Fast Multi-dimensional Diffusion MRI with Sparse Sampling and Model-based Deep Learning Reconstruction

神经退行性疾病是一个重要的公共卫生和经济问题， 全球残疾的主要原因。了解特定的退化过程， 在疾病过程中的进展对于开发靶向药物治疗和决定 治疗方案。此外，了解结构连接性的变化， 受影响的电路在开发电路特异性非侵入性脑刺激疗法中至关重要。扩散- 基于MRI的测定可以提供对（i）神经退行性病变高度敏感的微结构测量， （二）互联互通的变化。可以利用先进的建模方法来进一步增强 显微结构测量对潜在神经退行性过程的特异性。但他们的 实用性通常限于纯白色物质区域。在弥散MRI的典型空间分辨率下（~ 2 mm 各向同性体素尺寸），在大多数脑体素中存在显著的部分体积效应（例如，具有多个 组织类型、具有不同性质的异质纤维）。在全脑研究中， 通过先进的建模方法确定的疾病过程的特异性;它也有助于 连接映射不准确。另外，扩散参数编码空间当前限于 一个或两个低b值（B<2000 s/mm 2）的壳体，这限制了几个相关的唯一确定 微观结构参数该提案的主要目标是开发，验证和临床 本发明提供了一种扩散MRI测定的平移，其使得能够在亚像素处对扩散参数空间进行有效编码。 毫米体素分辨率，用于整个大脑中的关节微观结构和连接映射。我们的整体 假设所提出的框架可以显着提高微观结构建模的有效性， 大多数脑体素。拟议的开发将利用SNR高效的3D多厚片采集。 结合时间高效的稀疏k-q采样，编码将跨越多个b壳。以允许 几个相关的微观结构参数的独特测定，多房室T2信息将被 利用。拟议的发展将通过两种先进的重建方法实现：结构化低- 秩矩阵完成，一种新的MRI重建综合框架，具有多种功能 包括多回波成像和自校准重建;以及基于模型的深度学习，一种新的深度 架构，以系统的方式使用神经网络解决MR重建算法。这些 这些方法克服了与将3D多厚片采集扩展到多个厚片相关联的几个低效率。 k-q-TE空间中的三维成像。为了确保科学的严谨性，我们将全面验证我们的 使用不同量化的专用扩散体模沿着健康志愿者的技术 指标.我们还使用横断面多模态MRI研究验证了dMRI检测的能力。 亨廷顿病队列研究

Project Summary: Neurodegenerative disorders are a significant public health and economic problem and are the leading cause of disability worldwide. Understanding the specific degenerative processes that are actively progressing over the course of the illness is crucial for developing targeted drugs therapies and deciding treatment options. Additionally, understanding the structural connectivity changes to tease apart the specific circuitry affected is crucial in developing circuit specific non-invasive brain stimulation therapies. Diffusion- based MRI assays can provide microstructural measures that are highly sensitive to (i) the neurodegenerative processes and (ii) connectivity changes. Advanced modeling approaches can be utilized to further enhance the specificity of the microstructural measures to the underlying neurodegenerative processes. However, their utility is often limited to pure white matter regions. At the typical spatial resolution of diffusion MRI (~2mm isotropic voxel size), significant partial volume effects exist in most brain voxels (e.g., voxels with multiple tissue types, heterogenous fibers with different properties). In whole brain studies, this compromises the specificity of the disease processes identified by the advanced modeling approaches; it also contributes to inaccurate connectivity mapping. Additionally, the diffusion parameter encoding space is currently limited to one or two shells of low b-values (b<2000s/mm2), which limits the unique determination of several relevant microstructural parameters. The main objective of the proposal is the development, validation and clinical translation of a diffusion MRI assay that enable efficient encoding of diffusion parameter space at sub- millimeter voxel resolution for joint microstructure and connectivity mapping in the whole brain. Our overall hypothesis is that the proposed framework can significantly improve the validity of microstructural modeling in most brain voxels. The proposed development will make use of SNR-efficient 3D multi-slab acquisitions. Coupled with time-efficient sparse k-q sampling, the encoding will span over multiple b-shells. To allow the unique determination of several relevant microstructural parameters, multicompartmental T2 information will be utilized. The proposed developments will be enabled by two advanced reconstruction methods: structured low- rank matrix completion, a novel integrative framework for MRI reconstructions that enables several capabilities including multi-echo imaging and self-calibrating reconstruction; and model-based deep learning, a novel deep architecture to solve MR reconstruction algorithms using neural networks in a systematic fashion. These methods overcome several inefficiencies associated with extending the 3D multi-slab acquisition for multi- dimensional imaging in the k-q-TE space. To ensure scientific rigor, we will comprehensively validate our technology on dedicated diffusion phantoms along with healthy volunteers using different quantification metrics. We also validate the capability of the dMRI assay using a multi-modal MRI study in a cross-sectional study on a cohort of Huntington's disease.