Q-Space Guided Multi-Modal Translation Network for Diffusion-Weighted Image Synthesis

Diffusion-weighted imaging (DWI) enables non-invasive characterization of tissue microstructure, yet acquiring densely sampled q-space data remains time-consuming and impractical in many clinical settings. Existing deep learning methods are typically constrained by fixed q-space sampling, limiting their adaptability to variable sampling scenarios. In this paper, we propose a Q-space Guided Multi-Modal Translation Network (Q-MMTN) for synthesizing multi-shell, high-angular resolution DWI (MS-HARDI) from flexible q-space sampling, leveraging commonly acquired structural data (e.g., T1- and T2-weighted MRI). Q-MMTN integrates the hybrid encoder and multi-modal attention fusion mechanism to effectively extract both local and global complementary information from multiple modalities. This design enhances feature representation and, together with a flexible q-space-aware embedding, enables dynamic modulation of internal features without relying on fixed sampling schemes. Additionally, we introduce a set of task-specific constraints, including adversarial, reconstruction, and anatomical consistency losses, which jointly enforce anatomical fidelity and signal realism. These constraints guide Q-MMTN to accurately capture the intrinsic and nonlinear relationships between directional DWI signals and q-space information. Extensive experiments across four lifespan datasets of children, adolescents, young and older adults demonstrate that Q-MMTN outperforms existing methods, including 1D-qDL, 2D-qDL, MESC-SD, and Q-GAN in estimating parameter maps and fiber tracts with fine-grained anatomical 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-MMTN.