ImaSim, a software tool for basic education of medical x-ray imaging in radiotherapy and radiology

Landry, Guillaume; DeBlois, F; Verhaegen, Frank

doi:10.3389/fphy.2013.00022

Cited by 21 publications

(17 citation statements)

References 23 publications

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“…The CT energy spectrum can be measured by, e.g., a Compton spectrometer, see Duisterwinkel et al, 26 or it can be approximated by calculation, e.g., using ImaSim. [22][23][24][25] But the results for the SPR estimations are suspected to be dependent on the accuracy of the energy spectra determination. However, the results for Han's method have been shown to be comparable to the results using the method by Yang et al, 7 and the SPR parametrization compared favourably to Han's method.…”

Section: Discussionmentioning

confidence: 99%

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A robust empirical parametrization of proton stopping power using dual energy CT

et al. 2016

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Section: Discussionmentioning

confidence: 99%

“…However, the CT energy spectra were generated using ImaSim (Refs. [22][23][24][25] to obtain energy spectra, w E, j , for which the theoretical CT numbers for the Gammex inserts calculated from Eqs. (4) and (5) match the measured CT numbers.…”

Section: A3 Theoretical Ct Numbers For Evaluation Of the Spr Methodsmentioning

confidence: 99%

A robust empirical parametrization of proton stopping power using dual energy CT

et al. 2016

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“…22 Seven different X-ray spectra, equivalent to the ones used for the measurements, were generated with SpekCalc. [23][24][25] These spectra were used to simulate images of both the calibration and validation phantom with different diameters of 3, 5 and 7 cm, as shown in Figure 2.…”

Section: Imaging Protocolsmentioning

confidence: 99%

Optimizing dual energy cone beam CT protocols for preclinical imaging and radiation research

Schyns¹,

Almeida²,

Hoof³

et al. 2017

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Quantitative DECT imaging is feasible for small animal irradiators with an integrated CBCT system. To obtain the best results, optimizing the imaging protocols is required. Well-separated X-ray spectra and a sufficient dose level should be used to minimize the error and noise for Z and ρ. When no BHC is applied in the image reconstruction, the size of the calibration phantom should match the size of the imaged object to limit the influence of beam hardening effects. No significant differences in Z and ρ errors are observed between the two systems from different manufacturers. Advances in knowledge: This is the first study that investigates quantitative DECT imaging for small animal irradiators with an integrated CBCT system.

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“…X-ray Sim [ 5 ], which has a basic open-source version in the CPU, lacks flexibility in the available system geometries and is based on the projection of digital computer-aided design (CAD) models, thus hindering the direct use of real acquired images. A similar drawback is found in ImaSim [ 6 ], where objects are based on specific geometrical shapes and not voxels, thus precluding handling of voxelized objects such as actual CT datasets. CONRAD [ 7 ] is a Java-based software framework that uses GPU devices for hardware acceleration.…”

Section: Introductionmentioning

confidence: 86%

FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems

et al. 2017

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The availability of digital X-ray detectors, together with advances in reconstruction algorithms, creates an opportunity for bringing 3D capabilities to conventional radiology systems. The downside is that reconstruction algorithms for non-standard acquisition protocols are generally based on iterative approaches that involve a high computational burden.The development of new flexible X-ray systems could benefit from computer simulations, which may enable performance to be checked before expensive real systems are implemented. The development of simulation/reconstruction algorithms in this context poses three main difficulties. First, the algorithms deal with large data volumes and are computationally expensive, thus leading to the need for hardware and software optimizations. Second, these optimizations are limited by the high flexibility required to explore new scanning geometries, including fully configurable positioning of source and detector elements. And third, the evolution of the various hardware setups increases the effort required for maintaining and adapting the implementations to current and future programming models. Previous works lack support for completely flexible geometries and/or compatibility with multiple programming models and platforms.In this paper, we present FUX-Sim, a novel X-ray simulation/reconstruction framework that was designed to be flexible and fast. Optimized implementation for different families of GPUs (CUDA and OpenCL) and multi-core CPUs was achieved thanks to a modularized approach based on a layered architecture and parallel implementation of the algorithms for both architectures.A detailed performance evaluation demonstrates that for different system configurations and hardware platforms, FUX-Sim maximizes performance with the CUDA programming model (5 times faster than other state-of-the-art implementations). Furthermore, the CPU and OpenCL programming models allow FUX-Sim to be executed over a wide range of hardware platforms.

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ImaSim, a software tool for basic education of medical x-ray imaging in radiotherapy and radiology

Cited by 21 publications

References 23 publications

A robust empirical parametrization of proton stopping power using dual energy CT

A robust empirical parametrization of proton stopping power using dual energy CT

Optimizing dual energy cone beam CT protocols for preclinical imaging and radiation research

FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems

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