BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension - Ashraf, Ghulam Md; Chatzichronis, Stylianos; Alexiou, Athanasios; Kyriakopoulos, Nikolaos; Alghamdi, Badrah Saeed Ali; Tayeb, Haythum Osama; Alghamdi, Jamaan Salem; Khan, Waseem; Jalal, Manal Ben; Atta, Hazem Mahmoud (Frontiers in Aging Neuroscience, 2021)
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 is to create a computational tool for high precision measuring brain structural changes using the Fractal Dimension definition. The validation of the BrainFD software is based on MRI T1 weighted images from the OASIS-3 brain database, where each participant has multiple MRI scan sessions. The software is based on the Python and JAVA programming languages with main functionality the Fractal Dimension 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 participants' Clinical Dementia Rating scores. Two experiments were executed. The first was a cross-section study where data was separated into two Clinical Dementia Rating classes. In the second experiment, a model on multiple heterogeneous data was trained, and the Fractal Dimension calculation for each participant of the OASIS-3 database through multiple sessions was evaluated. The results suggest that Fractal Dimension variation efficiently describes the brain's structure complexity and the related cognitive decline. Additionally, the Fractal Dimension efficiently discriminates the two classes achieving 100% accuracy. It is shown that this classification outperforms the currently existing methods in terms of accuracy and dataset size. Therefore, the Fractal Dimension 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.