Leysen Siebe, Radwan Ahmed, Maes Frederik, Sunaert Stefan, Christiaens Daan
Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.
Imaging Neurosci (Camb). 2025 Aug 1;3. doi: 10.1162/IMAG.a.95. eCollection 2025.
Diffusion MRI (dMRI) plays a crucial role in studying tissue microstructure and fibre orientation. Due to the intricate nature of the dMRI signal, end users require representations that provide a straightforward interpretation. Currently, these representations rely on tissue-average estimations or simplified tissue models and are hence limited in their applicability to pathology. In this study, we propose a novel approach called LoRE-SD-a local response function estimation in spherical deconvolution. LoRE-SD minimises assumptions about tissue microstructure to improve the reconstruction of dMRI data in the presence of pathology. This is achieved by introducing a general signal representation that spans the most common multi-compartment microstructure models used in neuroimaging. Leveraging spherical deconvolution, LoRE-SD provides accurate estimations of the local fibre orientations, allowing tractography in the healthy and pathological brain. We evaluate this approach using simulations and in vivo data from a healthy volunteer and from patients with glioma. Comparing the results quantitatively with the state-of-the-art, we find that LoRE-SD accurately reconstructs fibre orientations across the brain while also significantly improving glioma reconstruction and fibre bundle estimation. Additionally, the tissue representation in LoRE-SD facilitates the generation of various image contrasts, including response function anisotropy and contrasts accentuating intra-axonal, extra-axonal, and free water spaces, which enables a more flexible approach for tractography. In conclusion, LoRE-SD introduces a framework for estimating a data-driven, local representation of tissue microstructure with minimal prior assumptions. This approach provides a new way to represent the human brain, pathology, and other organs using dMRI and opens avenues for defining novel image contrasts, which may benefit tractography.
扩散磁共振成像（dMRI）在研究组织微观结构和纤维方向方面起着至关重要的作用。由于dMRI信号的复杂性，终端用户需要能够提供直观解释的表示方法。目前，这些表示方法依赖于组织平均估计或简化的组织模型，因此在病理学中的适用性有限。在本研究中，我们提出了一种名为LoRE-SD的新方法——球面去卷积中的局部响应函数估计。LoRE-SD尽量减少对组织微观结构的假设，以改善在存在病理学情况下dMRI数据的重建。这是通过引入一种通用信号表示来实现的，该表示涵盖了神经成像中最常用的多室微观结构模型。利用球面去卷积，LoRE-SD提供了局部纤维方向的准确估计，从而能够在健康和病理大脑中进行纤维束成像。我们使用模拟以及来自健康志愿者和胶质瘤患者的体内数据对该方法进行评估。将结果与现有技术进行定量比较，我们发现LoRE-SD能够准确重建全脑的纤维方向，同时还显著改善了胶质瘤的重建和纤维束估计。此外，LoRE-SD中的组织表示有助于生成各种图像对比度，包括响应函数各向异性以及突出轴突内、轴突外和自由水空间的对比度，这为纤维束成像提供了一种更灵活的方法。总之，LoRE-SD引入了一个框架，用于在最少先验假设的情况下估计数据驱动的组织微观结构局部表示。这种方法为使用dMRI表示人类大脑、病理学和其他器官提供了一种新途径，并为定义新的图像对比度开辟了道路，这可能有利于纤维束成像。
