Encrypted | Login

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

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.

Evaluation of Image-Domain Training Frameworks for Deep Learning Based Denoising and Deconvolution
Patrick VanMeter Mayo Clinic

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
Q & A -
Graph includes member loads and excludes non-members. Include? A load is defined as someone coming to this page, not necesarily that they pressed play, or watched the entire presentation.