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Abstract #0276

Estimating Uncertainty in Deep Learning MRI Reconstruction using a Pixel Classification Image Reconstruction Framework

Kamlesh Pawar1,2, Gary F Egan1,2,3, and Zhaolin Chen1,4
1Monash Biomedical Imaging, Monash University, Melbourne, Australia, 2School of Psychological Sciences, Monash University, Melbourne, Australia, 3ARC Centre of Excellence for Integrative Brain Function, Monash University, Melbourne, Australia, 4Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia

Data-driven deep learning (DL) image reconstruction from undersampled data has become a mainstream research area in MR image reconstruction. The generalization of the model on unseen data and out of sample data distribution is still a concern for the adoption of the DL reconstruction. In this work, we present a method of risk assessment in DL MR image reconstruction by generating an uncertainty map along with the reconstructed image. The proposed method re-casts image reconstruction as a classification problem and the probability of each voxel intensity in the reconstructed image can be used to efficiently estimate its uncertainty.

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