Abstract #0724

Advanced Deep Learning Denoising for Accelerated 0.55T Prostate MRI

Nikola Janjusevic^1,2,3, Mary Bruno^1,3, Yuhui Huang^1,3, Jingjia Chen^1,3, Yao Wang², Hersh Chandarana^1,3, and Li Feng^1,3

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ²Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States, ³Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States

Synopsis

Keywords: Prostate, Prostate

Motivation: Low-Field MR offers a great platform for low-cost high-performance screening of prostate cancer, but it suffers from low SNR. Prolonged scan times are typically needed to achieve adequate SNR at low field.

Goal(s): In this work, we developed an advanced deep learning denoising method for rapid high spatial resolution prostate MRI at 0.55T.

Approach: The proposed approach was tested in T2-weighted prostate MRI. Supervised training was performed to denoise images acquired with different numbers of averages, corresponding to different scan times.

Results: Deep learning was able to denoise prostate images at high spatial resolution resulting acquisition time with 1-2 average.

Impact: The proposed denoising technique holds significant potential to promote the use of 0.55T MRI and other types of low-field MRI for prostate imaging and screening for prostate cancer, with reduced cost and greater accessibility.

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