مقالات پذیرفته شده در هشتمین کنگره بین المللی زیست پزشکی
Application of Diffusion Tensor Imaging in Multiple Sclerosis Detection
Application of Diffusion Tensor Imaging in Multiple Sclerosis Detection
Amirreza Sadeghinasab,1,*Mahmoud Mohammadi-Sadr,2Fatemeh Mazaheri,3
1. Department of Radiologic Technology, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. and Students Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. 2. Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran 3. Department of Radiologic Technology, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. and Students Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Introduction: Multiple sclerosis (MS) is a chronic neurodegenerative disease characterized by demyelination and axonal loss, leading to a wide range of neurological symptoms. Early and accurate diagnosis of MS is crucial for initiating timely and effective treatment. Diffusion Tensor Imaging (DTI) has emerged as a promising non-invasive technique for evaluating white matter microstructure, providing valuable insights into the pathophysiology of MS. This study aimed to investigate the potential of DTI in differentiating patients with MS from healthy controls and explore the correlation between DTI metrics and disease severity.
Methods: PubMed, Science Direct, Web of Science, and Google Scholar databases were explored up to August 2024, using different combinations of the keywords: " Multiple sclerosis ", " Diffusion Tensor Imaging ", " Neurodegenerative disease ", "Magnetic resonance imaging" and "Detection". Finally, six more recent and relevant records were included in the study.
Results: Patients with MS exhibited significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values compared to healthy controls in specific white matter regions, particularly in the corpus callosum, periventricular white matter, and brainstem. These findings indicate widespread white matter damage in MS patients. Furthermore, a significant correlation was observed between decreased FA and increased MD values with higher Expanded Disability Status Scale (EDSS) scores, suggesting that DTI metrics may reflect disease progression.
Conclusion: This study demonstrates the potential of DTI as a valuable tool for differentiating MS patients from healthy controls and assessing disease severity. By providing quantitative information about white matter microstructure, DTI can contribute to early diagnosis, monitoring disease progression, and evaluating treatment efficacy. Future studies with larger sample sizes are warranted to further explore the clinical utility of DTI in MS management and to investigate the potential of DTI as a prognostic biomarker.