Wu Ye, Liu Xiaoming, Zhang Xinyuan, Huynh Khoi Minh, Ahmad Sahar, Yap Pew-Thian
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Med Image Comput Comput Assist Interv. 2023 Oct;14227:152-162. doi: 10.1007/978-3-031-43993-3_15. Epub 2023 Oct 1.
Brain tissue microarchitecture is characterized by heterogeneous degrees of diffusivity and rates of transverse relaxation. Unlike standard diffusion MRI with a single echo time (TE), which provides information primarily on diffusivity, relaxation-diffusion MRI involves multiple TEs and multiple diffusion-weighting strengths for probing tissue-specific coupling between relaxation and diffusivity. Here, we introduce a relaxation-diffusion model that characterizes tissue apparent relaxation coefficients for a spectrum of diffusion length scales and at the same time factors out the effects of intra-voxel orientation heterogeneity. We examined the model with an in vivo dataset, acquired using a clinical scanner, involving different health conditions. Experimental results indicate that our model caters to heterogeneous tissue microstructure and can distinguish fiber bundles with similar diffusivities but different relaxation rates. Code with sample data is available at https://github.com/dryewu/RDSI.
脑组织微结构的特征在于扩散率和横向弛豫率的异质性程度。与具有单个回波时间（TE）的标准扩散磁共振成像不同，标准扩散磁共振成像主要提供关于扩散率的信息，弛豫扩散磁共振成像涉及多个TE和多个扩散加权强度，用于探测弛豫和扩散率之间的组织特异性耦合。在这里，我们引入了一个弛豫扩散模型，该模型表征了一系列扩散长度尺度下的组织表观弛豫系数，同时排除了体素内取向异质性的影响。我们使用临床扫描仪获取的涉及不同健康状况的体内数据集对该模型进行了检验。实验结果表明，我们的模型适用于异质组织微结构，并且能够区分具有相似扩散率但不同弛豫率的纤维束。带有示例数据的代码可在https://github.com/dryewu/RDSI获取。
