Abstract #4047

Practical Approaches to the Evaluation of Signal-to-Noise Ratio Performance with Deep Learning Denoising Image Reconstruction

Zihan Wang^1,2, Jayse M Weaver^2,3, Daiki Tamada, PhD^2,3, Diego Hernando, PhD^2,3, and Scott B Reeder, MD, PhD^1,2,3,4,5

¹Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ²Radiology, University of Wisconsin-Madison, Madison, WI, United States, ³Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ⁴Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁵Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

In this work, three methods for measuring signal-to-noise ratio (SNR) performance of deep learning (DL) denoising image reconstruction were evaluated. Images reconstructed using a vendor prototype DL reconstruction algorithm were compared to conventional Fourier reconstruction. Phantom experiments were performed using a single-channel head coil and a 32-channel head coil to assess the effects of parallel imaging acceleration in combination with DL reconstruction on SNR performance. We found excellent agreement between the three SNR measurement methods for both Fourier and DL reconstruction, and found that DL reconstructed images have similar g-factor performance patterns as Fourier reconstructed images.

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