Monte Carlo simulations for medical physics: From fundamental physics to cancer treatment

Guatelli, Susanna; Incerti, S.

doi:10.1016/j.ejmp.2017.01.002

Cited by 9 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geant4 is widely used in medical physics in critical applications such as verification of radiotherapy treatment planning systems, and the design of equipment for radiotherapy and nuclear medicine. It is also used in medical imaging for dosimetry, to improve detectors and reconstruction algorithms, and for radiation protection assessments (Guatelli et al 2017 [4], Archambault et al 2003 [5]). In the medical physics domain, Geant4 can be used in stand-alone applications or via software tools like GAMOS (Arce et al 2014 [6]), GATE (Jan et al 2011 [7]), PTSim (Akagi et al 2011 [8], Akagi et al 2014 [9]) and TOPAS (Perl et al 2012 [10]).…”

Section: Introductionmentioning

confidence: 99%

“…It is also used in medical imaging for dosimetry, to improve detectors and reconstruction algorithms, and for radiation protection assessments. 4,5 In the medical physics domain, Geant4 can be used in stand-alone applications or via software tools like GAMOS, 6 GATE, 7 PTSim, 8,9 and TOPAS. 10 Given the extensive use of Geant4 in medical physics, a systematic benchmarking of the accuracy of the Geant4 physics models in this domain is paramount.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Report on G4‐Med, a Geant4 benchmarking system for medical physics applications developed by the Geant4 Medical Simulation Benchmarking Group

et al. 2020

Self Cite

View full text Add to dashboard Cite

Geant4 is a Monte Carlo code extensively used in medical physics for a wide range of applications, such as dosimetry, micro-and nano-dosimetry, imaging, radiation protection and nuclear medicine. Geant4 is continuously evolving, so it is crucial to have a system that benchmarks this Monte Carlo code for medical physics against reference data and to perform regression testing. To respond to these needs, we developed G4-Med, a benchmarking and regression testing system of Geant4 for medical physics, that currently includes 18 tests. They range from the benchmarking of fundamental physics quantities to the testing of Monte Carlo simulation setups typical of medical physics applications. Both electromagnetic and hadronic physics processes and models within the pre-built, Geant4 physics lists are tested. The tests included in G4-Med are executed on the CERN computing infrastructure via the use of the geant-val web application, developed at CERN for Geant4 testing. The physical observables can be compared to reference data for benchmarking and to results of previous Geant4 versions for regression testing purposes. This paper describes the tests included in G4-Med and shows the results derived from the benchmarking of Geant4 10.5 against reference data. The results presented and discussed in this paper will aid users in tailoring physics lists to their particular application.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Report on G4‐Med, a Geant4 benchmarking system for medical physics applications developed by the Geant4 Medical Simulation Benchmarking Group

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Head Phantom: We created the HIGH RES HEAD digital head phantom with very high resolution, [86] for the development and evaluation of pCT image reconstruction algorithms and IMPT algorithms, but it can be used for other medical physics purposes as well. The phantom is now available as a computational DICOM head phantom to the Geant4 user community in the open source Geant4 MC simulation tool package [87]. The phantom represents a head of a 5-yearold child.…”

Section: Digital Phantomsmentioning

confidence: 99%

Developments in Mathematical Algorithms and Computational Tools for Proton CT and Particle Therapy Treatment Planning

Censor¹,

Schubert²,

Schulte³

2021

Preprint

View full text Add to dashboard Cite

We summarize recent results and ongoing activities in mathematical algorithms and computer science methods related to proton computed tomography (pCT) and intensitymodulated particle therapy (IMPT) treatment planning. Proton therapy necessitates a high level of delivery accuracy to exploit the selective targeting imparted by the Bragg peak. For this purpose, pCT utilizes the proton beam itself to create images. The technique works by sending a low-intensity beam of protons through the patient and measuring the position, direction, and energy loss of each exiting proton. The pCT technique allows reconstruction of the volumetric distribution of the relative stopping power (RSP) of the patient tissues for use in treatment planning and pre-treatment range verification. We have investigated new ways to make the reconstruction both efficient and accurate. Better accuracy of RSP also enables more robust inverse approaches to IMPT. For IMPT, we developed a framework for performing intensity-modulation of the proton pencil beams. We expect that these developments will lead to additional project work in the years to come, which requires a regular exchange between experts in the fields of mathematics, computer science, and medical physics. We have initiated such an exchange by organizing annual workshops on pCT and IMPT algorithm and technology developments. This report is, admittedly, tilted toward our interdisciplinary work and methods. We offer a comprehensive overview of results, problems, and challenges in pCT and IMPT with the aim of making other scientists wanting to tackle such issues and to strengthen their interdisciplinary collaboration by bringing together cutting-edge know-how from medicine, computer science, physics, and mathematics to bear on medical physics problems at hand.

show abstract

“…Monte Carlo (MC) simulations are considered as current gold standard for proton dose calculation (Guatelli and Incerti 2017). Due to a continuous increase in computational performance, they are nowadays more widely used clinically.…”

Section: Introductionmentioning

confidence: 99%

Material assignment for proton range prediction in Monte Carlo patient simulations using stopping-power datasets

et al. 2020

View full text Add to dashboard Cite

Motivation and objective. For each institute, the selection and calibration of the most suitable approach to assign material properties for Monte Carlo (MC) patient simulation in proton therapy is a major challenge. Current conventional approaches based on computed tomography (CT) depend on CT acquisition and reconstruction settings. This study proposes a material assignment approach, referred to as MATA (MATerial Assignment), which is independent of CT scanner properties and, therefore, universally applicable by any institute. Materials and methods. The MATA approach assigns material properties to the physical quantity stopping-power ratio (SPR) using a set of 40 material compositions specified for human tissues and linearly determined mass density. The application of clinically available CT-number-to-SPR conversion avoids the need for any further calibration. The MATA approach was validated with homogeneous and heterogeneous SPR datasets by assessing the SPR accuracy after material assignment obtained either based on dose scoring or determination of water-equivalent thickness. Finally, MATA was applied on patient datasets to evaluate dose differences induced by different approaches for material assignment and SPR prediction. Results. The deviation between the SPR after material assignment and the input SPR was close to zero in homogeneous datasets and below 0.002 (0.2% relative to water) in heterogeneous datasets, which was within the systematic uncertainty in SPR estimation. The comparison of different material assignment approaches revealed relevant differences in dose distribution and SPR. The comparison between two SPR prediction approaches, a standard look-up table and direct SPR determination from dual-energy CT, resulted in patient-specific mean proton range shifts between 1.3 mm and 4.8 mm. Conclusion. MATA eliminates the need for institution-specific adaptations of the material assignment. It allows for using any SPR dataset and thus facilitates the implementation of more accurate SPR prediction approaches. Hence, MATA provides a universal solution for patient modeling in MC-based proton treatment planning.

show abstract

Monte Carlo simulations for medical physics: From fundamental physics to cancer treatment

Cited by 9 publications

References 20 publications

Report on G4‐Med, a Geant4 benchmarking system for medical physics applications developed by the Geant4 Medical Simulation Benchmarking Group

Report on G4‐Med, a Geant4 benchmarking system for medical physics applications developed by the Geant4 Medical Simulation Benchmarking Group

Developments in Mathematical Algorithms and Computational Tools for Proton CT and Particle Therapy Treatment Planning

Material assignment for proton range prediction in Monte Carlo patient simulations using stopping-power datasets

Contact Info

Product

Resources

About