Encrypted | Login

2021 AAPM Virtual 63rd Annual Meeting - Session: AI in CT and CBCT: Image Enhancement and Synthesis Q & A

This video is currently visible only to AAPM Members, Affiliates and Developing Country Educational Associates. Embargo periods are typically one year from the date the presentation was first published.

Q & A

When you have finished the video, you may watch again, browse/search all videos, go back to the previous page, or watch another video in this session (see below). add this video to a playlist.

All videos in this session:
Convolutional Neural Network Based Metal Artifact Correction for Sparse View Dental CT Imaging - SeongJun Kim Yonsei University
Low-Dose CT Image Enhancement Through a Texture Transformer - Shiwei Zhou University of Texas at Arlington
Multi-Resolution Residual Deep Neural Network for Generating Synthetic CT Images with High HU Accuracy and Structural Fidelity - Wangjiang Wu Peking University Third Hospital
Renal Stone Quantification in Contrast Enhanced CT Using Convolutional Neural Network Assisted Dual-Energy Virtual Non-Contrast Imaging - Hao Gong Mayo Clinic
Synthesize 3D Realistic CT Textures and Anatomy in the XCAT Phantom Using Generative Adversarial Network (GAN) - Yijie Yuan Duke University
Task-Based Loss Function for Convolutional Neural Network-Based CT Denoising - Brandon Nelson Mayo Clinic
Evaluation of Image-Domain Training Frameworks for Deep Learning Based Denoising and Deconvolution - Patrick VanMeter Mayo Clinic
Graph includes member loads and non-member loads (0 total loads). Exclude non-member? A load is defined as someone coming to this page, not necesarily that they pressed play, or watched the entire presentation.