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

Lookup NU author(s): JJ Wyatt, Dr Rachel Pearson, Dr Ross Maxwell, Dr Hazel McCallum



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2023 The Author(s). 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.

Publication metadata

Author(s): Wyatt JJ, Kaushik S, Cozzini C, Pearson RA, Petit S, Capala M, Hernandez-Tamames JA, Hideghety K, Maxwell RJ, Wiesinger F, McCallum HM

Publication type: Article

Publication status: Published

Journal: Radiotherapy and Oncology

Year: 2023

Volume: 184

Print publication date: 01/07/2023

Online publication date: 06/05/2023

Acceptance date: 24/04/2023

Date deposited: 01/06/2023

ISSN (print): 0167-8140

ISSN (electronic): 1879-0887

Publisher: Elsevier Ireland Ltd


DOI: 10.1016/j.radonc.2023.109692

PubMed id: 37150446


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