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Abstract #3008

Classification of amyloid-β status using 3D quantitative-amplified Magnetic Resonance Imaging (3D q-aMRI) - A preliminary study

Itamar Terem1, Kyan Younes2, Skylar Weiss2, Andrew Dreisbach3, Yonatan Urman 1, Christina Young2, Elizabeth Mormino2, Samantha Holdsworth 4,5, and Kawin Setsompop3
1Electrical Engineering, Stanford, Stanford, CA, United States, 2Neurology & Neurological Sciences, Stanford, Stanford, CA, United States, 3Radiology, Stanford, Stanford, CA, United States, 4Mātai Medical Research Institute, Gisborne, New Zealand, 5Faculty of Medical & Health Sciences & Centre for Brain Research, University of Auckland, Auckland, New Zealand

Synopsis

Keywords: Alzheimer's Disease, Alzheimer's Disease, 3D q-aMRI, amyloid-β status, AD biomarker

Motivation: 3D q-aMRI is a technique for visualizing and quantifying pulsatile sub-voxel displacement in the brain. This study investigates the potential of pulsatile brain motion as a predictor of amyloid-β (Aβ) status.

Goal(s): Assess the potential of 3D q-aMRI in differentiating between Aβ-positive and Aβ-negative patients

Approach: The sub-voxel displacement field output from 3D q-aMRI was utilized in 46 patients –both Aβ+ and Aβ- –to train a multi-layer perceptron for classification purposes.

Results: Classification accuracy reached 74.2%, despite the limited sample size and use of a basic multi-layer perceptron (MLP) model, indicating detectable differences in brain motion between Aβ+ and Aβ- cases.


Impact: Abnormal amyloid-β (Aβ) buildup signals early Alzheimer’s but requires invasive assessments. This study used 3D q-aMRI to classify Aβ status via pulsatile brain motion, achieving 74.2% accuracy with a basic MLP model trained on sub-voxel displacement data.

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Keywords