ArticlePhysics and imaging in radiation oncology2023
Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen.
Article in Physics and imaging in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- MRI-enhanced online adaptive radiotherapy by virtual integration of a standalone MRI simulator and CBCT-based treatment delivery.Radiation oncology (London, England) · 2026Article
- Photon-counting detector computing tomography in treatment planning of head-and-neck cancer.Physics and imaging in radiation oncology · 2026Article
- Impact of bulk density assignment of bone on MRI-based abdominal region radiotherapy planning for MR-linac workflow.Journal of applied clinical medical physics · 2025Article
- Brain MR-only workflow in clinical practice: A comparison among generators for quality assurance and patient positioning.Journal of applied clinical medical physics · 2025Article
- Results of 2023 survey on the use of synthetic computed tomography for magnetic resonance Imaging-only radiotherapy: Current status and future steps.Physics and imaging in radiation oncology · 2024Article
- Current and future developments of synthetic computed tomography generation for radiotherapy.Physics and imaging in radiation oncology · 2023Article
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9 authors.
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Abstract
Background and purpose: The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions. Materials and methods: A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT. Results: The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path. Conclusion: The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%.
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