Danfeng Xie1, Yiran Li1, and Ze Wang1
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
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. Experimental results demonstrated the high transfer capability of DLASL for clinical studies.
Figure 4: The resulting T map of two-sample T-test (AD vs NC). The top row shows the results obtained by DLASL. The bottom row shows the results obtained by the non-DL-based conventional processing method[5]. From left to right: slices 95, 100, 105, 110, 115, 120 and 125. Display window: 4-6. P-value threshold is 0.001
Figure 3: The box plot of the CNR (top row) and SNR (bottom row) from 21 AD subjects' CBF maps and 24 NC subjects' CBF maps with different processing methods.