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Clinical validation of a commercially available deep learning software for synthetic CT generation for brain

BACKGROUND: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, bas...

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Autores principales: Lerner, Minna, Medin, Joakim, Jamtheim Gustafsson, Christian, Alkner, Sara, Siversson, Carl, Olsson, Lars E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025544/
https://www.ncbi.nlm.nih.gov/pubmed/33827619
http://dx.doi.org/10.1186/s13014-021-01794-6
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author Lerner, Minna
Medin, Joakim
Jamtheim Gustafsson, Christian
Alkner, Sara
Siversson, Carl
Olsson, Lars E.
author_facet Lerner, Minna
Medin, Joakim
Jamtheim Gustafsson, Christian
Alkner, Sara
Siversson, Carl
Olsson, Lars E.
author_sort Lerner, Minna
collection PubMed
description BACKGROUND: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, based on a convolutional neural network (CNN) algorithm, to enable MRI-only treatment planning for the brain in a clinical setting. METHODS: This prospective study included 20 patients with brain malignancies of which 14 had areas of resected skull bone due to surgery. A Dixon magnetic resonance (MR) acquisition sequence for sCT generation was added to the clinical brain MR-protocol. The corresponding sCT images were provided by the software MRI Planner (Spectronic Medical AB, Sweden). sCT images were rigidly registered and resampled to CT for each patient. Treatment plans were optimized on CT and recalculated on sCT images for evaluation of dosimetric and geometric endpoints. Further analysis was also performed for the post-surgical cases. Clinical robustness in patient setup verification was assessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively. RESULTS: All sCT images were successfully generated. Areas of bone resection due to surgery were accurately depicted. Mean absolute error of the sCT images within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differences were below 0.2% for parameters evaluated for both targets and organs at risk. Mean pass rate of global gamma (1%/1 mm) for all patients was 100.0 ± 0.0% within PTV and 99.1 ± 0.6% for the full dose distribution. No clinically relevant deviations were found in the CBCT-sCT vs CBCT-CT image registrations. In addition, mean values of voxel-wise patient specific geometric distortion in the Dixon images for sCT generation were below 0.1 mm for soft tissue, and below 0.2 mm for air and bone. CONCLUSIONS: This work successfully validated a commercially available CNN-based software for sCT generation. Results were comparable for sCT and CT images in both dosimetric and geometric evaluation, for both patients with and without anatomical anomalies. Thus, MRI Planner is feasible to use for radiotherapy treatment planning of brain tumours. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-021-01794-6.
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spelling pubmed-80255442021-04-08 Clinical validation of a commercially available deep learning software for synthetic CT generation for brain Lerner, Minna Medin, Joakim Jamtheim Gustafsson, Christian Alkner, Sara Siversson, Carl Olsson, Lars E. Radiat Oncol Research BACKGROUND: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, based on a convolutional neural network (CNN) algorithm, to enable MRI-only treatment planning for the brain in a clinical setting. METHODS: This prospective study included 20 patients with brain malignancies of which 14 had areas of resected skull bone due to surgery. A Dixon magnetic resonance (MR) acquisition sequence for sCT generation was added to the clinical brain MR-protocol. The corresponding sCT images were provided by the software MRI Planner (Spectronic Medical AB, Sweden). sCT images were rigidly registered and resampled to CT for each patient. Treatment plans were optimized on CT and recalculated on sCT images for evaluation of dosimetric and geometric endpoints. Further analysis was also performed for the post-surgical cases. Clinical robustness in patient setup verification was assessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively. RESULTS: All sCT images were successfully generated. Areas of bone resection due to surgery were accurately depicted. Mean absolute error of the sCT images within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differences were below 0.2% for parameters evaluated for both targets and organs at risk. Mean pass rate of global gamma (1%/1 mm) for all patients was 100.0 ± 0.0% within PTV and 99.1 ± 0.6% for the full dose distribution. No clinically relevant deviations were found in the CBCT-sCT vs CBCT-CT image registrations. In addition, mean values of voxel-wise patient specific geometric distortion in the Dixon images for sCT generation were below 0.1 mm for soft tissue, and below 0.2 mm for air and bone. CONCLUSIONS: This work successfully validated a commercially available CNN-based software for sCT generation. Results were comparable for sCT and CT images in both dosimetric and geometric evaluation, for both patients with and without anatomical anomalies. Thus, MRI Planner is feasible to use for radiotherapy treatment planning of brain tumours. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-021-01794-6. BioMed Central 2021-04-07 /pmc/articles/PMC8025544/ /pubmed/33827619 http://dx.doi.org/10.1186/s13014-021-01794-6 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Lerner, Minna
Medin, Joakim
Jamtheim Gustafsson, Christian
Alkner, Sara
Siversson, Carl
Olsson, Lars E.
Clinical validation of a commercially available deep learning software for synthetic CT generation for brain
title Clinical validation of a commercially available deep learning software for synthetic CT generation for brain
title_full Clinical validation of a commercially available deep learning software for synthetic CT generation for brain
title_fullStr Clinical validation of a commercially available deep learning software for synthetic CT generation for brain
title_full_unstemmed Clinical validation of a commercially available deep learning software for synthetic CT generation for brain
title_short Clinical validation of a commercially available deep learning software for synthetic CT generation for brain
title_sort clinical validation of a commercially available deep learning software for synthetic ct generation for brain
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025544/
https://www.ncbi.nlm.nih.gov/pubmed/33827619
http://dx.doi.org/10.1186/s13014-021-01794-6
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