantoms: hot Gaussian spheres on random lumpy warm background, and the anthropomorphic brain phantom, at high- and low-noise levels (64k and 90k counts, respectively), and reconstruct them using PFPA. We also perform limited comparative studies with selected competing state-of-the-art total variation (TV) and higher-order TV (HOTV) transform-based methods, and widely used post-filtered maximum-likelihood expectation-maximization. We investigate imaging performance of these methods using: Contrast-to-Noise Ratio (CNR), Ensemble Variance Images (EVI), Background Ensemble Noise (BEN), Normalized Mean-Square Error (NMSE), and Channelized Hotelling Observer (CHO) detectability. Each of the competing methods is independently optimized for each metric. We establish that the proposed method outperforms the other approaches in all image quality metrics except NMSE where it is matched by HOTV. The superiority of the proposed method is especially evident in the CHO detectability tests results. We also perform qualitative image evaluation for presence and severity of image artifacts where it also performs better in terms of suppressing "staircase" artifacts, as compared to TV methods. However, edge artifacts on high-contrast regions persist. We conclude that the proposed method may offer a powerful tool for detection tasks in high-noise SPECT imaging.
Original Publication Citation
Zheng, W., Li, S., Krol, A., Schmidtlein, C. R., Zeng, X., & Xu, Y. (2019). Sparsity promoting regularization for effective noise suppression in SPECT image reconstruction. Inverse Problems, 101985.R101982. doi:10.1088/1361-6420/ab23da
Zheng, Wei; Li, Si; Krol, Andrzej; Schmidtlein, C. Ross; Zeng, Xueying; and Xu, Yuesheng, "Sparsity Promoting Regularization for Effective Noise Suppression in SPECT Image Reconstruction" (2019). Mathematics & Statistics Faculty Publications. 161.
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