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Abstract #1960

Adaptive deep image reconstruction using G-SURE

Hemant Kumar Aggarwal1 and Mathews Jacob1
1University of Iowa, Iowa City, IA, United States

Deep learning image reconstruction algorithms often suffer from model mismatches when the acquisition scheme differs significantly from the forward model used during training. We introduce a Generalized Stein's Unbiased Risk Estimate (GSURE) loss metric to adapt or fine-tune the network to the measured k-space data, thus minimizing the impact of model misfit. Unlike current methods that rely on the mean square error in k-space, the proposed metric accounts for noise in the measurements. This makes the approach less vulnerable to overfitting, thus offering improved reconstruction quality compared to schemes that rely on mean-square error.

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