A<scp>PENELOPE</scp>‐based system for the automated Monte Carlo simulation of clinacs and voxelized geometries—application to far‐from‐axis fields

Sempau, J.; Badal, Andreu; Brualla, L.

doi:10.1118/1.3643029

Cited by 249 publications

(247 citation statements)

References 27 publications

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“…To simulate multiprojection scans, we began by generating scatter-free images using a voxelized version of the PENELOPE photon-electron transport code called penEasy. 35,36 We used the penEasy package (v. 2010-09-02) in ray-tracing mode and ran simulations in parallel on a 190-node cluster of AMD Opteron and Intel Xeon servers in order to generate large image sets quickly. In penEasy ray tracing, a noise-free mean image is generated by casting a small number of deterministic rays from the focal spot to different positions inside each pixel to estimate the probability of x rays arriving at the pixel without interacting along the track.…”

Section: Iib Multiprojection X-ray Imagingmentioning

confidence: 99%

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

et al. 2013

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Purpose: Digital breast tomosynthesis (DBT) is a promising breast cancer screening tool that has already begun making inroads into clinical practice. However, there is ongoing debate over how to quantitatively evaluate and optimize these systems, because different definitions of image quality can lead to different optimal design strategies. Powerful and accurate tools are desired to extend our understanding of DBT system optimization and validate published design principles. Methods: The authors developed a virtual trial framework for task-specific DBT assessment that uses digital phantoms, open-source x-ray transport codes, and a projection-space, spatial-domain observer model for quantitative system evaluation. The authors considered evaluation of reconstruction algorithms as a separate problem and focused on the information content in the raw, unfiltered projection images. Specifically, the authors investigated the effects of scan angle and number of angular projections on detectability of a small (3 mm diameter) signal embedded in randomly-varying anatomical backgrounds. Detectability was measured by the area under the receiver-operating characteristic curve (AUC). Experiments were repeated for three test cases where the detectability-limiting factor was anatomical variability, quantum noise, or electronic noise. The authors also juxtaposed the virtual trial framework with other published studies to illustrate its advantages and disadvantages. Results: The large number of variables in a virtual DBT study make it difficult to directly compare different authors' results, so each result must be interpreted within the context of the specific virtual trial framework. The following results apply to 25% density phantoms with 5.15 cm compressed thickness and 500 μm 3 voxels (larger 500 μm 2 detector pixels were used to avoid voxel-edge artifacts): 1. For raw, unfiltered projection images in the anatomical-variability-limited regime, AUC appeared to remain constant or increase slightly with scan angle. 2. In the same regime, when the authors fixed the scan angle, AUC increased asymptotically with the number of projections. The threshold number of projections for asymptotic AUC performance depended on the scan angle. In the quantum-and electronic-noise dominant regimes, AUC behaviors as a function of scan angle and number of projections sometimes differed from the anatomy-limited regime. For example, with a fixed scan angle, AUC generally decreased with the number of projections in the electronic-noise dominant regime. These results are intended to demonstrate the capabilities of the virtual trial framework, not to be used as optimization rules for DBT. Conclusions:The authors have demonstrated a novel simulation framework and tools for evaluating DBT systems in an objective, task-specific manner. This framework facilitates further investigation of image quality tradeoffs in DBT.

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Section: Iib Multiprojection X-ray Imagingmentioning

confidence: 99%

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

et al. 2013

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“…To perform the simulations, we have chosen Penelope2008 (Sempau et al 2011, Salvat et al 2008), a MC code whose reliability and performance have been widely tested (Ye et al 2004). Penelope2008 cross section data are those of the EPDL97 (Cullen et al 1997) and EEDL (Cullen et al 2001).…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

A simple analytical method for heterogeneity corrections in low dose rate prostate brachytherapy

et al. 2015

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Abstract. In low energy brachytherapy, the presence of tissue heterogeneities contributes significantly to the discrepancies observed between treatment plan and delivered dose. In this work, we present a simplified analytical dose calculation algorithm for heterogeneous tissue. We compare it with Monte Carlo computations and assess its suitability for integration in clinical treatment planning systems. The algorithm, named as RayStretch, is based on the classic equivalent path length method and TG-43 reference data. Analytical and Monte Carlo dose calculations using Penelope2008 are compared for a benchmark case: a prostate patient with calcifications. The results show a remarkable agreement between simulation and algorithm, the latter having in addition a high calculation speed. The proposed analytical model is compatible with clinical real-time treatment planning systems based on TG-43 consensus datasets for improving dose calculation and treatment quality in heterogeneous tissue. Moreover, the algorithm is applicable for any type of heterogeneities.

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“…The algorithm for mixed transport of charged particles implemented by PENELOPE algorithm led to its use in radiotherapy, simulating several irradiations and geometry of clinical situations in radiotherapy, which have shown a good concordance with experimental measurements using different irradiation techniques [17][18][19][20]. This chapter will approach two dosimetry tools: the MAGIC-f gel dosimeter and the PENELOPE-Monte code.…”

Section: Introductionmentioning

confidence: 99%

3D Dosimetric Tools in Radiotherapy for Photon Beams

Alva-Sánchez¹,

Pianoschi²

2013

Frontiers in Radiation Oncology

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APENELOPE‐based system for the automated Monte Carlo simulation of clinacs and voxelized geometries—application to far‐from‐axis fields

Cited by 249 publications

References 27 publications

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

A simple analytical method for heterogeneity corrections in low dose rate prostate brachytherapy

3D Dosimetric Tools in Radiotherapy for Photon Beams

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