  Contrast resolution beyond the limits of conventional cone-beam CT (CBCT)
systems is essential to high-quality imaging of the brain. We present a deep
learning reconstruction method (dubbed DL-Recon) that integrates physically
principled reconstruction models with DL-based image synthesis based on the
statistical uncertainty in the synthesis image. A synthesis network was
developed to generate a synthesized CBCT image (DL-Synthesis) from an
uncorrected filtered back-projection (FBP) image. To improve generalizability
(including accurate representation of lesions not seen in training), voxel-wise
epistemic uncertainty of DL-Synthesis was computed using a Bayesian inference
technique (Monte-Carlo dropout). In regions of high uncertainty, the DL-Recon
method incorporates information from a physics-based reconstruction model and
artifact-corrected projection data. Two forms of the DL-Recon method are
proposed: (i) image-domain fusion of DL-Synthesis and FBP (DL-FBP) weighted by
DL uncertainty; and (ii) a model-based iterative image reconstruction (MBIR)
optimization using DL-Synthesis to compute a spatially varying regularization
term based on DL uncertainty (DL-MBIR). The error in DL-Synthesis images was
correlated with the uncertainty in the synthesis estimate. Compared to FBP and
PWLS, the DL-Recon methods (both DL-FBP and DL-MBIR) showed ~50% reduction in
noise (at matched spatial resolution) and ~40-70% improvement in image
uniformity. Conventional DL-Synthesis alone exhibited ~10-60% under-estimation
of lesion contrast and ~5-40% reduction in lesion segmentation accuracy (Dice
coefficient) in simulated and real brain lesions, suggesting a lack of
reliability / generalizability for structures unseen in the training data.
DL-FBP and DL-MBIR improved the accuracy of reconstruction by directly
incorporating information from the measurements in regions of high uncertainty.
