  Due to the lack of standardized 3D cephalometric analytic methodology, 2D
cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes
are widely used for cephalometric analysis in dental CBCT systems. However,
compared with conventional X-ray film based cephalograms, such synthetic
cephalograms lack image contrast and resolution. In addition, the radiation
dose during the scan for 3D reconstruction causes potential health risks. In
this work, we propose a sigmoid-based intensity transform that uses the
nonlinear optical property of X-ray films to increase image contrast of
synthetic cephalograms. To improve image resolution, super resolution deep
learning techniques are investigated. For low dose purpose, the pixel-to-pixel
generative adversarial network (pix2pixGAN) is proposed for 2D cephalogram
synthesis directly from two CBCT projections. For landmark detection in the
synthetic cephalograms, an efficient automatic landmark detection method using
the combination of LeNet-5 and ResNet50 is proposed. Our experiments
demonstrate the efficacy of pix2pixGAN in 2D cephalogram synthesis, achieving
an average peak signal-to-noise ratio (PSNR) value of 33.8 with reference to
the cephalograms synthesized from 3D CBCT volumes. Pix2pixGAN also achieves the
best performance in super resolution, achieving an average PSNR value of 32.5
without the introduction of checkerboard or jagging artifacts. Our proposed
automatic landmark detection method achieves 86.7% successful detection rate in
the 2 mm clinical acceptable range on the ISBI Test1 data, which is comparable
to the state-of-the-art methods. The method trained on conventional
cephalograms can be directly applied to landmark detection in the synthetic
cephalograms, achieving 93.0% and 80.7% successful detection rate in 4 mm
precision range for synthetic cephalograms from 3D volumes and 2D projections
respectively.
