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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Published on: July 28, 2013

AutoDPS: An unsupervised diffusion model based method for multiple degradation removal in MRI.

Arunima Sarkar¹, Ayantika Das¹, Keerthi Ram²

¹Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), Chennai 600036, Tamil Nadu, India.

Computer Methods and Programs in Biomedicine

|March 2, 2025

Summary

This summary is machine-generated.

This study introduces AutoDPS, an unsupervised method for removing motion and undersampling artifacts in Magnetic Resonance Images (MRI). AutoDPS significantly improves image quality, offering a robust solution for corrupted MRI scans essential for accurate diagnosis.

Keywords:

Diffusion model Inverse problems MRI Motion correction

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Area of Science:

Medical Imaging
Artificial Intelligence
Image Processing

Background:

Magnetic Resonance Imaging (MRI) is crucial for diagnosis but susceptible to artifacts like motion and undersampling.
Existing deep learning methods often require paired or unpaired data to model degradations, which is impractical for complex MRI corruptions.
Diffusion models offer an unsupervised, degradation-independent approach suitable for restoring artifact-corrupted MRI.

Purpose of the Study:

To develop an unsupervised method for removing multiple corruptions in brain MRI.
To address the challenge of restoring MRI images corrupted by motion and undersampling artifacts.
To provide a robust and adaptable solution for enhancing MRI image quality without prior degradation modeling.

Main Methods:

Proposed AutoDPS, an unsupervised method leveraging Diffusion Posterior Sampling for corruption removal in brain MRI.
Implemented blind iterative solver for motion-related corruption parameter estimation.
Incorporated knowledge of undersampling patterns and corruption operations during the sampling process to guide image recovery.

Main Results:

AutoDPS achieved approximately 1.63 dB PSNR improvement for realistic 3D motion restoration over baselines.
Demonstrated ∼ 0.5 dB PSNR improvement for random motion with undersampling.
Showcased resilience to noise, generalization under domain shift, and adaptability to unseen corruptions.

Conclusions:

AutoDPS effectively removes multiple corruptions, particularly motion and undersampling, in MRI images.
The method shows promising results on realistic and composite artifacts, outperforming existing methods.
The developed code is publicly available, facilitating further research and application.