Keywords: Diffusion Reconstruction, Diffusion Reconstruction, q-space super-resolution
Motivation: The scanning time of high-angular resolution diffusion MRI increases linearly with the number of diffusion gradients, which limits its widespread use in clinical settings.
Goal(s): We aimed to facilitate high-fidelity and detail-preserving super-resolution for dMRI in q-space.
Approach: We propose a physical knowledge-guided residual DDPM-based method, Diff2-SRNet. This model divides the dMRI signal into Gaussian and non-Gaussian components, then utilizes the diffusion tensor model to represent the former part and leverages the strong generative capabilities of DDPM for the more complex non-Gaussian component.
Results: Experimental results demonstrate that the proposed Diff2-SRNet reconstructs HAR DWIs with higher fidelity and preserves better details.
Impact: The proposed method exhibits interpretability and reliability and shows a high potential to become a practical tool in a wide range of clinical and neuroscientific applications.
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