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

本文提出的研究旨在开发准确和鲁棒的方法来估计两者 逐体素扩散表示（扩散率或各向异性图、扩散张量或频谱图），以及 扩散加权磁共振的全局通路结构 （DW-MR）数据。虽然该算法将广泛适用于扩散MRI，但感兴趣的应用 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： 失真和噪声的形式，以及由于个体原因而导致的待重建结构中的固有不确定性， 潜在解剖结构的变异性。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 通过丰富的白质通路解剖模型来解决，通过在一组 主要路径已手动定义的科目。扩散测度的当前估计是 次优，因为它是基于在不考虑 然后在一系列后处理中近似地校正失真 步此外，白质通路的重建是劳动密集型的 由于需要人工干预来限制解决方案空间并引导纤维束成像 神经解剖学专业知识。通过解决这些问题，拟议的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， 即使对大量的科目也是实用的。正在制定的拟议方法主要是为了 解决常规体内研究中典型的数据质量问题。因此，我们将评估 并优化我们处理这些数据的方法。此外，我们将在离体脑上验证我们的方法 采集，其中长扫描中采集的高分辨率、高SNR图像的结果可用作 与常规质量图像结果进行比较的金标准。

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.