  A major remaining challenge for magnetic resonance-based attenuation
correction methods (MRAC) is their susceptibility to sources of MRI artifacts
(e.g. implants, motion) and uncertainties due to the limitations of MRI
contrast (e.g. accurate bone delineation and density, and separation of
air/bone). We propose using a Bayesian deep convolutional neural network that,
in addition to generating an initial pseudo-CT from MR data, also produces
uncertainty estimates of the pseudo-CT to quantify the limitations of the MR
data. These outputs are combined with MLAA reconstruction that uses the PET
emission data to improve the attenuation maps. With the proposed approach
(UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions
and show recovery of metal implants. In patients without implants, UpCT-MLAA
had acceptable but slightly higher RMSE than Zero-echo-time and Dixon Deep
pseudo-CT when compared to CTAC. In patients with metal implants, MLAA
recovered the metal implant; however, anatomy outside the implant region was
obscured by noise and crosstalk artifacts. Attenuation coefficients from the
pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal
implant region was estimated to have attenuation coefficients of air. UpCT-MLAA
estimated attenuation coefficients of metal implants alongside accurate
anatomic depiction outside of implant regions.
