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April 19, 2026Magnetic Resonance in MedicineOpen Access

Self‐Supervised Deep Learning Framework for Rician Distribution Based Denoising and Modeling of Multi‐b Prostate Diffusion MRI

Mustafa Abbas Wenyin Zhou Stephan E. Maier Fredrik Langkilde Stefan Kuczera

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Key Points

This research aims to enhance the quality of diffusion MRI images using a self-supervised deep learning framework.
Utilized a CNN-based approach for image denoising and modeling.
Focused on Rician distribution for analysis of multi-b prostate diffusion MRI.
Emphasized the need for larger training datasets for better outcomes.
Achieved higher quality diffusion-weighted images compared to traditional methods.
Significantly reduced computation times in image processing.
Indicated that further refinements are necessary for robustness and generalizability.

Implication

Self-supervised deep learning improves image quality in diffusion MRI, suggesting clinical applications.

Abstract

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Cite This Study

Abbas et al. (2026) studied this question.

synapsesocial.com/papers/69e8661d6e0dea528ddea81e https://doi.org/10.1002/mrm.70373

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