<i>penMesh</i>—Monte Carlo Radiation Transport Simulation in a Triangle Mesh Geometry

Badal, Andreu; Kyprianou, Iacovos S.; Banh, Diem Phuc; Badano, Aldo; Sempau, J.

doi:10.1109/tmi.2009.2021615

Cited by 43 publications

(32 citation statements)

References 24 publications

(30 reference statements)

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“…For modeling scatter, we generated a uniform phantom with the same shape as the structured phantoms and a nominal density 0.955 g/cm 3 (for 75%-25% adipose-fibroglandular) based on results of Hammerstein et al 38 We used the opensource MC-GPU code 39 with slight modifications to support partially-isocentric DBT geometries, to simulate one highexposure (10 12 source photons) acquisition at each projection angle. In MC-GPU, the source was defined as a fan beam with fixed polar (24 • ) and azimuthal (7 • ) aperture angles, producing a 27.6 × 8.0 cm 2 rectangular field (at θ = 0 • ).…”

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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“…Several public-domain and popular Monte Carlo code systems include MCNP (X-5 Monte Carlo Team 2003), MCNPX [151], EGS [152], Geant4 [6,7,153], PENELOPE [154][155][156], and Fluka [157,158].…”

Section: Monte Carlo Methods and Computer Codesmentioning

confidence: 99%

Computational Phantoms for Organ Dose Calculations in Radiation Protection and Imaging

2013

The Phantoms of Medical and Health Physics

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Dosimetry for ionizing radiation has to do with the determination of amount and distribution pattern of the energy deposited in a part or parts of the human body from internal or external radiation sources. To protect against occupational exposures, dose limits for radiosensitive organs are recommended by international organizations and are adopted as national regulations. In both diagnostic radiology and nuclear medicine, X-ray photons and gamma rays traverse through body tissues to form images of the anatomy, depositing radiation energy in organs along the pathway via secondary electrons. Accurate radiation dosimetry is essential but also quite challenging for three reasons: (1) there are many diverse exposure scenarios resulting in unique spatial and temporal relationships between the source and human body; (2) an exposure can involve multiple radiation types, each of which is governed by different radiation physics principles, such as photons (X-ray photons, gamma rays, and positrons), electrons, alpha particles, neutrons, and protons; (3) the human body consists of a large number of anatomical structures of diverse shape, composition and density, leading to complex radiation interaction patterns. Since it is inconvenient to place a dosimeter inside the human body, organ dose estimates have been obtained mostly using a physical phantom or a computational phantom that mimics the interior and exterior anatomical features of the human body.Historically, the term phantom was used in most radiological science literature to mean a physical model of the human body. In the radiation protection community, however, the term has also been used to refer to a mathematically defined anatomical model that is distinctly different from a physiologically based model such as that related to respiration or blood flow. In this chapter, the phrases, ''computational phantom'' and ''physical phantom,'' are used to avoid confusion.

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“…Moreover, PenMesh, a Monte Carlo code based on PENELOPE physics subroutines, can directly deal with PM geometries. 26 For the present work, the resulting NURBS and PM organs have been revoxelized by triangulation with Rhinoceros and then by voxelization with the software Binvox (Ref. 27).…”

Section: Whole Body Voxel Model Laura and Its Nurbs/pm Versionmentioning

confidence: 99%

Anthropomorphic dual-lattice voxel models for optimizing image quality and dose

et al. 2017

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Abstract. Using numerical simulations, the influence of various imaging parameters on the resulting image can be determined for various imaging technologies. To achieve this, visualization of fine tissue structures needed to evaluate the image quality with different radiation quality and dose is essential. The present work examines a method that employs simulations of the imaging process using Monte Carlo methods and a combination of a standard and higher resolution voxel models. A hybrid model, based on nonlinear uniform rational B-spline and polygon mesh surfaces, was constructed from an existing voxel model of a female patient of a resolution in the range of millimeters. The resolution of the hybrid model was 500 μm, i.e., substantially finer than that of the original model. Furthermore, a high resolution lung voxel model [ð0.11 mmÞ 3 voxel volume, slice thickness: 114 μm] was developed from the specimen of a left lung lobe. This has been inserted into the hybrid model, substituting its left lung lobe and resulting in a dual-lattice geometry model. "Dual lattice" means, in this context, the combination of voxel models with different resolutions. Monte Carlo simulations of radiographic imaging were performed and the fine structure of the lung was easily recognizable.

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penMesh—Monte Carlo Radiation Transport Simulation in a Triangle Mesh Geometry

Cited by 43 publications

References 24 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

Computational Phantoms for Organ Dose Calculations in Radiation Protection and Imaging

Anthropomorphic dual-lattice voxel models for optimizing image quality and dose

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