Abstract #1095

Deep learning-based uncertainty estimation: application in PET/MRI joint estimation of attenuation and activity for patients with metal implants

Andrew P. Leynes^1,2, Sangtae Ahn³, Kristen Wangerin³, Florian Wiesinger⁴, Thomas A. Hope¹, and Peder E.Z. Larson¹

¹University of California San Francisco, San Francisco, CA, United States, ²UC Berkeley - UC San Francisco Joint Graduate Program in Bioengineering, Berkeley and San Francisco, CA, United States, ³GE Global Research, Niskayuna, NY, United States, ⁴GE Global Research, Munich, Germany

PET image reconstruction requires accurate estimates of attenuation coefficients. Metal implants corrupt both the MRI and CT images and thus are not suitable for use in image reconstruction. In particular, the metal implant appears as a large signal void in the MRI and is incorrectly estimated as having the attenuation coefficients of air. We proposed to use Bayesian deep learning to identify the location of the metal implant and use it to guide PET joint estimation of attenuation and activity. We found that the metal implant is recovered and lesion uptake near the implant agree well with our reference data.

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