用可移动的扩散模拟物训练模型，实现多站点MRI数据无须真人跨站数据即可统一。
HARP: HARmonizing in-vivo diffusion MRI using Phantom-only training
- 基于模拟物的1D神经网络学习不同扫描仪间扩散信号差异
- 使各扫描仪间的FA、MD、GFA变异分别降低12%、10%、30%
- 适合大规模临床研究，无需复杂跨站人体数据
目的：多站点扩散MRI（dMRI）数据融合受扫描仪间差异影响，传统方法依赖大量跨站点匹配或流动的人体数据，难以获取。本文提出一种深度学习框架HARP，无需多站点在体人体数据即可实现dMRI数据统一。方法：HARP采用仅在易运输的扩散模拟物上训练的体素级1D神经网络，学习不同站点球谐系数间关系，不记忆空间结构。结果：在多种指标下显著降低扫描仪间差异。定量结果显示，与扫描-重扫标准误差相比，各站点间FA、MD、GFA的标准误差分别降低12%、10%和30%，且保留了纤维方向与追踪结果。结论：HARP是首个仅使用模拟物数据实现dMRI统一的方法，为大规模临床研究中量化dMRI的可行性与可扩展性提供了重要突破。
原文摘要 · Abstract (English)
Purpose: Combining multi-site diffusion MRI (dMRI) data is hindered by inter-scanner variability, which confounds subsequent analysis. Previous harmonization methods require large, matched or traveling human subjects from multiple sites, which are impractical to acquire in many situations. This study aims to develop a deep learning-based dMRI harmonization framework that eliminates the reliance on multi-site in-vivo traveling human data for training. Methods: HARP employs a voxel-wise 1D neural network trained on an easily transportable diffusion phantom. The model learns relationships between spherical harmonics coefficients of different sites without memorizing spatial structures. Results: HARP reduced inter-scanner variability levels significantly in various measures. Quantitatively, it decreased inter-scanner variability as measured by standard error in FA (12%), MD (10%), and GFA (30%) with scan-rescan standard error as the baseline, while preserving fiber orientations and tractography after harmonization. Conclusion: We believe that HARP represents an important first step toward dMRI harmonization using only phantom data, thereby obviating the need for complex, matched in vivo multi-site cohorts. This phantom-only strategy substantially enhances the feasibility and scalability of quantitative dMRI for large-scale clinical studies.
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