Machine log file-based dose verification using novel iterative CBCT reconstruction algorithm in commercial software during volumetric modulated arc therapy for prostate cancer patients

Inui, Shoki; Nishio, Teiji; Ueda, Yoshihiro; Ohira, Satoshi; Ueda, Hikari; S, Ono; Miyazaki, Michihiko; Koike, Masahiko; Koyama, Koji

doi:10.1016/j.ejmp.2021.11.004

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Instructors in medical imaging/ physics could use it as a convenient tool to demonstrate an entire imaging chain in CT and image guidance in IGRT. For instance, the study [29] evaluated the performance of a commercial iterative algorithm in log file-based dose verification. We believe the proposed toolkit could provide more algorithms to perform a comparative study.…”

Section: Discussionmentioning

confidence: 99%

TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research

Wang

Biguri

et al. 2022

Physica Medica

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Section: Discussionmentioning

confidence: 99%

TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research

Wang

Biguri

et al. 2022

Physica Medica

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Machine Log File and Calibration Errors-based Patient-specific Quality Assurance (QA) for Volumetric Modulated Arc Therapy (VMAT)

Ma,

Mou,

Beeraka

et al. 2023

CPD

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Introduction:: Dose reconstructed based on linear accelerator (linac) log-files is one of the widely used solutions to perform patient-specific quality assurance (QA). However, it has a drawback that the accuracy of log-file is highly dependent on the linac calibration. The objective of the current study is to represent a new practical approach for a patient-specific QA during Volumetric modulated arc therapy (VMAT) using both log-file and calibration errors of linac. Methods:: A total of six cases, including two head and neck neoplasms, two lung cancers, and two rectal carcinomas, were selected. The VMAT-based delivery was optimized by the TPS of Pinnacle^3 subsequently, using Elekta Synergy VMAT linac (Elekta Oncology Systems, Crawley, UK), which was equipped with 80 Multi-leaf collimators (MLCs) and the energy of the ray selected at 6 MV. Clinical mode log-file of this linac was used in this study. A series of test fields validate the accuracy of log-file. Then, six plans of test cases were delivered and log-file of each was obtained. The log-file errors were added to the corresponding plans through the house script and the first reconstructed plan was obtained. Later, a series of tests were performed to evaluate the major calibration errors of the linac (dose-rate, gantry angle, MLC leaf position) and the errors were added to the first reconstruction plan to generate the second reconstruction plan. At last, all plans were imported to Pinnacle and recalculated dose distribution on patient CT and ArcCheck phantom (SUN Nuclear). For the former, both target and OAR dose differences between them were compared. For the latter, γ was evaluated by ArcCheck, and subsequently, the surface dose differences between them were performed. Results:: Accuracy of log-file was validated. If error recordings in the log file were only considered, there were four arcs whose proportion of control points with gantry angle errors more than ± 1°larger than 35%. Errors of leaves within ± 0.5 mm were 95% for all arcs. The distinctness of a single control point MU was bigger, but the distinctness of cumulative MU was smaller. The maximum, minimum, and mean doses for all targets were distributed between -6.79E-02-0.42%, -0.38-0.4%, 2.69E-02-8.54E-02% respectively, whereas for all OAR, the maximum and mean dose were distributed between -1.16-2.51%, -1.21-3.12% respectively. For the second reconstructed dose: the maximum, minimum, and mean dose for all targets was distributed between 0.0995~5.7145%, 0.6892~4.4727%, 0.5829~1.8931% separately. Due to OAR, maximum and mean dose distribution was observed between -3.1462~6.8920%, -6.9899~1.9316%, respectively. Conclusion:: Patient-specific QA based on the log-file could reflect the accuracy of the linac execution plan, which usually has a small influence on dose delivery. When the linac calibration errors were considered, the reconstructed dose was closer to the actual delivery and the developed method was accurate and practical other: N/A

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A joint learning framework for multisite CBCT-to-CT translation using a hybrid CNN-transformer synthesizer and a registration network

Hu,

Cheng,

Wei

et al. 2024

Front. Oncol.

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BackgroundCone-beam computed tomography (CBCT) is a convenient method for adaptive radiation therapy (ART), but its application is often hindered by its image quality. We aim to develop a unified deep learning model that can consistently enhance the quality of CBCT images across various anatomical sites by generating synthetic CT (sCT) images.MethodsA dataset of paired CBCT and planning CT images from 135 cancer patients, including head and neck, chest and abdominal tumors, was collected. This dataset, with its rich anatomical diversity and scanning parameters, was carefully selected to ensure comprehensive model training. Due to the imperfect registration, the inherent challenge of local structural misalignment of paired dataset may lead to suboptimal model performance. To address this limitation, we propose SynREG, a supervised learning framework. SynREG integrates a hybrid CNN-transformer architecture designed for generating high-fidelity sCT images and a registration network designed to correct local structural misalignment dynamically during training. An independent test set of 23 additional patients was used to evaluate the image quality, and the results were compared with those of several benchmark models (pix2pix, cycleGAN and SwinIR). Furthermore, the performance of an autosegmentation application was also assessed.ResultsThe proposed model disentangled sCT generation from anatomical correction, leading to a more rational optimization process. As a result, the model effectively suppressed noise and artifacts in multisite applications, significantly enhancing CBCT image quality. Specifically, the mean absolute error (MAE) of SynREG was reduced to 16.81 ± 8.42 HU, whereas the structural similarity index (SSIM) increased to 94.34 ± 2.85%, representing improvements over the raw CBCT data, which had the MAE of 26.74 ± 10.11 HU and the SSIM of 89.73 ± 3.46%. The enhanced image quality was particularly beneficial for organs with low contrast resolution, significantly increasing the accuracy of automatic segmentation in these regions. Notably, for the brainstem, the mean Dice similarity coefficient (DSC) increased from 0.61 to 0.89, and the MDA decreased from 3.72 mm to 0.98 mm, indicating a substantial improvement in segmentation accuracy and precision.ConclusionsSynREG can effectively alleviate the differences in residual anatomy between paired datasets and enhance the quality of CBCT images.

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Machine log file-based dose verification using novel iterative CBCT reconstruction algorithm in commercial software during volumetric modulated arc therapy for prostate cancer patients

Cited by 3 publications

References 29 publications

TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research

TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research

Machine Log File and Calibration Errors-based Patient-specific Quality Assurance (QA) for Volumetric Modulated Arc Therapy (VMAT)

A joint learning framework for multisite CBCT-to-CT translation using a hybrid CNN-transformer synthesizer and a registration network

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