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基于参考图像的压缩感知磁共振扩散张量成像
引用本文:刘良友,李兆同,张泽茹,夏一帆,高嵩. 基于参考图像的压缩感知磁共振扩散张量成像[J]. 中国医学物理学杂志, 2021, 0(3): 323-326. DOI: DOI:10.3969/j.issn.1005-202X.2021.03.010
作者姓名:刘良友  李兆同  张泽茹  夏一帆  高嵩
作者单位:1.北京大学医学技术研究院, 北京 100191; 2.北京大学医学人文学院, 北京 100191
基金项目:国家自然科学基金(12075011,82071280);北京市自然科学基金(7202093);西藏自治区重点研发计划(XZ202001ZY0005G)。
摘    要:基于参考图像的压缩感知磁共振扩散张量成像方法,利用相邻方向的扩散加权图像差异较小的特点,采用压缩感知理论实现快速扩散张量成像,回顾性选取扩散张量图像数据进行实验研究,在采样率为50%的均匀分布辐射线欠采样方式下进行基于参考图像的压缩感知扩散张量图像重建,结果表明重建后的扩散加权图的平均结构相似性(MSSIM)和峰值信噪比(PSNR)分别为0.904±0.044、(37.92±3.89) dB,各向异性分数图的MSSIM和PSNR分别为0.992、41.64 dB。因此,该方法在保证重建图像质量的前提下,可显著缩短数据采集时间,减少由于时间过长引起的图像伪影等问题。

关 键 词:扩散张量成像  高角分辨率扩散成像  参考图像  压缩感知

Compressed sensing diffusion tensor imaging based on reference image
LIU Liangyou,LI Zhaotong,ZHANG Zeru,XIAYifan,GAO Song. Compressed sensing diffusion tensor imaging based on reference image[J]. Chinese Journal of Medical Physics, 2021, 0(3): 323-326. DOI: DOI:10.3969/j.issn.1005-202X.2021.03.010
Authors:LIU Liangyou  LI Zhaotong  ZHANG Zeru  XIAYifan  GAO Song
Affiliation:1. Institute of Medical Technology, Peking University, Beijing 100191, China 2. School of Health Humanities, Peking University, Beijing 100191, China
Abstract:A method of compressed sensing diffusion tensor imaging based on reference image which utilizes the small differences between diffusion-weighted images in adjacent directions and compressed sensing theory to realize fast diffusion tensor imaging is proposed in the study,and diffusion tensor image data are retrospectively selected for experimental research.Compressed sensing diffusion tensor image reconstruction is performed based on reference image under a uniformly distributed radiation under-sampling method with a sampling rate of 50%.The results show that the mean structural similarity and peak signal-noise ratio of the reconstructed diffusion-weighted images are 0.904±0.044 and(37.92±3.89)dB,respectively,and those of fractional anisotropy images are 0.992 and 41.64 dB,respectively.Under the premise of ensuring the quality of the reconstructed image,using the proposed method can significantly shorten data acquisition time and reduce image artifacts caused by long imaging time.
Keywords:diffusion tensor imaging  high angular resolution diffusion imaging  reference image  compressed sensing
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