Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules, offering the capability to inspect tissue microstructures and is the only in-vivo method to reconstruct white matter fiber tracts non-invasively. The DWI signal can be analysed with the diffusion tensor imaging (DTI) model to estimate the directionality of water diffusion within voxels. Several scalar metrics, including axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD), and fractional anisotropy (FA), can be further derived from DTI to quantitatively summarise the microstructural integrity of brain tissue. These scalar metrics have played an important role in understanding the organisation and health of brain tissue at a microscopic level in clinical studies. However, reliable DTI metrics rely on DWI acquisitions with high gradient directions, which often go beyond the commonly used clinical protocols. To enhance the utility of clinically acquired DWI and save scanning time for robust DTI analysis, this work proposes DirGeo-DTI, a deep learning-based method to estimate reliable DTI metrics even from a set of DWIs acquired with the minimum theoretical number (6) of gradient directions. DirGeo-DTI leverages directional encoding and geometric constraints to facilitate the training process. Two public DWI datasets were used for evaluation, demonstrating the effectiveness of the proposed method. Extensive experimental results show that the proposed method achieves the best performance compared to existing DTI enhancement methods and potentially reveals further clinical insights with routine clinical DWI scans.

翻译：扩散加权成像（DWI）是一种对水分子扩散性敏感的磁共振成像（MRI）技术，能够检测组织微观结构，并且是唯一能够无创重建白质纤维束的体内方法。DWI信号可通过扩散张量成像（DTI）模型进行分析，以估计体素内水扩散的方向性。从DTI可进一步推导出多个标量指标，包括轴向扩散率（AD）、平均扩散率（MD）、径向扩散率（RD）和分数各向异性（FA），用于定量概括脑组织的微观结构完整性。这些标量指标在临床研究中对于在微观层面理解脑组织的结构与健康状况发挥了重要作用。然而，可靠的DTI指标依赖于具有高梯度方向数的DWI采集，这通常超出了常规临床协议的范围。为提升临床采集DWI的实用性并节省扫描时间以实现稳健的DTI分析，本研究提出了DirGeo-DTI，这是一种基于深度学习的方法，即使从仅具有理论最小数量（6个）梯度方向的DWI集合中，也能估计出可靠的DTI指标。DirGeo-DTI利用方向性编码和几何约束来促进训练过程。使用两个公开的DWI数据集进行评估，结果证明了所提方法的有效性。大量实验结果表明，与现有的DTI增强方法相比，所提方法取得了最佳性能，并有可能通过常规临床DWI扫描揭示更深入的临床见解。