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ORIGINAL RESEARCH ARTICLE

Algorithm development and metal oxide nanoparticle analysis in magnetic resonance imaging: Advancing neurodegenerative disease diagnostics

Daniela Gomes Bornal1† ,  Hulder Henrique Zaparoli1† ,  Marina Piacenti-Silva2 ,  Paulo Noronha Lisboa-Filho2 ,  Marcela de Oliveira2*
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1 Postgraduate Program in Science and Technology of Materials - POSMAT, School of Sciences/São Paulo State University, Bauru, São Paulo, Brazil
2 Department of Physics and Meteorology, School of Sciences/São Paulo State University, Bauru, São Paulo, Brazil
AIH, 3947 https://doi.org/10.36922/aih.3947
Submitted: 14 June 2024 | Accepted: 28 August 2024 | Published: 9 October 2024
(This article belongs to the Special Issue Artificial intelligence for diagnosing brain diseases)
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Magnetic resonance imaging (MRI) is critical in the diagnosis of neurodegenerative diseases, enabling the detection of brain lesions. Recent research has examined metallic nanoparticles (NPs) as MRI contrast agents (CAs) that can enhance lesion visibility by altering relaxation times. This study investigates the effects of metal oxide NPs on MRI relaxation times and brain lesion signals and proposes an algorithm for automated relaxation time determination using these NPs. The utilized NPs were synthesized using the sol‒gel method and characterized using Fourier-transform infrared spectroscopy and X-ray diffraction. MRI scans were performed on a phantom infused with varying concentrations of each metal oxide NP to assess changes in pixel signal intensities and relaxation rates. Our analysis involved segmenting the MRI images to focus on regions with different NP concentrations. The algorithm computed the longitudinal relaxation time for each region, revealing that Fe2O3 NPs exhibited the most substantial effect on signal intensity and relaxation time. The results indicated a high correlation (r = 0.9977), demonstrating strong agreement and confirming the reliability of our method. Our findings suggest that metallic oxide NPs, particularly Fe2O3, can considerably alter magnetization and act as effective negative CAs in MRI. These capabilities can improve the monitoring and treatment efficacy of neurodegenerative diseases. Our method for quantifying longitudinal relaxation times can potentially enhance routine clinical MRI assessments, offering a promising tool for future clinical applications.

Keywords
Magnetic resonance imaging
Algorithm
Longitudinal relaxation time (T1)
Signal intensity
Funding
This study was funded by the Brazilian agency Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP 2020/03022-9, 2019/16362-5 and 2017/20032-5).
Conflict of interest
The authors declare that they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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