BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension
A few methods and tools are available for the quantitative measurement of the brain volume targeting mainly brain volume loss. However, several factors, such as the clinical conditions, the time of the day, the type of MRI machine, the brain volume artifacts, the pseudoatrophy, and the variations am...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2021.765185/full |
| _version_ | 1818869989815353344 |
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| author | Ghulam Md Ashraf Ghulam Md Ashraf Stylianos Chatzichronis Stylianos Chatzichronis Athanasios Alexiou Athanasios Alexiou Nikolaos Kyriakopoulos Badrah Saeed Ali Alghamdi Badrah Saeed Ali Alghamdi Badrah Saeed Ali Alghamdi Haythum Osama Tayeb Haythum Osama Tayeb Jamaan Salem Alghamdi Waseem Khan Manal Ben Jalal Hazem Mahmoud Atta |
| author_facet | Ghulam Md Ashraf Ghulam Md Ashraf Stylianos Chatzichronis Stylianos Chatzichronis Athanasios Alexiou Athanasios Alexiou Nikolaos Kyriakopoulos Badrah Saeed Ali Alghamdi Badrah Saeed Ali Alghamdi Badrah Saeed Ali Alghamdi Haythum Osama Tayeb Haythum Osama Tayeb Jamaan Salem Alghamdi Waseem Khan Manal Ben Jalal Hazem Mahmoud Atta |
| author_sort | Ghulam Md Ashraf |
| collection | DOAJ |
| description | A few methods and tools are available for the quantitative measurement of the brain volume targeting mainly brain volume loss. However, several factors, such as the clinical conditions, the time of the day, the type of MRI machine, the brain volume artifacts, the pseudoatrophy, and the variations among the protocols, produce extreme variations leading to misdiagnosis of brain atrophy. While brain white matter loss is a characteristic lesion during neurodegeneration, the main objective of this study was to create a computational tool for high precision measuring structural brain changes using the fractal dimension (FD) definition. The validation of the BrainFD software is based on T1-weighted MRI images from the Open Access Series of Imaging Studies (OASIS)-3 brain database, where each participant has multiple MRI scan sessions. The software is based on the Python and JAVA programming languages with the main functionality of the FD calculation using the box-counting algorithm, for different subjects on the same brain regions, with high accuracy and resolution, offering the ability to compare brain data regions from different subjects and on multiple sessions, creating different imaging profiles based on the Clinical Dementia Rating (CDR) scores of the participants. Two experiments were executed. The first was a cross-sectional study where the data were separated into two CDR classes. In the second experiment, a model on multiple heterogeneous data was trained, and the FD calculation for each participant of the OASIS-3 database through multiple sessions was evaluated. The results suggest that the FD variation efficiently describes the structural complexity of the brain and the related cognitive decline. Additionally, the FD efficiently discriminates the two classes achieving 100% accuracy. It is shown that this classification outperforms the currently existing methods in terms of accuracy and the size of the dataset. Therefore, the FD calculation for identifying intracranial brain volume loss could be applied as a potential low-cost personalized imaging biomarker. Furthermore, the possibilities measuring different brain areas and subregions could give robust evidence of the slightest variations to imaging data obtained from repetitive measurements to Physicians and Radiologists. |
| first_indexed | 2024-12-19T11:59:54Z |
| format | Article |
| id | doaj.art-b44eff16c4a040e8a5bf1663ac55ef7c |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-12-19T11:59:54Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-b44eff16c4a040e8a5bf1663ac55ef7c2022-12-21T20:22:32ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652021-11-011310.3389/fnagi.2021.765185765185BrainFD: Measuring the Intracranial Brain Volume With Fractal DimensionGhulam Md Ashraf0Ghulam Md Ashraf1Stylianos Chatzichronis2Stylianos Chatzichronis3Athanasios Alexiou4Athanasios Alexiou5Nikolaos Kyriakopoulos6Badrah Saeed Ali Alghamdi7Badrah Saeed Ali Alghamdi8Badrah Saeed Ali Alghamdi9Haythum Osama Tayeb10Haythum Osama Tayeb11Jamaan Salem Alghamdi12Waseem Khan13Manal Ben Jalal14Hazem Mahmoud Atta15Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, GreeceDepartment of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW, AustraliaDepartment of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW, AustraliaAFNP Med Austria, Vienna, AustriaMRI Department, 251 General Airforce Hospital, Athens, GreecePre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Physiology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi ArabiaThe Neuroscience Research Unit, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi ArabiaThe Neuroscience Research Unit, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi ArabiaDivision of Neurology, Department of Internal Medicine, King Abdulaziz University, Jeddah, Saudi Arabia0Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia1Department of Radiology, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia1Department of Radiology, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia2Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi ArabiaA few methods and tools are available for the quantitative measurement of the brain volume targeting mainly brain volume loss. However, several factors, such as the clinical conditions, the time of the day, the type of MRI machine, the brain volume artifacts, the pseudoatrophy, and the variations among the protocols, produce extreme variations leading to misdiagnosis of brain atrophy. While brain white matter loss is a characteristic lesion during neurodegeneration, the main objective of this study was to create a computational tool for high precision measuring structural brain changes using the fractal dimension (FD) definition. The validation of the BrainFD software is based on T1-weighted MRI images from the Open Access Series of Imaging Studies (OASIS)-3 brain database, where each participant has multiple MRI scan sessions. The software is based on the Python and JAVA programming languages with the main functionality of the FD calculation using the box-counting algorithm, for different subjects on the same brain regions, with high accuracy and resolution, offering the ability to compare brain data regions from different subjects and on multiple sessions, creating different imaging profiles based on the Clinical Dementia Rating (CDR) scores of the participants. Two experiments were executed. The first was a cross-sectional study where the data were separated into two CDR classes. In the second experiment, a model on multiple heterogeneous data was trained, and the FD calculation for each participant of the OASIS-3 database through multiple sessions was evaluated. The results suggest that the FD variation efficiently describes the structural complexity of the brain and the related cognitive decline. Additionally, the FD efficiently discriminates the two classes achieving 100% accuracy. It is shown that this classification outperforms the currently existing methods in terms of accuracy and the size of the dataset. Therefore, the FD calculation for identifying intracranial brain volume loss could be applied as a potential low-cost personalized imaging biomarker. Furthermore, the possibilities measuring different brain areas and subregions could give robust evidence of the slightest variations to imaging data obtained from repetitive measurements to Physicians and Radiologists.https://www.frontiersin.org/articles/10.3389/fnagi.2021.765185/fullagingbiomarkersfractal dimensionintracranial brain volumeMRIneuroinformatics |
| spellingShingle | Ghulam Md Ashraf Ghulam Md Ashraf Stylianos Chatzichronis Stylianos Chatzichronis Athanasios Alexiou Athanasios Alexiou Nikolaos Kyriakopoulos Badrah Saeed Ali Alghamdi Badrah Saeed Ali Alghamdi Badrah Saeed Ali Alghamdi Haythum Osama Tayeb Haythum Osama Tayeb Jamaan Salem Alghamdi Waseem Khan Manal Ben Jalal Hazem Mahmoud Atta BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension Frontiers in Aging Neuroscience aging biomarkers fractal dimension intracranial brain volume MRI neuroinformatics |
| title | BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension |
| title_full | BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension |
| title_fullStr | BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension |
| title_full_unstemmed | BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension |
| title_short | BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension |
| title_sort | brainfd measuring the intracranial brain volume with fractal dimension |
| topic | aging biomarkers fractal dimension intracranial brain volume MRI neuroinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2021.765185/full |
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