Retraining-free multi-task MR image restoration using a pretrained diffusion model with Bayesian optimization.

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Abstract

Magnetic resonance imaging (MRI) often requires long scan times, and efforts to shorten scans can lead to image degradation, such as undersampling artifacts, increased noise, and reduced spatial resolution. A restoration framework that can address multiple types of degradation without task-specific retraining would therefore be valuable. In this study, we evaluated whether a pretrained score-based diffusion model combined with Bayesian optimization, termed task guidance design with Bayesian optimization (TGD-BO), could provide retraining-free, multi-task MR image restoration for compressed sensing (CS) reconstruction, super-resolution (SR), and denoising (DN). TGD-BO incorporates data-consistency guidance and gradient guidance into the reverse diffusion process and parameterizes their time-varying strengths using compact low-dimensional B-spline-based guidance schedules. For each task, the schedule was optimized by Bayesian optimization using four paired degraded/reference slices from a single adaptation case, and the resulting task-specific schedule was then applied unchanged to an independent test set. In single-task experiments, TGD-BO improved upon the fixed-guidance diffusion baseline in denoising, compressed-sensing reconstruction, and super-resolution, although the improvement in super-resolution was modest. Compared with task-specific supervised baselines, TGD-BO did not uniformly achieve higher peak signal-to-noise ratio (PSNR) or structural similarity (SSIM); in super-resolution, it showed lower PSNR and SSIM but better perceptual similarity as assessed by learned perceptual image patch similarity (LPIPS). In joint-task experiments, schedules optimized directly for DN + CS and DN + SR yielded numerically better image-quality metrics than applying the corresponding single-task schedules. Sensitivity analyses showed that the selected schedule and test performance were unchanged when 1, 4, or 8 slices from the same adaptation case were used, whereas changing the adaptation case substantially affected test performance. These results suggest the feasibility of adapting a single pretrained diffusion prior across multiple MR image restoration tasks through guidance calibration without task-specific weight retraining, while indicating that robustness to adaptation-case selection and computational cost require further investigation.

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