AI 中文总结
针对密集多壳扩散MRI采集时间长的问题，提出空间掩码集框架合成稀疏多壳扩散MRI信号，在HCP100数据集上的实验显示其信号NMSE低于现有模型，实现了更优性能。
AI 中文摘要
密集多壳扩散MRI可提供丰富的q空间信息，但采集时间较长。我们提出一种空间掩码集框架用于稀疏多壳扩散MRI信号合成，该模型将观测测量值视为无序集，利用局部3×3×3邻域获取空间上下文，预测中心体素的径向阶数为6的SHORE系数，这些系数可通过解析解码以合成任意q空间位置的信号。训练结合按壳的梯度丢弃、密集信号监督及旋转一致的SHORE目标，确保稀疏输入信号在数据增强时仍与系数监督对齐。我们在保留的HCP100白质体素上进行评估，通过保留参考采集中测量的扩散加权信号的有限子集实现评估。所提方法的信号NMSE低于解析q空间模型及针对任意输入输出q空间采样设计的最先进连续dMRI信号合成模型；在b=1000、10个输入梯度的设置下，其NMSE为2.70%，较该连续模型相对降低22.4%。重建的b=1000信号的分数各向异性提供了张量衍生的互补终点，尽管解析模型在评估的q空间中具有更高的密集信号NMSE，但在分数各向异性上仍具竞争力。代码实现可在指定网址获取。
英文摘要
Dense multi-shell diffusion MRI provides rich q-space information but requires long acquisition times. We propose a spatial masked-set framework for sparse multi-shell diffusion MRI signal synthesis. The model treats observed measurements as an unordered set, uses a local $3 \times 3 \times 3$ neighborhood for spatial context, and predicts radial-order-6 SHORE coefficients for the center voxel. The coefficients can then be decoded analytically to synthesize signals at arbitrary q-space locations. Training combines shell-wise gradient dropping, dense signal supervision, and rotation-consistent SHORE targets so that sparse input signals remain aligned with their coefficient supervision under augmentation. We evaluate on held-out HCP100 white-matter voxels by retaining limited subsets of measured diffusion-weighted signals from the reference acquisition. The proposed method achieves lower signal NMSE than both analytical q-space models and a state-of-the-art continuous dMRI signal synthesis model designed for arbitrary input and output q-space sampling. In the $b=1000$ setting with 10 input gradients, it achieves $2.70\%$ NMSE, a $22.4\%$ relative reduction over this continuous model. Fractional anisotropy on reconstructed $b=1000$ signals provides a complementary tensor-derived endpoint, with analytical models remaining competitive for FA despite higher dense-signal NMSE across the evaluated q-space. The implementation is available on \href{https://github.com/xmindflow/SHOREPred}{https://github.com/xmindflow/SHOREPred}.
CommentsAccepted at MICCAI CDMRI Workshop 2026
