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

时域MR扩散测度估计的惩罚似然算法

基本信息

批准号：
8292088
负责人：
Anastasia Yendiki
金额：
$ 23.29万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-07-01 至 2013-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8292088
关键词：
Accounting Address Algorithms Alzheimer&apos s Disease Anatomy Anisotropy Brain Brain region Cerebrum Corticospinal Tracts Data Data Quality Development Diffuse Diffusion Diffusion Magnetic Resonance Imaging Discrimination Disease Evaluation Fiber Fourier Transform Frequencies Goals Gold Image Imaging Techniques Impairment Individual Intervention Lead Magnetic Resonance Magnetic Resonance Imaging Manuals Maps Measurement Measures Medical Methods Modeling Monitor Morphologic artifacts Motor Multiple Sclerosis Nature Newborn Infant Noise Pathway interactions Patients Physics Population Process Research Research Personnel Residual state Resolution Scanning Schizophrenia Series Side Solutions Stroke Structure Testing Time Training Uncertainty Water Weight Work base clinically relevant data acquisition design image processing imaging modality improved in vivo interest magnetic field reconstruction statistics tool white matter

项目摘要

The research proposed herein aims at developing accurate and robust methods for the estimation of both voxel-wise diffusion representations (diffusivity or anisotropy maps, diffusion tensor or spectrum maps) and global pathway structure from diffusion-weighted magnetic resonance (DW-MR) 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 probabilistic and it models two types of uncertainty that are present in DW-MR data: uncertainty introduced by the imaging process in the form of distortions and noise, and inherent uncertainty In the structures to be reconstructed due to individual variability in the underlying anatomy. The former will be addressed by accurate modeling of diffusion MR physics, including the effects of magnetic field inhomogeneities, eddy currents, and noise. The latter will be addressed by rich models of white-matter pathway anatomy, obtained by training the model on a set of subjects where major pathways have been defined manually. Cun'ently estimation of diffusion measures is suboptimal in that it is based on distorted images that are reconstructed without consideration for the underiying MR physics and then corrected for the distortions approximately in a series of post-processing steps. In addition reconstruction of white-matter pathways is labor-intensive because of the need for manual intervention to constrain the solution space and guide the tractography with neuroanatomical expertise. By addressing these issues the proposed project will make estimates of diffusion measures more accurate. It will also automate the reconstruction of white-matter pathways, making such studies practical even for large numbers of subjects. The proposed methods are being developed primarily to address the artifacts present at the data quality that is typical of routine in vivo studies. Thus we will evaluate and optimize our approach on such data. In addition, we will validate our methods on ex vivo brain acquisitions, where results from high-resolution, high-SNR images acquired in long scans can be used as a gold standard for comparison to results from routine-quality images.

本文提出的研究旨在开发准确而稳健的方法来估计两者体素扩散表示（扩散率或各向异性图、扩散张量或频谱图）和扩散加权磁共振的全局通路结构（DW-MR）数据。尽管这些算法将广泛适用于扩散 MRI，但兴趣的应用是大脑白质结构的成像。所提出的方法是概率性的，它模拟了两个 DW-MR 数据中存在的不确定性类型：成像过程引入的不确定性扭曲和噪声的形式，以及由于个体因素而要重建的结构中固有的不确定性基础解剖结构的变异性。前者将通过扩散 MR 的精确建模来解决物理学，包括磁场不均匀性、涡流和噪声的影响。后者将是通过丰富的白质通路解剖模型来解决这个问题，这些模型是通过在一组主要途径已手动定义的科目。目前扩散措施的估计为次优，因为它基于失真图像，而这些图像是在不考虑基于 MR 物理原理，然后在一系列后处理中大致校正失真步骤。此外，白质通路的重建是劳动密集型的因为需要手动干预来限制解决方案空间并指导纤维束成像神经解剖学专业知识。通过解决这些问题，拟议项目将对扩散进行估计测量更准确。它还将自动重建白质通路，使得这样的即使对于大量科目，研究也是实用的。所提出的方法主要是为了解决常规体内研究典型的数据质量中存在的伪影。因此我们将评估并优化我们对此类数据的方法。此外，我们将在离体大脑上验证我们的方法采集，其中在长扫描中采集的高分辨率、高信噪比图像的结果可用作与常规质量图像结果进行比较的黄金标准。