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

Mustafa Abbas¹, Wenyin Zhou^1,2, Stephan E Maier³

¹Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Magnetic Resonance in Medicine

|April 20, 2026

Summary

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This summary is machine-generated.

Convolutional neural networks (CNNs) significantly improve denoising and Rician bias correction in diffusion-weighted (DW) images. This AI approach drastically reduces computation time for generating high-quality DW images and biomarker maps.

Area of Science:

Medical Imaging
Artificial Intelligence in Radiology
Diffusion-Weighted MRI

Background:

Diffusion-weighted (DW) imaging is crucial for characterizing tissue microstructure.
Traditional denoising and bias correction methods in DW imaging can be computationally intensive and may introduce artifacts.
Accurate modeling of the diffusion signal is essential for reliable biomarker quantification.

Purpose of the Study:

To evaluate convolutional neural networks (CNNs) for enhanced and accelerated denoising and Rician bias correction in multi-b value DW images.
To assess the performance of different CNN architectures and signal models for simultaneous signal modeling and bias correction.
To compare the CNN-based approach with a conventional model-based method (OBSIDIAN) in terms of image quality, parameter estimation, and computation time.

Keywords:

ADC Rician bias correction denoising diffusion prostate

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Main Methods:

Self-supervised training of CNNs (U-Net, Attention U-Net, Residual Attention U-Net) using multi-b value prostate DW images from 46 individuals.
CNNs were trained to output model parameter maps for synthesizing DW images and ADC maps, incorporating Rician bias correction.
Exploration of signal models including biexponential, kurtosis, and gamma distributions, with and without Rician bias correction, and with/without noise map input.

Main Results:

CNN-generated synthetic DW images demonstrated comparable quality to the OBSIDIAN method.
CNN models produced less noisy ADC and parameter maps than OBSIDIAN, with good quantitative agreement for ADC values.
Rician bias correction was essential for accurate results, while noise map input had a less pronounced effect.
Computation time was reduced from several hours (OBSIDIAN) to seconds using CNNs.

Conclusions:

CNN-based methods offer a promising approach for clinical DW imaging, delivering higher quality images and biomarker maps.
The significant reduction in computation time makes CNNs highly attractive for practical applications.
Further research with larger datasets is recommended to enhance generalizability and robustness of the CNN models.