Explore innovative approaches to image reconstruction in radiation therapy through this insightful talk by Xun Jia from Johns Hopkins University. Delve into the techniques of modeling prior information to enhance the accuracy and efficiency of image reconstruction processes, crucial for improving treatment planning and delivery in radiation therapy. Gain valuable insights into the latest advancements in deep learning applications for medical imaging, specifically tailored for oncology and radiotherapy contexts.
Modeling Prior Information to Guide Image Reconstruction in Radiation Therapy
Overview
Syllabus
Modeling prior information to guide image reconstruction in radiation therapy
Taught by
Yale Radiology and Biomedical Imaging
Tags
Related Courses
-
On Bayesian Models with Networks for Reconstruction and Detection
-
Prior Knowledge-Driven Machine Intelligence to Maximize the Potential of Low-Dose Spectral CT
-
Cancer Image Segmentation for Head and Neck Radiation Therapy Planning
-
Deep Learning-Based MR Image Reconstruction and Contrast Conversion
-
SPACE Webinar Series: Adaptive Dynamic Scanning and Task-Driven Image Reconstruction
-
Geometric Understanding of Supervised and Unsupervised Deep Learning for Biomedical Image Reconstruction
