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Updated: May 12, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Physics-informed neural networks for denoising high b-value diffusion-weighted images.

Qiaoling Lin¹, Fan Yang², Yang Yan³

¹Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361102, Fujian, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|June 10, 2025

Summary

We developed a Physics-Informed neural network for high b-value Diffusion-Weighted Imaging (DWI) denoising (PIND). PIND significantly enhances image quality and reduces acquisition time, aiding in more sensitive tumor diagnosis.

Keywords:

Denoising Diffusion-weighted imaging Neural networks Reader study

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Last Updated: May 12, 2026

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Area of Science:

Medical Imaging
Artificial Intelligence
Biophysics

Background:

Diffusion-weighted imaging (DWI) is crucial for tumor diagnosis, relying on water molecule diffusion measurements.
High b-value DWI requires stronger magnetic resonance imaging (MRI) gradients but suffers from low signal-to-noise ratio (SNR) due to signal decay.
Effective noise reduction is essential for high b-value DWI to improve diagnostic accuracy.

Purpose of the Study:

To propose a novel Physics-Informed neural network for high b-value DWI denoising (PIND).
To leverage physics-informed loss and low b-value DWI data for enhanced noise suppression.
To evaluate PIND's performance in improving image quality and preserving diffusion characteristics.

Main Methods:

Developed a Physics-Informed neural network (PIND) incorporating physics-informed loss.
Utilized prior information from high signal-to-noise ratio low b-value DWI images.
Conducted experiments on a prostate DWI dataset (125 subjects).

Main Results:

PIND improved peak signal-to-noise ratio from 31.25 dB to 36.28 dB and structural similarity index from 0.77 to 0.92.
Achieved 98% accuracy in apparent diffusion coefficient (ADC) values while saving 83% data acquisition time.
Radiologist evaluation confirmed PIND's superior performance in overall quality, SNR, artifact suppression, and lesion conspicuity.

Conclusions:

PIND effectively denoises high b-value DWI images, enhancing diagnostic sensitivity.
The method significantly reduces MRI acquisition time without compromising diagnostic accuracy.
PIND shows strong potential for improving clinical DWI applications and tumor detection.