An optimized framework for cone‐beam computed tomography‐based online evaluation for proton therapy

Abstract

BackgroundClinical evidence has demonstrated that proton therapy can achieve comparable tumor control probabilities compared to conventional photon therapy but with the added benefit of sparing healthy tissues. However, proton therapy is sensitive to inter‐fractional anatomy changes. Online pre‐fraction evaluation can effectively verify proton dose before delivery to patients, but there is a lack of guidelines for implementing this workflow.PurposeThe purpose of this study is to develop a cone‐beam CT‐based (CBCT) online evaluation framework for proton therapy that enables knowledge transparency and evaluates the efficiency and accuracy of each essential component.MethodsTwenty‐three patients with various lesion sites were included to conduct a retrospective study of implementing the proposed CBCT evaluation framework for the clinic. The framework was implemented on the RayStation 11B Research platform. Two synthetic CT (sCT) methods, corrected CBCT (cCBCT), and virtual CT (vCT), were used, and the ground truth images were acquired from the same‐day deformed quality assurance CT (dQACT) for the comparisons. The evaluation metrics for the framework include time efficiency, dose‐difference distributions (gamma passing rates), and water equivalent thickness (WET) distributions.ResultsThe mean online CBCT evaluation times were 1.6 ± 0.3 min and 1.9 ± 0.4 min using cCBCT and vCT, respectively. The dose calculation and deformable image registration dominated the evaluation efficiency, and accounted for 33% and 30% of the total evaluation time, respectively. The sCT generation took another 19% of the total time. Gamma passing rates were greater than 91% and 97% using 1%/1 mm and 2%/2 mm criteria, respectively. When the appropriate sCT was chosen, the target mean WET difference from the reference were less than 0.5 mm. The appropriate sCT method choice determined the uncertainty for the framework, with the cCBCT being superior for head‐and‐neck patient evaluation and vCT being better for lung patient evaluation.ConclusionsAn online CBCT evaluation framework was proposed to identify the use of the optimal sCT algorithm regarding efficiency and dosimetry accuracy. The framework is extendable to adopt advanced imaging methods and has the potential to support online adaptive radiotherapy to enhance patient benefits. It could be implemented into clinical use in the future.

A retrospective study on the investigation of potential dosimetric benefits of online adaptive proton therapy for head and neck cancer

“…One challenge that we encountered in this retrospective study was the poor soft tissue contrast and large HU uncertainties of the CBCT images acquired by the on‐board CBCT imaging system of existing proton Linac systems, which prevented the direct use of the CBCT images for daily dose evaluation and online plan adaptation. To address this issue, we employed our previously developed CBCT correction framework, 15 by first deforming the planning CT images to the daily CBCT images using the deformable registration algorithm provided in RayStation and then transferring the air cavities from the CBCT images to the deformed planning CT images. This strategy aimed to utilize the accurate HU values from the planning CT images while persevering the patient's actual treatment anatomy captured in the CBCT images.…”

“… Step 3 : The acquired CBCT images cannot be directly used for daily dose evaluation and online plan adaptation, as the existing on‐board CBCT imaging system suffers from severe image artifacts mainly due to scatter contamination, which impairs the soft tissue contrast and leads to large Hounsfield Unit (HU) uncertainties. Hence, we will perform CBCT image correction at this step using our previously developed CBCT correction framework 15 Specifically, we will deform the planning CT images to the daily CBCT images via deformable image registration, using the ANACONDA algorithm 16 provided in RayStation, in order to use the correct HU numbers from the planning CT images while preserving the patient's actual anatomy captured on the CBCT images. In addition, as the nasal cavity filling can vary from time to time, we will copy the air cavities from the CBCT images to the deformed planning CT images to preserve the actual nasal cavity filling.…”

DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy

“…Since the models were trained with CBCT images, the generated images were not directly suitable for dose calculation. Therefore, all images, both real and generated, were first converted to corrected CBCTs using the correction algorithm available in Raystation, which has shown promising results for dose calculation with protons (Chang et al 2023, Reiners et al 2023, Taasti et al 2023. For the entire planning study, we considered the corrected CBCT 1 as the planning image, as this was also the reference image of the DDPMs.…”

Deformable anthropomorphic pelvis phantom for dose accumulation verification

“…Some examples include dice similarity coefficient (DSC), mean distance to agreement, and Hausdorff distance. Meanwhile, the credibility of synthetic CT is often evaluated through gamma analysis of dose distributions (Thing et al 2022, Chang et al 2023.…”