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Feng H, Holmes JM, Vora SA, Stoker JB, Bues M, Wong WW, Sio TS, Foote RL, Patel SH, Shen J, Liu W. Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy. Phys Med Biol 2024; 69:10.1088/1361-6560/ad0b64. [PMID: 37944480 PMCID: PMC11009986 DOI: 10.1088/1361-6560/ad0b64] [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: 07/12/2023] [Accepted: 11/09/2023] [Indexed: 11/12/2023]
Abstract
Purpose. To enhance an in-house graphic-processing-unit accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both dose calculation and optimization for pencil beam scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS).Methods and materials. A module to simulate VPs passing through patient-specific aperture blocks was developed and integrated in VPMC based on simulation results of realistic particles (primary protons and their secondaries). To validate the aperture block module, VPMC was first validated by an opensource MC code, MCsquare, in eight water phantom simulations with 3 cm thick brass apertures: four were with aperture openings of 1, 2, 3, and 4 cm without a range shifter, while the other four were with same aperture opening configurations with a range shifter of 45 mm water equivalent thickness. Then, VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small targets (average volume 8.4 c.c. with range of 0.4-43.3 c.c.). Finally, 3 typical patients were selected for robust optimization with aperture blocks using VPMC.Results. In the water phantoms, 3D gamma passing rate (2%/2 mm/10%) between VPMC and MCsquare was 99.71 ± 0.23%. In the patient geometries, 3D gamma passing rates (3%/2 mm/10%) between VPMC/MCsquare and RayStation MC were 97.79 ± 2.21%/97.78 ± 1.97%, respectively. Meanwhile, the calculation time was drastically decreased from 112.45 ± 114.08 s (MCsquare) to 8.20 ± 6.42 s (VPMC) with the same statistical uncertainties of ~0.5%. The robustly optimized plans met all the dose-volume-constraints (DVCs) for the targets and OARs per our institutional protocols. The mean calculation time for 13 influence matrices in robust optimization by VPMC was 41.6 s and the subsequent on-the-fly 'trial-and-error' optimization procedure took only 71.4 s on average for the selected three patients.Conclusion. VPMC has been successfully enhanced to model aperture blocks in dose calculation and optimization for the PBSPT-based SRS.
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Affiliation(s)
- Hongying Feng
- College of Mechanical and Power Engineering, China Three Gorges University, Yichang, Hubei 443002, People’s Republic of China
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
- Department of Radiation Oncology, Guangzhou Concord Cancer Center, Guangzhou, Guangdong, 510555, People’s Republic of China
| | - Jason M Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
| | - Sujay A Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
| | - Joshua B Stoker
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
| | - William W Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
| | - Terence S Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
| | - Robert L Foote
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55902, United States of America
| | - Samir H Patel
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, United States of America
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Sarkar V, Paxton A, Su F, Price R, Nelson G, Szegedi M, James SS, Salter BJ. An evaluation of the use of DirectSPR images for proton planning in the RayStation treatment planning software. J Appl Clin Med Phys 2023; 24:e13900. [PMID: 36625438 PMCID: PMC10161080 DOI: 10.1002/acm2.13900] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 12/20/2022] [Accepted: 12/24/2022] [Indexed: 01/11/2023] Open
Abstract
An important source of uncertainty in proton therapy treatment planning is the assignment of stopping-power ratio (SPR) from CT data. A commercial product is now available that creates an SPR map directly from dual-energy CT (DECT). This paper investigates the use of this new product in proton treatment planning and compares the results to the current method of assigning SPR based on a single-energy CT (SECT). Two tissue surrogate phantoms were CT scanned using both techniques. The SPRs derived from single-energy CT and by DirectSPR™ were compared to measured values. SECT-based values agreed with measurements within 4% except for low density lung and high density bone, which differed by 13% and 8%, respectively. DirectSPR™ values were within 2% of measured values for all tissues studied. Both methods were also applied to scanned containers of three types of animal tissue, and the expected range of protons of two different energies was calculated in the treatment planning system and compared to the range measured using a multi-layer ion chamber. The average difference between range measurements and calculations based on SPR maps from dual- and single-energy CT, respectively, was 0.1 mm (0.07%) versus 2.2 mm (1.5%). Finally, a phantom was created using a layer of various tissue surrogate plugs on top of a 2D ion chamber array. Dose measurements on this array were compared to predictions using both single- and dual-energy CTs and SPR maps. While standard gamma pass rates for predictions based on DECT-derived SPR maps were slightly higher than those based on single-energy CT, the differences were generally modest for this measurement setup. This study showed that SPR maps created by the commercial product from dual-energy CT can successfully be used in RayStation to generate proton dose distributions and that these predictions agree well with measurements.
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Affiliation(s)
| | - Adam Paxton
- University of Utah, Salt Lake City, Utah, USA
| | - Fanchi Su
- University of Utah, Salt Lake City, Utah, USA
| | - Ryan Price
- University of Utah, Salt Lake City, Utah, USA
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Yang Y, Rwigema JCM, Vargas C, Yu NY, Keole SR, Wong WW, Schild SE, Bues M, Liu W, Shen J. Technical note: Investigation of dose and LET d effect to rectum and bladder by using non-straight laterals in prostate cancer receiving proton therapy. Med Phys 2022; 49:7428-7437. [PMID: 36208196 DOI: 10.1002/mp.16008] [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: 06/01/2022] [Revised: 09/02/2022] [Accepted: 09/22/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Parallel-opposed lateral beams are the conventional beam arrangements in proton therapy for prostate cancer. However, when considering linear energy transfer (LET) and RBE effects, alternative beam arrangements should be investigated. PURPOSE To investigate the dose and dose averaged LET (LETd ) impact of using new beam arrangements rotating beams 5°-15° posteriorly to the laterals in prostate cancer treated with pencil-beam-scanning (PBS) proton therapy. METHODS Twenty patients with localized prostate cancer were included in this study. Four proton treatment plans for each patient were generated utilizing 0°, 5°, 10°, and 15° posterior oblique beam pairs relative to parallel-opposed lateral beams. Dose-volume histograms (DVHs) from posterior oblique beams were analyzed. Dose-LETd -volume histogram (DLVH) was employed to study the difference in dose and LETd with each beam arrangement. DLVH indices, V ( d , l ) $V( {d,l} )$ , defined as the cumulative absolute volume that has a dose of at least d (Gy[RBE]) and a LETd of at least l (keV/µm), were calculated for both the rectum and bladder to the whole group of patients and two-sub groups with and without hydrogel spacer. These metrics were tested using Wilcoxon signed-rank test. RESULTS Rotating beam angles from laterals to slightly posterior by 5°-15° reduced high LETd volumes while it increased the dose volume in the rectum and increased LETd in bladders. Beam angles rotated five degrees posteriorly from laterals (i.e., gantry in 95° and 265°) are proposed since they achieved the optimal balance of better LETd sparing and minimal dose increase in the rectum. A reduction of V(50 Gy[RBE], 2.6 keV/µm) from 7.41 to 3.96 cc (p < 0.01), and a slight increase of V(50 Gy[RBE], 0 keV/µm) from 20.1 to 21.6 cc (p < 0.01) were observed for the group without hydrogel spacer. The LETd sparing was less effective for the group with hydrogel spacer, which achieved the reduction of V(50 Gy[RBE], 2.6 keV/µm) from 4.28 to 2.10 cc (p < 0.01). CONCLUSIONS Posterior oblique angle plans improved LETd sparing of the rectum while sacrificing LETd sparing in the bladder in the treatment of prostate cancer with PBS. Beam angle modification from laterals to slightly posterior may be a strategy to redistribute LETd and perhaps reduce rectal toxicity risks in prostate cancer patients treated with PBS. However, the effect is reduced for patients with hydrogel spacer.
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Affiliation(s)
- Yunze Yang
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | | | - Carlos Vargas
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Nathan Y Yu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Sameer R Keole
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - William W Wong
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
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Zou W, Kim H, Diffenderfer ES, Carlson DJ, Koch CJ, Xiao Y, Teo BK, Kim MM, Metz JM, Fan Y, Maity A, Koumenis C, Busch TM, Wiersma R, Cengel KA, Dong L. A phenomenological model of proton FLASH oxygen depletion effects depending on tissue vasculature and oxygen supply. Front Oncol 2022; 12:1004121. [PMID: 36518319 PMCID: PMC9742361 DOI: 10.3389/fonc.2022.1004121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/11/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Radiation-induced oxygen depletion in tissue is assumed as a contributor to the FLASH sparing effects. In this study, we simulated the heterogeneous oxygen depletion in the tissue surrounding the vessels and calculated the proton FLASH effective-dose-modifying factor (FEDMF), which could be used for biology-based treatment planning. Methods The dose and dose-weighted linear energy transfer (LET) of a small animal proton irradiator was simulated with Monte Carlo simulation. We deployed a parabolic partial differential equation to account for the generalized radiation oxygen depletion, tissue oxygen diffusion, and metabolic processes to investigate oxygen distribution in 1D, 2D, and 3D solution space. Dose and dose rates, particle LET, vasculature spacing, and blood oxygen supplies were considered. Using a similar framework for the hypoxic reduction factor (HRF) developed previously, the FEDMF was derived as the ratio of the cumulative normoxic-equivalent dose (CNED) between CONV and UHDR deliveries. Results Dynamic equilibrium between oxygen diffusion and tissue metabolism can result in tissue hypoxia. The hypoxic region displayed enhanced radio-resistance and resulted in lower CNED under UHDR deliveries. In 1D solution, comparing 15 Gy proton dose delivered at CONV 0.5 and UHDR 125 Gy/s, 61.5% of the tissue exhibited ≥20% FEDMF at 175 μm vasculature spacing and 18.9 μM boundary condition. This percentage reduced to 34.5% and 0% for 8 and 2 Gy deliveries, respectively. Similar trends were observed in the 3D solution space. The FLASH versus CONV differential effect remained at larger vasculature spacings. A higher FLASH dose rate showed an increased region with ≥20% FEDMF. A higher LET near the proton Bragg peak region did not appear to alter the FLASH effect. Conclusion We developed 1D, 2D, and 3D oxygen depletion simulation process to obtain the dynamic HRF and derive the proton FEDMF related to the dose delivery parameters and the local tissue vasculature information. The phenomenological model can be used to simulate or predict FLASH effects based on tissue vasculature and oxygen concentration data obtained from other experiments.
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Holmes J, Shen J, Patel SH, Wong WW, Foote RL, Bues M, Liu W. Collimating individual beamlets in pencil beam scanning proton therapy, a dosimetric investigation. Front Oncol 2022; 12:1031340. [PMID: 36439436 PMCID: PMC9692234 DOI: 10.3389/fonc.2022.1031340] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/27/2022] [Indexed: 03/26/2024] Open
Abstract
The purpose of this work is to investigate collimating individual proton beamlets from a dosimetric perspective and to introduce a new device concept, the spot scanning aperture (SSA). The SSA consists of a thin aperture with a small cylindrical opening attached to a robotics system, which allows the aperture to follow and align with individual beamlets during spot delivery. Additionally, a range shifter is incorporated (source-side) for treating shallow depths. Since the SSA trims beamlets spot by spot, the patient-facing portion of the device only needs to be large enough to trim a single proton beamlet. The SSA has been modelled in an open-source Monte-Carlo-based dose engine (MCsquare) to characterize its dosimetric properties in water at depths between 0 and 10 cm while varying the following parameters: the aperture material, thickness, distance to the water phantom, distance between the aperture and attached range shifter, and the aperture opening radius. Overall, the SSA greatly reduced spot sizes for all the aperture opening radii that were tested (1 - 4 mm), especially in comparison with the extended range shifter (ranger shifter placed at 30 cm from patient); greater than 50% when placed less than 10 cm away from the patient at depths in water less than 50 mm. The peak to entrance dose ratio and linear energy transfer was found to depend on the thickness of the aperture and therefore the aperture material. Neutron production rates were also investigated and discussed.
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Affiliation(s)
- Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - Samir H. Patel
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - Robert L. Foote
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
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Chang C, Charyyev S, Harms J, Slopsema R, Wolf J, Refai D, Yoon T, McDonald MW, Bradley JD, Leng S, Zhou J, Yang X, Lin L. A component method to delineate surgical spine implants for proton Monte Carlo dose calculation. J Appl Clin Med Phys 2022; 24:e13800. [PMID: 36210177 PMCID: PMC9859997 DOI: 10.1002/acm2.13800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/09/2022] [Accepted: 09/22/2022] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Metallic implants have been correlated to local control failure for spinal sarcoma and chordoma patients due to the uncertainty of implant delineation from computed tomography (CT). Such uncertainty can compromise the proton Monte Carlo dose calculation (MCDC) accuracy. A component method is proposed to determine the dimension and volume of the implants from CT images. METHODS The proposed component method leverages the knowledge of surgical implants from medical supply vendors to predefine accurate contours for each implant component, including tulips, screw bodies, lockers, and rods. A retrospective patient study was conducted to demonstrate the feasibility of the method. The reference implant materials and samples were collected from patient medical records and vendors, Medtronic and NuVasive. Additional CT images with extensive features, such as extended Hounsfield units and various reconstruction diameters, were used to quantify the uncertainty of implant contours. RESULTS For in vivo patient implant estimation, the reference and the component method differences were 0.35, 0.17, and 0.04 cm3 for tulips, screw bodies, and rods, respectively. The discrepancies by a conventional threshold method were 5.46, 0.76, and 0.05 cm3 , respectively. The mischaracterization of implant materials and dimensions can underdose the clinical target volume coverage by 20 cm3 for a patient with eight lumbar implants. The tulip dominates the dosimetry uncertainty as it can be made from titanium or cobalt-chromium alloys by different vendors. CONCLUSIONS A component method was developed and demonstrated using phantom and patient studies with implants. The proposed method provides more accurate implant characterization for proton MCDC and can potentially enhance the treatment quality for proton therapy. The current proof-of-concept study is limited to the implant characterization for lumbar spine. Future investigations could be extended to cervical spine and dental implants for head-and-neck patients where tight margins are required to spare organs at risk.
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Affiliation(s)
- Chih‐Wei Chang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Serdar Charyyev
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Joseph Harms
- Department of Radiation OncologyUniversity of AlabamaBirminghamAlabamaUSA
| | - Roelf Slopsema
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Jonathan Wolf
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Daniel Refai
- Department of NeurosurgeryEmory UniversityAtlantaGeorgiaUSA
| | - Tim Yoon
- Department of OrthopaedicsEmory UniversityAtlantaGeorgiaUSA
| | - Mark W. McDonald
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Jeffrey D. Bradley
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Shuai Leng
- Department of RadiologyMayo ClinicRochesterMinnesotaUSA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA,Department of Biomedical InformaticsEmory UniversityAtlantaGeorgiaUSA
| | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
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Shan J, Feng H, Morales DH, Patel SH, Wong WW, Fatyga M, Bues M, Schild SE, Foote RL, Liu W. Virtual particle Monte Carlo: A new concept to avoid simulating secondary particles in proton therapy dose calculation. Med Phys 2022; 49:6666-6683. [PMID: 35960865 PMCID: PMC9588716 DOI: 10.1002/mp.15913] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND In proton therapy dose calculation, Monte Carlo (MC) simulations are superior in accuracy but more time consuming, compared to analytical calculations. Graphic processing units (GPUs) are effective in accelerating MC simulations but may suffer thread divergence and racing condition in GPU threads that degrades the computing performance due to the generation of secondary particles during nuclear reactions. PURPOSE A novel concept of virtual particle (VP) MC (VPMC) is proposed to avoid simulating secondary particles in GPU-accelerated proton MC dose calculation and take full advantage of the computing power of GPU. METHODS Neutrons and gamma rays were ignored as escaping from the human body; doses of electrons, heavy ions, and nuclear fragments were locally deposited; the tracks of deuterons were converted into tracks of protons. These particles, together with primary and secondary protons, are considered to be the realistic particles. Histories of primary and secondary protons were replaced by histories of multiple VPs. Each VP corresponded to one proton (either primary or secondary). A continuous-slowing-down-approximation model, an ionization model, and a large angle scattering event model corresponding to nuclear interactions were developed for VPs by generating probability distribution functions (PDFs) based on simulation results of realistic particles using MCsquare. For efficient calculations, these PDFs were stored in the Compute Unified Device Architecture textures. VPMC was benchmarked with TOPAS and MCsquare in phantoms and with MCsquare in 13 representative patient geometries. Comparisons between the VPMC calculated dose and dose measured in water during patient-specific quality assurance (PSQA) of the selected 13 patients were also carried out. Gamma analysis was used to compare the doses derived from different methods and calculation efficiencies were also compared. RESULTS Integrated depth dose and lateral dose profiles in both homogeneous and inhomogeneous phantoms all matched well among VPMC, TOPAS, and MCsquare calculations. The 3D-3D gamma passing rates with a criterion of 2%/2 mm and a threshold of 10% was 98.49% between MCsquare and TOPAS and 98.31% between VPMC and TOPAS in homogeneous phantoms, and 99.18% between MCsquare and TOPAS and 98.49% between VPMC and TOPAS in inhomogeneous phantoms, respectively. In patient geometries, the 3D-3D gamma passing rates with 2%/2 mm/10% between dose distributions from VPMC and MCsquare were 98.56 ± 1.09% in patient geometries. The 2D-3D gamma analysis with 3%/2 mm/10% between the VPMC calculated dose distributions and the 2D measured planar dose distributions during PSQA was 98.91 ± 0.88%. VPMC calculation was highly efficient and took 2.84 ± 2.44 s to finish for the selected 13 patients running on four NVIDIA Ampere GPUs in patient geometries. CONCLUSION VPMC was found to achieve high accuracy and efficiency in proton therapy dose calculation.
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Affiliation(s)
- Jie Shan
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | | | - Samir H. Patel
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Mirek Fatyga
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Steven E. Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Robert L. Foote
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, 55902, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
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Yang Y, Patel SH, Bridhikitti J, Wong WW, Halyard MY, McGee LA, Rwigema JCM, Schild SE, Vora SA, Liu T, Bues M, Fatyga M, Foote RL, Liu W. Exploratory study of seed spots analysis to characterize dose and linear energy transfer effect in adverse event initialization of pencil beam scanning proton therapy. Med Phys 2022; 49:6237-6252. [PMID: 35820062 DOI: 10.1002/mp.15859] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/20/2022] [Accepted: 07/06/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Both dose and linear-energy-transfer (LET) could play a substantial role in adverse event (AE) initialization of cancer patients treated with pencil-beam-scanning proton therapy (PBS). However, not all the voxels within the AE regions are directly induced from the dose and LET effect. It is important to study the synergistic effect of dose and LET in AE initialization by only including a subset of voxels that are dosimetrically important. PURPOSE To perform exploratory investigation of the dose and LET effects upon AE initialization in PBS using seed spots analysis. METHODS 113 head and neck (H&N) cancer patients receiving curative PBS were included. Among them, 20 patients experienced unanticipated CTCAEv4.0 grade≥3 AEs (AE group) and 93 patients did not (control group). Within the AE group, 13 AE patients were included in the seed spot analysis to derive the descriptive features of AE initialization and the remaining 7 mandible osteoradionecrosis patients and 93 control patients were used to derive the feature-based volume constraint of mandible osteoradionecrosis. The AE regions were contoured and the corresponding dose-LET volume histograms (DLVHs) of AE regions were generated for all patients in the AE group. We selected high LET voxels (the highest 5% of each dose bin) with a range of moderate to high dose (≥∼40 Gy[RBE]) as critical voxels. Critical voxels which were contiguous with each other were grouped into clusters. Each cluster was considered as a potential independent seed spot for AE initialization. Seed spots were displayed in a 2D dose-LET plane based on their mean dose and LET to derive the descriptive features of AE initialization. A volume constraint of mandible osteoradionecrosis was then established based on the extracted features using a receiver operating characteristic curve. RESULTS The product of dose and LET (xBD) was found to be a descriptive feature of seed spots leading to AE initialization in this preliminary study. The derived xBD volume constraint for mandible osteoradionecrosis showed good performance with an area-under-curve of 0.87 (sensitivity of 0.714 and specificity of 0.807 in the leave-one-out cross validation) for the very limited patient data included in this study. CONCLUSION Our exploratory study showed that both dose and LET were observed to be important in AE initializations. The derived xBD volume constraint could predict mandible osteoradionecrosis reasonably well in the very limited H&N cancer patient data treated with PBS included in this study. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yunze Yang
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Samir H Patel
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Jidapa Bridhikitti
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - William W Wong
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Michele Y Halyard
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Lisa A McGee
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | | | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Sujay A Vora
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Tianming Liu
- Department of Computer Science, the University of Georgia, Athens, Georgia, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Mirek Fatyga
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Robert L Foote
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
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Zhang J, Liang Y, Yang C. A primary proton integral depth dose calculation model corrected with straight scattering track approximation. Radiat Phys Chem Oxf Engl 1993 2022. [DOI: 10.1016/j.radphyschem.2022.110283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Chang CW, Gao Y, Wang T, Lei Y, Wang Q, Pan S, Sudhyadhom A, Bradley JD, Liu T, Lin L, Zhou J, Yang X. Dual-energy CT based mass density and relative stopping power estimation for proton therapy using physics-informed deep learning. Phys Med Biol 2022; 67:10.1088/1361-6560/ac6ebc. [PMID: 35545078 PMCID: PMC10410526 DOI: 10.1088/1361-6560/ac6ebc] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 05/11/2022] [Indexed: 11/12/2022]
Abstract
Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim of this study is to develop a physics-informed deep learning (PIDL) framework to derive accurate mass density and relative stopping power maps from dual-energy computed tomography (DECT) images. The PIDL framework allows deep learning (DL) models to be trained with a physics loss function, which includes a physics model to constrain DL models. Five DL models were implemented including a fully connected neural network (FCNN), dual-FCNN (DFCNN), and three variants of residual networks (ResNet): ResNet-v1 (RN-v1), ResNet-v2 (RN-v2), and dual-ResNet-v2 (DRN-v2). An artificial neural network (ANN) and the five DL models trained with and without physics loss were explored to evaluate the PIDL framework. Two empirical DECT models were implemented to compare with the PIDL method. DL training data were from CIRS electron density phantom 062M (Computerized Imaging Reference Systems, Inc., Norfolk, VA). The performance of DL models was tested by CIRS adult male, adult female, and 5-year-old child anthropomorphic phantoms. For density map inference, the physics-informed RN-v2 was 3.3%, 2.9% and 1.9% more accurate than ANN for the adult male, adult female, and child phantoms. The physics-informed DRN-v2 was 0.7%, 0.6%, and 0.8% more accurate than DRN-v2 without physics training for the three phantoms, respectfully. The results indicated that physics-informed training could reduce uncertainty when ANN/DL models without physics training were insufficient to capture data structures or derived significant errors. DL models could also achieve better image noise control compared to the empirical DECT parametric mapping methods. The proposed PIDL framework can potentially improve proton range uncertainty by offering accurate material properties conversion from DECT.
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Affiliation(s)
- Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Yuan Gao
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Qian Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Shaoyan Pan
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30308, United States of America
| | - Atchar Sudhyadhom
- Department of Radiation Oncology, Harvard Medical School, Boston, MA 02115, United States of America
| | - Jeffrey D Bradley
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30308, United States of America
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11
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Wang W, Chang Y, Liu Y, Liang Z, Liao Y, Qin B, Liu X, Yang Z. Feasibility study of fast intensity-modulated proton therapy dose prediction method using deep neural networks for prostate cancer. Med Phys 2022; 49:5451-5463. [PMID: 35543109 DOI: 10.1002/mp.15702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/20/2022] [Accepted: 04/28/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Compared to the pencil-beam algorithm, the Monte-Carlo (MC) algorithm is more accurate for dose calculation but time-consuming in proton therapy. To solve this problem, this study uses deep learning to provide fast 3D dose prediction for prostate cancer patients treated with intensity-modulated proton therapy (IMPT). METHODS A novel recurrent U-net (RU-net) architecture was trained to predict the 3D dose distribution. Doses, CT images, and beam spot information from IMPT plans were used to train the RU-net with a 5-fold cross-validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the Peak-MU model, this work develops the Multi-MU model that adopted more comprehensive inputs and was trained with a combinational loss function. The dose difference between the prediction dose and MC dose was evaluated with gamma analysis, dice similarity coefficient (DSC), and dose-volume histogram (DVH) metrics. The Monte-Carlo dropout was also added to the network to quantify the uncertainty of the model. RESULTS Compared to the Peak-MU model, the Multi-MU model led to smaller mean absolute errors (3.03% vs. 2.05%, p = 0.005), higher gamma-passing rate (2mm, 3%: 97.42% vs. 93.69%, p = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (CTV D98% : 3.03% vs. 6.08%, p = 0.017; in Bladder V30: 3.08% vs. 5.28%, p = 0.028; and in Bladder V20: 3.02% vs. 4.42%, p = 0.017). Considering more prior knowledge, the Multi-MU model had better-predicted accuracy with a prediction time of less than half a second for each fold. The mean uncertainty value of the Multi-MU model is 0.46%, with a dropout rate of 10%. CONCLUSION This method was a nearly real-time IMPT dose prediction algorithm with accuracy comparable to the PB analytical algorithms used in prostate cancer. This RU-net might be used in plan robustness optimization and robustness evaluation in the future. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Wei Wang
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yu Chang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yilin Liu
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, 77030-3722, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Zhikai Liang
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yicheng Liao
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Bin Qin
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xu Liu
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Zhiyong Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
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12
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A Universal Range Shifter and Range Compensator Can Enable Proton Pencil Beam Scanning Single-Energy Bragg Peak FLASH-RT Treatment Using Current Commercially Available Proton Systems. Int J Radiat Oncol Biol Phys 2022; 113:203-213. [PMID: 35101597 DOI: 10.1016/j.ijrobp.2022.01.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 11/30/2021] [Accepted: 01/07/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE Transmission beams have been proposed for ultra-high dose (or FLASH) proton planning, limiting the organ sparing potentials of proton therapy. By pulling back the ranges of the highest energy proton beams and compensating proton ranges to adapt to the target distally, the exit dose of proton beams can be eliminated to better protect organs at risk while still preserving FLASH dose rate delivery. METHOD AND MATERIALS An inverse planning tool was developed to optimize intensity modulated proton therapy using a single-energy layer for FLASH radiation therapy planning. The range pull-backs were calculated to stop single-energy proton beams at the distal edge of the target. The spot map and weights of each field were optimized to achieve a sufficient dose rate using proton beam Bragg peaks. A C-shape target in phantom, along with 6 consecutive lung cancer patients previously treated using proton stereotactic body radiation therapy were planned using this novel Bragg Peak method and also transmission technique. Dosimetry characteristics and 3-dimensional dose rate were investigated. RESULTS The minimum monitor units (MU) for transmission and Bragg peak plans were 400 MU/spot and 1200 MU/spot, respectively, corresponding to spot peak dose rates of 670 GyRBE (relative biological effectiveness) per second and 1950 GyRBE per second. Bragg peak plans yield a generally comparable target uniformity while significantly reducing dose spillage volume from the low to medium dose level. For all the 6 lung cases delivery of 34 GyRBE in 1 fraction, assessing Radiation Therapy Oncology Group 0915 constraints, the lung V7GyRBE volume was reduced by up to 32% (P = .001) for Bragg peak plans. The transmission plans tended to generate 2.4% higher FLASH dose rate coverage (V40GyRBE/s) versus Bragg peak plans over the major organs at risk. However, Bragg peak plans could also reach the FLASH radiation therapy threshold of V40GyRBE/s using a higher MU/spot and sophisticated dose-rate optimization algorithm. CONCLUSIONS This first proof-of-concept study has demonstrated this novel method of combining range pull-back and powerful inverse optimization capable of achieving FLASH dose rate based on currently available machine parameters using a single-energy Bragg peak. Similar target coverage and uniformity can be maintained by Bragg peak FLASH plans while substantially improving the sparing of organs at risk compared with transmission plans.
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13
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Li H, Dong L, Bert C, Chang J, Flampouri S, Jee KW, Lin L, Moyers M, Mori S, Rottmann J, Tryggestad E, Vedam S. Report of AAPM Task Group 290: Respiratory motion management for particle therapy. Med Phys 2022; 49:e50-e81. [PMID: 35066871 PMCID: PMC9306777 DOI: 10.1002/mp.15470] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 12/28/2021] [Accepted: 01/05/2022] [Indexed: 11/16/2022] Open
Abstract
Dose uncertainty induced by respiratory motion remains a major concern for treating thoracic and abdominal lesions using particle beams. This Task Group report reviews the impact of tumor motion and dosimetric considerations in particle radiotherapy, current motion‐management techniques, and limitations for different particle‐beam delivery modes (i.e., passive scattering, uniform scanning, and pencil‐beam scanning). Furthermore, the report provides guidance and risk analysis for quality assurance of the motion‐management procedures to ensure consistency and accuracy, and discusses future development and emerging motion‐management strategies. This report supplements previously published AAPM report TG76, and considers aspects of motion management that are crucial to the accurate and safe delivery of particle‐beam therapy. To that end, this report produces general recommendations for commissioning and facility‐specific dosimetric characterization, motion assessment, treatment planning, active and passive motion‐management techniques, image guidance and related decision‐making, monitoring throughout therapy, and recommendations for vendors. Key among these recommendations are that: (1) facilities should perform thorough planning studies (using retrospective data) and develop standard operating procedures that address all aspects of therapy for any treatment site involving respiratory motion; (2) a risk‐based methodology should be adopted for quality management and ongoing process improvement.
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Affiliation(s)
- Heng Li
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Lei Dong
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christoph Bert
- Department of Radiation Oncology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Joe Chang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stella Flampouri
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Kyung-Wook Jee
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Liyong Lin
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Michael Moyers
- Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, China
| | - Shinichiro Mori
- Research Center for Charged Particle Therapy, National Institute of Radiological Sciences, Chiba, Japan
| | - Joerg Rottmann
- Center for Proton Therapy, Proton Therapy Singapore, Proton Therapy Pte Ltd, Singapore
| | - Erik Tryggestad
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
| | - Sastry Vedam
- Department of Radiation Oncology, University of Maryland, Baltimore, USA
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14
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Cohilis M, Hong L, Janssens G, Rossomme S, Sterpin E, Lee JA, Souris K. Development and validation of an automatic commissioning tool for the Monte Carlo dose engine in myQA iON. Phys Med 2022; 95:1-8. [PMID: 35051680 DOI: 10.1016/j.ejmp.2022.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 01/07/2022] [Accepted: 01/08/2022] [Indexed: 12/14/2022] Open
Abstract
Independent dose verification with Monte Carlo (MC) simulations is an important feature of proton therapy quality assurance (QA). However, clinical integration of such tools often generates an additional and complex workload for medical physicists. The preparation of the necessary clinical inputs, such as the machine beam model, should therefore be automated. In this work, a methodology for automatic MC commissioning has been devised, validated, and developed into a MATLAB tool for the users of myQA iON, the recent QA platform of IBA Dosimetry. With this workflow, all necessary parameters can easily be tuned using dedicated optimization methods. For the geometrical beam parameters (phase space), the assumption of a single or double Gaussian is made. To model the energy spectrum, a Gaussian function is assumed and parameters are optimized using either MC simulations or a library of pre-computed Bragg peaks. For the absolute dose calibration, commissioning fields can be reproduced with the dose engine to retrieve the necessary parameters. We discuss in a first time the tool efficiency and show that one can optimize all parameters in less than 4 min per energy with excellent accuracy. We then validate a beam model obtained with the tool by simulating homogeneous spread-out Bragg peaks (SOBPs) and patient QA plans previously measured in water. An average range agreement of 0.29 ± 0.34 mm is achieved for the SOBPs while 3%/3 mm local gamma passing rates reach 99.3% on average over all 62 measured patient QA planes, which is well within clinical tolerances.
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Affiliation(s)
- M Cohilis
- Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique (IREC), MIRO Lab, Brussels, Belgium
| | - L Hong
- University of Florida Proton Therapy Institute, Jacksonville, FL, USA
| | - G Janssens
- Ion Beam Applications, Louvain-la-Neuve, Belgium
| | - S Rossomme
- Ion Beam Applications, Louvain-la-Neuve, Belgium
| | - E Sterpin
- Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique (IREC), MIRO Lab, Brussels, Belgium; KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
| | - J A Lee
- Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique (IREC), MIRO Lab, Brussels, Belgium
| | - K Souris
- Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique (IREC), MIRO Lab, Brussels, Belgium.
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15
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Shi C, Lin H, Huang S, Xiong W, Hu L, Choi I, Press R, Hasan S, Simone C, Chhabra A. Comprehensive Evaluation of Carbon-Fiber-Reinforced Polyetheretherketone (CFR-PEEK) Spinal Hardware for Proton and Photon Planning. Technol Cancer Res Treat 2022; 21:15330338221091700. [PMID: 35410544 PMCID: PMC9009152 DOI: 10.1177/15330338221091700] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Purpose: To evaluate a novel spine implant, carbon-fiber-reinforced polyetheretherketone (CFR-PEEK), for proton and photon treatment planning. Materials and Methods: We compared target coverage and sparing of organs-at-risk (OARs) for a spinal phantom with 4 different spine configurations: (a) normal (no implant); (b) Titanium; (c) CFR-PEEK; and (d) hybrid (CFR-PEEK with Titanium tulip head). The spinal phantom was imaged via computed tomography (CT) scan, and the iterative Metal Artifact Reduction (iMAR) CT set was used for planning. A representative spinal chordoma target and associated OARs were contoured. The prescription dose was 50 Gy to the initial target volume, followed by a 24 Gy boost, for which multi-field optimization (MFO) proton plans were developed with a 3 mm setup and 3.5% range uncertainties. For photon planning, volumetric modulated arc therapy (VMAT) plans were developed for the initial and boost plans. OAR dose constraints were set according to our institutional guidelines. Results: For the 4 spine configurations, the proton plans achieved similar nominal target coverage and OARs sparing. While evaluating coverage and OAR dose under uncertainty scenario analysis for initial clinical target volume (CTV) 50 Gy 95% and 90% coverage, higher means and the narrower band of doses variations were achieved for the normal and CFR-PEEK plans. Similarly, uncertainty analysis of spinal cord Dmax showed tighter distribution for normal and CFR-PEEK plans. Overall plan quality showed no significant difference for photon planning when compared to normal spine versus other inserts. However, for proton planning, there is a larger difference for the normal spine insert scenario versus the Titanium insert scenario. For each insert scenario comparison between photon and proton plans, there was a larger difference for OARs: heart and spinal cord. Conclusion: The CFR-PEEK implant has similar clinical properties to a normal spine for proton planning, allowing us to pass protons through the material and achieve superior target coverage and OAR sparing under nominal and uncertainty conditions.
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Affiliation(s)
| | - Haibo Lin
- New York Proton Center, New York, NY, USA
| | | | | | - Lei Hu
- New York Proton Center, New York, NY, USA
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16
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Lin L, Taylor PA, Shen J, Saini J, Kang M, Simone CB, Bradley JD, Li Z, Xiao Y. NRG Oncology Survey of Monte Carlo Dose Calculation Use in US Proton Therapy Centers. Int J Part Ther 2021; 8:73-81. [PMID: 34722813 PMCID: PMC8489489 DOI: 10.14338/ijpt-d-21-00004] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/08/2021] [Indexed: 11/21/2022] Open
Abstract
Purpose/Objective(s) Monte Carlo (MC) dose calculation has appeared in primary commercial treatment-planning systems and various in-house platforms. Dual-energy computed tomography (DECT) and metal artifact reduction (MAR) techniques complement MC capabilities. However, no publications have yet reported how proton therapy centers implement these new technologies, and a national survey is required to determine the feasibility of including MC and companion techniques in cooperative group clinical trials. Materials/Methods A 9-question survey was designed to query key clinical parameters: scope of MC utilization, validation methods for heterogeneities, clinical site-specific imaging guidance, proton range uncertainties, and how implants are handled. A national survey was distributed to all 29 operational US proton therapy centers on 13 May 2019. Results We received responses from 25 centers (86% participation). Commercial MC was most commonly used for primary plan optimization (16 centers) or primary dose evaluation (18 centers), while in-house MC was used more frequently for secondary dose evaluation (7 centers). Based on the survey, MC was used infrequently for gastrointestinal, genitourinary, gynecology and extremity compared with other more heterogeneous disease sites (P < .007). Although many centers had published DECT research, only 3/25 centers had implemented DECT clinically, either in the treatment-planning system or to override implant materials. Most centers (64%) treated patients with metal implants on a case-by-case basis, with a variety of methods reported. Twenty-four centers (96%) used MAR images and overrode the surrounding tissue artifacts; however, there was no consensus on how to determine metal dimension, materials density, or stopping powers. Conclusion The use of MC for primary dose calculation and optimization was prevalent and, therefore, likely feasible for clinical trials. There was consensus to use MAR and override tissues surrounding metals but no consensus about how to use DECT and MAR for human tissues and implants. Development and standardization of these advanced technologies are strongly encouraged for vendors and clinical physicists.
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Affiliation(s)
| | | | | | - Jatinder Saini
- Seattle Cancer Care Alliance Proton Therapy Center, Seattle, WA, USA
| | | | | | | | - Zuofeng Li
- Department of Radiation Oncology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Ying Xiao
- University of Pennsylvania, Philadelphia, PA, USA
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17
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Rana S, Rosenfeld AB. Impact of proton dose calculation algorithms on the interplay effect in PBS proton based SBRT lung plans. Biomed Phys Eng Express 2021; 7. [PMID: 34029212 DOI: 10.1088/2057-1976/abfea8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/06/2021] [Indexed: 01/02/2023]
Abstract
Purpose. The purpose of the current study was to investigate the impact of RayStation analytical pencil beam (APB) and Monte Carlo (MC) algorithms on the interplay effect in pencil beam scanning (PBS) proton-based stereotactic body radiation therapy (SBRT) lung plans.Methods. The currentin-silicoplanning study was designed for a total dose of 5000 cGy(RBE) with a fractional dose of 1000 cGy(RBE). First, three sets of nominal plans were generated for each patient: (a) APB optimization followed by APB dose calculation (PB-PB), (b) APB optimization followed by MC dose calculation (PB-MC), and (c) MC optimization followed by MC dose calculation (MC-MC). Second, for each patient, two sets of volumetric repainting plans (five repaintings) - PB-MCVR5and MC-MCVR5were generated based on PB-MC and MC-MC, respectively. Dosimetric differences between APB and MC algorithms were calculated on the nominal and interplay dose-volume-histograms (DVHs).Results. Interplay evaluation in non-volumetric repainting plans showed that APB algorithm overestimated the target coverage by up to 8.4% for D95%and 10.5% for D99%, whereas in volumetric repainting plans, APB algorithm overestimated by up to 5.3% for D95%and 7.0% for D99%. Interplay results for MC calculations showed a decrease in D95%and D99%by average differences of 3.5% and 4.7%, respectively, in MC-MC plans and by 1.8% and 3.0% in MC-MCVR5plans.Conclusion. In PBS proton-based SBRT lung plans, the combination of APB algorithm and interplay effect reduced the target coverage. This may result in inferior local control. The use of MC algorithm for both optimization and final dose calculations in conjunction with the volumetric repainting technique yielded superior target coverage.
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Affiliation(s)
- Suresh Rana
- Department of Medical Physics, The Oklahoma Proton Center, Oklahoma City, OK, United States of America.,Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, FL, United States of America.,Department of Radiation Oncology, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, United States of America.,Centre for Medical Radiation Physics (CMRP), University of Wollongong, Wollongong, NSW, Australia
| | - Anatoly B Rosenfeld
- Centre for Medical Radiation Physics (CMRP), University of Wollongong, Wollongong, NSW, Australia
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Zhang X, Hu Z, Zhang G, Zhuang Y, Wang Y, Peng H. Dose calculation in proton therapy using a discovery cross-domain generative adversarial network (DiscoGAN). Med Phys 2021; 48:2646-2660. [PMID: 33594673 DOI: 10.1002/mp.14781] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 01/21/2021] [Accepted: 02/12/2021] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Accurate dose calculation is a critical step in proton therapy. A novel machine learning-based approach was proposed to achieve comparable accuracy to that of Monte Carlo simulation while reducing the computational time. METHODS Computed tomography-based patient phantoms were used and three treatment sites were selected (thorax, head, and abdomen), comprising different beam pathways and beam energies. The training data were generated using Monte Carlo simulations. A discovery cross-domain generative adversarial network (DiscoGAN) was developed to perform the mapping between two domains: stopping power and dose, with HU values from CT images incorporated as auxiliary features. The accuracy of dose calculation was quantitatively evaluated in terms of mean relative error (MRE) and mean absolute error (MAE). The relationship between the DiscoGAN performance and other factors such as absolute dose, beam energy and location within the beam cross-section (center and off-center lines) was examined. RESULTS The DiscoGAN model is found to be effective in dose calculation. For the abdominal case, the MRE is found to 1.47% (mean), 3.30% (maximum) and 0.67% (minimum). For the thoracic case, the MRE is found to ~2.43% (mean), 4.80% (maximum) and 0.71% (minimum). For the head case, the MRE is found to ~2.83% (mean), 4.84% (maximum) and 1.01% (minimum). Comparable accuracy is found in the independent validation dataset (different CT images), achieving a mean MRE of ~1.65% (thorax), 4.02% (head) and 1.64% (abdomen). For the energy span between 80 and 130 MeV, no strong dependency of accuracy on beam energy is found. The results imply that no systematic deviation, either over-dose or under-dose, occurs between the predicted dose and raw dose. CONCLUSION The DiscoGAN framework demonstrates great potential as a tool for dose calculation in proton therapy, achieving comparable accuracy yet being more efficient relative to Monte Carlo simulation. Its comparison with the pencil beam algorithm (PBA) will be the next step of our research. If successful, our proposed approach is expected to find its use in more advanced applications such as inverse planning and adaptive proton therapy.
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Affiliation(s)
- Xiaoke Zhang
- Department of Medical Physics, Wuhan University, Wuhan, 430072, China
| | - Zongsheng Hu
- Department of Medical Physics, Wuhan University, Wuhan, 430072, China
| | - Guoliang Zhang
- Department of Medical Physics, Wuhan University, Wuhan, 430072, China
| | - Yongdong Zhuang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Yuenan Wang
- Department of Radiation Oncology, Peking University Shenzhen Hospital, No. 1120, Lianhua Road, Futian District, Shenzhen, Guangdong Province, 518036, China
| | - Hao Peng
- Department of Medical Physics, Wuhan University, Wuhan, 430072, China.,ProtonSmart Ltd, Wuhan, 430072, China
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Wu C, Nguyen D, Xing Y, Montero AB, Schuemann J, Shang H, Pu Y, Jiang S. Improving Proton Dose Calculation Accuracy by Using Deep Learning. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021; 2:015017. [PMID: 35965743 PMCID: PMC9374098 DOI: 10.1088/2632-2153/abb6d5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/18/2020] [Accepted: 09/09/2020] [Indexed: 12/28/2022] Open
Abstract
Introduction Pencil beam (PB) dose calculation is fast but inaccurate due to the approximations when dealing with inhomogeneities. Monte Carlo (MC) dose calculation is the most accurate method but it is time consuming. The aim of this study was to develop a deep learning model that can boost the accuracy of PB dose calculation to the level of MC dose by converting PB dose to MC dose for different tumor sites. Methods The proposed model uses the PB dose and CT image as inputs to generate the MC dose. We used 290 patients (90 head and neck, 93 liver, 75 prostate and 32 lung) to train, validate, and test the model. For each tumor site, we performed four numerical experiments to explore various combinations of training datasets. Results Training the model on data from all tumor sites together and using the dose distribution of each individual beam as input yielded the best performance for all four tumor sites. The average gamma passing rate (1mm/1%) between the converted and the MC dose was 92.8%, 92.7%, 89.7% and 99.6% for head and neck, liver, lung, and prostate test patients, respectively. The average dose conversion time for a single field was less than 4 seconds. The trained model can be adapted to new datasets through transfer learning. Conclusions Our deep learning-based approach can quickly boost the accuracy of PB dose to that of MC dose. The developed model can be added to the clinical workflow of proton treatment planning to improve dose calculation accuracy.
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Affiliation(s)
- Chao Wu
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Yixun Xing
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Ana Barragan Montero
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
- Molecular Imaging Radiation Oncology (MIRO) Laboratory, UCLouvain, Brussels, Belgium
| | - Jan Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
| | - Haijiao Shang
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Yuehu Pu
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
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Liu C, Zheng D, Bradley JA, Mailhot Vega RB, Zhang Y, Indelicato DJ, Mendenhall N, Liang X. Incorporation of the LETd-weighted biological dose in the evaluation of breast intensity-modulated proton therapy plans. Acta Oncol 2021; 60:252-259. [PMID: 33063569 DOI: 10.1080/0284186x.2020.1834141] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
PURPOSE To evaluate the LETd-weighted biological dose to OARs in proton therapy for breast cancer and to study the relationship of the LETd-weighted biological dose relative to the standard dose (RBE = 1.1) and thereby to provide estimations of the biological dose uncertainties with the standard dose calculations (RBE = 1.1) commonly used in clinical practice. METHOD This study included 20 patients who received IMPT treatment to the whole breast/chest wall and regional lymph nodes. The LETd distributions were calculated along with the physical dose using an open-source Monte Carlo simulation package, MCsquare. Using the McMahon linear model, the LETd-weighted biological dose was computed from the physical dose and LETd. OAR doses were compared between the Dose (RBE = 1.1) and the LETd-weighted biological dose, on brachial plexus, rib, heart, esophagus, and Ipsilateral lung. RESULTS On average, the LETd-weighted biological dose compared to the Dose (RBE = 1.1) was higher by 8% for the brachial plexus D0.1 cc, 13% for the ribs D0.5 cc, 24% for mean heart dose, and 10% for the esophagus D0.1 cc, respectively. The LETd-weighted doses to the Ipsilateral lung V5, V10, and V20 were comparable to the Dose (RBE = 1.1). No statistically significant difference in biological dose enhancement to OARs was observed between the intact breast group and the CW group, with the exception of the ribs: the CW group experienced slightly greater biological dose enhancement (13% vs. 12%, p = 0.04) to the ribs than the intact breast group. CONCLUSION Enhanced biological dose was observed compared to standard dose with assumed RBE of 1.1 for the heart, ribs, esophagus, and brachial plexus in breast/CW and regional nodal IMPT plans. Variable RBE models should be considered in the evaluation of the IMPT breast plans, especially for OARs located near the end of range of a proton beam. Clinical outcome studies are needed to validate model predictions for clinical toxicities.
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Affiliation(s)
- Chunbo Liu
- Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, FL, USA
- School of Physical Sciences, University of Science and Technology of China, Hefei, China
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Julie A. Bradley
- Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Raymond B. Mailhot Vega
- Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Yawei Zhang
- Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Daniel J. Indelicato
- Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Nancy Mendenhall
- Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Xiaoying Liang
- Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, FL, USA
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21
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Liu C, Ho MW, Park J, Hsi WC, Liang X, Li Z, Song Y, Feng H, Zhang Y. Fast MCsquare-Based Independent Dose Verification Platform for Pencil Beam Scanning Proton Therapy. Technol Cancer Res Treat 2021; 20:15330338211033076. [PMID: 34338058 PMCID: PMC8326813 DOI: 10.1177/15330338211033076] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 05/17/2021] [Accepted: 06/11/2021] [Indexed: 11/16/2022] Open
Abstract
PURPOSE To commission MCsquare (a multi-cores CPU-based dose calculation engine) for pencil beam scanning (PBS) proton therapy, integrate it into RayStation treatment plan system (TPS) to create a dedicated platform for fast independent dose verification. METHOD A MCsquare-based independent dose verification platform (MC2InRS) was developed to realize automatic dose re-calculation for clinical use, including data preparation, dose calculation, 2D/3D gamma analysis. MCsquare was commissioned based on in-air lateral dose profiles, integrated depth dose, and the absolute dose of different beam energies for Proteus®ONE. MC2InRS was validated with measurement data using various targets and depths in a water phantom. This study also investigated 15 clinical cases to demonstrate the feasibility and effectiveness of MC2InRS platform in clinic practice. RESULTS Between simulation and measurement, the distal range differences at 80% (R80) and 20% (R20) dose levels for each energy were below 0.05 mm, and 0.1 mm, respectively, and the absolute dose differences were below 0.5%. 29 out of 36 QA planes reached a 100% gamma passing rate (GPR) for 2%/2mm criteria, and a minimum of 98.3% gamma was obtained in water phantom between simulation and measurement. For the 15 clinical cases investigated, the average 2D GPR (2%/2mm) was 95.4%, 99.3% for MCsquare vs. measurement, MCsquare vs. TPS, respectively. The average 3D GPR (2%/2mm) was 98.9%, 95.3% for MCsquare vs. TPS in water, and computed tomography (CT), respectively. CONCLUSION MC2InRS, a fast, independent dose verification platform, has been developed to perform dose verification with high accuracy and efficiency for Pencil Bream Scanning (PBS). Its potential to be applied in routine clinical practice has also been discussed.
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Affiliation(s)
- Chunbo Liu
- School of Physical Sciences, University of Science and Technology of China, Hefei, Anhui, China
- University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
| | - Meng Wei Ho
- University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
- Department of Radiation Oncology, University of Florida, Gainesville, FL, USA
| | - Jiyeon Park
- University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
- Department of Radiation Oncology, University of Florida, Gainesville, FL, USA
| | - Wen Chien Hsi
- University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
- Department of Radiation Oncology, University of Florida, Gainesville, FL, USA
| | - Xiaoying Liang
- University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
- Department of Radiation Oncology, University of Florida, Gainesville, FL, USA
| | - Zuofeng Li
- University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
- Department of Radiation Oncology, University of Florida, Gainesville, FL, USA
| | - Yuntao Song
- School of Physical Sciences, University of Science and Technology of China, Hefei, Anhui, China
- Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Hansheng Feng
- Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Yawei Zhang
- University of Florida Health Proton Therapy Institute, Jacksonville, FL, USA
- Department of Radiation Oncology, University of Florida, Gainesville, FL, USA
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22
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Shang C, Evans G, Rahman M, Lin L. Beam characteristics of the first clinical 360° rotational single gantry room scanning pencil beam proton treatment system and comparisons against a multi‐room system. J Appl Clin Med Phys 2020; 21:266-271. [PMID: 32790244 PMCID: PMC7497910 DOI: 10.1002/acm2.12984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 06/21/2020] [Accepted: 06/23/2020] [Indexed: 11/07/2022] Open
Affiliation(s)
- Charles Shang
- South Florida Proton Therapy Institute Delray Beach FL USA
| | - Grant Evans
- South Florida Proton Therapy Institute Delray Beach FL USA
| | | | - Liyong Lin
- Emory Proton Therapy Center Atlanta GA USA
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23
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Deng W, Younkin JE, Souris K, Huang S, Augustine K, Fatyga M, Ding X, Cohilis M, Bues M, Shan J, Stoker J, Lin L, Shen J, Liu W. Technical Note: Integrating an open source Monte Carlo code "MCsquare" for clinical use in intensity-modulated proton therapy. Med Phys 2020; 47:2558-2574. [PMID: 32153029 DOI: 10.1002/mp.14125] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 02/27/2020] [Accepted: 02/27/2020] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To commission an open source Monte Carlo (MC) dose engine, "MCsquare" for a synchrotron-based proton machine, integrate it into our in-house C++-based I/O user interface and our web-based software platform, expand its functionalities, and improve calculation efficiency for intensity-modulated proton therapy (IMPT). METHODS We commissioned MCsquare using a double Gaussian beam model based on in-air lateral profiles, integrated depth dose of 97 beam energies, and measurements of various spread-out Bragg peaks (SOBPs). Then we integrated MCsquare into our C++-based dose calculation code and web-based second check platform "DOSeCHECK." We validated the commissioned MCsquare based on 12 different patient geometries and compared the dose calculation with a well-benchmarked GPU-accelerated MC (gMC) dose engine. We further improved the MCsquare efficiency by employing the computed tomography (CT) resampling approach. We also expanded its functionality by adding a linear energy transfer (LET)-related model-dependent biological dose calculation. RESULTS Differences between MCsquare calculations and SOBP measurements were <2.5% (<1.5% for ~85% of measurements) in water. The dose distributions calculated using MCsquare agreed well with the results calculated using gMC in patient geometries. The average 3D gamma analysis (2%/2 mm) passing rates comparing MCsquare and gMC calculations in the 12 patient geometries were 98.0 ± 1.0%. The computation time to calculate one IMPT plan in patients' geometries using an inexpensive CPU workstation (Intel Xeon E5-2680 2.50 GHz) was 2.3 ± 1.8 min after the variable resolution technique was adopted. All calculations except for one craniospinal patient were finished within 3.5 min. CONCLUSIONS MCsquare was successfully commissioned for a synchrotron-based proton beam therapy delivery system and integrated into our web-based second check platform. After adopting CT resampling and implementing LET model-dependent biological dose calculation capabilities, MCsquare will be sufficiently efficient and powerful to achieve Monte Carlo-based and LET-guided robust optimization in IMPT, which will be done in the future studies.
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Affiliation(s)
- Wei Deng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - James E Younkin
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Kevin Souris
- Center for Molecular Imaging and Experimental Radiotherapy, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, 1200, Brussels, Belgium
| | - Sheng Huang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kurt Augustine
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Mirek Fatyga
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Xiaoning Ding
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Marie Cohilis
- Center for Molecular Imaging and Experimental Radiotherapy, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, 1200, Brussels, Belgium
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Jie Shan
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Joshua Stoker
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Liyong Lin
- Emory Proton Therapy Center, Emory University, Atlanta, GA, USA
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
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24
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Faddegon B, Ramos-Méndez J, Schuemann J, McNamara A, Shin J, Perl J, Paganetti H. The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research. Phys Med 2020; 72:114-121. [PMID: 32247964 DOI: 10.1016/j.ejmp.2020.03.019] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 02/06/2020] [Accepted: 03/19/2020] [Indexed: 01/02/2023] Open
Abstract
PURPOSE This paper covers recent developments and applications of the TOPAS TOol for PArticle Simulation and presents the approaches used to disseminate TOPAS. MATERIALS AND METHODS Fundamental understanding of radiotherapy and imaging is greatly facilitated through accurate and detailed simulation of the passage of ionizing radiation through apparatus and into a patient using Monte Carlo (MC). TOPAS brings Geant4, a reliable, experimentally validated MC tool mainly developed for high energy physics, within easy reach of medical physicists, radiobiologists and clinicians. Requiring no programming knowledge, TOPAS provides all of the flexibility of Geant4. RESULTS After 5 years of development followed by its initial release, TOPAS was subsequently expanded from its focus on proton therapy physics to incorporate radiobiology modeling. Next, in 2018, the developers expanded their user support and code maintenance as well as the scope of TOPAS towards supporting X-ray and electron therapy and medical imaging. Improvements have been achieved in user enhancement through software engineering and a graphical user interface, calculational efficiency, validation through experimental benchmarks and QA measurements, and either newly available or recently published applications. A large and rapidly increasing user base demonstrates success in our approach to dissemination of this uniquely accessible and flexible MC research tool. CONCLUSIONS The TOPAS developers continue to make strides in addressing the needs of the medical community in applications of ionizing radiation to medicine, creating the only fully integrated platform for four-dimensional simulation of all forms of radiotherapy and imaging with ionizing radiation, with a design that promotes inter-institutional collaboration.
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Affiliation(s)
- Bruce Faddegon
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
| | - José Ramos-Méndez
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Jan Schuemann
- Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Aimee McNamara
- Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Jungwook Shin
- Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Joseph Perl
- SLAC National Accelerator Laboratory, Menlo Park, USA
| | - Harald Paganetti
- Massachusetts General Hospital and Harvard Medical School, Boston, USA
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25
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Kazemifar S, Barragán Montero AM, Souris K, Rivas ST, Timmerman R, Park YK, Jiang S, Geets X, Sterpin E, Owrangi A. Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors. J Appl Clin Med Phys 2020; 21:76-86. [PMID: 32216098 PMCID: PMC7286008 DOI: 10.1002/acm2.12856] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 02/10/2020] [Accepted: 02/14/2020] [Indexed: 12/23/2022] Open
Abstract
PURPOSE The purpose of this study was to address the dosimetric accuracy of synthetic computed tomography (sCT) images of patients with brain tumor generated using a modified generative adversarial network (GAN) method, for their use in magnetic resonance imaging (MRI)-only treatment planning for proton therapy. METHODS Dose volume histogram (DVH) analysis was performed on CT and sCT images of patients with brain tumor for plans generated for intensity-modulated proton therapy (IMPT). All plans were robustly optimized using a commercially available treatment planning system (RayStation, from RaySearch Laboratories) and standard robust parameters reported in the literature. The IMPT plan was then used to compute the dose on CT and sCT images for dosimetric comparison, using RayStation analytical (pencil beam) dose algorithm. We used a second, independent Monte Carlo dose calculation engine to recompute the dose on both CT and sCT images to ensure a proper analysis of the dosimetric accuracy of the sCT images. RESULTS The results extracted from RayStation showed excellent agreement for most DVH metrics computed on the CT and sCT for the nominal case, with a mean absolute difference below 0.5% (0.3 Gy) of the prescription dose for the clinical target volume (CTV) and below 2% (1.2 Gy) for the organs at risk (OARs) considered. This demonstrates a high dosimetric accuracy for the generated sCT images, especially in the target volume. The metrics obtained from the Monte Carlo doses mostly agreed with the values extracted from RayStation for the nominal and worst-case scenarios (mean difference below 3%). CONCLUSIONS This work demonstrated the feasibility of using sCT generated with a GAN-based deep learning method for MRI-only treatment planning of patients with brain tumor in intensity-modulated proton therapy.
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Affiliation(s)
- Samaneh Kazemifar
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ana M Barragán Montero
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium
| | - Kevin Souris
- Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium
| | - Sara T Rivas
- Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium
| | - Robert Timmerman
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yang K Park
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xavier Geets
- Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium.,Department of Radiation Oncology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Edmond Sterpin
- Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université catholique de Louvain, Brussels, Belgium.,Department of Oncology, Laboratory of Experimental Radiotherapy, KULeuven, Leuven, Belgium
| | - Amir Owrangi
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Chang CW, Huang S, Harms J, Zhou J, Zhang R, Dhabaan A, Slopsema R, Kang M, Liu T, McDonald M, Langen K, Lin L. A standardized commissioning framework of Monte Carlo dose calculation algorithms for proton pencil beam scanning treatment planning systems. Med Phys 2020; 47:1545-1557. [PMID: 31945191 DOI: 10.1002/mp.14021] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 01/03/2020] [Accepted: 01/04/2020] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Treatment planning systems (TPSs) from different vendors can involve different implementations of Monte Carlo dose calculation (MCDC) algorithms for pencil beam scanning (PBS) proton therapy. There are currently no guidelines for validating non-water materials in TPSs. Furthermore, PBS-specific parameters can vary by 1-2 orders of magnitude among different treatment delivery systems (TDSs). This paper proposes a standardized framework on the use of commissioning data and steps to validate TDS-specific parameters and TPS-specific heterogeneity modeling to potentially reduce these uncertainties. METHODS A standardized commissioning framework was developed to commission the MCDC algorithms of RayStation 8A and Eclipse AcurosPT v13.7.20 using water and non-water materials. Measurements included Bragg peak depth-dose and lateral spot profiles and scanning field outputs for Varian ProBeam. The phase-space parameters were obtained from in-air measurements and the number of protons per MU from output measurements of 10 × 10 cm2 square fields at a 2 cm depth. Spot profiles and various PBS field measurements at additional depths were used to validate TPS. Human tissues in TPS, Gammex phantom materials, and artificial materials were used for the TPS benchmark and validation. RESULTS The maximum differences of phase parameters, spot sigma, and divergence between MCDC algorithms are below 4.5 µm and 0.26 mrad in air, respectively. Comparing TPS to measurements at depths, both MC algorithms predict the spot sigma within 0.5 mm uncertainty intervals, the resolution of the measurement device. Beam Configuration in AcurosPT is found to underestimate number of protons per MU by ~2.5% and requires user adjustment to match measured data, while RayStation is within 1% of measurements using Auto model. A solid water phantom was used to validate the range accuracy of non-water materials within 1% in AcurosPT. CONCLUSIONS The proposed standardized commissioning framework can detect potential issues during PBS TPS MCDC commissioning processes, and potentially can shorten commissioning time and improve dosimetric accuracies. Secondary MCDC can be used to identify the root sources of disagreement between primary MCDC and measurement.
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Affiliation(s)
- Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Sheng Huang
- Memorial Sloan Kettering Cancer Center, New York City, NY, 10065, USA
| | - Joseph Harms
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Rongxiao Zhang
- Department of Radiation Oncology, Dartmouth College, Hanover, NH, USA
| | - Anees Dhabaan
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Roelf Slopsema
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Minglei Kang
- New York Proton Center, New York, NY, 10035, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Mark McDonald
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Katja Langen
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
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Wagenaar D, Tran LT, Meijers A, Marmitt GG, Souris K, Bolst D, James B, Biasi G, Povoli M, Kok A, Traneus E, van Goethem MJ, Langendijk JA, Rosenfeld AB, Both S. Validation of linear energy transfer computed in a Monte Carlo dose engine of a commercial treatment planning system. Phys Med Biol 2020; 65:025006. [PMID: 31801119 DOI: 10.1088/1361-6560/ab5e97] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
The relative biological effectiveness (RBE) of protons is highly variable and difficult to quantify. However, RBE is related to the local ionization density, which can be related to the physical measurable dose weighted linear energy transfer (LETD). The aim of this study was to validate the LETD calculations for proton therapy beams implemented in a commercially available treatment planning system (TPS) using microdosimetry measurements and independent LETD calculations (Open-MCsquare (MCS)). The TPS (RayStation v6R) was used to generate treatment plans on the CIRS-731-HN anthropomorphic phantom for three anatomical sites (brain, nasopharynx, neck) for a spherical target (Ø = 5 cm) with uniform target dose to calculate the LETD distribution. Measurements were performed at the University Medical Center Groningen proton therapy center (Proteus Plus, IBA) using a µ +-probe utilizing silicon on insulator microdosimeters capable of detecting lineal energies as low as 0.15 keV µm-1 in tissue. Dose averaged mean lineal energy [Formula: see text] depth-profiles were measured for 70 and 130 MeV spots in water and for the three treatment plans in water and an anthropomorphic phantom. The [Formula: see text] measurements were compared to the LETD calculated in the TPS and MCS independent dose calculation engine. D · [Formula: see text] was compared to D · LETD in terms of a gamma-index with a distance-to-agreement criteria of 2 mm and increasing dose difference criteria to determine the criteria for which a 90% pass rate was accomplished. Measurements of D · [Formula: see text] were in good agreement with the D · LETD calculated in the TPS and MCS. The 90% passing rate threshold was reached at different D · LETD difference criteria for single spots (TPS: 1% MCS: 1%), treatment plans in water (TPS: 3% MCS: 6%) and treatment plans in an anthropomorphic phantom (TPS: 6% MCS: 1%). We conclude that D · LETD calculations accuracy in the RayStation TPS and open MCSquare are within 6%, and sufficient for clinical D · LETD evaluation and optimization. These findings remove an important obstacle in the road towards clinical implementation of D · LETD evaluation and optimization of proton therapy treatment plans. Novelty and significance The dose weighed linear energy transfer (LETD) distribution can be calculated for proton therapy treatment plans by Monte Carlo dose engines. The relative biological effectiveness (RBE) of protons is known to vary with the LETD distribution. Therefore, there exists a need for accurate calculation of clinical LETD distributions. Previous LETD validations have focused on general purpose Monte Carlo dose engines which are typically not used clinically. We present the first validation of mean lineal energy [Formula: see text] measurements of the LETD against calculations by the Monte Carlo dose engines of the Raystation treatment planning system and open MCSquare.
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Affiliation(s)
- Dirk Wagenaar
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Author to whom any correspondence should be addressed
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28
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Han Y. Current status of proton therapy techniques for lung cancer. Radiat Oncol J 2019; 37:232-248. [PMID: 31918460 PMCID: PMC6952710 DOI: 10.3857/roj.2019.00633] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 12/26/2019] [Indexed: 12/11/2022] Open
Abstract
Proton beams have been used for cancer treatment for more than 28 years, and several technological advancements have been made to achieve improved clinical outcomes by delivering more accurate and conformal doses to the target cancer cells while minimizing the dose to normal tissues. The state-of-the-art intensity modulated proton therapy is now prevailing as a major treatment technique in proton facilities worldwide, but still faces many challenges in being applied to the lung. Thus, in this article, the current status of proton therapy technique is reviewed and issues regarding the relevant uncertainty in proton therapy in the lung are summarized.
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Affiliation(s)
- Youngyih Han
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
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29
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Cohilis M, Sterpin E, Lee JA, Souris K. A noise correction of the
γ
‐index method for Monte Carlo dose distribution comparison. Med Phys 2019; 47:681-692. [DOI: 10.1002/mp.13888] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 09/19/2019] [Accepted: 10/04/2019] [Indexed: 11/11/2022] Open
Affiliation(s)
- Marie Cohilis
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC) Université catholique de Louvain 1200 Brussels Belgium
| | - Edmond Sterpin
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC) Université catholique de Louvain 1200 Brussels Belgium
- Department of Oncology, Laboratory of Experimental Radiotherapy Katholieke Universiteit Leuven 3000 Leuven Belgium
| | - John A. Lee
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC) Université catholique de Louvain 1200 Brussels Belgium
| | - Kevin Souris
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC) Université catholique de Louvain 1200 Brussels Belgium
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30
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Kostiukhina N, Palmans H, Stock M, Georg D, Knäusl B. Dynamic lung phantom commissioning for 4D dose assessment in proton therapy. Phys Med Biol 2019; 64:235001. [PMID: 31652424 DOI: 10.1088/1361-6560/ab5132] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Anthropomorphic phantoms mimicking organ and tumor motion of patients are essential for end-to-end testing of motion mitigation techniques in ion beam therapy. In this work a commissioning procedure developed with the in-house designed respiratory phantom ARDOS (Advanced Radiation DOSimetry system) is presented. The phantom was tested and benchmarked for 4D dose verification in proton therapy, which included: characterization of the tissue equivalent materials from computed tomography (CT) imaging, assessment of dose calculation accuracy in critical structures of the phantom, and testing various detectors for proton dosimetry in the ARDOS phantom. To prove the validity of the CT calibration curve, measured relative stopping powers (RSP) of the ARDOS materials were compared with values from CTs: original and overwritten with known material parameters. Override of rib- and soft-tissue phantom components improved RSP accuracy while inhomogeneous lung tissue, represented by the balsa wood, was better modelled by the CT Hounsfield units. Monte Carlo (MC) dose calculations were benchmarked against measurements with a reference Farmer chamber embedded in ARDOS material samples showing less than 3% relative dose difference. Differences between MC calculated dose distributions and those calculated by analytical algorithms for the ARDOS geometry were higher than 20% of the prescribed dose, depending on the position in the phantom. Pinpoint ionization chambers and thermoluminescence dosimeters showed differences of up to 5.5% compared to MC dose calculations for all lung setups in the static phantom. They were also able to detect dose distortions due to motion. EBT3 film dosimetry was shown to be suitable for 2D relative dose characterization, which could provide extended information on dose distributions in the penumbra area. The presented methodology and results can be used for drafting general recommendations for dynamic phantom commissioning, which is an essential step towards end-to-end evaluation of motion mitigation techniques in ion beam therapy.
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Affiliation(s)
- N Kostiukhina
- Department of Radiation Oncology, Division Medical Radiation Physics, Medical University of Vienna/AKH Vienna, Vienna, Austria. Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria. Author to whom correspondence should be addressed
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31
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Schreuder AN, Bridges DS, Rigsby L, Blakey M, Janson M, Hedrick SG, Wilkinson JB. Validation of the RayStation Monte Carlo dose calculation algorithm using realistic animal tissue phantoms. J Appl Clin Med Phys 2019; 20:160-171. [PMID: 31541536 PMCID: PMC6806482 DOI: 10.1002/acm2.12733] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 07/29/2019] [Accepted: 08/12/2019] [Indexed: 01/23/2023] Open
Abstract
PURPOSE The aim of this study is to validate the RayStation Monte Carlo (MC) dose algorithm using animal tissue neck phantoms and a water breast phantom. METHODS Three anthropomorphic phantoms were used in a clinical setting to test the RayStation MC dose algorithm. We used two real animal necks that were cut to a workable shape while frozen and then thawed before being CT scanned. Secondly, we made a patient breast phantom using a breast prosthesis filled with water and placed on a flat surface. Dose distributions in the animal and breast phantoms were measured using the MatriXX PT device. RESULTS The measured doses to the neck and breast phantoms compared exceptionally well with doses calculated by the analytical pencil beam (APB) and MC algorithms. The comparisons between APB and MC dose calculations and MatriXX PT measurements yielded an average depth difference for best gamma agreement of <1 mm for the neck phantoms. For the breast phantom better average gamma pass rates between measured and calculated dose distributions were observed for the MC than for the APB algorithms. CONCLUSIONS The MC dose calculations are more accurate than the APB calculations for the static phantoms conditions we evaluated, especially in areas where significant inhomogeneous interfaces are traversed by the beam.
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Affiliation(s)
| | | | - Lauren Rigsby
- Provision Center for Proton Therapy – KnoxvilleKnoxvilleTNUSA
| | - Marc Blakey
- Provision Center for Proton Therapy – KnoxvilleKnoxvilleTNUSA
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