IEEE Trans Med Imaging. 2021 Mar;40(3):916-927. doi: 10.1109/TMI.2020.3042765. Epub 2021 Mar 2.
Multi-compartment models (MCM) are increasingly used to characterize the brain white matter microstructure from diffusion-weighted imaging (DWI). Their use in clinical studies is however limited by the inability to resample an MCM image towards a common reference frame, or to construct atlases from such brain microstructure models. We propose to solve this problem by first identifying that these two tasks amount to the same problem. We propose to tackle it by viewing it as a simplification problem, solved thanks to spectral clustering and the definition of semi-metrics between several usual compartments encountered in the MCM literature. This generic framework is evaluated for two models: the multi-tensor model where individual fibers are modeled as individual tensors and the diffusion direction imaging (DDI) model that differentiates intra- and extra-axonal components of each fiber. Results on simulated data, simulated transformations and real data show the ability of our method to well interpolate MCM images of these types. We finally present as an application an MCM template of normal controls constructed using our approach.
多腔室模型（MCM）越来越多地用于从扩散加权成像（DWI）中描述脑白质的微观结构。然而，它们在临床研究中的应用受到限制，因为无法将 MCM 图像重新采样到共同参考框架中，或者无法从这些脑微观结构模型构建图谱。我们建议通过首先确定这两个任务是相同的问题来解决这个问题。我们建议通过将其视为简化问题来解决，这要归功于光谱聚类以及在 MCM 文献中遇到的几种常见腔室之间的半度量定义。该通用框架针对两种模型进行了评估：多张量模型，其中将单个纤维建模为单个张量，以及扩散方向成像（DDI）模型，该模型区分了每个纤维的轴内和轴外成分。模拟数据、模拟变换和真实数据的结果表明，我们的方法能够很好地插值这些类型的 MCM 图像。我们最后提出了一种应用，即使用我们的方法构建的正常对照的 MCM 模板。
