GraphSVR：面向扩散MRI的q空间感知图基切片到体素配准

中文摘要 AI 辅助

扩散加权成像（DWI）对受试者运动仍然高度敏感，尤其是在时间效率高的协议和易运动人群中。虽然切片到体素配准（SVR）可以减轻切片间和堆栈间的错位，但扩散MRI由于扩散方向依赖的对比度以及需要在共同参考框架内对齐数十次测量的要求，引入了额外的复杂性，实际上构成了一个4D配准问题。现有方法主要依赖顺序建模或成对相似性，在稀疏梯度采样或严重运动下往往性能下降。我们提出了GraphSVR，一种用于DWI中4D SVR配准的q空间感知图基框架。GraphSVR将切片组表示为采集结构图中的节点，边编码时间邻近性、空间切片几何和扩散编码关系。图神经网络预测全局一致的堆栈级刚性运动，以自监督、零样本方式优化，仅使用解剖参考图像，无需成对的地面真值运动。我们使用完全合成的扩散模拟和来自真实采集的基于重组的现实模拟来评估GraphSVR，这些模拟具有可控的运动严重程度和梯度稀疏性。性能通过网格误差（毫米）和相对于已知地面真值变换的旋转误差来量化。在严重运动下，与标准DWI运动校正方法FSL eddy相比，GraphSVR将网格误差和旋转误差降低了73%，在稀疏方向设置中收益最大。这些结果表明，通过图基推理显式建模采集结构可提高4D DWI运动估计的鲁棒性和全局一致性。代码可在以下网址获取：此https URL。

英文摘要

Diffusion-weighted imaging (DWI) remains highly vulnerable to subject motion, particularly in time-efficient protocols and in motion-prone populations. While slice-to-volume registration (SVR) can mitigate inter-slice and inter-stack misalignment, diffusion MRI introduces additional complexity due to diffusion-direction-dependent contrast and the requirement to align dozens of measurements within a common reference frame, effectively yielding a 4D registration problem. Existing approaches rely primarily on sequential modeling or pairwise similarity and often degrade under sparse gradient sampling or severe motion. We introduce GraphSVR, a q-space-aware graph-based framework for 4D SVR registration in DWI. GraphSVR represents slice groups as nodes in an acquisition-structured graph, with edges encoding temporal proximity, spatial slice geometry and diffusion encoding relationships. A graph neural network predicts globally consistent stack-wise rigid motion, optimized in a self-supervised, zero-shot manner using only an anatomical reference image, without requiring paired ground-truth motion. We evaluate GraphSVR using both fully synthetic diffusion simulations and realistic recombination-based simulations from real acquisitions with controllable motion severity and gradient sparsity. Performance is quantified using grid error (mm) and rotation error relative to known ground-truth transforms. Under severe motion, GraphSVR reduces grid error and rotation error by 73% compared to FSL eddy, the standard DWI motion-correction method, with the largest gains observed in sparse-direction regimes. These results demonstrate that explicitly modeling acquisition structure through graph-based reasoning improves robustness and global consistency in 4D DWI motion estimation. Code is available at https://github.com/nogakertes/GraphSVR.git.

发表机构

• Technion – Israel Institute of Technology（以色列理工学院）
• Institute of Science and Technology Austria (ISTA)（奥地利科学技术学院）

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