Work

Application of Denoising Diffusion Probabilistic Methods in Fetal MRI

Year: 2025

Type: article

Abstract: Magnetic resonance imaging (MRI) is a common type of medical image acquisition that can also be used to diagnose early diseases. In this sense, fetal MRI is a non-invasive method to generate high-quality fetal volumes and to perform important clinical analysis. However, image denoising is necessary in many situations to ensure accurate evaluations. Thus, approaches such as Denoising Diffusion Probabilistic Methods (DDPM) have emerged and reached great results in this kind of task. In this work, we applied two DDPM-based approaches (an original and an improved one) besides two self-supervised deep models in a fetal MRI dataset. The results showed that, for the used fetal MRI dataset, the improved DDPM, named I-DDPM outperformed the counterparts considering two evaluation metrics for image quality, the Peak Signal-to-Noise Ratio (PSNR) and Root Mean Squared Error (RMSE).

Source: Revista de Informática Teórica e Aplicada

DOI: 10.22456/2175-2745.143785

Authors: Ana Claudia Souza Vidal de Negreiros, Gilson Giraldi, Heron Werner

Institutions: Universidade Federal Rural do Semi-Árido, Laboratório Nacional de Computação Científica, Pontifícia Universidade Católica do Rio de Janeiro

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: diamond

Funders: Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Defence and Security Accelerator