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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