# MRI Reconstruction with Side Information using Diffusion Models arXiv: https://arxiv.org/abs/2303.14795 ยท HTML: https://mlsanity.com/paper/2303.14795/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. - Builds on 21 of its 40 references that are in the map. - First posted on arXiv 26 Mar 2023. ## Placed in - Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/) ## Builds on (most cited first, 21 of 21) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019) - [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017) - [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017) - [Compressed Sensing using Generative Models](https://mlsanity.com/paper/1703.03208/) (2017) - [Score-based diffusion models for accelerated MRI](https://mlsanity.com/paper/2110.05243/) (2021) - [Robust Compressed Sensing MRI with Deep Generative Priors](https://mlsanity.com/paper/2108.01368/) (2021) - [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022) - [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018) - [Adaptive Diffusion Priors for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.05876/) (2022) - [DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior](https://mlsanity.com/paper/2001.03799/) (2020) - [Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models](https://mlsanity.com/paper/2202.01479/) (2022) - [Unsupervised Medical Image Translation Using Cycle-MedGAN](https://mlsanity.com/paper/1903.03374/) (2019) - [SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation](https://mlsanity.com/paper/2203.06823/) (2022) - [Instance-Optimal Compressed Sensing via Posterior Sampling](https://mlsanity.com/paper/2106.11438/) (2021) - [DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis](https://mlsanity.com/paper/1904.12894/) (2019) - [Prior Image-Constrained Reconstruction using Style-Based Generative Models](https://mlsanity.com/paper/2102.12525/) (2021) - [Coupled Dictionary Learning for Multi-contrast MRI Reconstruction](https://mlsanity.com/paper/1806.09930/) (2018) - [Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation](https://mlsanity.com/paper/1511.06631/) (2015) - [Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging](https://mlsanity.com/paper/1910.03273/) (2019) - [Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging](https://mlsanity.com/paper/1906.11410/) (2019) ## Built on by (most cited first, 15 of 15) - [Score-Based Generative Models for PET Image Reconstruction](https://mlsanity.com/paper/2308.14190/) (2023) - [Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications](https://mlsanity.com/paper/2310.19460/) (2023) - [Deep Learning for Accelerated and Robust MRI Reconstruction: a Review](https://mlsanity.com/paper/2404.15692/) (2024) - [A Trust-Guided Approach to MR Image Reconstruction with Side Information](https://mlsanity.com/paper/2501.03021/) (2025) - [Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach](https://mlsanity.com/paper/2309.08688/) (2023) - [A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling](https://mlsanity.com/paper/2409.13477/) (2024) - [Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models](https://mlsanity.com/paper/2311.09349/) (2023) - [An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse Problems](https://mlsanity.com/paper/2405.15643/) (2024) - [Accelerating MRI with Longitudinally-informed Latent Posterior Sampling](https://mlsanity.com/paper/2407.00537/) (2024) - [A Comprehensive Survey on Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2503.07097/) (2025) - [Inference-Time Search Using Side Information for Diffusion-Based Image Reconstruction](https://mlsanity.com/paper/2510.03352/) (2025) - [A Unified Framework for Multimodal Image Reconstruction and Synthesis using Denoising Diffusion Models](https://mlsanity.com/paper/2602.08249/) (2026) - [Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications](https://mlsanity.com/paper/2309.08568/) (2023) - [L-TGVN: Leveraging Longitudinal Priors for Personalized Rapid MRI](https://mlsanity.com/paper/2606.04419/) (2026) - [Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior](https://mlsanity.com/paper/2609.01959/) (2026) ## Abstract (arXiv) Magnetic resonance imaging (MRI) exam protocols consist of multiple contrast-weighted images of the same anatomy to emphasize different tissue properties. Due to the long acquisition times required to collect fully sampled k-space measurements, it is common to only collect a fraction of k-space for each scan and subsequently solve independent inverse problems for each image contrast. Recently, there has been a push to further accelerate MRI exams using data-driven priors, and generative models in particular, to regularize the ill-posed inverse problem of image reconstruction. These methods have shown promising improvements over classical methods. However, many of the approaches neglect the additional information present in a clinical MRI exam like the multi-contrast nature of the data and treat each scan as an independent reconstruction. In this work we show that by learning a joint Bayesian prior over multi-contrast data with a score-based generative model we are able to leverage the underlying structure between random variables related to a given imaging problem. This leads to an improvement in image reconstruction fidelity over generative models that rely only on a marginal prior over the image contrast of interest.