Abstract #4773

Accuracy of Deep Learning-based Signal-to-noise Measurements using Air Recon DL

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

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

Synopsis

Keywords: Data Acquisition, Data AnalysisNoise estimates in Deep Learning-based image reconstruction (DLR) from background regions lead to artificially low noise estimates and thus inaccurate SNR. Noise estimate accuracy was improved using the difference between two identical acquisitions. SNR increased in DLR compared to standard reconstruction in clinical sequences over a range of acquired signal averages and voxel sizes. With DLR, varying signal averages had little effect on the measured SNR in fast spin echo acquisitions, while SNR increased by more than three-fold in single-shot fast spin echo. However, with DLR, SNR no longer follows predicable behavior, therefore sequence optimizations need to be performed experimentally.

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