Penalized-likelihood Algorithms for Time-Domain MR Diffusion Measure Estimation

描述（由申请人提供）： 项目概要：本文提出的研究旨在通过补偿数据中的高水平噪声和失真，从扩散加权磁共振（MR）数据中获得扩散表示（图像，张量，频谱）的鲁棒估计。虽然该算法将广泛适用于扩散MRI，但感兴趣的应用是脑白质结构的成像。所提出的方法是一个惩罚似然（PL）框架，其中的扩散表示估计通过最大化的目标函数，该目标函数包括一个似然项，适合的解决方案的原始MR数据加上一个正则化项，惩罚过于嘈杂的解决方案。该算法将利用来自扫描仪的原始时域数据，避免过度简化的傅里叶变换数据模型。该框架的第一个组成部分，涉及PL方法与磁场不均匀性校正张量估计，正在原型，并将在指导阶段的奖励。在后面的阶段，这些组件将被纳入扩散谱估计。在开发的同时，将使用高分辨率离体数据作为金标准来评估方法并优化可能性和正则化项的相对权重，即，平滑的量。该项目符合候选人的长期职业目标，即建立一个高质量的独立研究项目，研究跨越不同模式的医学成像逆问题。它还将促进候选人的直接目标，成为扩散MR数据分析的专家，并通过翻译她以前在发射断层扫描统计重建工作中获得的技能来推进这一领域。指导阶段将在MGH/哈佛/麻省理工学院Martinos生物医学成像中心进行。候选人将利用该中心的尖端MRI设施和专业知识，以及其合作机构的世界级教育机会。她的职业发展计划包括MR数据采集培训;与该领域专家的咨询; MR物理学和神经科学课程;研讨会和科学会议。作为启动自己的独立研究计划的一部分，候选人将指导一名研究生，该研究生将有望为该项目做出贡献。相关性：从弥散加权MR数据中提取的信息用于医学，例如，监测中风患者的大脑功能;检测精神分裂症、多发性硬化症和阿尔茨海默氏症等疾病的影响;评估新生儿大脑发育;研究大脑区域的连通性。这项工作的长期目标是开发算法，提高质量的措施估计扩散加权MR数据。因此，它有可能使这一广泛和不断增长的医疗应用受益，并促进公共卫生的重要领域。

DESCRIPTION (provided by applicant): Project summary: The research proposed herein aims at obtaining robust estimates of diffusion representations (images, tensors, spectra) from diffusion-weighted magnetic resonance (MR) data, by compensating for the high levels of noise and distortions in the data. Although the algorithms will be widely applicable to diffusion MRI, the application of interest is the imaging of cerebral white-matter structures. The proposed approach is that of a penalized likelihood (PL) framework, where the diffusion representations are estimated by maximizing an objective function that consists of a likelihood term that fits the solution to the raw MR data plus a regularization term that penalizes overly noisy solutions. The algorithms will utilize the raw time-domain data from the scanner, avoiding the oversimplified Fourier transform data model. The first components of the framework, involving a PL approach to tensor estimation with magnetic field inhomogeneity correction, are being prototyped and will be completed during the mentored phase of the award. In later stages, these components will be incorporated in diffusion spectrum estimation. In parallel to development, high-resolution ex vivo data will be used as a gold standard to evaluate the methods and optimize the relative weighting of the likelihood and regularization terms, i.e., the amount of smoothing. The project fits the candidate's long-term career goal of establishing a high-quality independent research program on inverse problems in medical imaging that spans different modalities. It will also facilitate the candidate's immediate goals of becoming an expert in diffusion MR data analysis and advancing this field by translating the skills acquired in her previous work in statistical reconstruction for emission tomography. The mentored phase will be performed at the MGH/Harvard/MIT Martinos Center for Biomedical Imaging. The candidate will take advantage of the cutting-edge MRI facilities and expertise at the Center, as well as the world-class educational opportunities at its collaborating institutions. Her career development plan includes training in MR data acquisition; consultations with experts of the field; coursework in MR physics and neuroscience; seminars and scientific meetings. As part of launching her own independent research program, the candidate will mentor a graduate student who will be expected to contribute to this project. Relevance: Information extracted from diffusion-weighted MR data is used in medicine, e.g., to monitor brain function in stroke patients; to detect the effects of diseases such as schizophrenia, multiple sclerosis and Alzheimer's; to assess newborn brain development; and to research connectivity of brain regions. The long- term objective of this work is to develop algorithms that enhance the quality of the measures estimated from diffusion-weighted MR data. As such, it has the potential to benefit this wide and growing range of medical applications and promote important areas of public health.