Generation and Evaluation of Synthetic Computed Tomography (CT) from Cone-Beam CT (CBCT) by Incorporating Feature-Driven Loss into Intensity-Based Loss Functions in Deep Convolutional Neural Network.

Deep convolutional neural network (CNN) helped enhance image quality of cone-beam computed tomography (CBCT) by generating synthetic CT. Most of the previous works, however, trained network by intensity-based loss functions, possibly undermining to promote image feature similarity. The verifications were not sufficient to demonstrate clinical applicability, either.

The Effect of Precursor Composition on the Structural Properties of Nanocrystalline Diamond Films.

Nanocrystalline diamond (NCD) films were grown by hot filament CVD and the precursor composition dependence of the structural properties was examined. Films grown at 1 and 2 CH4 Vol% were found to be NCD layers with grain sizes of ~23-25 nm while films grown at 3-5 Vol% were identified as the mixtures of microcrystalline diamond and graphitic phase. The sp2/sp3 bonded carbon ratio in the grown films increased as the CH4 content increased up to 3 Vol% and then decreased beyond 4 Vol%.