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Self Supervised Denoising Diffusion Probabilistic Models for Abdominal DW-MRI
Serge Vasylechko1, Onur Afacan1, Sila Kurugol1
1QUIN Lab, Department of Radiology, Boston Children's Hospital, Harvard Medical School.
Computational Diffusion MRI : MICCAI Workshop
|May 13, 2024
Summary
This study introduces a new self-supervised denoising method for abdominal diffusion MRI. It enhances image quality and accuracy, even with single-direction images, improving disease detection.
Area of Science:
- Medical Imaging
- Radiology
- Biomedical Engineering
Background:
- Quantitative diffusion-weighted MRI (DW-MRI) is crucial for abdominal disease detection.
- Low signal-to-noise ratio (SNR) at high b-values limits DW-MRI accuracy.
- Current methods to improve SNR, like averaging multiple directions, increase scan time and introduce artifacts.
Purpose of the Study:
- To develop a novel parameter estimation technique for denoising diffusion-weighted images (DWIs).
- To enable accurate DW-MRI with single diffusion gradient direction images.
- To overcome SNR limitations in abdominal quantitative diffusion MRI.
Main Methods:
- Proposed a self-supervised diffusion denoising probabilistic model (ssDDPM).
- The model effectively denoises diffusion-weighted images.
- The technique works on single diffusion gradient direction images, reducing scan time.
Main Results:
- The ssDDPM effectively denoises abdominal DWIs.
- Improved SNR and image quality were achieved.
- Accurate parameter estimation is possible even with single-direction images.
Conclusions:
- The proposed ssDDPM offers a promising solution for accurate abdominal quantitative diffusion MRI.
- This method addresses SNR limitations and reduces artifacts.
- The technique facilitates faster and more reliable disease assessment using DW-MRI.
