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

具有稀疏采样和基于模型的深度学习重建的快速多维扩散 MRI

• 批准号: 10428538
• 负责人: Merry Mani
• 金额: $ 34.03万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10428538
• 关键词: 3-Dimensional; Acceleration; Affect; Algorithms; Alzheimer' s Disease; Animal Model; Architecture; Biological Assay; Biological Markers; Brain; Cause of Death; Clinical Research; Complement; Coupled; Cross-Sectional Studies; Data; Demyelinations; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disadvantaged; Disease; Effectiveness of Interventions; Ensure; Fiber; Financial compensation; Health; Human; Huntington Disease; Image; Intervention; Iron; Joints; Lead; Learning Module; Link; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Measures; Methods; Modeling; Morphologic artifacts; Motion; Myelin; Nerve Degeneration; Neurodegenerative Disorders; Neurons; Outcome; Parkinson Disease; Pattern; Persons; Pharmaceutical Preparations; Phase II/III Trial; Physics; Play; Process; Property; Protocols documentation; Public Health; Recovery; Resolution; Sampling; Signal Transduction; Specificity; Structure; Study models; Symptoms; Techniques; Technology; Time; Tissue Model; Tissues; Translations; Validation; Water; Work; World Health Organization; advanced disease; axonal degeneration; base; biophysical model; brain circuitry; clinical translation; cohort; deep learning; deep learning model; design; disability; effectiveness evaluation; health economics; healthy volunteer; high resolution imaging; image reconstruction; improved; in vivo; multimodality; neural network; neuroinflammation; neuron loss; noninvasive brain stimulation; novel; physical property; reconstruction; targeted treatment; white matter

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.

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