Work

Physics-Assisted Deep Learning Denoising for Stabilized IMPULSED dMRI Microenvironment Parameter Fitting

Year: 2026

Type: preprint

Abstract: Objective: Diffusion-weighted MRI (dMRI) is a powerful tool for quantifying cellular microenvironment parameters. However, the inherently low signal-to-noise ratio (SNR) of dMRI can compromise the accuracy and reliability of parameter estimation. This study proposes a physics-assisted deep learning (DL)-based denoising framework designed to enhance dMRI signal quality and improve the robustness of subsequent biophysical model fitting, with potential relevance to low-SNR settings such as clinical 1.5 T MRI acquisitions. Approach: evaluation using HeLa and MC38 cell lines. Main results: , while reducing the mean overall fitting failure rate from 17.7% to 0%. Significance: datasets.

Source: PubMed

DOI: 10.48550/arxiv.2608.27681

Authors: Li, Wen, Dai, Yan, Rodriguez, Arely Perez, Aguilera, Todd, Deng, Jie, Jia, Xun

Institutions: Johns Hopkins University, The University of Texas Southwestern Medical Center

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institutes of Health