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作 者:刘元元[1] 杨育昕 朱庆永 崔卓须 程静 刘聪聪 梁栋[1,3] 朱燕杰[1] LIU Yuan-yuan;YANG Yu-xin;ZHU Qing-yong;CUI Zhuo-xu;CHENG Jing;LIU Cong-cong;LIANG Dong;ZHU Yan-jie(Paul C.Lauterbur Research Centre for Biomedical Imaging,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;Department of Biomedical Engineering,Chongqing University of Technology,Chongqing 400054,China;Research Center for Medical AI,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China)
机构地区:[1]保罗C.劳特伯生物医学成像研究中心,中国科学院深圳先进技术研究院,广东深圳518055 [2]药学与生物工程学院,重庆理工大学,重庆400054 [3]医学人工智能研究中心,中国科学院深圳先进技术研究院,广东深圳518055
出 处:《波谱学杂志》2022年第3期243-257,共15页Chinese Journal of Magnetic Resonance
基 金:中国科学院磁共振技术联盟仪器设备功能开发技术创新项目(2020GZL006);国家重点研发计划课题(2020YFA0712200);广东省基础与应用基础研究基金项目(2021A1515110540);深圳市优秀科技创新人才培养项目(RCYX20210609104444089);中国博士后科学基金面上项目(2020M682990,2021M69331,2021M703390);广东省磁共振成像与多模系统重点实验室(2020B1212060051).
摘 要:定量磁共振成像(MRI)可量化组织特性,是科学研究和临床研究的重要工具.旋转坐标系下的自旋-晶格弛豫时间(T_(1ρ))能反映水与大分子之间的低频交互作用,在3 T及以上的高场环境下,T_(1ρ)受水和不稳定质子之间化学交换的影响较大,通过测量弛豫率随自旋锁定场强度的变化而得到其分布情况(T_(1ρ)散布),可用于分析和量化质子的交换过程,因此T_(1ρ)散布是一种重要的定量MRI技术.然而,获得不同自旋锁定场强下T_(1ρ)加权图像的时间过长,限制了其应用范围.针对这一问题,本研究提出一种基于多弛豫信号补偿策略的快速T_(1ρ)散布成像方法.该方法将不同锁定频率下的T_(1ρ)加权图像补偿到同一信号强度水平,并结合低秩与稀疏建立重建模型.实验结果表明,该方法在加速倍数高达7倍时仍获得了较好的重建结果.Magnetic resonance imaging(MRI)can quantify characteristic values of tissues,serving as an important tool for scientific and clinical research.Magnetic resonance T_(1ρ)relaxation time reflects the low-frequency motional processes between water and macromolecules.At high fields of 3 T and above,T_(1ρ)is greatly affected by the chemical exchange between water and exchangeable protons,and T_(1ρ)dispersion measured with varying spin-lock fields can be utilized to analyze and quantify the proton exchange process.However,it is time-consuming to obtain T_(1ρ)-weighted images with different spin-lock fields,which limits its application.To solve this problem,a fast T_(1ρ)dispersion imaging method based on multiple relaxation signal compensation strategy is proposed in this work,which compensates the T_(1ρ)-weighted images at different locking frequencies to the same signal strength level,and combines the low-rank plus sparse model in the reconstruction.Experimental results show that the proposed method achieves good reconstruction results even when the acceleration factor is up to 7.
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