Point-based digitally reconstructed radiograph

Shen, Aodong; Luo, Limin

doi:10.1109/icpr.2008.4761753

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Primary processes refer to the attenuation of primary photons in the object, while secondary processes include scatter, beam hardening, detector response characteristics and noise. Conventional methods to render DRRs are either based on raytracing to model the primary process and/or statistical methods such as Monte-Carlo simulation to model the primary and/or secondary processes [32][33][34][35][36]. While raytracing is robust and can be computationally efficient, a significant amount of image pre-and postprocessing is required to incorporate the effect of the X-ray source spectrum and secondary effects to render a realistic DRR.…”

Section: Introductionmentioning

confidence: 99%

RealDRR – Rendering of realistic digitally reconstructed radiographs using locally trained image-to-image translation

Dhont

Verellen

Mollaert

et al. 2020

Radiotherapy and Oncology

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Section: Introductionmentioning

confidence: 99%

RealDRR – Rendering of realistic digitally reconstructed radiographs using locally trained image-to-image translation

Dhont

Verellen

Mollaert

et al. 2020

Radiotherapy and Oncology

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“…It is usually a complicated process to design an appropriate test chain . One has to choose a suitable X-ray emissive source and detector and determine the optimal parameters (tube voltage, tube current, geometry adjustment and exposure time) [1,2]. Therefore, it has great significance to study X-ray imaging simulation system.…”

Section: Introductionmentioning

confidence: 99%

A Fast Ray-Tracing Algorithm for X-Ray Imaging Simulation

Fu¹,

Shen²,

Luo³

2012

AMM

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X-ray radiography has more and more application value and X-ray simulation system has practical significance for improving the quality of X-ray imaging and replacing the expensive devices but it’s a time-consuming work. This paper proposes an improved fast ray-tracing method extended from Siddon’s and Zhao’s methods. For a 2D array, we compute level by level which has natural modes, by comparing the incident x-coordinate with the characteristic constant. In the case of 3D models, the volume data are back projected into 1D linked list and compute indices and lengths similarly like the case of 2D. Compared with Zhao’s method, the new method avoids skipping parametric planes and computing several starting voxels and ending voxels in one level. The time consumed in the new algorithm has reduced by 5/6 regarding the conventional Siddon’s algorithm.

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