Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{178599,
        author = {Bharat Bhushan Dagur and Rukamanee and Dr. Ashish Kumar Shukla and Neelam Rao and Shivanshu Chauhan},
        title = {MRI AS A DIAGNOSTIC AND PROGNOSTIC TOOL IN ALZHEIMER'S DISEASE: A COMPREHENSIVE REVIEW},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4079-4085},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178599},
        abstract = {Aim: To explore and summarize the role of Magnetic Resonance Imaging (MRI) as a diagnostic and prognostic tool in Alzheimer's disease (AD), with a focus on structural, functional, and advanced imaging techniques. Objectives: To review MRI findings characteristic of AD, To evaluate the relevance of imaging biomarkers for diagnosis and prognosis, To discuss future directions in MRI applications for AD. Materials and Methods: This review is based on a comprehensive analysis of peer-reviewed literature from 2000 to 2024, sourced from databases such as PubMed, Scopus, and Web of Science. Studies focusing on MRI modalities used in AD diagnosis and prognosis were included. Results: Structural MRI identifies hallmark features of AD, such as hippocampal atrophy and cortical thinning. Functional MRI highlights altered connectivity patterns, and Diffusion Tensor Imaging (DTI) detects white matter degradation. Quantitative biomarkers such as medial temporal lobe atrophy (MTA) scores and volumetric analyses have shown high diagnostic and prognostic value. Advanced techniques like MRS, ASL, and QSM, along with AI integration, further enhance diagnostic accuracy. Conclusion: MRI is an essential, non-invasive tool that significantly contributes to the early diagnosis, monitoring, and prognosis of AD. Continuous advancements in imaging techniques and their integration with artificial intelligence hold promise for improving patient outcomes and understanding AD pathophysiology.},
        keywords = {Alzheimer's disease, MRI, diagnosis, prognosis, neuroimaging, hippocampal atrophy, biomarkers, brain volumetry.},
        month = {May},
        }