  Low-dose dental cone beam computed tomography (CBCT) has been increasingly
used for maxillofacial modeling. However, the presence of metallic inserts,
such as implants, crowns, and dental filling, causes severe streaking and
shading artifacts in a CBCT image and loss of the morphological structures of
the teeth, which consequently prevents accurate segmentation of bones. A
two-stage metal artifact reduction method is proposed for accurate 3D low-dose
maxillofacial CBCT modeling, where a key idea is to utilize explicit tooth
shape prior information from intra-oral scan data whose acquisition does not
require any extra radiation exposure. In the first stage, an image-to-image
deep learning network is employed to mitigate metal-related artifacts. To
improve the learning ability, the proposed network is designed to take
advantage of the intra-oral scan data as side-inputs and perform multi-task
learning of auxiliary tooth segmentation. In the second stage, a 3D
maxillofacial model is constructed by segmenting the bones from the dental CBCT
image corrected in the first stage. For accurate bone segmentation, weighted
thresholding is applied, wherein the weighting region is determined depending
on the geometry of the intra-oral scan data. Because acquiring a paired
training dataset of metal-artifact-free and metal artifact-affected dental CBCT
images is challenging in clinical practice, an automatic method of generating a
realistic dataset according to the CBCT physics model is introduced. Numerical
simulations and clinical experiments show the feasibility of the proposed
method, which takes advantage of tooth surface information from intra-oral scan
data in 3D low dose maxillofacial CBCT modeling.
