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An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is an irreversible brain disease that severely damages human thinking and memory. Early diagnosis plays an important part in the prevention and treatment of AD. Neuroimaging-based computer-aided diagnosis (CAD) has shown that deep learning methods using multimodal image...

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Autores principales: Song, Juan, Zheng, Jian, Li, Ping, Lu, Xiaoyuan, Zhu, Guangming, Shen, Peiyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521941/
https://www.ncbi.nlm.nih.gov/pubmed/34713109
http://dx.doi.org/10.3389/fdgth.2021.637386
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author Song, Juan
Zheng, Jian
Li, Ping
Lu, Xiaoyuan
Zhu, Guangming
Shen, Peiyi
author_facet Song, Juan
Zheng, Jian
Li, Ping
Lu, Xiaoyuan
Zhu, Guangming
Shen, Peiyi
author_sort Song, Juan
collection PubMed
description Alzheimer's disease (AD) is an irreversible brain disease that severely damages human thinking and memory. Early diagnosis plays an important part in the prevention and treatment of AD. Neuroimaging-based computer-aided diagnosis (CAD) has shown that deep learning methods using multimodal images are beneficial to guide AD detection. In recent years, many methods based on multimodal feature learning have been proposed to extract and fuse latent representation information from different neuroimaging modalities including magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose positron emission tomography (FDG-PET). However, these methods lack the interpretability required to clearly explain the specific meaning of the extracted information. To make the multimodal fusion process more persuasive, we propose an image fusion method to aid AD diagnosis. Specifically, we fuse the gray matter (GM) tissue area of brain MRI and FDG-PET images by registration and mask coding to obtain a new fused modality called “GM-PET.” The resulting single composite image emphasizes the GM area that is critical for AD diagnosis, while retaining both the contour and metabolic characteristics of the subject's brain tissue. In addition, we use the three-dimensional simple convolutional neural network (3D Simple CNN) and 3D Multi-Scale CNN to evaluate the effectiveness of our image fusion method in binary classification and multi-classification tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset indicate that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it outperforms state-of-the-art methods for AD diagnosis.
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spelling pubmed-85219412021-10-27 An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis Song, Juan Zheng, Jian Li, Ping Lu, Xiaoyuan Zhu, Guangming Shen, Peiyi Front Digit Health Digital Health Alzheimer's disease (AD) is an irreversible brain disease that severely damages human thinking and memory. Early diagnosis plays an important part in the prevention and treatment of AD. Neuroimaging-based computer-aided diagnosis (CAD) has shown that deep learning methods using multimodal images are beneficial to guide AD detection. In recent years, many methods based on multimodal feature learning have been proposed to extract and fuse latent representation information from different neuroimaging modalities including magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose positron emission tomography (FDG-PET). However, these methods lack the interpretability required to clearly explain the specific meaning of the extracted information. To make the multimodal fusion process more persuasive, we propose an image fusion method to aid AD diagnosis. Specifically, we fuse the gray matter (GM) tissue area of brain MRI and FDG-PET images by registration and mask coding to obtain a new fused modality called “GM-PET.” The resulting single composite image emphasizes the GM area that is critical for AD diagnosis, while retaining both the contour and metabolic characteristics of the subject's brain tissue. In addition, we use the three-dimensional simple convolutional neural network (3D Simple CNN) and 3D Multi-Scale CNN to evaluate the effectiveness of our image fusion method in binary classification and multi-classification tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset indicate that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it outperforms state-of-the-art methods for AD diagnosis. Frontiers Media S.A. 2021-02-26 /pmc/articles/PMC8521941/ /pubmed/34713109 http://dx.doi.org/10.3389/fdgth.2021.637386 Text en Copyright © 2021 Song, Zheng, Li, Lu, Zhu and Shen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Digital Health
Song, Juan
Zheng, Jian
Li, Ping
Lu, Xiaoyuan
Zhu, Guangming
Shen, Peiyi
An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis
title An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis
title_full An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis
title_fullStr An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis
title_full_unstemmed An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis
title_short An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis
title_sort effective multimodal image fusion method using mri and pet for alzheimer's disease diagnosis
topic Digital Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521941/
https://www.ncbi.nlm.nih.gov/pubmed/34713109
http://dx.doi.org/10.3389/fdgth.2021.637386
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