Comparison of CBCT conversion methods for dose calculation in the head and neck region

Irmak, Sinan; Georg, Dietmar; Lechner, Wolfgang

doi:10.1016/j.zemedi.2020.05.007

Cited by 15 publications

(15 citation statements)

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“…Analysed dose parameters for target and OAR structures as well as gamma index showed good agreement between pCT, sCT, and converted CBCT plans with dose differences within 2% with a few exceptions. For the conversion of CBCT images, multiple methods were proposed which were evaluated in detail in previous studies 36,38 . The calculations with the density overriding method showed acceptable results in head and neck in the previous studies 38,39 which were also identified in this work.…”

Section: Discussionmentioning

confidence: 61%

“…In order to investigate the CBCT based QA for sCT, the population based method was chosen as an alternative to the density overriding method. While this method is very labor intensive during the commissioning, it leads to accurate results, 36 provided that adequate regular QA procedures are performed on the CBCT and CT imaging systems. This method can be applied to any TPS to calculate the CBCT based dose without the requirement of additional features.…”

Section: Discussionmentioning

confidence: 99%

“…For the population‐based dose calculation approach 34–36 CT and CBCT images were first rigidly registered and the following volumes of interests (VOIs) were delineated on pCT images which represent different tissue classes, i.e. skull, bone, spinal cord, muscle in the proximity of neck area, brain, brainstem, air cavities, and air outside the body region.…”

Section: Methodsmentioning

confidence: 99%

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Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region

et al. 2021

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Purpose: In the past years, many different neural network-based conversion techniques for synthesising CTs (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, e.g. for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated.Methods: 41 patients with tumours in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method (CBCT RS ) and a population-based dose calculation method (CBCT P op ) were employed for CBCT based dose calculation. The dose distributions were analysed using 3D global gamma analysis applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs at risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs.Results: The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60 HU for MAE. Using the 2%/2mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0±0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0±0.8% and 99.1±0.8% for the CBCT RS and CBCT P op , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4±1.6% and 99.2±0.6% for CBCT RS and CBCT P op , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%. Conclusion:The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed-up.

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Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region

et al. 2021

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“…Giacometti et al [ 20 ] show the currently used DR procedures based on CBCT. Despite continuous improvements in the technology used to create CBCT images, direct calculations of dose distributions on CBCT images are still subject to a greater error than calculations made on CT images [ 21 ]. One of the methods to reduce inaccuracies of dose recalculation is based on the use of CBCT images to transform the CT images used in planning.…”

Section: Discussionmentioning

confidence: 99%

Dosimetric Comparison of Ultra-Hypofractionated and Conventionally Fractionated Radiation Therapy Boosts for Patients with High-Risk Prostate Cancer

Piotrowski

Yartsev

Krawczyk

et al. 2022

Life

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Recent comparison of an ultra-hypofractionated radiotherapy (UF-RT) boost to a conventionally fractionated (CF-RT) option showed similar toxicity and disease control outcomes. An analysis of the treatment plans for these patients is needed for evaluating calculated doses for different organs, treatment beam-on time, and requirements for human and financial resources. Eighty-six plans for UF-RT and 93 plans for CF-RT schemes were evaluated. The biologically equivalent dose, EQD2, summed for the first phase and the boost, was calculated for dose-volume parameters for organs at risk (OARs), as well as for the PTV1. ArcCHECK measurements for the boost plans were used for a comparison of planned and delivered doses. Monitor units and beam-on times were recorded by the Eclipse treatment planning system. Statistical analysis was performed with a significance level of 0.05. Dosimetric parameter values for OARs were well within tolerance for both groups. EQD2 for the PTV1 was on average 84 Gy for UF-RT patients and 76 Gy for CF-RT patients. Gamma passing rate for planned/delivered doses comparison was above 98% for both groups with 3 mm/3% distance to agreement/dose difference criteria. Total monitor units per fraction were 647 ± 94 and 2034 ± 570 for CF-RT and UF-RT, respectively. The total delivery time for boost radiation for the patients in the UF-RT arm was, on average, four times less than the total time for a conventional regimen with statistically equal clinical outcomes for the two arms in this study.

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“…This allows for the transfer of HU values or dose information from the planning CT onto the CBCT images. 6,[15][16][17][18][19][20] However, correlation of anatomical regions and densities between planning CT and CBCT after deformable image registration may contain registration errors. 19,21,22 More recently, Deep Learning methods have been investigated extensively to generate high quality CBCT images from standard CBCT images and learning from the gold standard pCT images, known as synthetic CT. [23][24][25][26][27] While these methods can achieve remarkable CT number accuracy, their ability to consistently generate high-fidelity synthetic CT images for various patient sizes, anatomical regions, and radiotherapy treatment setups is still under investigation.…”

Section: Introductionmentioning

confidence: 99%

2D antiscatter grid and scatter sampling based CBCT method for online dose calculations during CBCT guided radiation therapy of pelvis

Bayat,

Miller,

Park

et al. 2023

Medical Physics

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BackgroundOnline dose calculations before the delivery of radiation treatments have applications in dose delivery verification, online adaptation of treatment plans, and simulation‐free treatment planning. While dose calculations by directly utilizing CBCT images are desired, dosimetric accuracy can be compromised due to relatively lower HU accuracy in CBCT images.PurposeIn this work, we propose a novel CBCT imaging pipeline to enhance the accuracy of CBCT‐based dose calculations in the pelvis region. Our approach aims to improve the HU accuracy in CBCT images, thereby improving the overall accuracy of CBCT‐based dose calculations prior to radiation treatment delivery.MethodsAn in‐house developed quantitative CBCT pipeline was implemented to address the CBCT raw data contamination problem. The pipeline combines algorithmic data correction strategies and 2D antiscatter grid‐based scatter rejection to achieve high CT number accuracy. To evaluate the effect of the quantitative CBCT pipeline on CBCT‐based dose calculations, phantoms mimicking pelvis anatomy were scanned using a linac‐mounted CBCT system, and a gold standard multidetector CT used for treatment planning (pCT). A total of 20 intensity‐modulated treatment plans were generated for five targets, using 6 and 10 MV flattening filter‐free beams, and utilizing small and large pelvis phantom images. For each treatment plan, four different dose calculations were performed using pCT images and three CBCT imaging configurations: quantitative CBCT, clinical CBCT protocol, and a high‐performance 1D antiscatter grid (1D ASG). Subsequently, dosimetric accuracy was evaluated for both targets and organs at risk as a function of patient size, target location, beam energy, and CBCT imaging configuration.ResultsWhen compared to the gold‐standard pCT, dosimetric errors in quantitative CBCT‐based dose calculations were not significant across all phantom sizes, beam energies, and treatment sites. The largest error observed was 0.6% among all dose volume histogram metrics and evaluated dose calculations. In contrast, dosimetric errors reached up to 7% and 97% in clinical CBCT and high‐performance ASG CBCT‐based treatment plans, respectively. The largest dosimetric errors were observed in bony targets in the large phantom treated with 6 MV beams. The trends of dosimetric errors in organs at risk were similar to those observed in the targets.ConclusionsThe proposed quantitative CBCT pipeline has the potential to provide comparable dose calculation accuracy to the gold‐standard planning CT in photon radiation therapy for the abdomen and pelvis. These robust dose calculations could eliminate the need for density overrides in CBCT images and enable direct utilization of CBCT images for dose delivery monitoring or online treatment plan adaptations before the delivery of radiation treatments.

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Comparison of CBCT conversion methods for dose calculation in the head and neck region

Cited by 15 publications

References 61 publications

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region

Dosimetric Comparison of Ultra-Hypofractionated and Conventionally Fractionated Radiation Therapy Boosts for Patients with High-Risk Prostate Cancer

2D antiscatter grid and scatter sampling based CBCT method for online dose calculations during CBCT guided radiation therapy of pelvis

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