Abstract #4619

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

Evan McNabb¹, Véronique Fortier^1,2,3,4, and Ives R. Levesque^3,4,5

¹Medical Imaging, McGill University Health Centre, Montreal, QC, Canada, ²Department of Radiology, McGill University, Montreal, QC, Canada, ³Medical Physics Unit, McGill University, Montreal, QC, Canada, ⁴Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada, ⁵Research Institute of the McGill University, Montreal, QC, Canada

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging

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.

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