祝贺段嘉毓同学的论文被国际权威杂志Physics in Medicine and Biology接受 - 个人简介 - 牟 轩沁
段嘉毓同学为第一作者的论文“Image Quality Guided Iterative Reconstruction for Low-dose CT Based on CT Image Statistics”被本领域的国际权威杂志Physics in Medicine and Biology杂志接受发表，在此表示祝贺！
Title：Image Quality Guided Iterative Reconstruction for Low-dose CT Based on CT Image Statistics
Authors：Jiayu Duan and Xuanqin Mou
Iterative reconstruction framework shows predominance in low dose and incomplete data situation. In the iterative reconstruction framework, there are two components, i.e., fidelity term aims to maintain the structure details of the reconstructed object, and the regularization term uses prior information to suppress the artifacts such as noise. A regularization parameter balances them, aiming to find a good trade-off between noise and resolution. Currently, the regularization parameters are selected as a rule of thumb or some prior knowledge assumption is required, which limits practical uses. Furthermore, the computation cost of regularization parameter selection is also heavy. In this paper, we address this problem by introducing CT image quality assessment (IQA) into the iterative reconstruction framework. Several steps are involved during the study. First, we analyze the CT image statistics using the dual dictionary method. Regularities are observed and concluded, revealing the relationship among the regularization parameter, iterations, and CT image quality. Second, with derivation and simplification of DDL procedure, a CT IQA metric named SODVAC is designed. SODVAC locates the optimal regularization parameter that results in the reconstructed image with distinct structures and with no noise or little noise. Third, we propose a general image-quality guided iterative reconstruction (QIR) framework and give a specific framework example (sQIR) by introducing SODVAC into the iterative reconstruction framework.. sQIR simultaneously optimizes the reconstructed image and the regularization parameter during the iterations. Results confirm the effectiveness of the proposed method. No prior information needed and low computation costs are the advantages of our method compared with existing state-of-the-art L-curve and ZIP selection strategies.