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，可靠的方法来估计两者体素扩散表示（扩散率或各向异性图，扩散张量或光谱图）和扩散加权磁共振的全球途径结构（DW-MR）数据。尽管该算法将广泛适用于扩散MRI，但利益应用是大脑白物结构的成像。提出的方法是概率，它模拟了两个 DW-MR数据中存在的不确定性类型：成像过程中引入的不确定性扭曲和噪声的形式，以及由于个体而要重建的结构中固有的不确定性基础解剖结构的可变性。前者将通过精确建模扩散MR来解决物理学，包括磁场不均匀性的影响，涡流和噪声。后者将是通过富裕模型的白人途径解剖结构来解决，通过训练该模型在一组中获得手动定义了主要途径的受试者。估计扩散措施的估计是次优的是，它基于不考虑未考虑的扭曲图像 MR物理学不足，然后在一系列后期处理中校正大约扭曲步骤。此外，重建白物途径是劳动密集型由于需要手动干预来限制解决方案空间，并用神经解剖专业知识。通过解决这些问题，拟议项目将估算扩散衡量更准确。它还将自动化白色途径的重建，使这样甚至针对大量受试者进行研究。提出的方法主要是开发解决以常规体内研究的典型数据质量所存在的伪像。因此我们将评估并在此类数据上优化我们的方法。此外，我们将验证我们在体内大脑上的方法收购，在高分辨率的结果中，以长期扫描获取的高SNR图像可以用作与常规质量图像的结果进行比较的金标准。