Stability of conventional and machine learning‐based tumor auto‐segmentation techniques using undersampled dynamic radial bSSFP acquisitions on a 0.35 T hybrid MR‐linac system-Reference-Cited by-同舟云学术

Stability of conventional and machine learning‐based tumor auto‐segmentation techniques using undersampled dynamic radial bSSFP acquisitions on a 0.35 T hybrid MR‐linac system

Published:2021-01-09 Issue:2 Volume:48 Page:587-596
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Friedrich Florian¹²,Hörner‐Rieber Juliane³⁴⁵⁶,Renkamp C. Katharina³⁴⁵,Klüter Sebastian³⁴⁵,Bachert Peter¹²,Ladd Mark E.¹²⁷,Knowles Benjamin R.¹

Affiliation:

1. Division of Medical Physics in Radiology German Cancer Research Center (DKFZ) Im Neuenheimer Feld 280 Heidelberg69120Germany

2. Faculty of Physics and Astronomy University of Heidelberg Im Neuenheimer Feld 226 Heidelberg69120Germany

3. Department of Radiation Oncology University Hospital of Heidelberg Im Neuenheimer Feld 400 Heidelberg69120Germany

4. Heidelberg Institute of Radiation Oncology (HIRO) Im Neuenheimer Feld 400 Heidelberg69120Germany

5. National Center for Radiation Research in Oncology (NCRO) Im Neuenheimer Feld 400 Heidelberg69120Germany

6. Clinical Cooperation Unit Radiation OncologyGerman Cancer Research Center (DKFZ) Im Neuenheimer Feld 280 Heidelberg69120Germany

7. Faculty of Medicine University of Heidelberg Im Neuenheimer Feld 672 Heidelberg69120Germany

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14659

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