FFA-DMRI : A Network Based on Feature Fusion and Attention Mechanism for Brain MRI Denoising

Copyright © 2020 Hong, Huang, Yang, Li, Qian and Cai..

Magnetic Resonance Imaging (MRI) is an indispensable tool in the diagnosis of brain diseases due to painlessness and safety. Nevertheless, Rician noise is inevitably injected during the image acquisition process, which leads to poor observation and interferes with the treatment. Owing to the complexity of Rician noise, using the elimination method of Gaussian to remove it does not perform well. Therefore, the feature fusion and attention network (FFA-DMRI) is proposed to separate noise from observed MRI. Inspired by the attention-guided CNN network (ADNet) and Convolutional block attention module (CBAM), a spatial attention mechanism has been specially designed to obtain the area of interest in MRI. Furthermore, the feature fusion block concatenates local with global information, which makes full use of the multilevel structure and boosts the expressive ability of network. The comprehensive experiments on Alzheimer's disease neuroimaging initiative dataset (ADNI) have demonstrated high effectiveness of FFA-DMRI with maintaining the crucial brain details. Moreover, in terms of visual inspections, the denoising results are also consistent with human perception.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

Frontiers in neuroscience - 14(2020) vom: 23., Seite 577937

Sprache:

Englisch

Beteiligte Personen:

Hong, Dan [VerfasserIn]
Huang, Chenxi [VerfasserIn]
Yang, Chenhui [VerfasserIn]
Li, Jianpeng [VerfasserIn]
Qian, Yunhan [VerfasserIn]
Cai, Chunting [VerfasserIn]

Links:

Volltext

Themen:

Attention mechanism
Brain
Denoising
Feature fusion
Journal Article
Magnetic resonance imaging

Anmerkungen:

Date Revised 13.10.2020

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.3389/fnins.2020.577937

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM316115878