Abstract #3881

Uncertainty Estimation for Deep Learning-Based Enhancement of Undersampled Dual-Echo Steady-State Knee MRI

Ashmita Deb^1,2, Shu-Fu Shih^1,2, Zhaohuan Zhang^1,2, and Holden H Wu^1,2

¹Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States, ²Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Data Analysis, Image EnhancementDeep learning (DL) based image enhancement of undersampled 3D dual-echo steady-state knee MRI can achieve faster computation times compared to compressed sensing reconstruction. However, it is hard to interpret how DL models work. This introduces the risk of DL-enhanced images containing inaccuracies without the user’s knowledge and thus confounding diagnosis. This work aimed to calculate pixel-wise uncertainty maps for DL-enhanced images by incorporating Monte Carlo Dropout into a 2D UNET to estimate epistemic uncertainty. Analysis showed that the DL-enhanced images achieved good image quality and the spatial uncertainty maps reflected errors, compared to reference images.

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