Abstract #4807

A Convergence Analysis for Projected Fast Iterative Soft-thresholding Algorithm under Radial Sampling MRI

Biao Qu¹, Zuwen Zhang², Yewei Chen², Chen Qian², Taishan Kang³, Jianzhong Lin³, Lihua Chen⁴, Zhigang Wu⁵, Jiazheng Wang⁵, Gaofeng Zheng¹, and Xiaobo Qu²

¹Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China, ²Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China, ³Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University,, Xiamen, China, ⁴Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China, ⁵Philips, Beijing, China

Synopsis

Keywords: Image Reconstruction, Image ReconstructionRadial sampling is a fast magnetic resonance imaging technique. The projected fast iterative soft-thresholding algorithm (pFISTA) has shown the advantage to solved tight frame sparse reconstruction model for removing undersampling image artifacts. However, the convergence of this algorithm under radial sampling has not been clearly set up. In this work, the authors derived a theoretical convergence condition for this algorithm and an optimal step size was further suggested to allow the fastest convergence. Verifications were made in vivo data of static brain imaging and dynamic contrast-enhanced (DCE) liver imaging, demonstrating that the recommended parameter allowed fast convergence in radial MRI.

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