Comprehensive dose evaluation of a Deep Learning based synthetic Computed Tomography algorithm for pelvic Magnetic Resonance-only radiotherapy.

Autor: Wyatt JJ; Translational and Clinical Research Institute, Newcastle University, Newcastle, UK; Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle, UK. Electronic address: jonathanwyatt@nhs.net., Kaushik S; GE Healthcare, Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland., Cozzini C; GE Healthcare, Munich, Germany., Pearson RA; Translational and Clinical Research Institute, Newcastle University, Newcastle, UK; Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle, UK., Petit S; Department of Radiotherapy, Erasmus MC Cancer Institute, Rotterdam, the Netherlands., Capala M; Department of Radiotherapy, Erasmus MC Cancer Institute, Rotterdam, the Netherlands., Hernandez-Tamames JA; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands., Hideghéty K; Department of Oncotherapy, University of Szeged, Szeged, Hungary., Maxwell RJ; Translational and Clinical Research Institute, Newcastle University, Newcastle, UK., Wiesinger F; GE Healthcare, Munich, Germany., McCallum HM; Translational and Clinical Research Institute, Newcastle University, Newcastle, UK; Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle, UK.
Jazyk: angličtina
Zdroj: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology [Radiother Oncol] 2023 Jul; Vol. 184, pp. 109692. Date of Electronic Publication: 2023 May 06.
DOI: 10.1016/j.radonc.2023.109692
Abstrakt: Background and Purpose: Magnetic Resonance (MR)-only radiotherapy enables the use of MR without the uncertainty of MR-Computed Tomography (CT) registration. This requires a synthetic CT (sCT) for dose calculations, which can be facilitated by a novel Zero Echo Time (ZTE) sequence where bones are visible and images are acquired in 65 seconds. This study evaluated the dose calculation accuracy for pelvic sites of a ZTE-based Deep Learning sCT algorithm developed by GE Healthcare.
Materials and Methods: ZTE and CT images were acquired in 56 pelvic radiotherapy patients in the radiotherapy position. A 2D U-net convolutional neural network was trained using pairs of deformably registered CT and ZTE images from 36 patients. In the remaining 20 patients the dosimetric accuracy of the sCT was assessed using cylindrical dummy Planning Target Volumes (PTVs) positioned at four different central axial locations, as well as the clinical treatment plans (for prostate (n = 10), rectum (n = 4) and anus (n = 6) cancers). The sCT was rigidly and deformably registered, the plan recalculated and the doses compared using mean differences and gamma analysis.
Results: Mean dose differences to the PTV D98% were ≤ 0.5% for all dummy PTVs and clinical plans (rigid registration). Mean gamma pass rates at 1%/1 mm were 98.0 ± 0.4% (rigid) and 100.0 ± 0.0% (deformable), 96.5 ± 0.8% and 99.8 ± 0.1%, and 95.4 ± 0.6% and 99.4 ± 0.4% for the clinical prostate, rectum and anus plans respectively.
Conclusions: A ZTE-based sCT algorithm with high dose accuracy throughout the pelvis has been developed. This suggests the algorithm is sufficiently accurate for MR-only radiotherapy for all pelvic sites.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Conflicts of Interest Sandeep Kaushik, Cristina Cozzini and Florian Wiesinger are employees of GE Healthcare.
(Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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