A Patient-Specific Autosegmentation Strategy Using Multi-Input Deformable Image Registration for Magnetic Resonance Imaging–Guided Online Adaptive Radiation Therapy: A Feasibility Study-Reference-Cited by-同舟云学术

A Patient-Specific Autosegmentation Strategy Using Multi-Input Deformable Image Registration for Magnetic Resonance Imaging–Guided Online Adaptive Radiation Therapy: A Feasibility Study

Published:2020-11 Issue:6 Volume:5 Page:1350-1358
ISSN:2452-1094
Container-title:Advances in Radiation Oncology
language:en
Short-container-title:Advances in Radiation Oncology

Author:

Zhang Ying,Paulson Eric,Lim Sara,Hall William A.,Ahunbay Ergun,Mickevicius Nikolai J.,Straza Michael W.,Erickson Beth,Li X. Allen

Funder

Medical College of Wisconsin

Cancer Center and Froedtert Hospital Foundation

MCW Meinerz and Fotsch Foundations

Manteia Med.

Elekta AB

Publisher

Elsevier BV

Subject

Radiology Nuclear Medicine and imaging,Oncology

Reference27 articles.

1. MRI commissioning of 1.5T MR-linac systems - A multi-institutional study;Tijssen;Radiother Oncol,2019

2. Texture-based, automatic contour validation for online adaptive replanning: A feasibility study on abdominal organs;Zhang;Med Phys,2019

3. Online adaptive radiation therapy: Implementation of a new process of care;Lamb;Cureus,2017

4. Deformable image registration based on similarity-steered CNN regression;Cao;Med Image Comput Comput Assist Interv,2017

5. Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: A multi-institutional study;Loi;Med Phys,2018

Cited by 27 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Automated segmentation in pelvic radiotherapy: A comprehensive evaluation of ATLAS-, machine learning-, and deep learning-based models;Physica Medica;2024-09

2. MR-Guided Radiotherapy in the Pediatric and Adolescent Patient;A Practical Guide to MR-Linac;2024

3. NRG Oncology Assessment of Artificial Intelligence Deep Learning–Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions;International Journal of Radiation Oncology*Biology*Physics;2023-11

4. Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system;Physics in Medicine & Biology;2023-09-12

5. Investigation of autosegmentation techniques on T2‐weighted MRI for off‐line dose reconstruction in MR‐linac workflow for head and neck cancers;Medical Physics;2023-07-20