Unsupervised learning for deformable registration of thoracic CT and cone‐beam CT based on multiscale features matching with spatially adaptive weighting-Reference-Cited by-同舟云学术

Unsupervised learning for deformable registration of thoracic CT and cone‐beam CT based on multiscale features matching with spatially adaptive weighting

Published:2020-10-07 Issue:11 Volume:47 Page:5632-5647
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Duan Luwen¹²,Ni Xinye³⁴,Liu Qi¹²,Gong Lun⁵,Yuan Gang²,Li Ming²,Yang Xiaodong²,Fu Tianxiao⁶,Zheng Jian²

Affiliation:

1. School of Biomedical Engineering University of Science and Technology of China Hefei230026 China

2. Department of Medical Imaging Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou215163 China

3. Radiotherapy Department Second People’s Hospital of ChangzhouNanjing Medical University Changzhou213003 China

4. Center for Medical Physics Nanjing Medical University Changzhou213003 China

5. The Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education Tianjin University Tianjin300072 China

6. Department of Radiation Oncology The First Affiliated Hospital Of Soochow University Suzhou215006 China

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14464

Reference50 articles.

1. Evaluation of accuracy and reproducibility of a relocatable maxillary fixation system for fractionated intracranial stereotactic radiation therapy

2. Incorporating organ movements in IMRT treatment planning for prostate cancer: Minimizing uncertainties in the inverse planning process

3. The ANACONDA algorithm for deformable image registration in radiotherapy

4. Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapy

5. Commissioning experience with cone-beam computed tomography for image-guided radiation therapy

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