Keywords: AI Diffusion Models, Diffusion/other diffusion imaging techniques
Motivation: Diffusion-Weighted Imaging (DWI) suffers from low resolution. Post-acquisition super-resolution can effectively enhance the resolution of DWIs.
Goal(s): We propose a novel post-acquisition DWI super-resolution method based on the conditioned diffusion model.
Approach: We design an effective condition based on Track Density Imaging (TDI), which contains rich high-resolution information. Furthermore, we consider low-resolution DWIs as another condition to preserve the original information of images.
Results: Extensive experiments on HCP data show that our model is effective in DWI super-resolution and outperforms the cutting-edge models.
Impact: To enhance the resolution of DWIs, we propose a super-resolution method based on conditioned diffusion model. This is beneficial to the clinical practice of DWI.
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