Score-based diffusion models for accelerated MRI

  title={Score-based diffusion models for accelerated MRI},
  author={Hyungjin Chung and Jong-Chul Ye},
  journal={Medical image analysis},

Conditional Score-Based Reconstructions for Multi-contrast MRI

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

High-Frequency Space Diffusion Models for Accelerated MRI

Experiments show that HFS-SDE based reconstruction method outperforms the parallel imaging, supervised deep learning, and existing VE- and VP-S DEs-based methods in terms of reconstruction accuracy and improves the stability of MR reconstruction and accelerates sampling procedure of reverse diffusion.

Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models

Diffusion models have emerged as the new state-of-the-art generative model with high quality samples, with intriguing properties such as mode coverage and high flexibil-ity. They have also been shown

Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction

A fully-sampled-data-free score-based diffusion model for MRI reconstruction, which learns the fully sampled MR image prior in a self-supervised manner on undersampled data by Bayesian deep learning and can reconstruct the MR image by performing conditioned Langevin Markov chain Monte Carlo sampling.

Accelerated Motion Correction for MRI using Score-Based Generative Models

This work proposes a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using score-based generative models and demonstrates its framework on retrospectively accelerated 2D brain MRI corrupted by rigid motion.

One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging

— Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. However, lowering the radiation dose will significantly degrade the image

Stable deep MRI reconstruction using Generative Priors

A novel deep neural network based regularizer which is trained in an unsupervised setting on reference magnitude images only, which encodes higher-level domain statistics which is demonstrated by synthesizing images without data.

Towards performant and reliable undersampled MR reconstruction via diffusion model sampling

The proposed DiffuseRecon achieves SoTA performances reconstructing from raw acquisition signals in fastMRI and SKM-TEA, and proposes an accelerated, coarse-to-fine Monte-Carlo sampling scheme to approximate the most likely reconstruction candidate.

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

DOLCE is presented, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior that achieves the SOTA performance on drastically different types of images.

MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion

This work proposes a new denoising method based on score-based reverse diffusion sampling, which overcomes all the aforementioned drawbacks and establishes state-of-the-art performance while having desirable properties which prior MMSE denoisers did not have.

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

This work proposes a fully unsupervised technique for inverse problem solving, leveraging the recently introduced score-based generative models, and introduces a sampling method to reconstruct an image consistent with both the prior and the observed measurements.

DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction With Deep T1 Prior

    Bo ZhouS. K. Zhou
    Computer Science
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
A Dual Domain Recurrent Network (DuDoRNet) with deep T1 prior embedded to simultaneously recover k-space and images for accelerating the acquisition of MRI with a long imaging protocol and is customized for dual domain restorations from undersampled MRI data.

Variational Diffusion Models

A family of diffusion-based generative models that obtain state-of-the-art likelihoods on standard image density estimation benchmarks are introduced, and it is shown how to use the model as part of a bits-back compression scheme, and demonstrate lossless compression rates close to the theoretical optimum.

MRI Reconstruction Using Deep Energy-Based Model

A novel regularization strategy is introduced in this article which takes advantage of self-adversarial cogitation of the deep energy-based model to boost the performance of Magnetic Resonance Imaging (MRI) reconstruction.

Score Matching Model for Unbounded Data Score

This paper introduces Unbounded Diffusion Model (UDM) that resolves the score diverging problem with an easily applicable modification to any diffusion models and introduces a new SDE that overcomes the theoretic and practical limitations of Variance Exploding SDE.

Sliced Score Matching: A Scalable Approach to Density and Score Estimation

It is demonstrated that sliced score matching can learn deep energy-based models effectively, and can produce accurate score estimates for applications such as variational inference with implicit distributions and training Wasserstein Auto-Encoders.

Robust Compressed Sensing MRI with Deep Generative Priors

This paper trains a generative prior on brain scans from the fastMRI dataset, and shows that posterior sampling via Langevin dynamics achieves high quality reconstructions and is robust to changes in the ground-truth distribution and measurement process.

Generative Modeling by Estimating Gradients of the Data Distribution

A new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching, which allows flexible model architectures, requires no sampling during training or the use of adversarial methods, and provides a learning objective that can be used for principled model comparisons.

Score-Based Generative Modeling through Stochastic Differential Equations

This work presents a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by Slowly removing the noise.

Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint

An iterative reconstruction method for undersampled radial MRI which is based on a nonlinear optimization, allows for the incorporation of prior knowledge with use of penalty functions, and deals with data from multiple coils is developed.