What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Denoising Diffusion Probabilistic Model with Dual-domain Entropy and Stochastic Differential Equations on High Undersampling Reconstruction

Key Points

EDU-DDPM improves reconstruction fidelity in MRI with high undersampling factors, outperforming existing models.
Our method demonstrates better performance with 8-fold undersampled knee and brain datasets on fastMRI.
The composite loss function incorporates information entropy and stochastic differential equations for enhanced results.
This technique offers significant improvements over compressed sensing methods for TOF-MRA at 7T.

Abstract

Motivation: Using denoising diffusion probabilistic models (DDPM) for MRI reconstruction remains challenging on high-fold under-sampling. Goal(s): To improve the reconstruction performance of 8-fold under-sampled data by a modified DDPM. Approach: A composite loss function based on information entropy, dual-domain difference, and uniform-static-SDE (stochastic differential equations) is proposed, called EDU-DDPM. Our algorithm has been tested on fastMRI and compared to compressed sensing (CS) TOF-MRA data acquired at 7T. Results: Compared to previous DDPM, our method performs better in single-channel knee and multi-channel brain datasets on fastMRI with 8-fold under-sampling. Additionally, it outperforms CS reconstruction in Time-of-flight (TOF-MRA) acquired in variable density Poisson sampling pattern. Impact: The proposed EDU-DDPM significantly improves MRI reconstruction at high subsampling factors, outperforming DDPM on fastMRI and compressive sensing on 7T TOF-MRA data. This advancement enhances fidelity of reconstruction.

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