用灵活采样数据生成高分辨率弥散加权图像，提升细节保真度。
Q-space Guided Collaborative Attention Translation Network for Flexible Diffusion-Weighted Images Synthesis
- 通过协同注意力机制融合多模态信息，动态适应不同采样方案。
- 在HCP数据集上优于1D-qDL等方法，准确重建纤维轨迹和参数图。
- 适合临床与研究中不固定采样策略的弥散MRI生成任务。
本文提出一种新型Q-space引导的协同注意力翻译网络（Q-CATN），用于从灵活q空间采样数据中合成多壳层高角分辨弥散加权成像（MS-HARDI），仅依赖常规结构MRI数据。Q-CATN采用协同注意力机制，有效提取多模态互补信息，并根据灵活q空间信息动态调整内部表示，无需固定采样方案。同时引入多种任务特定约束，以保持弥散图像的解剖保真度，使模型能准确学习方向性弥散信号分布与q空间之间的内在关系。在人类连接组计划（HCP）数据集上的大量实验表明，Q-CATN在定量与定性层面均优于1D-qDL、2D-qDL、MESC-SD和QGAN等现有方法，能更好还原纤维束及参数图的精细结构。其对灵活采样策略的兼容性，凸显其在临床与科研中的应用潜力。代码已开源：https://github.com/Idea89560041/Q-CATN。
原文摘要 · Abstract (English)
This study, we propose a novel Q-space Guided Collaborative Attention Translation Networks (Q-CATN) for multi-shell, high-angular resolution DWI (MS-HARDI) synthesis from flexible q-space sampling, leveraging the commonly acquired structural MRI data. Q-CATN employs a collaborative attention mechanism to effectively extract complementary information from multiple modalities and dynamically adjust its internal representations based on flexible q-space information, eliminating the need for fixed sampling schemes. Additionally, we introduce a range of task-specific constraints to preserve anatomical fidelity in DWI, enabling Q-CATN to accurately learn the intrinsic relationships between directional DWI signal distributions and q-space. Extensive experiments on the Human Connectome Project (HCP) dataset demonstrate that Q-CATN outperforms existing methods, including 1D-qDL, 2D-qDL, MESC-SD, and QGAN, in estimating parameter maps and fiber tracts both quantitatively and qualitatively, while preserving fine-grained details. Notably, its ability to accommodate flexible q-space sampling highlights its potential as a promising toolkit for clinical and research applications. Our code is available at https://github.com/Idea89560041/Q-CATN.
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