Q-space Guided Collaborative Attention Translation Network for Flexible Diffusion-Weighted Images Synthesis
This addresses a domain-specific problem for medical imaging researchers and clinicians by enabling flexible DWI synthesis without fixed sampling schemes, though it appears incremental as it builds on existing translation networks with novel mechanisms.
The study tackled the problem of synthesizing multi-shell, high-angular resolution diffusion-weighted images from flexible q-space sampling by proposing Q-CATN, which outperformed existing methods like 1D-qDL and QGAN on the HCP dataset in estimating parameter maps and fiber tracts quantitatively and qualitatively.
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.