Evaluating the dosimetric effect of intra-fractional variations in deep inspiration breath-hold radiotherapy – a proof-of-concept study-Reference-Cited by-同舟云学术

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Evaluating the dosimetric effect of intra-fractional variations in deep inspiration breath-hold radiotherapy – a proof-of-concept study

Published:2023-09-22 Issue:10 Volume:62 Page:1246-1250
ISSN:0284-186X
Container-title:Acta Oncologica
language:en
Short-container-title:Acta Oncologica

Author:

Håkansson K.¹^ORCID,Josipovic M.¹²,Ottosson W.³,Behrens C. P.³⁴^ORCID,Vogelius I. R.¹²,Persson G.²³^ORCID

Affiliation:

1. Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark

2. Department of Clinical Medicine, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark

3. Department of Oncology, Copenhagen University Hospital – Herlev and Gentofte, Copenhagen, Denmark

4. Department of Health Technology, Technical University of Denmark, Roskilde, Denmark

Publisher

Medical Journals Sweden AB

Subject

Radiology, Nuclear Medicine and imaging,Oncology,Hematology,General Medicine

Link

https://www.tandfonline.com/doi/pdf/10.1080/0284186X.2023.2259084

Reference16 articles.

1. Dosimetric evaluation of lung tumor immobilization using breath hold at deep inspiration

2. The advantage of deep-inspiration breath-hold and cone-beam CT based soft-tissue registration for locally advanced lung cancer radiotherapy

3. Deep inspiration breath hold in locally advanced lung cancer radiotherapy: validation of intrafractional geometric uncertainties in the INHALE trial

4. Deep inspiration breath-hold radiotherapy for lung cancer: impact on image quality and registration uncertainty in cone beam CT image guidance

5. Deep inspiration breath hold radiotherapy for locally advanced lung cancer: Comparison of different treatment techniques on target coverage, lung dose and treatment delivery time

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1. Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data;Physics and Imaging in Radiation Oncology;2024-04

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