This document discusses using score-based diffusion models for accelerated MRI. It proposes training diffusion models on magnitude MRI images to reconstruct complex-valued images and even extend to parallel imaging. The models are agnostic to sub-sampling patterns and show superior performance, especially for high-frequency detail. They also demonstrate high generalization capacity, trained only on knee images but able to generalize to other anatomies and contrasts. The models can also provide multiple posterior samples to estimate reconstruction uncertainty.

1. Score-based diffusion models for accelerated MRI
Hyungjin Chung and Jong Chul Ye
Hyungjin Chung

2. Introduction

3. Inverse problems: examples
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𝐲
𝐱
• Inpainting
⨀
• Deblurring (Deconvolution)
𝐱 𝐲
∗
• CS-MRI
𝓕
⨀

4. Inverse problems: examples
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• CS-MRI (multi-coil)
https://ajiljalal.github.io/DeepMRI.html

5. Inverse problems: examples
• Computed tomography
𝐱
𝑹 𝑷
𝑹𝑷𝐲
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6. What we want: sampling from the posterior
𝐱 ∼ 𝑝(𝐱|𝐲)
𝐲 = 𝒜𝐱 + 𝜼
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7. Score-based diffusion models for accelerated MRI
Chung & Ye. MeDIA 2022
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8. Score-based diffusion models for accelerated MRI
• Agnostic to forward model
(sub-sampling pattern)
• Superior performance,
especially high-frequency
detail
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Chung & Ye. MeDIA 2022

9. Score-based diffusion models for accelerated MRI
• Trained on DICOM (magnitude) images
• Able to reconstruct complex-valued image data at inference time
• Even extends to parallel imaging by reconstructing coil-wise
• Very high generalization capacity
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Chung & Ye. MeDIA 2022

10. Score-based diffusion models for accelerated MRI
Trained only on knee images, generalizes to other anatomy & contrast
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Chung & Ye. MeDIA 2022

11. Score-based diffusion models for accelerated MRI
Trained only on knee images, generalizes to other anatomy & contrast
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Chung & Ye. MeDIA 2022

12. Multiple posterior samples - uncertainty
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Chung & Ye. MeDIA 2022