Development of an anthropomorphic multimodality pelvic phantom for quantitative evaluation of a deep‐learning‐based synthetic computed tomography generation technique-Reference-Cited by-同舟云学术

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Development of an anthropomorphic multimodality pelvic phantom for quantitative evaluation of a deep‐learning‐based synthetic computed tomography generation technique

Published:2022-05-17 Issue:8 Volume:23 Page:
ISSN:1526-9914
Container-title:Journal of Applied Clinical Medical Physics
language:en
Short-container-title:J Applied Clin Med Phys

Author:

Jin Hyeongmin¹,Lee Sung Young¹,An Hyun Joon¹²³,Choi Chang Heon¹²³,Chie Eui Kyu¹²³⁴,Wu Hong‐Gyun¹²³⁴^ORCID,Park Jong Min¹²³⁴⁵,Park Sukwon⁶,Kim Jung‐in¹²³

Affiliation:

1. Department of Radiation Oncology Seoul National University Hospital Seoul Republic of Korea

2. Institute of Radiation Medicine Seoul National University Medical Research Center Seoul Republic of Korea

3. Biomedical Research Institute Seoul National University Hospital Seoul Republic of Korea

4. Department of Radiation Oncology Seoul National University College of Medicine Seoul Republic of Korea

5. Robotics Research Laboratory for Extreme Environments Advanced Institute of Convergence Technology Suwon Republic of Korea

6. Department of Radiation Oncology Myongji Hospital Goyang‐si Gyeonggi‐do Republic of Korea

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging,Instrumentation,Radiation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/acm2.13644

Reference39 articles.

1. MRI simulation for radiotherapy treatment planning

2. The future of image-guided radiotherapy will be MR guided

3. MR-based synthetic CT generation using a deep convolutional neural network method

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