1
|
Chen J, Chen K, OuYang M, Wang G, Bai P, You H. Evaluation of dose delivery based on deformed CT using a commercial software for lung cancer. Sci Rep 2024; 14:14470. [PMID: 38914766 PMCID: PMC11196743 DOI: 10.1038/s41598-024-65381-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 06/19/2024] [Indexed: 06/26/2024] Open
Abstract
This study employed a commercial software velocity to perform deformable registration and dose calculation on deformed CT images, aiming to assess the accuracy of dose delivery during the radiotherapy for lung cancers. A total of 20 patients with lung cancer were enrolled in this study. Adaptive CT (ACT) was generated by deformed the planning CT (pCT) to the CBCT of initial radiotherapy fraction, followed by contour propagation and dose recalculation. There was not significant difference between volumes of GTV and CTV calculated from the ACT and pCT. However, significant differences in dice similarity coefficient (DSC) and coverage ratio (CR) between GTV and CTV were observed, with lower values for GTV volumes below 15 cc. The mean differences in dose corresponding to 95% of the GTV, GTV-P, CTV, and CTV-P between ACT and pCT were - 0.32%, 4.52%, 2.17%, and 4.71%, respectively. For the dose corresponding to 99%, the discrepancies were - 0.18%, 8.35%, 1.92%, and 24.96%, respectively. These differences in dose primarily appeared at the edges of the target areas. Notably, a significant enhancement of dose corresponding to 1 cc for spinal cord was observed in ACT, compared with pCT. There was no statistical difference in the mean dose of lungs and heart. In general, for lung cancer patients, anatomical motion may result in both CTV and GTV moving outside the original irradiation region. The dose difference within the original target area was small, but the difference in the planning target area was considerable.
Collapse
Affiliation(s)
- Jihong Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Kaiqiang Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Min OuYang
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China
| | - Guohua Wang
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China
| | - Penggang Bai
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Hongqiang You
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China.
| |
Collapse
|
2
|
Haertter A, Salerno M, Koger B, Kennedy C, Alonso‐Basanta M, Dong L, Teo B, Li T. ACR benchmark testing of a novel high-speed ring-gantry linac kV-CBCT system. J Appl Clin Med Phys 2024; 25:e14299. [PMID: 38520072 PMCID: PMC11087172 DOI: 10.1002/acm2.14299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/21/2023] [Accepted: 01/16/2024] [Indexed: 03/25/2024] Open
Abstract
A new generation cone-beam computed tomography (CBCT) system with new hardware design and advanced image reconstruction algorithms is available for radiation treatment simulation or adaptive radiotherapy (HyperSight CBCT imaging solution, Varian Medical Systems-a Siemens Healthineers company). This study assesses the CBCT image quality metrics using the criteria routinely used for diagnostic CT scanner accreditation as a first step towards the future use of HyperSight CBCT images for treatment planning and target/organ delineations. Image performance was evaluated using American College of Radiology (ACR) Program accreditation phantom tests for diagnostic computed tomography systems (CTs) and compared HyperSight images with a standard treatment planning diagnostic CT scanner (Siemens SOMATOM Edge) and with existing CBCT systems (Varian TrueBeam version 2.7 and Varian Halcyon version 2.0). Image quality performance for all Varian HyperSight CBCT vendor-provided imaging protocols were assessed using ACR head and body ring CT phantoms, then compared to existing imaging modalities. Image quality analysis metrics included contrast-to-noise (CNR), spatial resolution, Hounsfield number (HU) accuracy, image scaling, and uniformity. All image quality assessments were made following the recommendations and passing criteria provided by the ACR. The Varian HyperSight CBCT imaging system demonstrated excellent image quality, with the majority of vendor-provided imaging protocols capable of passing all ACR CT accreditation standards. Nearly all (8/11) vendor-provided protocols passed ACR criteria using the ACR head phantom, with the Abdomen Large, Pelvis Large, and H&N vendor-provided protocols produced HU uniformity values slightly exceeding passing criteria but remained within the allowable minor deviation levels (5-7 HU maximum differences). Compared to other existing CT and CBCT imaging modalities, both HyperSight Head and Pelvis imaging protocols matched the performance of the SOMATOM CT scanner, and both the HyperSight and SOMATOM CT substantially surpassed the performance of the Halcyon 2.0 and TrueBeam version 2.7 systems. Varian HyperSight CBCT imaging system could pass almost all tests for all vendor-provided protocols using ACR accreditation criteria, with image quality similar to those produced by diagnostic CT scanners and significantly better than existing linac-based CBCT imaging systems.
Collapse
Affiliation(s)
- Allison Haertter
- Department of Radiation OncologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Michael Salerno
- Department of Radiation OncologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Brandon Koger
- Department of Radiation OncologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Christopher Kennedy
- Department of Radiation OncologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Lei Dong
- Department of Radiation OncologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Boon‐Keng Teo
- Department of Radiation OncologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Taoran Li
- Department of Radiation OncologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| |
Collapse
|
3
|
Bayat F, Miller B, Park Y, Yu Z, Alexeev T, Thomas D, Stuhr K, Kavanagh B, Miften M, Altunbas C. 2D antiscatter grid and scatter sampling based CBCT method for online dose calculations during CBCT guided radiation therapy of pelvis. Med Phys 2024; 51:3053-3066. [PMID: 38043086 PMCID: PMC11008043 DOI: 10.1002/mp.16867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/31/2023] [Accepted: 11/15/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Online 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. PURPOSE In 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. METHODS An 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. RESULTS When 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. CONCLUSIONS The 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.
Collapse
Affiliation(s)
- Farhang Bayat
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Brian Miller
- Department of Radiation Oncology, The University of Arizona, College of Medicine, Tucson, AZ 85719
| | - Yeonok Park
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Zhelin Yu
- Department of Computer Science and Engineering, University of Colorado Denver, 1200 Larimer Street, Denver, CO, 80204
| | - Timur Alexeev
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - David Thomas
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Kelly Stuhr
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Cem Altunbas
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| |
Collapse
|
4
|
Lavrova E, Garrett MD, Wang YF, Chin C, Elliston C, Savacool M, Price M, Kachnic LA, Horowitz DP. Adaptive Radiation Therapy: A Review of CT-based Techniques. Radiol Imaging Cancer 2023; 5:e230011. [PMID: 37449917 PMCID: PMC10413297 DOI: 10.1148/rycan.230011] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/18/2023] [Accepted: 05/10/2023] [Indexed: 07/18/2023]
Abstract
Adaptive radiation therapy is a feedback process by which imaging information acquired over the course of treatment, such as changes in patient anatomy, can be used to reoptimize the treatment plan, with the end goal of improving target coverage and reducing treatment toxicity. This review describes different types of adaptive radiation therapy and their clinical implementation with a focus on CT-guided online adaptive radiation therapy. Depending on local anatomic changes and clinical context, different anatomic sites and/or disease stages and presentations benefit from different adaptation strategies. Online adaptive radiation therapy, where images acquired in-room before each fraction are used to adjust the treatment plan while the patient remains on the treatment table, has emerged to address unpredictable anatomic changes between treatment fractions. Online treatment adaptation places unique pressures on the radiation therapy workflow, requiring high-quality daily imaging and rapid recontouring, replanning, plan review, and quality assurance. Generating a new plan with every fraction is resource intensive and time sensitive, emphasizing the need for workflow efficiency and clinical resource allocation. Cone-beam CT is widely used for image-guided radiation therapy, so implementing cone-beam CT-guided online adaptive radiation therapy can be easily integrated into the radiation therapy workflow and potentially allow for rapid imaging and replanning. The major challenge of this approach is the reduced image quality due to poor resolution, scatter, and artifacts. Keywords: Adaptive Radiation Therapy, Cone-Beam CT, Organs at Risk, Oncology © RSNA, 2023.
Collapse
Affiliation(s)
- Elizaveta Lavrova
- From the Department of Radiation Oncology, Columbia University Irving
Medical Center, 622 W 168th St, New York, NY 10032 (E.L., M.D.G., Y.F.W., C.C.,
C.E., M.S., M.P., L.A.K., D.P.H.); and Herbert Irving Comprehensive Cancer
Center, New York, NY (C.C., L.A.K., D.P.H.)
| | - Matthew D. Garrett
- From the Department of Radiation Oncology, Columbia University Irving
Medical Center, 622 W 168th St, New York, NY 10032 (E.L., M.D.G., Y.F.W., C.C.,
C.E., M.S., M.P., L.A.K., D.P.H.); and Herbert Irving Comprehensive Cancer
Center, New York, NY (C.C., L.A.K., D.P.H.)
| | - Yi-Fang Wang
- From the Department of Radiation Oncology, Columbia University Irving
Medical Center, 622 W 168th St, New York, NY 10032 (E.L., M.D.G., Y.F.W., C.C.,
C.E., M.S., M.P., L.A.K., D.P.H.); and Herbert Irving Comprehensive Cancer
Center, New York, NY (C.C., L.A.K., D.P.H.)
| | - Christine Chin
- From the Department of Radiation Oncology, Columbia University Irving
Medical Center, 622 W 168th St, New York, NY 10032 (E.L., M.D.G., Y.F.W., C.C.,
C.E., M.S., M.P., L.A.K., D.P.H.); and Herbert Irving Comprehensive Cancer
Center, New York, NY (C.C., L.A.K., D.P.H.)
| | - Carl Elliston
- From the Department of Radiation Oncology, Columbia University Irving
Medical Center, 622 W 168th St, New York, NY 10032 (E.L., M.D.G., Y.F.W., C.C.,
C.E., M.S., M.P., L.A.K., D.P.H.); and Herbert Irving Comprehensive Cancer
Center, New York, NY (C.C., L.A.K., D.P.H.)
| | - Michelle Savacool
- From the Department of Radiation Oncology, Columbia University Irving
Medical Center, 622 W 168th St, New York, NY 10032 (E.L., M.D.G., Y.F.W., C.C.,
C.E., M.S., M.P., L.A.K., D.P.H.); and Herbert Irving Comprehensive Cancer
Center, New York, NY (C.C., L.A.K., D.P.H.)
| | - Michael Price
- From the Department of Radiation Oncology, Columbia University Irving
Medical Center, 622 W 168th St, New York, NY 10032 (E.L., M.D.G., Y.F.W., C.C.,
C.E., M.S., M.P., L.A.K., D.P.H.); and Herbert Irving Comprehensive Cancer
Center, New York, NY (C.C., L.A.K., D.P.H.)
| | - Lisa A. Kachnic
- From the Department of Radiation Oncology, Columbia University Irving
Medical Center, 622 W 168th St, New York, NY 10032 (E.L., M.D.G., Y.F.W., C.C.,
C.E., M.S., M.P., L.A.K., D.P.H.); and Herbert Irving Comprehensive Cancer
Center, New York, NY (C.C., L.A.K., D.P.H.)
| | - David P. Horowitz
- From the Department of Radiation Oncology, Columbia University Irving
Medical Center, 622 W 168th St, New York, NY 10032 (E.L., M.D.G., Y.F.W., C.C.,
C.E., M.S., M.P., L.A.K., D.P.H.); and Herbert Irving Comprehensive Cancer
Center, New York, NY (C.C., L.A.K., D.P.H.)
| |
Collapse
|
5
|
TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research. Phys Med 2022; 102:33-45. [PMID: 36088800 DOI: 10.1016/j.ejmp.2022.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/08/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022] Open
Abstract
We presented TIGRE-VarianCBCT, an open-source toolkit Matlab-GPU for Varian on-board cone-beam CT with particular emphasis to address challenges in raw data preprocessing, artifacts correction, tomographic reconstruction and image post-processing. The aim of this project is to provide not only a tool to bridge the gap between clinical usage of CBCT scan data and research algorithms but also a framework that breaks down the imaging chain into individual processes so that research effort can be focused on a specific part. The entire imaging chain, module-based architecture, data flow and techniques used in the creation of the toolkit are presented. Raw scan data are first decoded to extract X-ray fluoro image series and set up the imaging geometry. Data conditioning operations including scatter correction, normalization, beam-hardening correction, ring removal are performed sequentially. Reconstruction is supported by TIGRE with FDK as well as a variety of iterative algorithms. Pixel-to-HU mapping is calibrated by a CatphanTM 504 phantom. Imaging dose in CTDIw is calculated in an empirical formula. The performance was validated on real patient scans with good agreement with respect to vendor-designed program. Case studies in scan protocol optimization, low dose imaging and iterative algorithm comparison demonstrated its substantial potential in performing scan data based clinical studies. The toolkit is released under the BSD license, imposing minimal restrictions on its use and distribution. The toolkit is accessible as a module at https://github.com/CERN/TIGRE.
Collapse
|
6
|
Treatment Planning in Intraoperative Radiation Therapy (IORT): Where Should We Go? Cancers (Basel) 2022; 14:cancers14143532. [PMID: 35884591 PMCID: PMC9319593 DOI: 10.3390/cancers14143532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/05/2022] [Accepted: 07/11/2022] [Indexed: 02/04/2023] Open
Abstract
As opposed to external beam radiation therapy (EBRT), treatment planning systems (TPS) dedicated to intraoperative radiation therapy (IORT) were not subject to radical modifications in the last two decades. However, new treatment regimens such as ultrahigh dose rates and combination with multiple treatment modalities, as well as the prospected availability of dedicated in-room imaging, call for important new features in the next generation of treatment planning systems in IORT. Dosimetric accuracy should be guaranteed by means of advanced dose calculation algorithms, capable of modelling complex scattering phenomena and accounting for the non-tissue equivalent materials used to shape and compensate electron beams. Kilovoltage X-ray based IORT also presents special needs, including the correct description of extremely steep dose gradients and the accurate simulation of applicators. TPSs dedicated to IORT should also allow real-time imaging to be used for treatment adaptation at the time of irradiation. Other features implemented in TPSs should include deformable registration and capability of radiobiological planning, especially if unconventional irradiation schemes are used. Finally, patient safety requires that the multiple features be integrated in a comprehensive system in order to facilitate control of the whole process.
Collapse
|
7
|
Czajkowski P, Piotrowski T. Evaluation of the accuracy of dose delivery in stereotactic radiotherapy using the Velocity commercial software. Phys Med 2022; 95:133-139. [DOI: 10.1016/j.ejmp.2022.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 01/31/2022] [Accepted: 02/04/2022] [Indexed: 12/18/2022] Open
|
8
|
Shinde P, Jadhav A, Shankar V, Gupta KK, Dhoble NS, Dhoble SJ. Evaluation of kV-CBCT based 3D dose calculation accuracy and its validation using delivery fluence derived dose metrics in Head and Neck Cancer. Phys Med 2022; 96:32-45. [PMID: 35217498 DOI: 10.1016/j.ejmp.2022.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 10/19/2022] Open
Abstract
PURPOSE The purpose of this study is to evaluate the dosimetric impact of Hounsfield unit (HU) variations in kilovoltage cone-beam computed tomography (kV-CBCT) based 3D dose calculation accuracy in the treatment planning system and its validation using measured treatment delivery dose (MTDD) derived dose metrics for Volumetric Modulated Arc Therapy (VMAT) and Intensity Modulated Radiotherapy (IMRT) plans in Head and Neck (HN) Cancer. METHODS CBCT dose calculation accuracy was evaluated for 8 VMAT plans on inhomogeneous phantom and 40 VMAT and IMRT plans of HN Cancer patients and validated using ArcCHECK diode array MTDD derived 3D dose metric on CT and CBCT. RESULTS The mean percentage dose difference between CBCT and CT in TPS (ΔD(CBCT-CT)TPS) and 3DVH (ΔD(CBCT-CT)3DVH) were compared for the corresponding evaluation dose metrics (D98%, D95%, D50%, D2%, Dmax, D1cc, D0.03cc, Dmean) of all PTVs and OARs in phantom and patients. ΔD(CBCT-CT)TPS and ΔD(CBCT-CT)3DVH for all evaluation dose points of all PTVs and OARs were less than 2.55% in phantom and 2.4% in HN patients. The Pearson correlation coefficient (r) between ΔD(CBCT-CT)TPS and ΔD(CBCT-CT)3DVH for all dose points in all PTVs and OARs showed a strong to moderate correlation in phantom and patients with p < 0.001. CONCLUSIONS This study evaluated and validated the potential feasibility of kV-CBCT for treatment plan 3D dose reconstruction in clinical decision making for Adaptive radiotherapy on CT in Head and Neck cancer.
Collapse
Affiliation(s)
- Prashantkumar Shinde
- Department of Physics, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur 440033, India
| | - Anand Jadhav
- Department of Radiation Oncology, Sir H N Reliance Foundation Hospital and Research Centre, Mumbai 400004, India
| | - V Shankar
- Department of Radiation Oncology, Apollo Cancer Center, Chennai 600035, India
| | - Karan Kumar Gupta
- Department of Chemical Engineering, National Taiwan University, Taipei, Taiwan, ROC.
| | - Nirupama S Dhoble
- Department of Chemistry, Sevadal Mahila Mahavidhyalay, Nagpur 440015, India
| | - Sanjay J Dhoble
- Department of Physics, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur 440033, India.
| |
Collapse
|
9
|
Inui S, Nishio T, Ueda Y, Ohira S, Ueda H, Washio H, Ono S, Miyazaki M, Koizumi M, Konishi K. Machine log file-based dose verification using novel iterative CBCT reconstruction algorithm in commercial software during volumetric modulated arc therapy for prostate cancer patients. Phys Med 2021; 92:24-31. [PMID: 34837857 DOI: 10.1016/j.ejmp.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 11/06/2021] [Accepted: 11/16/2021] [Indexed: 10/19/2022] Open
Abstract
PURPOSE To evaluate the utility of the use of iterative cone-beam computed tomography (CBCT) for machine log file-based dose verification during volumetric modulated arc therapy (VMAT) for prostate cancer patients. METHODS All CBCT acquisition data were used to reconstruct images with the Feldkamp-Davis-Kress algorithm (FDK-CBCT) and the novel iterative algorithm (iCBCT). The Hounsfield unit (HU)-electron density curves for CBCT images were created using the Advanced Electron Density Phantom. The I'mRT and anthropomorphic phantoms were irradiated with VMAT after CBCT registration. Subsequently, fourteen prostate cancer patients received VMAT after CBCT registration. Machine log files and both CBCT images were exported to the PerFRACTION software, and a 3D patient dose was reconstructed. Mean dose for planning target volume (PTV), the bladder, and rectum and the 3D gamma analysis were evaluated. RESULTS For the phantom studies, the variation of HU values was observed at the central position surrounding the bones in FDK-CBCT. There were almost no changes in the difference of doses at the isocenter between measurement and reconstructed dose for planning CT (pCT), FDK-CBCT, and iCBCT. Mean dose differences of PTV, rectum, and bladder between iCBCT and pCT were approximately 2% lower than those between FDK-CBCT and pCT. For the clinical study, average gamma analysis for 2%/2 mm was 98.22% ± 1.07 and 98.81% ± 1.25% in FDK-CBCT and iCBCT, respectively. CONCLUSIONS A similar machine log file-based dose verification accuracy is obtained for FDK-CBCT and iCBCT during VMAT for prostate cancer patients.
Collapse
Affiliation(s)
- Shoki Inui
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Suita, Japan.
| | - Teiji Nishio
- Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Hikari Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Hayate Washio
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Graduate School of Health Sciences, Kumamoto University, Kumamoto, Japan
| | - Shunsuke Ono
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masayoshi Miyazaki
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masahiko Koizumi
- Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Suita, Japan
| | - Koji Konishi
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| |
Collapse
|