Abstract
ObjectivesMRI-only radiotherapy (RT) provides a workflow to decrease the geometric uncertainty introduced by the image registration process between MRI and CT data and to streamline the RT planning. Despite the recent availability of validated synthetic CT (sCT) methods for the head region, there are no clinical implementations reported for brain tumors. Based on a preceding validation study of sCT, this study aims to investigate MRI-only brain RT through a prospective clinical feasibility study with endpoints for dosimetry and patient setup.Material and MethodsTwenty-one glioma patients were included. MRI Dixon images were used to generate sCT images using a CE-marked deep learning-based software. RT treatment plans were generated based on MRI delineated anatomical structures and sCT for absorbed dose calculations. CT scans were acquired but strictly used for sCT quality assurance (QA). Prospective QA was performed prior to MRI-only treatment approval, comparing sCT and CT image characteristics and calculated dose distributions. Additional retrospective analysis of patient positioning and dose distribution gamma evaluation was performed.ResultsTwenty out of 21 patients were treated using the MRI-only workflow. A single patient was excluded due to an MRI artifact caused by a hemostatic substance injected near the target during surgery preceding radiotherapy. All other patients fulfilled the acceptance criteria. Dose deviations in target were within ±1% for all patients in the prospective analysis. Retrospective analysis yielded gamma pass rates (2%, 2 mm) above 99%. Patient positioning using CBCT images was within ± 1 mm for registrations with sCT compared to CT.ConclusionWe report a successful clinical study of MRI-only brain radiotherapy, conducted using both prospective and retrospective analysis. Synthetic CT images generated using the CE-marked deep learning-based software were clinically robust based on endpoints for dosimetry and patient positioning.
Highlights
Radiotherapy (RT) is an important part of treatment for patients with brain malignancies, such as glioma
To facilitate the implementation of magnetic resonance imaging (MRI)-only RT planning for brain tumors, this study aimed to introduce a new workflow in our clinic based on solely MR images
The substance gave rise to a signal loss in the MR images which the synthetic CT (sCT) generation software interpreted as bone
Summary
Radiotherapy (RT) is an important part of treatment for patients with brain malignancies, such as glioma. In recent years a workflow based on MRI without CT imaging has evolved, referred to as MRI-only radiotherapy [1,2,3]. Excluding CT from the workflow enables reduced spatial uncertainties in the final dose plan since the otherwise required image registration between the CT and the MR images is not needed [4, 5]. MRI-only radiotherapy provides a more streamlined workflow which may reduce both time and costs [1]. The Hounsfield units (HU) containing electron density information for absorbed dose calculations are not directly present in the MR images. To bridge this gap, synthetic CT (sCT) images, generated based on MRI information, are introduced to provide the necessary HU. Many successful sCT generation methods for brain have been presented in the literature, starting from methods which assumed a homogeneous attenuation value inside the head [6] to state-of-the-art deep learning-based methods in recent publications [7,8,9,10,11,12]
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