Abstract #0276

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

Kamlesh Pawar^1,2, Gary F Egan^1,2,3, and Zhaolin Chen^1,4

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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