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

项目摘要：神经退行性疾病是一个重大的公共卫生和经济问题， 是全球导致残疾的主要原因。了解主动的特定退化过程 在疾病过程中的进展对于开发靶向药物疗法和决定 治疗方案。此外，了解结构连接性的变化以梳理特定的 受影响的回路在开发回路特异性非侵入性脑刺激疗法中是至关重要的。扩散- 基于MRI的分析可以提供对(I)神经退行性变高度敏感的微观结构测量 进程和(Ii)连接性变化。可以利用先进的建模方法来进一步增强 微结构措施对潜在的神经退行性变过程的特异性。然而，他们的 效用通常仅限于纯白质区域。在弥散磁共振的典型空间分辨率下(~2 mm 各向同性体素大小)，在大多数脑体素(例如，具有多个 组织类型、具有不同特性的异质纤维)。在全脑研究中，这损害了 高级建模方法确定的疾病过程的特殊性；它还有助于 连接映射不准确。此外，扩散参数编码空间当前被限制为 一个或两个低b值的外壳(b&lt；2000/mm2)，这限制了对几个相关的 微观结构参数。该提案的主要目标是开发、验证和临床应用 一种扩散磁共振成像分析的翻译，其使得能够在亚 毫米级体素分辨率，用于整个大脑的关节微结构和连接映射。我们的整体 假设所提出的框架可以显著提高微结构建模的有效性 大多数脑体素。拟议的开发将利用SNR效率高的3D多平板采集。 再加上时间效率高的稀疏k-q采样，编码将跨越多个b-外壳。以允许 几个相关微观结构参数的唯一确定，多间隔T2信息将 被利用了。拟议的开发将通过两种先进的重建方法实现：结构化低成本- 秩矩阵补全，一种用于MRI重建的新型集成框架，支持多种功能 包括多回波成像和自校准重建；基于模型的深度学习，一种新的深度学习 体系结构，以系统的方式使用神经网络来解决MR重建算法。这些 方法克服了将3D多平板采集扩展为多平板采集的几个低效率问题 K-q-te空间中的维度成像。为确保科学严谨，我们将全面验证我们的 利用不同量化方法建立健康志愿者专用扩散模体的技术 指标。我们还使用横断面的多模式MRI研究来验证dMRI分析的能力 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.