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Abstract #3624

Deep Learning-Based Adaptive Noise Reduction for Improving Image Quality of 1.5T MR Images

Shigeru Kiryu1, Yasutaka Sugano2, Tomoyuki Ohta3, and Kuni Ohtomo4
1Radiology, International University of Health, School of medicine, Chiba, Japan, 2Canon Medical Systems Corporation, Kanagawa, Japan, 3International University of Health and Welfare Hospital, Tochigi, Japan, 4International University of Health and Welfare, Tochigi, Japan

We assessed the performance of the Deep Learning-based Reconstruction (dDLR) technique in improving 1.5T MR images. Eleven volunteers underwent MR imaging at 3T and 1.5T on the same day with the same imaging parameters. We applied the dDLR to the 1.5T image data (dDLR-1.5T), and then compared the 1.5T and dDLR-1.5T datasets with reference to the 3T dataset. The structure similarity of dDLR-1.5T was higher than that of 1.5T and dDLR increased SNR at 1.5T. The dDLR technique improves the image quality of MR images obtained at 1.5T.

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