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Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System

Magnetic resonance elastography (MRE) is used to assess the stiffness of the liver to rule out cirrhosis or fibrosis. The image, nevertheless, is regarded as shear-wave imaging and does not depict any anatomical features. Multimodality medical image fusion (MMIF), such as the fusion of MRE with comp...

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Autores principales: Alharbi, Fayez M, Besbes, Faten R, Almutairi, Basim S, Alotaibi, Abdullah T, Fatani, Faiq F, Besbes, Hatem R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569364/
https://www.ncbi.nlm.nih.gov/pubmed/37842423
http://dx.doi.org/10.7759/cureus.45109
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author Alharbi, Fayez M
Besbes, Faten R
Almutairi, Basim S
Alotaibi, Abdullah T
Fatani, Faiq F
Besbes, Hatem R
author_facet Alharbi, Fayez M
Besbes, Faten R
Almutairi, Basim S
Alotaibi, Abdullah T
Fatani, Faiq F
Besbes, Hatem R
author_sort Alharbi, Fayez M
collection PubMed
description Magnetic resonance elastography (MRE) is used to assess the stiffness of the liver to rule out cirrhosis or fibrosis. The image, nevertheless, is regarded as shear-wave imaging and does not depict any anatomical features. Multimodality medical image fusion (MMIF), such as the fusion of MRE with computed tomography (CT) scan or magnetic resonance imaging (MRI), can help doctors optimize the advantages of each imaging technique. As a result, perceptions serve as valid and valuable assessment criteria. The contrast sensitivity function (CSF), which describes the rates of visual contrast sensitivity through the changing of spatial frequencies, is used mathematically to characterize the human visual system (HVS). As a result, we suggest novel methods for fusing images that use discrete wavelets transform (DWT) based on HVS and CSF models. Images from MRI or CT scan were combined with MRE images, and the outcomes were assessed both subjectively and objectively. Visual inspection of merging images was done throughout the qualitative analysis. The CT-MRE fused images in all four datasets were shown to be superior at maintaining bones and spatial resolution, despite the MRI-MRE being better at exhibiting soft tissues and contrast resolution. It is clear from all four datasets that the liver soft tissue in MRI and CT images mixed successfully with the red-colored stiffness distribution seen in MRE images. The proposed approach outperformed DWT, which produced visual artifacts such as signal loss. Quantitative evaluation using mean, standard deviation, and entropy showed that the generated images from the proposed technique performed better than the source images and DWT. Additionally, peak signal-to-noise ratio, mean square error, correlation coefficient, and structural similarity index measure were employed to compare the two fusion approaches, namely, MRI-MRE and CT-MRE. The comparison did not show the superiority of one approach over the other. In conclusion, both subjective and objective evaluation approaches revealed that the combined images contained more information and characteristics. Hence, the proposed method might be a useful procedure to diagnose and localize the stiffness regions on the liver soft tissue by fusion of MRE with MRI or CT.
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spelling pubmed-105693642023-10-13 Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System Alharbi, Fayez M Besbes, Faten R Almutairi, Basim S Alotaibi, Abdullah T Fatani, Faiq F Besbes, Hatem R Cureus Medical Physics Magnetic resonance elastography (MRE) is used to assess the stiffness of the liver to rule out cirrhosis or fibrosis. The image, nevertheless, is regarded as shear-wave imaging and does not depict any anatomical features. Multimodality medical image fusion (MMIF), such as the fusion of MRE with computed tomography (CT) scan or magnetic resonance imaging (MRI), can help doctors optimize the advantages of each imaging technique. As a result, perceptions serve as valid and valuable assessment criteria. The contrast sensitivity function (CSF), which describes the rates of visual contrast sensitivity through the changing of spatial frequencies, is used mathematically to characterize the human visual system (HVS). As a result, we suggest novel methods for fusing images that use discrete wavelets transform (DWT) based on HVS and CSF models. Images from MRI or CT scan were combined with MRE images, and the outcomes were assessed both subjectively and objectively. Visual inspection of merging images was done throughout the qualitative analysis. The CT-MRE fused images in all four datasets were shown to be superior at maintaining bones and spatial resolution, despite the MRI-MRE being better at exhibiting soft tissues and contrast resolution. It is clear from all four datasets that the liver soft tissue in MRI and CT images mixed successfully with the red-colored stiffness distribution seen in MRE images. The proposed approach outperformed DWT, which produced visual artifacts such as signal loss. Quantitative evaluation using mean, standard deviation, and entropy showed that the generated images from the proposed technique performed better than the source images and DWT. Additionally, peak signal-to-noise ratio, mean square error, correlation coefficient, and structural similarity index measure were employed to compare the two fusion approaches, namely, MRI-MRE and CT-MRE. The comparison did not show the superiority of one approach over the other. In conclusion, both subjective and objective evaluation approaches revealed that the combined images contained more information and characteristics. Hence, the proposed method might be a useful procedure to diagnose and localize the stiffness regions on the liver soft tissue by fusion of MRE with MRI or CT. Cureus 2023-09-12 /pmc/articles/PMC10569364/ /pubmed/37842423 http://dx.doi.org/10.7759/cureus.45109 Text en Copyright © 2023, Alharbi et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Medical Physics
Alharbi, Fayez M
Besbes, Faten R
Almutairi, Basim S
Alotaibi, Abdullah T
Fatani, Faiq F
Besbes, Hatem R
Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System
title Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System
title_full Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System
title_fullStr Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System
title_full_unstemmed Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System
title_short Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System
title_sort fusion of magnetic resonance elastography images with computed tomography and magnetic resonance imaging using the human visual system
topic Medical Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569364/
https://www.ncbi.nlm.nih.gov/pubmed/37842423
http://dx.doi.org/10.7759/cureus.45109
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