Likelihood-Separable Diffusion Inference for Multi-Image MRI Super-Resolution

• URL: http://arxiv.org/abs/2601.14030v1
• Date: Tue, 20 Jan 2026 14:53:20 GMT
• Title: Likelihood-Separable Diffusion Inference for Multi-Image MRI Super-Resolution
• Authors: Samuel W. Remedios, Zhangxing Bian, Shuwen Wei, Aaron Carass, Jerry L. Prince, Blake E. Dewey,
• Abstract summary: We generalize diffusion-based inverse single-image problem solvers for multi-image super-resolution (MISR) MRI.<n>We show that the DPS likelihood correction allows an exactly-separable gradient decomposition across independently acquired measurements.<n>Our results achieve state-of-the-art super-resolution of anisotropic MRI volumes and, critically, enable reconstruction of near-isotropic anatomy from routine 2D multi-slice acquisitions.
• Score: 3.3307176205207383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models are the current state-of-the-art for solving inverse problems in imaging. Their impressive generative capability allows them to approximate sampling from a prior distribution, which alongside a known likelihood function permits posterior sampling without retraining the model. While recent methods have made strides in advancing the accuracy of posterior sampling, the majority focuses on single-image inverse problems. However, for modalities such as magnetic resonance imaging (MRI), it is common to acquire multiple complementary measurements, each low-resolution along a different axis. In this work, we generalize common diffusion-based inverse single-image problem solvers for multi-image super-resolution (MISR) MRI. We show that the DPS likelihood correction allows an exactly-separable gradient decomposition across independently acquired measurements, enabling MISR without constructing a joint operator, modifying the diffusion model, or increasing network function evaluations. We derive MISR versions of DPS, DMAP, DPPS, and diffusion-based PnP/ADMM, and demonstrate substantial gains over SISR across $4\times/8\times/16\times$ anisotropic degradations. Our results achieve state-of-the-art super-resolution of anisotropic MRI volumes and, critically, enable reconstruction of near-isotropic anatomy from routine 2D multi-slice acquisitions, which are otherwise highly degraded in orthogonal views.

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