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Abstract #2412

Improving ASL MRI Sensitivity for Clinical Applications Using Transfer Learning-based Deep Learning

Danfeng Xie1, Yiran Li1, and Ze Wang1
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States

This study represents the first effort to apply transfer learning of Deep learning-based ASL denoising (DLASL) method on clinical ASL data. Pre-trained with young healthy subjects’ data, DLASL method showed improved Contrast-to-Noise Ratio (CNR) and Signal-to-Noise Ratio (SNR) and higher sensitivity for detecting the AD related hypoperfusion patterns compared with the conventional method. Experimental results demonstrated the high transfer capability of DLASL for clinical studies.

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