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Updated: Jun 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

Self-supervised denoising diffusion probabilistic models for abdominal DW-MRI.

Serge Didenko Vasylechko^1,2, Andy Tsai^1,2, Onur Afacan^1,2

¹Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.

Magnetic Resonance in Medicine

|May 2, 2025

Summary

This summary is machine-generated.

A new self-supervised denoising diffusion probabilistic model (ssDDPM) enhances abdominal diffusion-weighted MRI quality for pediatric liver lesions. This method improves apparent diffusion coefficient map accuracy and lesion detection using single-repetition scans.

Keywords:

ADC DWI abdominal MRI diffusion lesion self‐supervised

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Abdominal diffusion-weighted MRI (DW-MRI) quality is often compromised by motion artifacts, especially with single-repetition (NEX = 1) protocols.
Motion-induced blurring obscures small lesions and reduces the accuracy of apparent diffusion coefficient (ADC) maps, hindering diagnosis.
Current clinical standards involve averaging multiple repetitions, increasing scan time and potentially exacerbating motion effects.

Purpose of the Study:

To improve the quality of abdominal DW-MRI acquired with single-repetition protocols (NEX = 1).
To enhance apparent diffusion coefficient (ADC) map accuracy and lesion conspicuity at high b-values.
To reduce motion-related blurring that obscures small lesions in pediatric abdominal scans.

Main Methods:

A self-supervised denoising diffusion probabilistic model (ssDDPM) was developed for multi-b-value DW-MRI.
The ssDDPM incorporates diffusion signal decay (ADC) model constraints into its training loss function.
Trained on pediatric liver tumor data, ssDDPM denoised single-repetition images and was compared against Non-Local Means (NLM) and Deep Image Prior (DIP) techniques.

Main Results:

The ssDDPM significantly outperformed NLM and DIP in image quality, lesion conspicuity, and ADC map accuracy.
Radiologist assessments favored ssDDPM, and Receiver Operating Characteristic (ROC) analysis showed improved lesion discrimination.
ADC estimates from ssDDPM were more precise and accurate compared to motion-free reference data.

Conclusions:

The ssDDPM effectively denoises single-repetition DW-MRI images, reducing motion artifacts and improving image quality.
This method enhances the diagnostic utility for pediatric liver lesions by providing more accurate ADC maps and better lesion visibility.
ssDDPM offers a more efficient diagnostic tool with reduced scan times compared to current practices.