Anatomical evaluation of deep-learning synthetic computed tomography images generated from male pelvis cone-beam computed tomography

Anatomical evaluation of deep-learning synthetic computed tomography images generated from male pelvis cone-beam computed tomography

Background and purpose: To improve cone-beam computed tomography (CBCT), deep-learning (DL)-models are being explored to generate synthetic CTs (sCT). The sCT evaluation is mainly focused on image quality and CT number accuracy. However, correct representation of daily anatomy of the CBCT is also im...

Full description

Bibliographic Details
Main Authors: Yvonne J.M. de Hond, Camiel E.M. Kerckhaert, Maureen A.J.M. van Eijnatten, Paul M.A. van Haaren, Coen W. Hurkmans, Rob H.N. Tijssen
Format: Article
Language:English
Published: Elsevier 2023-01-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Deep-Learning
Synthetic computed tomography
Cone-beam computed tomography
Average surface distance
Anatomical comparison
Volume difference
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631623000076

Similar Items

Similar Items