Accuracy of SNR estimates and relative changes in SNR with applied Deep Learning-based Reconstruction in the brain and abdomen

Motivation: Improved SNR characterization of images with Deep Learning-based image reconstruction (DLR). Goal(s): To evaluate whether SNR measurements using improved noise estimates demonstrate expected SNR changes with common sequence acquisition modifications in images reconstructed using DLR. Approach: Sequentially acquired images were used to calculate theoretically accurate noise estimates in the brain and abdomen. Each dataset consisted of two sequentially acquired T2-weighted series. Five distinct SNR estimates were calculated from the same data. Results: SNR estimates using region-based noise measurements were less accurate than sequentially acquired estimates. Furthermore, these SNR estimates cannot reliably measure relative SNR with common sequence parameters modifications. Impact: SNR measured from sequential images in the brain and abdomen demonstrated that DLR improved SNR. Results from single-image noise estimates were inaccurate. Further, results from known parameter modifications demonstrated that DLR underestimated the expected relative SNR differences in all methods.