Abstract #1038

Early Diagnosis of Dementia (AD/MCI/Normal Aging) Based on CBF-Maps Derived from ASL–MRI and Artificial Intelligence

Soroor Kalantari¹, Fardin Samadi Khosh Mehr², Mohammad Soltani¹, Mehdi Maghbooli³, Zahra Rezaei⁴, Soheila Borji¹, Behzad Memari¹, Mohammad Bayat¹, Behnaz Eslami⁵, and Hamidreza Saligheh Rad⁶

¹Department of Radiology, Zanjan University of Medical Science, Zanjan, Iran (Islamic Republic of), ²Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), ³Department of Neurology, Zanjan University of Medical Science, Zanjan, Iran (Islamic Republic of), ⁴Department of Computer and Electrical Engineering, University of Kashan, Kashan, Iran (Islamic Republic of), ⁵Tehran Islamic Azad University, Tehran, Iran (Islamic Republic of), ⁶Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Department of Medical Physics and Biomedical Engineering, Tehran university of Medical Science, Tehran, Iran (Islamic Republic of)

This study aims to investigate the use of high-level de-noising and machine-learning methods applied on ASL-MRI dataset acquired at 1.5T, and in order to to find important regions in the brain for the classification of patients with AD and MCI and normal aging. Automated classification and prediction methods recognizing perfusion changes in specific subregions of the brain are applied to pseudo-continuous ASL-derived CBF-maps, predicting the diagnosis of Alzheimer's disease, mild cognitive impairment, and normal cognition. Due to alarming prevalence of AD, machine-learning approaches for ASL- MRI are used to develop computer-aided diagnosis (CAD) tools for clinical and screening targets, assisting early diagnosis of the AD process.

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