School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Center for Robotics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Biomed Res Int. 2016;2016:2738231. doi: 10.1155/2016/2738231. Epub 2016 Dec 20.
Quantitative susceptibility mapping (QSM) has shown its potential for anatomical and functional MRI, as it can quantify, for tissues, magnetic biomarkers and contrast agents which have differential susceptibilities to the surroundings substances. For reconstructing the QSM with a single orientation, various methods have been proposed to identify a unique solution for the susceptibility map. Bayesian QSM approach is the major type which uses various regularization terms, such as a piece-wise constant, a smooth, a sparse, or a morphological prior. Six QSM algorithms with or without structure prior are systematically discussed to address the structure prior effects. The methods are evaluated using simulations, phantom experiments with the given susceptibility, and human brain data. The accuracy and image quality of QSM were increased when using structure prior in the simulation and phantom compared to same regularization term without it, respectively. The image quality of QSM method using the structure prior is better comparing, respectively, to the method without it by either sharpening the image or reducing streaking artifacts . The structure priors improve the performance of the various QSMs using regularized minimization including L1, L2, and TV norm.
定量磁敏感图（QSM）已经在解剖学和功能 MRI 中显示出了其潜力，因为它可以定量地测量组织中的磁敏感标志物和对比剂，这些标志物和对比剂对周围物质的磁敏感性不同。为了用单个方向重建 QSM，已经提出了各种方法来为磁化率图确定唯一的解。贝叶斯 QSM 方法是主要类型，它使用各种正则化项，如分段常数、平滑、稀疏或形态学先验。本文系统地讨论了六种带有或不带有结构先验的 QSM 算法，以解决结构先验的影响。该方法使用仿真、具有给定磁化率的体模实验和人脑数据进行了评估。与没有使用结构先验的情况相比，在仿真和体模中使用结构先验可以分别提高 QSM 的准确性和图像质量。与没有使用结构先验的情况相比，使用结构先验的 QSM 方法的图像质量分别通过锐化图像或减少条纹伪影来得到改善。结构先验提高了包括 L1、L2 和 TV 范数在内的正则化最小化的各种 QSM 的性能。
