1038
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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)
Automated classification methods showed excellent performance to distinguish AD versus normal cognitive group (ACC: 100%, AUC: 0.88), AD versus MCI (acuracy:%88, AUC: 0.90) and MCI versus normal cognitive group (ACC: 95%, AUC: 1)
Figure 2. Absolute perfusion image using voxel-wise calibration (left) and after correction volume effects around the edge of the brain (right)
Figure 3. CBF map extracted from the kinetic model (left). The image of estimated PVs of gray matter (center), and the estimated gray matter perfusion (right)