# Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction arXiv: https://arxiv.org/abs/2309.00783 ยท HTML: https://mlsanity.com/paper/2309.00783/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. - Builds on 20 of its 45 references that are in the map. - First posted on arXiv 2 Sep 2023. ## Placed in - Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/) ## Builds on (most cited first, 20 of 20) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015) - [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021) - [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022) - [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017) - [Score-based diffusion models for accelerated MRI](https://mlsanity.com/paper/2110.05243/) (2021) - [Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction](https://mlsanity.com/paper/2112.05146/) (2021) - [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022) - [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017) - [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022) - [Adaptive Diffusion Priors for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.05876/) (2022) - [A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction](https://mlsanity.com/paper/1703.00555/) (2017) - [Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction](https://mlsanity.com/paper/2203.03623/) (2022) - [Towards performant and reliable undersampled MR reconstruction via diffusion model sampling](https://mlsanity.com/paper/2203.04292/) (2022) - [Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models](https://mlsanity.com/paper/2202.01479/) (2022) - [DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training](https://mlsanity.com/paper/1810.00302/) (2018) - [An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities](https://mlsanity.com/paper/2109.09738/) (2021) - [MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping](https://mlsanity.com/paper/2101.08413/) (2021) ## Built on by (most cited first, 22 of 22) - [Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach](https://mlsanity.com/paper/2408.06634/) (2024) - [Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation](https://mlsanity.com/paper/2409.11703/) (2024) - [Hy-Facial: Hybrid Feature Extraction by Dimensionality Reduction Methods for Enhanced Facial Expression Classification](https://mlsanity.com/paper/2509.26614/) (2025) - [Style Transfer: From Stitching to Neural Networks](https://mlsanity.com/paper/2409.00606/) (2024) - [G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving](https://mlsanity.com/paper/2410.14710/) (2024) - [On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction](https://mlsanity.com/paper/2511.19641/) (2025) - [Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction](https://mlsanity.com/paper/2405.02958/) (2024) - [Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting](https://mlsanity.com/paper/2410.23318/) (2024) - [EquiReg: Equivariance Regularized Diffusion for Inverse Problems](https://mlsanity.com/paper/2505.22973/) (2025) - [MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction](https://mlsanity.com/paper/2402.03383/) (2024) - [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024) - [Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models](https://mlsanity.com/paper/2505.08142/) (2025) - [Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction](https://mlsanity.com/paper/2506.23311/) (2025) - [Guided MRI Reconstruction via Schr"odinger Bridge](https://mlsanity.com/paper/2411.14269/) (2024) - [A Comprehensive Survey on Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2503.07097/) (2025) - [MDPG: Multi-domain Diffusion Prior Guidance for MRI Reconstruction](https://mlsanity.com/paper/2506.23701/) (2025) - [q3-MuPa: Quick, Quiet, Quantitative Multi-Parametric MRI using Physics-Informed Diffusion Models](https://mlsanity.com/paper/2512.23726/) (2025) - [Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping](https://mlsanity.com/paper/2608.11942/) (2026) - [Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors](https://mlsanity.com/paper/2505.00044/) (2025) - [Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization](https://mlsanity.com/paper/2505.11518/) (2025) - [Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI](https://mlsanity.com/paper/2610.04028/) (2026) - [Stochastic Finite-Transition Learning and Exploration for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2610.13095/) (2026) ## Abstract (arXiv) This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain. Our method is applied to multi-coil MRI and quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the frequency and parameter domains. The prior MRI physics are used as embeddings in the diffusion model, enforcing data consistency to guide the training and sampling process, characterizing MRI k-space encoding in MRI reconstruction, and leveraging MR signal modeling for qMRI reconstruction. Furthermore, a gradient descent optimization is incorporated into the diffusion steps, enhancing feature learning and improving denoising. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors. Notably, it maintains great reconstruction accuracy and efficiency for static and quantitative MRI reconstruction across diverse anatomical structures. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains.