Damon Bruce M, Guzman Roberto Pineda, Lockard Carly A, Zhou Xingyu
Carle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana IL USA 61801.
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN USA 37232.
bioRxiv. 2024 Aug 30:2024.08.29.610343. doi: 10.1101/2024.08.29.610343.
The internal arrangement of a muscle's fibers with respect to its mechanical line of action (muscle architecture) is a major determinant of muscle function. Muscle architecture can be quantified using diffusion tensor magnetic resonance imaging-based tractography, which propagates streamlines from a set of seed points by integrating vectors that represent the direction of greatest water diffusion (and by inference, the local fiber orientation). Previous work has demonstrated that tractography outcomes are sensitive to the method for defining seed points, but this sensitivity has not been fully examined. To do so, we developed a realistic simulated muscle architecture and implemented four novel methods for tract seeding: seeding along the muscle-aponeurosis boundary with an updated procedure for rounding seed points prior to lookup in the muscle boundary mask and diffusion tensor matrix (APO-3); voxel-based seeding throughout the muscle volume at a user-specified spatial frequency (VXL-1); voxel-based seeding throughout the muscle volume at a variable spatial frequency (VXL-2), and seeding near external and internal muscle boundaries (VXL-3). We then implemented these methods in an example human dataset. The updated aponeurosis seeding procedures allow more accurate and robust tract propagation from seed points. The voxel-based seeding methods had quantification outcomes that closely matched the updated aponeurosis seeding method. Further, the voxel-based methods can accelerate the overall workflow and may be beneficial in high throughput analysis of multi-muscle datasets. Continued evaluation of these methods in a wider range of muscle architectures is warranted.
肌肉纤维相对于其力学作用线的内部排列(肌肉结构)是肌肉功能的主要决定因素。肌肉结构可以使用基于扩散张量磁共振成像的纤维束成像来量化,该方法通过整合代表最大水扩散方向的向量(并据此推断局部纤维方向),从一组种子点传播流线。先前的研究表明,纤维束成像结果对定义种子点的方法敏感,但这种敏感性尚未得到充分研究。为此,我们开发了一种逼真的模拟肌肉结构,并实施了四种新的纤维束种子点设定方法:沿着肌肉 - 腱膜边界进行种子点设定,并在查找肌肉边界掩码和扩散张量矩阵之前采用更新的程序对种子点进行舍入(APO - 3);以用户指定的空间频率在整个肌肉体积内进行基于体素的种子点设定(VXL - 1);以可变空间频率在整个肌肉体积内进行基于体素的种子点设定(VXL - 2),以及在肌肉内外边界附近进行种子点设定(VXL - 3)。然后,我们在一个示例人体数据集中实施了这些方法。更新后的腱膜种子点设定程序允许从种子点进行更准确和稳健的纤维束传播。基于体素的种子点设定方法的量化结果与更新后的腱膜种子点设定方法密切匹配。此外,基于体素的方法可以加速整个工作流程,并且可能有利于多肌肉数据集的高通量分析。有必要在更广泛的肌肉结构范围内继续评估这些方法。