MRI-only based treatment with a commercial deep-learning generation method for synthetic CT of brain

Abstract

ObjectivesTo show feasibility of synthetic computed tomography (sCT) images generated using a commerciallyavailable software, enabling MRI-only treatment planning for the brain in a clinical setting.Patients and Methods20 and 16 patients with brain malignancies, including post-surgical cases, were included for validationand treatment, respectively. Dixon MR images of the skull were exported to the MRI Planner software(Spectronic Medical AB), which utilizes convolutional neural network algorithms for sCT generation.In the validation study, sCT images were rigidly registered and resampled to CT geometry for eachpatient. Treatment plans were optimized on CT and retrospectively recalculated on sCT images forevaluation of dosimetric and geometric endpoints. Clinical robustness in patient setup verification wasassessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively.The treatment study was performed on sCT images, using CT solely for QA purposes.ResultsAll sCT images were successfully generated in the validation study. Mean absolute error of the sCTimages within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differenceswere below 0.2%. Mean pass rate of global gamma (1%/1mm) for all patients was 100.0 ± 0.0 % withinPTV and 99.1 ± 0.6 % for the full dose distribution. No clinically relevant deviations were found in theCBCT-sCT vs CBCT-CT image registrations. Areas of bone resection due to surgery were accuratelydepicted in the sCT images. Finally, treatment success rate was 15/16. One patient was excluded due tosCT artifacts from a haemostatic substance injected during surgery.Conclusion15 patients have successfully received MRI-only RT for brain tumours using the validated commerciallyavailable sCT software. Validation showed comparable results between sCT and CT images for bothdosimetric and geometric endpoint