Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen

Riccardo Dal Bello; Mariia Lapaeva; Agustina La Greca Saint-Esteven; Philipp Wallimann; Manuel Günther; Ender Konukoglu; Nicolaus Andratschke; Matthias Guckenberger; Stephanie Tanadini-Lang

doi:10.1016/j.phro.2023.100464

Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen

Phys Imaging Radiat Oncol. 2023 Jun 26:27:100464. doi: 10.1016/j.phro.2023.100464. eCollection 2023 Jul.

Affiliations

¹ Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
² Artificial Intelligence and Machine Learning Group, Department of Informatics, University of Zurich, Zurich, Switzerland.
³ Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland.

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%.

Keywords: MR-Linac; MR-guided radiotherapy; Neural network; PSQA; Quality assurance; synthetic CT.