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Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning
BACKGROUND AND PURPOSE: Magnetic Resonance Imaging (MRI)-only workflow eliminates the MRI-computed tomography (CT) registration inaccuracy, which degrades radiotherapy (RT) treatment accuracy. For an MRI-only workflow MRI sequences need to be converted to synthetic-CT (sCT). The purpose of this stud...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9667284/ https://www.ncbi.nlm.nih.gov/pubmed/36405564 http://dx.doi.org/10.1016/j.phro.2022.10.002 |
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author | Masitho, Siti Szkitsak, Juliane Grigo, Johanna Fietkau, Rainer Putz, Florian Bert, Christoph |
author_facet | Masitho, Siti Szkitsak, Juliane Grigo, Johanna Fietkau, Rainer Putz, Florian Bert, Christoph |
author_sort | Masitho, Siti |
collection | PubMed |
description | BACKGROUND AND PURPOSE: Magnetic Resonance Imaging (MRI)-only workflow eliminates the MRI-computed tomography (CT) registration inaccuracy, which degrades radiotherapy (RT) treatment accuracy. For an MRI-only workflow MRI sequences need to be converted to synthetic-CT (sCT). The purpose of this study was to evaluate a commercially available artificial intelligence (AI)-based sCT generation for dose calculation and 2D/2D kV-image daily positioning for brain RT workflow. MATERIALS AND METHODS: T1-VIBE DIXON was acquired at the 1.5 T MRI for 26 patients in RT setup for sCTs generation. For each patient, a volumetric modulated arc therapy (VMAT) plan was optimized on the CT, then recalculated on the sCT; and vice versa. sCT-based digitally reconstructed radiographs (DRRs) were fused with stereoscopic X-ray images recorded as image guidance for clinical treatments. Dosimetric differences between planned/recalculated doses and the differences between the calculated and recorded clinical couch shift/rotation were evaluated. RESULTS: Mean ΔD(50) between planned/recalculated doses for target volumes ranged between −0.2 % and 0.2 %; mean ΔD(50) and ΔD(0.01ccm) were −0.6 % and 1.6 % and −1.4 % and 1.0 % for organ-at-risks, respectively. Differences were tested for clinical equivalence using intervals ±2 % (dose), ±1mm (translation), and ±1° (rotation). Dose equivalence was found using ±2 % interval (p < 0.001). The median differences between lat./long./vert. couch shift between CT-based/sCT-based DRRs were 0.3 mm/0.2 mm/0.3 mm (p < 0.05); median differences between lat./long./vert. couch rotation were −1.5°/0.1°/0.1° (after improvement of RT setup: −0.4°/−0.1°/−0.4°, p < 0.05). CONCLUSIONS: This in-silico study showed that the AI-based sCT provided equivalent results to the CT for dose calculation and daily stereoscopic X-ray positioning when using an optimal RT setup during MRI acquisition. |
format | Online Article Text |
id | pubmed-9667284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-96672842022-11-17 Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning Masitho, Siti Szkitsak, Juliane Grigo, Johanna Fietkau, Rainer Putz, Florian Bert, Christoph Phys Imaging Radiat Oncol Original Research Article BACKGROUND AND PURPOSE: Magnetic Resonance Imaging (MRI)-only workflow eliminates the MRI-computed tomography (CT) registration inaccuracy, which degrades radiotherapy (RT) treatment accuracy. For an MRI-only workflow MRI sequences need to be converted to synthetic-CT (sCT). The purpose of this study was to evaluate a commercially available artificial intelligence (AI)-based sCT generation for dose calculation and 2D/2D kV-image daily positioning for brain RT workflow. MATERIALS AND METHODS: T1-VIBE DIXON was acquired at the 1.5 T MRI for 26 patients in RT setup for sCTs generation. For each patient, a volumetric modulated arc therapy (VMAT) plan was optimized on the CT, then recalculated on the sCT; and vice versa. sCT-based digitally reconstructed radiographs (DRRs) were fused with stereoscopic X-ray images recorded as image guidance for clinical treatments. Dosimetric differences between planned/recalculated doses and the differences between the calculated and recorded clinical couch shift/rotation were evaluated. RESULTS: Mean ΔD(50) between planned/recalculated doses for target volumes ranged between −0.2 % and 0.2 %; mean ΔD(50) and ΔD(0.01ccm) were −0.6 % and 1.6 % and −1.4 % and 1.0 % for organ-at-risks, respectively. Differences were tested for clinical equivalence using intervals ±2 % (dose), ±1mm (translation), and ±1° (rotation). Dose equivalence was found using ±2 % interval (p < 0.001). The median differences between lat./long./vert. couch shift between CT-based/sCT-based DRRs were 0.3 mm/0.2 mm/0.3 mm (p < 0.05); median differences between lat./long./vert. couch rotation were −1.5°/0.1°/0.1° (after improvement of RT setup: −0.4°/−0.1°/−0.4°, p < 0.05). CONCLUSIONS: This in-silico study showed that the AI-based sCT provided equivalent results to the CT for dose calculation and daily stereoscopic X-ray positioning when using an optimal RT setup during MRI acquisition. Elsevier 2022-10-22 /pmc/articles/PMC9667284/ /pubmed/36405564 http://dx.doi.org/10.1016/j.phro.2022.10.002 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Article Masitho, Siti Szkitsak, Juliane Grigo, Johanna Fietkau, Rainer Putz, Florian Bert, Christoph Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning |
title | Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning |
title_full | Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning |
title_fullStr | Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning |
title_full_unstemmed | Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning |
title_short | Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning |
title_sort | feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: two-way dose validation and 2d/2d kv-image-based positioning |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9667284/ https://www.ncbi.nlm.nih.gov/pubmed/36405564 http://dx.doi.org/10.1016/j.phro.2022.10.002 |
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