1
|
Tseng W, Liu H, Yang Y, Liu C, Furutani K, Beltran C, Lu B. Performance assessment of variant UNet-based deep-learning dose engines for MR-Linac-based prostate IMRT plans. Phys Med Biol 2023; 68:175004. [PMID: 37499682 DOI: 10.1088/1361-6560/aceb2c] [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: 03/10/2023] [Accepted: 07/27/2023] [Indexed: 07/29/2023]
Abstract
Objective. UNet-based deep-learning (DL) architectures are promising dose engines for traditional linear accelerator (Linac) models. Current UNet-based engines, however, were designed differently with various strategies, making it challenging to fairly compare the results from different studies. The objective of this study is to thoroughly evaluate the performance of UNet-based models on magnetic-resonance (MR)-Linac-based intensity-modulated radiation therapy (IMRT) dose calculations.Approach. The UNet-based models, including the standard-UNet, cascaded-UNet, dense-dilated-UNet, residual-UNet, HD-UNet, and attention-aware-UNet, were implemented. The model input is patient CT and IMRT field dose in water, and the output is patient dose calculated by DL model. The reference dose was calculated by the Monaco Monte Carlo module. Twenty training and ten test cases of prostate patients were included. The accuracy of the DL-calculated doses was measured using gamma analysis, and the calculation efficiency was evaluated by inference time.Results. All the studied models effectively corrected low-accuracy doses in water to high-accuracy patient doses in a magnetic field. The gamma passing rates between reference and DL-calculated doses were over 86% (1%/1 mm), 98% (2%/2 mm), and 99% (3%/3 mm) for all the models. The inference times ranged from 0.03 (graphics processing unit) to 7.5 (central processing unit) seconds. Each model demonstrated different strengths in calculation accuracy and efficiency; Res-UNet achieved the highest accuracy, HD-UNet offered high accuracy with the fewest parameters but the longest inference, dense-dilated-UNet was consistently accurate regardless of model levels, standard-UNet had the shortest inference but relatively lower accuracy, and the others showed average performance. Therefore, the best-performing model would depend on the specific clinical needs and available computational resources.Significance. The feasibility of using common UNet-based models for MR-Linac-based dose calculations has been explored in this study. By using the same model input type, patient training data, and computing environment, a fair assessment of the models' performance was present.
Collapse
Affiliation(s)
- Wenchih Tseng
- Department of Radiation Oncology, University of Florida, Gainesville, FL 32610-0385, United States of America
| | - Hongcheng Liu
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611-6595, United States of America
| | - Yu Yang
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611-6595, United States of America
| | - Chihray Liu
- Department of Radiation Oncology, University of Florida, Gainesville, FL 32610-0385, United States of America
| | - Keith Furutani
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224-0001, United States of America
| | - Chris Beltran
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224-0001, United States of America
| | - Bo Lu
- Department of Radiation Oncology, University of Florida, Gainesville, FL 32610-0385, United States of America
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224-0001, United States of America
| |
Collapse
|
2
|
Babapour H, Semyari S, Yadollahi M, Majdaeen M, Abedi-Firouzjah R, Ataei G. Assessing the Effect of Directional Bremsstrahlung Splitting on the Output Spectra and Parameters Using BEAMnrc Monte Carlo Simulation Package. Biomed Eng Comput Biol 2022; 13:11795972221138473. [PMCID: PMC9716629 DOI: 10.1177/11795972221138473] [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: 08/18/2022] [Accepted: 10/26/2022] [Indexed: 12/05/2022] Open
Abstract
Introduction: EGSnrc software package is one of the computational packages for Monte Carlo simulation in radiation therapy and has several subset codes. Directional bremsstrahlung splitting (DBS) is a technique that applies braking radiations in interactions in this software. This study aimed to evaluate the effect of this technique on the simulation time, uncertainty, particle number of phase-space data, and photon beam spectrum resulting from a medical linear accelerator (LINAC). Materials and methods: The gantry of the accelerator, including the materials and geometries of different parts, was simulated using the BEAMnrc code (a subset code in the EGSnrc package). The phase-space data were recorded in different parts of the LINAC. The DBS values (1, 10, 100, and 1000) were changed, and their effects were evaluated on the simulation parameters and output spectra. Results: Increasing the DBS value from 1 to 1000 resulted in an increase in the simulation time from 1.778 to 11.310 hours, and increasing the number of particles in the phase-space plane (5 590 732-180 328 382). When the DBS had been picked up from 1 to 100, the simulation uncertainty decreased by about 1.29%. In addition, the DBS increment value from 100 to 1000 leads to an increase in uncertainty and simulation time of about 0.71% and 315%, respectively. Conclusion: Although using the DBS technique reduces the simulation time or uncertainty, increasing the DBS from a specific value, equal to 100 in our study, increases simulation uncertainties and times. Therefore, we propose considering a specific DBS value as we obtained for the Monte Carlo simulation of photon beams produced by linear accelerators.
Collapse
Affiliation(s)
- Hamed Babapour
- Department of Radiotherapy and Oncology, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | - Somayeh Semyari
- Department of Physic, Imam Khomeini International University, Qazvin, Iran
| | - Masoumeh Yadollahi
- Department of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran
| | - Mehrsa Majdaeen
- Department of Radiotherapy and Oncology, Razi Hospital, Guilan University of Medical Sciences, Rasht, Iran
| | | | - Gholamreza Ataei
- Department of Radiology Technology, Faculty of Paramedical Sciences, Babol University of Medical Science, Babol, Iran,Gholamreza Ataei, Department of Radiology Technology, Faculty of Paramedical Sciences, Babol University of Medical Sciences, Babol, 47176-47745, Iran.
| |
Collapse
|
3
|
Chiuyo J, Lugendo I, Muhogora W. Determination of dose distributions by monte-carlo simulation of 6 MV photon beam of varian vitalbeam accelerator using geant4 multithreaded code. J Med Phys 2022; 47:181-188. [PMID: 36212206 PMCID: PMC9543005 DOI: 10.4103/jmp.jmp_139_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/03/2022] [Accepted: 02/22/2022] [Indexed: 11/11/2022] Open
Abstract
Background: Accuracy of dose delivery in radiation therapy is a primary requirement for effective cancer treatment. In practice, dose delivery accuracy of ±5% is desired. To achieve this accuracy level, an accurate method for calculating the dose distributions in the tumor volume is required. Monte-Carlo method is one of the methods considered to be the most accurate for calculating dose distributions. Materials and Methods: G4 linac-MT code was used to simulate a 6 MV photon beam. The initial electron beam parameters were tuned to validate the beam modeling from depth doses and beam profile. The dose distributions measured in water phantom were compared to the calculated dose distributions based on gamma index criterion. Results: The beam tuning showed the initial electron energy, sigma and full width at half maximum of 6.2 MeV, 0.8 MeV, and 1.18 mm, respectively, best match the measured dose distributions. The gamma index tests showed the calculated depth doses and beam profile were generally comparable with measurements, passing the standard acceptance criterion of 2%/2 mm. The simulated photon beam was justified by the index of beam quality, which showed excellent agreement with measured doses with a discrepancy of 0.1%. Conclusion: The observed agreement confirm the accuracy of the simulated 6 MV photon beam. It can therefore be used as radiation source for calculating dose distributions and further investigations aimed at improving dose delivery and planning in cancer patients.
Collapse
|
4
|
Amato E, Gnesin S, Cicone F, Auditore L. Fundamentals of internal radiation dosimetry. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00142-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
|
5
|
Auditore L, Pistone D, Amato E, Italiano A. Monte Carlo methods in nuclear medicine. Nucl Med Mol Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00136-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
|
6
|
Ibáñez P, Villa-Abaunza A, Vidal M, Guerra P, Graullera S, Illana C, Udías JM. XIORT-MC: A real-time MC-based dose computation tool for low- energy X-rays intraoperative radiation therapy. Med Phys 2021; 48:8089-8106. [PMID: 34658039 DOI: 10.1002/mp.15291] [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] [Received: 01/28/2021] [Revised: 09/20/2021] [Accepted: 10/06/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The INTRABEAM system is a miniature accelerator for low-energy X-ray Intra-Operative Radiation Therapy (IORT), and it could benefit from a fast and accurate dose computation tool. With regards to accuracy, dose computed with Monte Carlo (MC) simulations are the gold standard, however, they require a large computational effort and consequently they are not suitable for real-time dose planning. This work presents a comparison of the implementation on Graphics Processing Unit (GPU) of two different dose calculation algorithms based on MC phase-space (PHSP) information to compute dose distributions for the INTRABEAM device within seconds and with the accuracy of realistic MC simulations. METHODS The MC-based algorithms we present incorporate photoelectric, Compton and Rayleigh effects for the interaction of low-energy X-rays. XIORT-MC (X-ray Intra-Operative Radiation Therapy Monte Carlo) includes two dose calculation algorithms; a Woodcock-based MC algorithm (WC-MC) and a Hybrid MC algorithm (HMC), and it is implemented in CPU and in GPU. Detailed MC simulations have been generated to validate our tool in homogeneous and heterogeneous conditions with all INTRABEAM applicators, including three clinically realistic CT-based simulations. A performance study has been done to determine the acceleration reached with the code, in both CPU and GPU implementations. RESULTS Dose distributions were obtained with the HMC and the WC-MC and compared to standard reference MC simulations with more than 95% voxels fulfilling a 7%-0.5 mm gamma evaluation in all the cases considered. The CPU-HMC is 100 times more efficient than the reference MC, and the CPU-WC-MC is about 50 times more efficient. With the GPU implementation, the particle tracking of the WC-MC is faster than the HMC, with the extraction of the particle's information from the PHSP file taking a major part of the time. However, thanks to the variance reduction techniques implemented in the HMC, up to 400 times less particles are needed in the HMC to reach the same level of noise than the WC-MC. Therefore, in our implementation for INTRABEAM energies, the HMC is about 1.3 times more efficient than the WC-MC in an NVIDIA GeForce GTX 1080 Ti card and about 5.5 times more efficient in an NVIDIA GeForce RTX 3090. Dose with noise below 5% has been obtained in realistic situations in less than 5 s with the WC-MC and in less than 0.5 s with the HMC. CONCLUSIONS The XIORT-MC is a dose computation tool designed to take full advantage of modern GPUs, making possible to obtain MC-grade accurate dose distributions within seconds. Its high speed allows a real-time dose calculation that includes the realistic effects of the beam in voxelized geometries of patients. It can be used as a dose-planning tool in the operating room during a XIORT treatment with any INTRABEAM device.
Collapse
Affiliation(s)
- Paula Ibáñez
- Nuclear Physics Group, EMFTEL and IPARCOS, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain.,Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain
| | - Amaia Villa-Abaunza
- Nuclear Physics Group, EMFTEL and IPARCOS, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain
| | - Marie Vidal
- Nuclear Physics Group, EMFTEL and IPARCOS, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain.,Department of Radiotherapy, Centre Antoine-Lacassagne, Nice, France
| | - Pedro Guerra
- Department of Electronic Engineering, ETSIT, CEI Moncloa, Universidad Politécnica de Madrid, Madrid, Spain.,Biomedical Research Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.,Tres Cantos, MedLumics S.L., Madrid, Spain
| | | | | | - José Manuel Udías
- Nuclear Physics Group, EMFTEL and IPARCOS, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain.,Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain
| |
Collapse
|
7
|
Aamri H, Fielding A, Aamry A, Sulieman A, Tamam N, Alkhorayef M, Bradley DA. Comparison between PRIMO and EGSnrc Monte Carlo models of the Varian True Beam linear accelerator. Radiat Phys Chem Oxf Engl 1993 2021. [DOI: 10.1016/j.radphyschem.2020.109013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
8
|
RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning. Int J Radiat Oncol Biol Phys 2020; 108:802-812. [DOI: 10.1016/j.ijrobp.2020.04.045] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 04/23/2020] [Accepted: 04/30/2020] [Indexed: 12/20/2022]
|
9
|
Mirzapour M, Hadad K, Faghihi R, Hamilton RJ, Watchman CJ. Fast Monte-Carlo Photon Transport Employing GPU-Based Parallel Computation. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020. [DOI: 10.1109/trpms.2020.2972202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
10
|
Ghareeb F, Esposito A, Lencart J, Santos JA. Localized extra focal dose collimator angle dependence during VMAT: An out-of-field Monte Carlo study using PRIMO software. Radiat Phys Chem Oxf Engl 1993 2020. [DOI: 10.1016/j.radphyschem.2020.108694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
11
|
McNair H. Image guided radiotherapy moving towards real time adaptive radiotherapy; global positioning system for radiotherapy? Tech Innov Patient Support Radiat Oncol 2019; 12:1-2. [PMID: 32095548 PMCID: PMC7033764 DOI: 10.1016/j.tipsro.2019.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
|
12
|
Ghareeb F, Lencart J, Oliveira J, Santos JAM. Characterization of Extrafocal Dose Influence on the Out-of-Field Dose Distribution by Monte Carlo Simulations and Dose Measurements. HEALTH PHYSICS 2019; 117:489-503. [PMID: 31033708 DOI: 10.1097/hp.0000000000001079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Out-of-field scattered and transmitted extrafocal radiation may induce secondary cancer in long-term survivors of external radiotherapy. Pediatric patients have higher life expectancy and tend to receive higher secondary radiation damage due to geometric and biological factors. The goal of this study is to characterize the location and the magnitude of extrafocal dose regions in the case of three-dimensional conformal radiotherapy and volumetric arc therapy, to apply this information to clinical treatment cases, and to provide mitigation strategies. Extrafocal dose has been investigated in a Varian TrueBeam linac equipped with a high-definition 120 multileaf collimator using different physical and virtual phantoms, dose calculation (including Monte Carlo techniques), and dose measurement methods. All Monte Carlo calculations showed excellent agreement with measurements. Treatment planning system calculations failed to provide reliable results out of the treatment field. Both Monte Carlo calculations and dose measurements showed regions with higher dose (extrafocal dose areas) when compared to the background. These areas start to be noticeable beyond 11 cm from the isocenter in the direction perpendicular to the multileaf collimator leaves' travel direction. Out-of-field extrafocal doses up to 160% of the mean dose transmitted through the closed multileaf collimator were registered. Two overlapping components were observed in the extrafocal distribution: the first is an almost elliptical blurred dose distribution, and the second is a well-defined rectangular dose distribution. Extra precautions should be taken into consideration when treating pediatric patients with a high-definition 120 multileaf collimator to avoid directing the extrafocal radiation into a radiosensitive organ during external beam therapy.
Collapse
Affiliation(s)
- Firass Ghareeb
- Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), Porto, Portugal
| | - Joana Lencart
- Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), Porto, Portugal
- Medical Physics Department, Portuguese Oncology Institute of Porto (IPO Porto), Porto, Portugal
| | - Jorge Oliveira
- Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), Porto, Portugal
| | - João A M Santos
- Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), Porto, Portugal
- Medical Physics Department, Portuguese Oncology Institute of Porto (IPO Porto), Porto, Portugal
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), University of Porto, Porto, Portugal
| |
Collapse
|
13
|
SIMU-RAD programme: a learning tool for radiation (photons and charged particles) interaction. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2019. [DOI: 10.2478/pjmpe-2019-0025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Radiation education is necessary for a wide variety of people, such as radiation workers particularly for students of secondary school and higher education institution who learn radiation sciences. The fact that we could not see or feel radiation makes it difficult to understand it. The use of radiation trajectories shown on a personal computer should be useful to overcome this difficulty. In order to understand radiation behaviour inside the material, we have developed a Simu-Rad (Copyright: LY2018002738) by using Monte Carlo simulation programme. One who has no programming knowledge is able to simulate photons in a material through the developed programme. The program could become a computer aided learning tool for radiation related courses. We aim to facilitate lecturer from ‘The Traditional Classroom’ to ‘The Flipped Classroom’ for radiation education concerning in the era of IR 4.0. To validate our radiation simulator, we calculate photon linear attenuation coefficient (µ) of an aluminium material which commonly used as a filter in diagnostic radiology. µ is one of the main characteristics to understand how the radiation attenuated inside the materials. We calculate at energy photon of 662 keV (Cs-137 radiation source) to compare our results of µ with the XCOM database. Consequently, the results from the developed simulator comparable with the database verified our programme to be used for radiation study.
Collapse
|
14
|
Maqsudur Rashid A, Ramalingam L, Al-Jawadi A, Moustaid-Moussa N, Moussa H. Low dose radiation, inflammation, cancer and chemoprevention. Int J Radiat Biol 2018; 95:506-515. [DOI: 10.1080/09553002.2018.1484194] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Al Maqsudur Rashid
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA
| | - Latha Ramalingam
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, USA
- Obesity Research Cluster, Texas Tech University, Lubbock, TX, USA
| | - Arwa Al-Jawadi
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, USA
- Obesity Research Cluster, Texas Tech University, Lubbock, TX, USA
| | - Naima Moustaid-Moussa
- Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, USA
- Obesity Research Cluster, Texas Tech University, Lubbock, TX, USA
| | - Hanna Moussa
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA
- Obesity Research Cluster, Texas Tech University, Lubbock, TX, USA
| |
Collapse
|
15
|
Doerner E, Caprile P. Technical Note: An hybrid parallel implementation for EGSnrc Monte Carlo user codes. Med Phys 2018; 45:3969-3973. [PMID: 29870055 DOI: 10.1002/mp.13033] [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: 03/05/2018] [Revised: 05/09/2018] [Accepted: 05/26/2018] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The purpose of this study was to present a parallel solution for the EGSnrc Monte Carlo code system combining MPI and OpenMP programming models as an alternative to the provided implementation, based on the use of a batch-queueing system (BQS). METHODS Relying on a previous implementation based on OpenMP by E. Doerner and P. Caprile [Med. Phys. 44, 6672 (2017)], this work incorporates MPI features to efficiently distribute the simulation on current high-performance computing (HPC) systems. These features are introduced through properly defined macros, which are enabled depending on the compilation flags given by the user. The presented solution was benchmarked using the DOSXYZnrc code for a 6 MV clinical photon beam impinging on an homogeneous water phantom. RESULTS The platform validation against the serial run results confirmed that the introduction of new features does not modify the final dose distribution. The performance tests indicated that the new implementation was able to handle efficiently the workload distribution among the computing units available. Using all the resources available, the hybrid simulation was 10% faster than the MPI only solution and 30% faster than the BQS implementation. CONCLUSIONS The hybrid method presented is a viable solution to parallelize MC simulations using the EGSnrc codes in distributed computing systems in an simple and efficient way, taking advantage of the available resources and giving the user the possibility of choosing between different parallelization schemes (only OpenMP/MPI or a combination of both).
Collapse
Affiliation(s)
- Edgardo Doerner
- Institute of Physics, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago, 7820436, Chile
| | - Paola Caprile
- Institute of Physics, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago, 7820436, Chile
| |
Collapse
|
16
|
Barragán Montero AM, Souris K, Sanchez-Parcerisa D, Sterpin E, Lee JA. Performance of a hybrid Monte Carlo-Pencil Beam dose algorithm for proton therapy inverse planning. Med Phys 2017; 45:846-862. [PMID: 29159915 DOI: 10.1002/mp.12688] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 11/09/2017] [Accepted: 11/12/2017] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Analytical algorithms have a limited accuracy when modeling very heterogeneous tumor sites. This work addresses the performance of a hybrid dose optimizer that combines both Monte Carlo (MC) and pencil beam (PB) dose engines to get the best trade-off between speed and accuracy for proton therapy plans. METHODS The hybrid algorithm calculates the optimal spot weights (w) by means of an iterative optimization process where the dose at each iteration is computed by using a precomputed dose influence matrix based on the conventional PB plus a correction term c obtained from a MC simulation. Updates of c can be triggered as often as necessary by calling the MC dose engine with the last corrected values of w as input. In order to analyze the performance of the hybrid algorithm against dose calculation errors, it was applied to a simplistic water phantom for which several test cases with different errors were simulated, including proton range uncertainties. Afterwards, the algorithm was used in three clinical cases (prostate, lung, and brain) and benchmarked against full MC-based optimization. The influence of different stopping criteria in the final results was also investigated. RESULTS The hybrid algorithm achieved excellent results provided that the estimated range in a homogeneous material is the same for the two dose engines involved, i.e., PB and MC. For the three patient cases, the hybrid plans were clinically equivalent to those obtained with full MC-based optimization. Only a single update of c was needed in the hybrid algorithm to fulfill the clinical dose constraints, which represents an extra computation time to obtain c that ranged from 1 (brain) to 4 min (lung) with respect to the conventional PB-based optimization, and an estimated average gain factor of 14 with respect to full MC-based optimization. CONCLUSION The hybrid algorithm provides an improved trade-off between accuracy and speed. This algorithm can be immediately considered as an option for improving dose calculation accuracy of commercial analytical treatment planning systems, without a significant increase in the computation time (≪5 min) with respect to current PB-based optimization.
Collapse
Affiliation(s)
- Ana María Barragán Montero
- Université catholique de Louvain, Institut de Recherche Exp érimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Kevin Souris
- Université catholique de Louvain, Institut de Recherche Exp érimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Daniel Sanchez-Parcerisa
- Facultad de Ciencias Físicas, Departamento de Física Atómica, UCM - Universidad Complutense de Madrid, Grupo de Física Nuclear, Molecular y Nuclear, CEI Moncloa, Madrid, Spain
| | - Edmond Sterpin
- Université catholique de Louvain, Institut de Recherche Exp érimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium.,KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
| | - John Aldo Lee
- Université catholique de Louvain, Institut de Recherche Exp érimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| |
Collapse
|
17
|
Menten MJ, Wetscherek A, Fast MF. MRI-guided lung SBRT: Present and future developments. Phys Med 2017; 44:139-149. [PMID: 28242140 DOI: 10.1016/j.ejmp.2017.02.003] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 01/25/2017] [Accepted: 02/07/2017] [Indexed: 12/25/2022] Open
Abstract
Stereotactic body radiotherapy (SBRT) is rapidly becoming an alternative to surgery for the treatment of early-stage non-small cell lung cancer patients. Lung SBRT is administered in a hypo-fractionated, conformal manner, delivering high doses to the target. To avoid normal-tissue toxicity, it is crucial to limit the exposure of nearby healthy organs-at-risk (OAR). Current image-guided radiotherapy strategies for lung SBRT are mostly based on X-ray imaging modalities. Although still in its infancy, magnetic resonance imaging (MRI) guidance for lung SBRT is not exposure-limited and MRI promises to improve crucial soft-tissue contrast. Looking beyond anatomical imaging, functional MRI is expected to inform treatment decisions and adaptations in the future. This review summarises and discusses how MRI could be advantageous to the different links of the radiotherapy treatment chain for lung SBRT: diagnosis and staging, tumour and OAR delineation, treatment planning, and inter- or intrafractional motion management. Special emphasis is placed on a new generation of hybrid MRI treatment devices and their potential for real-time adaptive radiotherapy.
Collapse
Affiliation(s)
- Martin J Menten
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
| | - Andreas Wetscherek
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Martin F Fast
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
| |
Collapse
|
18
|
Ziegenhein P, Kozin IN, Kamerling CP, Oelfke U. Towards real-time photon Monte Carlo dose calculation in the cloud. Phys Med Biol 2017; 62:4375-4389. [PMID: 28141583 DOI: 10.1088/1361-6560/aa5d4e] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Near real-time application of Monte Carlo (MC) dose calculation in clinic and research is hindered by the long computational runtimes of established software. Currently, fast MC software solutions are available utilising accelerators such as graphical processing units (GPUs) or clusters based on central processing units (CPUs). Both platforms are expensive in terms of purchase costs and maintenance and, in case of the GPU, provide only limited scalability. In this work we propose a cloud-based MC solution, which offers high scalability of accurate photon dose calculations. The MC simulations run on a private virtual supercomputer that is formed in the cloud. Computational resources can be provisioned dynamically at low cost without upfront investment in expensive hardware. A client-server software solution has been developed which controls the simulations and transports data to and from the cloud efficiently and securely. The client application integrates seamlessly into a treatment planning system. It runs the MC simulation workflow automatically and securely exchanges simulation data with the server side application that controls the virtual supercomputer. Advanced encryption standards were used to add an additional security layer, which encrypts and decrypts patient data on-the-fly at the processor register level. We could show that our cloud-based MC framework enables near real-time dose computation. It delivers excellent linear scaling for high-resolution datasets with absolute runtimes of 1.1 seconds to 10.9 seconds for simulating a clinical prostate and liver case up to 1% statistical uncertainty. The computation runtimes include the transportation of data to and from the cloud as well as process scheduling and synchronisation overhead. Cloud-based MC simulations offer a fast, affordable and easily accessible alternative for near real-time accurate dose calculations to currently used GPU or cluster solutions.
Collapse
Affiliation(s)
- Peter Ziegenhein
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, SM2 5NG, United Kingdom
| | | | | | | |
Collapse
|
19
|
Expert system classifier for adaptive radiation therapy in prostate cancer. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2017; 40:337-348. [PMID: 28290067 DOI: 10.1007/s13246-017-0535-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 02/09/2017] [Indexed: 10/20/2022]
Abstract
A classifier-based expert system was developed to compare delivered and planned radiation therapy in prostate cancer patients. Its aim is to automatically identify patients that can benefit from an adaptive treatment strategy. The study predominantly addresses dosimetric uncertainties and critical issues caused by motion of hollow organs. 1200 MVCT images of 38 prostate adenocarcinoma cases were analyzed. An automatic daily re-contouring of structures (i.e. rectum, bladder and femoral heads), rigid/deformable registration and dose warping was carried out to simulate dose and volume variations during therapy. Support vector machine, K-means clustering algorithms and similarity index analysis were used to create an unsupervised predictive tool to detect incorrect setup and/or morphological changes as a consequence of inadequate patient preparation due to stochastic physiological changes, supporting clinical decision-making. After training on a dataset that was considered sufficiently dosimetrically stable, the system identified two equally sized macro clusters with distinctly different volumetric and dosimetric baseline properties and defined thresholds for these two clusters. Application to the test cohort resulted in 25% of the patients located outside the two macro clusters thresholds and which were therefore suspected to be dosimetrically unstable. In these patients, over the treatment course, mean volumetric changes of 30 and 40% for rectum and bladder were detected which possibly represents values justifying adjustment of patient preparation, frequent re-planning or a plan-of-the-day strategy. Based on our research, by combining daily IGRT images with rigid/deformable registration and dose warping, it is possible to apply a machine learning approach to the clinical setting obtaining useful information for a decision regarding an individualized adaptive strategy. Especially for treatments influenced by the movement of hollow organs, this could reduce inadequate treatments and possibly reduce toxicity, thereby increasing overall RT efficacy.
Collapse
|
20
|
Souris K, Lee JA, Sterpin E. Fast multipurpose Monte Carlo simulation for proton therapy using multi- and many-core CPU architectures. Med Phys 2016; 43:1700. [PMID: 27036568 DOI: 10.1118/1.4943377] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Accuracy in proton therapy treatment planning can be improved using Monte Carlo (MC) simulations. However the long computation time of such methods hinders their use in clinical routine. This work aims to develop a fast multipurpose Monte Carlo simulation tool for proton therapy using massively parallel central processing unit (CPU) architectures. METHODS A new Monte Carlo, called MCsquare (many-core Monte Carlo), has been designed and optimized for the last generation of Intel Xeon processors and Intel Xeon Phi coprocessors. These massively parallel architectures offer the flexibility and the computational power suitable to MC methods. The class-II condensed history algorithm of MCsquare provides a fast and yet accurate method of simulating heavy charged particles such as protons, deuterons, and alphas inside voxelized geometries. Hard ionizations, with energy losses above a user-specified threshold, are simulated individually while soft events are regrouped in a multiple scattering theory. Elastic and inelastic nuclear interactions are sampled from ICRU 63 differential cross sections, thereby allowing for the computation of prompt gamma emission profiles. MCsquare has been benchmarked with the gate/geant4 Monte Carlo application for homogeneous and heterogeneous geometries. RESULTS Comparisons with gate/geant4 for various geometries show deviations within 2%-1 mm. In spite of the limited memory bandwidth of the coprocessor simulation time is below 25 s for 10(7) primary 200 MeV protons in average soft tissues using all Xeon Phi and CPU resources embedded in a single desktop unit. CONCLUSIONS MCsquare exploits the flexibility of CPU architectures to provide a multipurpose MC simulation tool. Optimized code enables the use of accurate MC calculation within a reasonable computation time, adequate for clinical practice. MCsquare also simulates prompt gamma emission and can thus be used also for in vivo range verification.
Collapse
Affiliation(s)
- Kevin Souris
- Center for Molecular Imaging and Experimental Radiotherapy, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Avenue Hippocrate 54, 1200 Brussels, Belgium and ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve 1348, Belgium
| | - John Aldo Lee
- Center for Molecular Imaging and Experimental Radiotherapy, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Avenue Hippocrate 54, 1200 Brussels, Belgium and ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve 1348, Belgium
| | - Edmond Sterpin
- Center for Molecular Imaging and Experimental Radiotherapy, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Avenue Hippocrate 54, 1200 Brussels, Belgium and Department of Oncology, Katholieke Universiteit Leuven, O&N I Herestraat 49, 3000 Leuven, Belgium
| |
Collapse
|
21
|
Kamerling CP, Fast MF, Ziegenhein P, Menten MJ, Nill S, Oelfke U. Real-time 4D dose reconstruction for tracked dynamic MLC deliveries for lung SBRT. Med Phys 2016; 43:6072. [PMID: 27806589 PMCID: PMC5965366 DOI: 10.1118/1.4965045] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 08/26/2016] [Accepted: 10/05/2016] [Indexed: 12/25/2022] Open
Abstract
PURPOSE This study provides a proof of concept for real-time 4D dose reconstruction for lung stereotactic body radiation therapy (SBRT) with multileaf collimator (MLC) tracking and assesses the impact of tumor tracking on the size of target margins. METHODS The authors have implemented real-time 4D dose reconstruction by connecting their tracking and delivery software to an Agility MLC at an Elekta Synergy linac and to their in-house treatment planning software (TPS). Actual MLC apertures and (simulated) target positions are reported to the TPS every 40 ms. The dose is calculated in real-time from 4DCT data directly after each reported aperture by utilization of precalculated dose-influence data based on a Monte Carlo algorithm. The dose is accumulated onto the peak-exhale (reference) phase using energy-mass transfer mapping. To investigate the impact of a potentially reducible safety margin, the authors have created and delivered treatment plans designed for a conventional internal target volume (ITV) + 5 mm, a midventilation approach, and three tracking scenarios for four lung SBRT patients. For the tracking plans, a moving target volume (MTV) was established by delineating the gross target volume (GTV) on every 4DCT phase. These were rigidly aligned to the reference phase, resulting in a unified maximum GTV to which a 1, 3, or 5 mm isotropic margin was added. All scenarios were planned for 9-beam step-and-shoot IMRT to meet the criteria of RTOG 1021 (3 × 18 Gy). The GTV 3D center-of-volume shift varied from 6 to 14 mm. RESULTS Real-time dose reconstruction at 25 Hz could be realized on a single workstation due to the highly efficient implementation of dose calculation and dose accumulation. Decreased PTV margins resulted in inadequate target coverage during untracked deliveries for patients with substantial tumor motion. MLC tracking could ensure the GTV target dose for these patients. Organ-at-risk (OAR) doses were consistently reduced by decreased PTV margins. The tracked MTV + 1 mm deliveries resulted in the following OAR dose reductions: lung V20 up to 3.5%, spinal cord D2 up to 0.9 Gy/Fx, and proximal airways D2 up to 1.4 Gy/Fx. CONCLUSIONS The authors could show that for patient data at clinical resolution and realistic motion conditions, the delivered dose could be reconstructed in 4D for the whole lung volume in real-time. The dose distributions show that reduced margins yield lower doses to healthy tissue, whilst target dose can be maintained using dynamic MLC tracking.
Collapse
Affiliation(s)
- Cornelis Ph Kamerling
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, United Kingdom
| | - Martin F Fast
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, United Kingdom
| | - Peter Ziegenhein
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, United Kingdom
| | - Martin J Menten
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, United Kingdom
| | - Simeon Nill
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, United Kingdom
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, United Kingdom
| |
Collapse
|
22
|
Guidi G, Maffei N, Meduri B, D'Angelo E, Mistretta GM, Ceroni P, Ciarmatori A, Bernabei A, Maggi S, Cardinali M, Morabito VE, Rosica F, Malara S, Savini A, Orlandi G, D'Ugo C, Bunkheila F, Bono M, Lappi S, Blasi C, Lohr F, Costi T. A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation. Phys Med 2016; 32:1659-1666. [PMID: 27765457 DOI: 10.1016/j.ejmp.2016.10.005] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 09/23/2016] [Accepted: 10/01/2016] [Indexed: 01/29/2023] Open
Abstract
PURPOSE To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset. MATERIALS AND METHODS 90 patients (pts) treated for head-neck cancer (H&N) formed a multicenter data-set. 41 H&N pts (45.6%) were considered for learning; 49 pts (54.4%) were used to test the tool. A homemade machine-learning classifier was developed to analyze volume and dose variations of parotid glands (PG). Using deformable image registration (DIR) and GPU, patients' conditions were analyzed automatically. Support Vector Machines (SVM) was used for time-series evaluation. "Inadequate" class identified patients that might benefit from replanning. Double-blind evaluation by two radiation oncologists (ROs) was carried out to validate day/week selected for re-planning by the classifier. RESULTS The cohort was affected by PG mean reduction of 23.7±8.8%. During the first 3weeks, 86.7% cases show PG deformation aligned with predefined tolerance, thus not requiring re-planning. From 4th week, an increased number of pts would potentially benefit from re-planning: a mean of 58% of cases, with an inter-center variability of 8.3%, showed "inadequate" conditions. 11% of cases showed "bias" due to DIR and script failure; 6% showed "warning" output due to potential positioning issues. Comparing re-planning suggested by tool with recommended by ROs, the 4th week seems the most favorable time in 70% cases. CONCLUSIONS SVM and decision-making tool was applied to overcome ART challenges. Pts would benefit from ART and ideal time for re-planning intervention was identified in this retrospective analysis.
Collapse
Affiliation(s)
- G Guidi
- Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy; Physics Department, Alma Mater Studiorum University of Bologna, Italy.
| | - N Maffei
- Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy
| | - B Meduri
- Radiation Oncology Department, Az. Ospedaliero Universitaria di Modena, Italy
| | - E D'Angelo
- Radiation Oncology Department, Az. Ospedaliero Universitaria di Modena, Italy
| | - G M Mistretta
- Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy
| | - P Ceroni
- Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy
| | - A Ciarmatori
- Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy; Radiotherapy Unit, Altnagelvin Hospital, Londonderry, United Kingdom
| | - A Bernabei
- Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy
| | - S Maggi
- Medical Physics Department, Az.Ospedaliero-Universitaria Ospedale Riuniti di Ancona, Italy
| | - M Cardinali
- Radiation Oncology Department, Az.Ospedaliero-Universitaria Ospedale Riuniti di Ancona, Italy
| | - V E Morabito
- Medical Physics Department, Az.Ospedaliero-Universitaria Ospedale Riuniti di Ancona, Italy
| | - F Rosica
- Medical Physics Department, AUSL4 Teramo, Italy
| | - S Malara
- Radiation Oncology Department, AUSL4 Teramo, Italy
| | - A Savini
- Medical Physics Department, AUSL4 Teramo, Italy
| | - G Orlandi
- Medical Physics Department, AUSL4 Teramo, Italy
| | - C D'Ugo
- Radiation Oncology Department, AUSL4 Teramo, Italy
| | - F Bunkheila
- Radiation Oncology Department, Az.Osp.Ospedali Riuniti Marche Nord di Pesaro, Italy
| | - M Bono
- Medical Physics Department, Az.Osp.Ospedali Riuniti Marche Nord di Pesaro, Italy
| | - S Lappi
- Medical Physics Department, Az.Osp.Ospedali Riuniti Marche Nord di Pesaro, Italy
| | - C Blasi
- Radiation Oncology Department, Az.Osp.Ospedali Riuniti Marche Nord di Pesaro, Italy
| | - F Lohr
- Radiation Oncology Department, Az. Ospedaliero Universitaria di Modena, Italy
| | - T Costi
- Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy
| |
Collapse
|
23
|
Fast MF, Kamerling CP, Ziegenhein P, Menten MJ, Bedford JL, Nill S, Oelfke U. Assessment of MLC tracking performance during hypofractionated prostate radiotherapy using real-time dose reconstruction. Phys Med Biol 2016; 61:1546-62. [PMID: 26816273 PMCID: PMC5390952 DOI: 10.1088/0031-9155/61/4/1546] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 11/25/2015] [Accepted: 12/16/2015] [Indexed: 11/16/2022]
Abstract
By adapting to the actual patient anatomy during treatment, tracked multi-leaf collimator (MLC) treatment deliveries offer an opportunity for margin reduction and healthy tissue sparing. This is assumed to be especially relevant for hypofractionated protocols in which intrafractional motion does not easily average out. In order to confidently deliver tracked treatments with potentially reduced margins, it is necessary to monitor not only the patient anatomy but also the actually delivered dose during irradiation. In this study, we present a novel real-time online dose reconstruction tool which calculates actually delivered dose based on pre-calculated dose influence data in less than 10 ms at a rate of 25 Hz. Using this tool we investigate the impact of clinical target volume (CTV) to planning target volume (PTV) margins on CTV coverage and organ-at-risk dose. On our research linear accelerator, a set of four different CTV-to-PTV margins were tested for three patient cases subject to four different motion conditions. Based on this data, we can conclude that tracking eliminates dose cold spots which can occur in the CTV during conventional deliveries even for the smallest CTV-to-PTV margin of 1 mm. Changes of organ-at-risk dose do occur frequently during MLC tracking and are not negligible in some cases. Intrafractional dose reconstruction is expected to become an important element in any attempt of re-planning the treatment plan during the delivery based on the observed anatomy of the day.
Collapse
Affiliation(s)
- M F Fast
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, UK
| | - C P Kamerling
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, UK
| | - P Ziegenhein
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, UK
| | - M J Menten
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, UK
| | - J L Bedford
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, UK
| | - S Nill
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, UK
| | - U Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5NG, UK
| |
Collapse
|