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Shi M, Cui S, Chuang C, Oderinde O, Kovalchuk N, Surucu M, Xing L, Han B. A time- and space-saving Monte Carlo simulation method using post-collimation generative adversarial network for dose calculation of an O-ring gantry Linac. Phys Med 2024; 119:103318. [PMID: 38382210 DOI: 10.1016/j.ejmp.2024.103318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/06/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024] Open
Abstract
PURPOSE This study explores the feasibility of employing Generative Adversarial Networks (GANs) to model the RefleXion X1 Linac. The aim is to investigate the accuracy of dose simulation and assess the potential computational benefits. METHODS The X1 Linac is a new radiotherapy machine with a binary multi-leaf collimation (MLC) system, facilitating innovative biology-guided radiotherapy. A total of 34 GAN generators, each representing a desired MLC aperture, were developed. Each generator was trained using a phase space file generated underneath the corresponding aperture, enabling the generation of particles and serving as a beam source for Monte Carlo simulation. Dose distributions in water were simulated for each aperture using both the GAN and phase space sources. The agreement between dose distributions was evaluated. The computational time reduction from bypassing the collimation simulation and storage space savings were estimated. RESULTS The percentage depth dose at 10 cm, penumbra, and full-width half maximum of the GAN simulation agree with the phase space simulation, with differences of 0.4 % ± 0.2 %, 0.32 ± 0.66 mm, and 0.26 ± 0.44 mm, respectively. The gamma passing rate (1 %/1mm) for the planar dose exceeded 90 % for all apertures. The estimated time-saving for simulating an plan using 5766 beamlets was 530 CPU hours. The storage usage was reduced by a factor of 102. CONCLUSION The utilization of the GAN in simulating the X1 Linac demonstrated remarkable accuracy and efficiency. The reductions in both computational time and storage requirements make this approach highly valuable for future dosimetry studies and beam modeling.
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Affiliation(s)
- Mengying Shi
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA; Department of Radiation Oncology, University of California, Irvine, Orange, CA, USA.
| | - Sunan Cui
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA; Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Cynthia Chuang
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | | | - Nataliya Kovalchuk
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | - Murat Surucu
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | - Bin Han
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
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Herraiz JL, Lopez-Montes A, Badal A. MCGPU-PET: An Open-Source Real-Time Monte Carlo PET Simulator. COMPUTER PHYSICS COMMUNICATIONS 2024; 296:109008. [PMID: 38145286 PMCID: PMC10735232 DOI: 10.1016/j.cpc.2023.109008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
Monte Carlo (MC) simulations are commonly used to model the emission, transmission, and/or detection of radiation in Positron Emission Tomography (PET). In this work, we introduce a new open-source MC software for PET simulation, MCGPU-PET, which has been designed to fully exploit the computing capabilities of modern GPUs to simulate the acquisition of more than 100 million coincidences per second from voxelized sources and material distributions. The new simulator is an extension of the PENELOPE-based MCGPU code previously used in cone-beam CT and mammography applications. We validated the accuracy of the accelerated code by comparing it to GATE and PeneloPET simulations achieving an agreement within 10 percent approximately. As an example application of the code for fast estimation of PET coincidences, a scan of the NEMA IQ phantom was simulated. A fully 3D sinogram with 6382 million true coincidences and 731 million scatter coincidences was generated in 54 seconds in one GPU. MCGPU-PET provides an estimation of true and scatter coincidences and spurious background (for positron-gamma emitters such as 124I) at a rate 3 orders of magnitude faster than CPU-based MC simulators. This significant speed-up enables the use of the code for accurate scatter and prompt-gamma background estimations within an iterative image reconstruction process.
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Affiliation(s)
- Joaquin L. Herraiz
- Complutense University of Madrid, EMFTEL, Grupo de Física Nuclear and IPARCOS, Madrid, 28040, Spain
- Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdiSSC), Madrid,28040, Spain
| | - Alejandro Lopez-Montes
- Complutense University of Madrid, EMFTEL, Grupo de Física Nuclear and IPARCOS, Madrid, 28040, Spain
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, United States of America
| | - Andreu Badal
- DIDSR, OSEL, CDRH, US Food and Drug Administration, Silver Spring, MD, 20993, USA
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3
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Auer B, Könik A, Fromme TJ, De Beenhouwer J, Kalluri KS, Lindsay C, Furenlid LR, Kuo PH, King MA. Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction. Phys Med Biol 2023; 68:10.1088/1361-6560/acbde2. [PMID: 36808915 PMCID: PMC10073298 DOI: 10.1088/1361-6560/acbde2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 02/21/2023] [Indexed: 02/23/2023]
Abstract
Objective.Monte-Carlo simulation studies have been essential for advancing various developments in single photon emission computed tomography (SPECT) imaging, such as system design and accurate image reconstruction. Among the simulation software available, Geant4 application for tomographic emission (GATE) is one of the most used simulation toolkits in nuclear medicine, which allows building systems and attenuation phantom geometries based on the combination of idealized volumes. However, these idealized volumes are inadequate for modeling free-form shape components of such geometries. Recent GATE versions alleviate these major limitations by allowing users to import triangulated surface meshes.Approach.In this study, we describe our mesh-based simulations of a next-generation multi-pinhole SPECT system dedicated to clinical brain imaging, called AdaptiSPECT-C. To simulate realistic imaging data, we incorporated in our simulation the XCAT phantom, which provides an advanced anatomical description of the human body. An additional challenge with the AdaptiSPECT-C geometry is that the default voxelized XCAT attenuation phantom was not usable in our simulation due to intersection of objects of dissimilar materials caused by overlap of the air containing regions of the XCAT beyond the surface of the phantom and the components of the imaging system.Main results.We validated our mesh-based modeling against the one constructed by idealized volumes for a simplified single vertex configuration of AdaptiSPECT-C through simulated projection data of123I-activity distributions. We resolved the overlap conflict by creating and incorporating a mesh-based attenuation phantom following a volume hierarchy. We then evaluated our reconstructions with attenuation and scatter correction for projections obtained from simulation consisting of mesh-based modeling of the system and the attenuation phantom for brain imaging. Our approach demonstrated similar performance as the reference scheme simulated in air for uniform and clinical-like123I-IMP brain perfusion source distributions.Significance.This work enables the simulation of complex SPECT acquisitions and reconstructions for emulating realistic imaging data close to those of actual patients.
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Affiliation(s)
- Benjamin Auer
- University of Massachusetts Chan Medical School, Department of Radiology, Worcester, MA, 01655, United States of America
- Brigham and Women's Hospital, Department of Radiology, Boston, MA, 02215, United States of America
| | - Arda Könik
- Dana-Farber Cancer Institute, Department of Imaging, Boston, MA, 02215, United States of America
| | - Timothy J Fromme
- Worcester Polytechnic Institute, Worcester, MA, 01609, United States of America
| | | | - Kesava S Kalluri
- University of Massachusetts Chan Medical School, Department of Radiology, Worcester, MA, 01655, United States of America
| | - Clifford Lindsay
- University of Massachusetts Chan Medical School, Department of Radiology, Worcester, MA, 01655, United States of America
| | - Lars R Furenlid
- James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, , United States of America
| | - Philip H Kuo
- Department of Medical Imaging, University of Arizona, Tucson, AZ, 85724, United States of America
| | - Michael A King
- University of Massachusetts Chan Medical School, Department of Radiology, Worcester, MA, 01655, United States of America
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4
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Sarrut D, Arbor N, Baudier T, Borys D, Etxebeste A, Fuchs H, Gajewski J, Grevillot L, Jan S, Kagadis GC, Kang HG, Kirov A, Kochebina O, Krzemien W, Lomax A, Papadimitroulas P, Pommranz C, Roncali E, Rucinski A, Winterhalter C, Maigne L. The OpenGATE ecosystem for Monte Carlo simulation in medical physics. Phys Med Biol 2022; 67:10.1088/1361-6560/ac8c83. [PMID: 36001985 PMCID: PMC11149651 DOI: 10.1088/1361-6560/ac8c83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/24/2022] [Indexed: 11/12/2022]
Abstract
This paper reviews the ecosystem of GATE, an open-source Monte Carlo toolkit for medical physics. Based on the shoulders of Geant4, the principal modules (geometry, physics, scorers) are described with brief descriptions of some key concepts (Volume, Actors, Digitizer). The main source code repositories are detailed together with the automated compilation and tests processes (Continuous Integration). We then described how the OpenGATE collaboration managed the collaborative development of about one hundred developers during almost 20 years. The impact of GATE on medical physics and cancer research is then summarized, and examples of a few key applications are given. Finally, future development perspectives are indicated.
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Affiliation(s)
- David Sarrut
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1294; INSA-Lyon; Université Lyon 1, Léon Bérard cancer center, Lyon, France
| | - Nicolas Arbor
- Université de Strasbourg, IPHC, CNRS, UMR7178, F-67037 Strasbourg, France
| | - Thomas Baudier
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1294; INSA-Lyon; Université Lyon 1, Léon Bérard cancer center, Lyon, France
| | - Damian Borys
- Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Ane Etxebeste
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1294; INSA-Lyon; Université Lyon 1, Léon Bérard cancer center, Lyon, France
| | - Hermann Fuchs
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Vienna, Währinger Gürtel 18-20, A-1090 Wien, Austria
| | - Jan Gajewski
- Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland
| | | | - Sébastien Jan
- Université Paris-Saclay, Inserm, CNRS, CEA, Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), F-91401 Orsay, France
| | - George C Kagadis
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece
| | - Han Gyu Kang
- National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Assen Kirov
- Memorial Sloan Kettering Cancer, New York, NY 10021, United States of America
| | - Olga Kochebina
- Université Paris-Saclay, Inserm, CNRS, CEA, Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), F-91401 Orsay, France
| | - Wojciech Krzemien
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, S. Lojasiewicza 11, 30-348 Krakow, Poland
- Centre for Theranostics, Jagiellonian University, Kopernika 40 St, 31 501 Krakow, Poland
| | - Antony Lomax
- Center for Proton Therapy, PSI, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | | | - Christian Pommranz
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, D-72076 Tuebingen, Germany
- Institute for Astronomy and Astrophysics, Eberhard Karls University Tuebingen, Sand 1, D-72076 Tuebingen, Germany
| | - Emilie Roncali
- University of California Davis, Departments of Biomedical Engineering and Radiology, Davis, CA 95616, United States of America
| | - Antoni Rucinski
- Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland
| | - Carla Winterhalter
- Center for Proton Therapy, PSI, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - Lydia Maigne
- Université Clermont Auvergne, Laboratoire de Physique de Clermont, CNRS, UMR 6533, F-63178 Aubière, France
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5
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Romero IO, Fang Y, Li C. Correlation between X-ray tube current exposure time and X-ray photon number in GATE. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:667-675. [PMID: 35466921 PMCID: PMC10754355 DOI: 10.3233/xst-221126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND X-ray image quality relies heavily on the emitted X-ray photon number which depends on X-ray tube current and exposure time. To accurately estimate the absorbed dose in an imaging protocol, it is better to simulate the X-ray imaging with a Monte Carlo platform such as GATE (Geant4 Application for Tomographic Emission). Although input of GATE is the X-ray photon number of the simulated X-ray tube, it lacks a good way to setup the photon number for a desired X-ray tube current setting. OBJECTIVE To provide a method to correlate the experimental X-ray tube current exposure time and the X-ray photon number in GATE. METHODS The accumulated radiation dose of a micro-computed tomography (CT) X-ray tube was recorded at different current exposure times with a general-purpose ion chamber. GATE was used to model the experimental microCT imaging system and calculate the total absorbed dose (cGy) in the sensitive volume of the ion chamber with different X-ray photon numbers. Linear regression models are used to establish a correlation between the estimated X-ray photon number and the X-ray tube settings. At first, one model establishes the relationship between the experimentally measured dose and the X-ray tube setting. Then, another model establishes a relationship between the simulated dose and the X-ray number in GATE. At last, by correlating these two models, a regression model to estimate the X-ray output number from an experimental X-ray tube setting (mAs) is obtained. RESULTS For a typical micro-CT scan, the X-ray tube is operated at 50 kVp and 0.5 mA for a 500 ms exposure time per projection (0.25 mAs). For these X-ray imaging parameters, the X-ray number per projection is estimated to be 3.613×106 with 1.0 mm Al filter. CONCLUSION The findings of this work provide an approach to correlate the experimental X-ray tube current exposure time to the X-ray photon number in the GATE simulation of the X-ray tube to more accurately determine radiation dose for an imaging protocol.
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Affiliation(s)
- Ignacio O. Romero
- Department of Bioengineering, University of California, Merced, Merced, CA, USA
| | - Yile Fang
- Department of Bioengineering, University of California, Merced, Merced, CA, USA
| | - Changqing Li
- Department of Bioengineering, University of California, Merced, Merced, CA, USA
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6
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Sarrut D, Bała M, Bardiès M, Bert J, Chauvin M, Chatzipapas K, Dupont M, Etxebeste A, M Fanchon L, Jan S, Kayal G, S Kirov A, Kowalski P, Krzemien W, Labour J, Lenz M, Loudos G, Mehadji B, Ménard L, Morel C, Papadimitroulas P, Rafecas M, Salvadori J, Seiter D, Stockhoff M, Testa E, Trigila C, Pietrzyk U, Vandenberghe S, Verdier MA, Visvikis D, Ziemons K, Zvolský M, Roncali E. Advanced Monte Carlo simulations of emission tomography imaging systems with GATE. Phys Med Biol 2021; 66:10.1088/1361-6560/abf276. [PMID: 33770774 PMCID: PMC10549966 DOI: 10.1088/1361-6560/abf276] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/26/2021] [Indexed: 12/13/2022]
Abstract
Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.
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Affiliation(s)
- David Sarrut
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | | | - Manuel Bardiès
- Cancer Research Institute of Montpellier, U1194 INSERM/ICM/Montpellier University, 208 Av des Apothicaires, F-34298 Montpellier cedex 5, France
| | - Julien Bert
- LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, F-29238, Brest, France
| | - Maxime Chauvin
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
| | | | | | - Ane Etxebeste
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | - Louise M Fanchon
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, United States of America
| | - Sébastien Jan
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Service Hospitalier Frédéric Joliot, F-91401, Orsay, France
| | - Gunjan Kayal
- CRCT, UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
- SCK CEN, Belgian Nuclear Research Centre, Boeretang 200, Mol 2400, Belgium
| | - Assen S Kirov
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, United States of America
| | - Paweł Kowalski
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
| | - Wojciech Krzemien
- High Energy Physics Division, National Centre for Nuclear Research, Otwock-Świerk, Poland
| | - Joey Labour
- Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France
| | - Mirjam Lenz
- FH Aachen University of Applied Sciences, Forschungszentrum Jülich, Jülich, Germany
- Faculty of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany
| | - George Loudos
- Bioemission Technology Solutions (BIOEMTECH), Alexandras Av. 116, Athens, Greece
| | | | - Laurent Ménard
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, F-91405 Orsay, France
- Université de Paris, IJCLab, F-91405 Orsay France
| | | | | | - Magdalena Rafecas
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Julien Salvadori
- Department of Nuclear Medicine and Nancyclotep molecular imaging platform, CHRU-Nancy, Université de Lorraine, F-54000, Nancy, France
| | - Daniel Seiter
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, 53705, United States of America
| | - Mariele Stockhoff
- Medical Image and Signal Processing (MEDISIP), Ghent University, Ghent, Belgium
| | - Etienne Testa
- Univ. Lyon, Univ. Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, F-69622, Villeurbanne, France
| | - Carlotta Trigila
- Department of Biomedical Engineering, University of California, Davis, CA 95616 United States of America
| | - Uwe Pietrzyk
- Faculty of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany
| | | | - Marc-Antoine Verdier
- Université Paris-Saclay, CNRS/IN2P3, IJCLab, F-91405 Orsay, France
- Université de Paris, IJCLab, F-91405 Orsay France
| | - Dimitris Visvikis
- LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, F-29238, Brest, France
| | - Karl Ziemons
- FH Aachen University of Applied Sciences, Forschungszentrum Jülich, Jülich, Germany
| | - Milan Zvolský
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Emilie Roncali
- Department of Biomedical Engineering, University of California, Davis, CA 95616 United States of America
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