| Tuesday 2:00 PM | TU-D-TRACK 3-1 : Convolutional Neural Network Based Metal Artifact Correction for Sparse View Dental CT Imaging S. Kim*, J. Ahn, B. Kim, J. Baek |
| Tuesday 2:07 PM | TU-D-TRACK 3-2 : Low-Dose CT Image Enhancement Through a Texture Transformer S. Zhou*, L. Yu, M. Jin |
| Tuesday 2:14 PM | TU-D-TRACK 3-3 : Multi-Resolution Residual Deep Neural Network for Generating Synthetic CT Images with High HU Accuracy and Structural Fidelity W. Wu*, J. Qu, J. Cai, R. Yang |
| Tuesday 2:21 PM | TU-D-TRACK 3-4 : Renal Stone Quantification in Contrast Enhanced CT Using Convolutional Neural Network Assisted Dual-Energy Virtual Non-Contrast Imaging H. Gong*, A. Ferrero, J. Marsh, N. Huber, J. Fletcher, C. McCollough, S. Leng |
| Tuesday 2:28 PM | TU-D-TRACK 3-5 : Synthesize 3D Realistic CT Textures and Anatomy in the XCAT Phantom Using Generative Adversarial Network (GAN) Y. Yuan*, L. Ren, Y. Chang |
| Tuesday 2:35 PM | TU-D-TRACK 3-6 : Task-Based Loss Function for Convolutional Neural Network-Based CT Denoising B. Nelson*, D. Gomez Cardona, N. Huber, A. Missert, L. Yu, C. McCollough |
| Tuesday 2:42 PM | TU-D-TRACK 3-7 : Evaluation of Image-Domain Training Frameworks for Deep Learning Based Denoising and Deconvolution P. VanMeter*, S. Hsieh, J. Marsh, N. Huber, A. Ferrero, C. McCollough |