KEYWORDS: Sensors, Monte Carlo methods, Photons, Computer simulations, Convolution, Computed tomography, Detection and tracking algorithms, Scintillators, Signal attenuation, Signal detection
Accurate quantitative reconstruction in kV cone-beam computed tomography (CBCT) is challenged by the presence of secondary radiations (scattering, fluorescence, and bremsstrahlung photons) coming from the object and from the detector itself. The authors present a simulation study of the CBCT imaging chain and its integration into a comprehensive correction algorithm. A layer model of the flat-panel detector is built in a Monte Carlo environment in order to help in localizing and analyzing the secondary radiations. The contribution of these events to the final image is estimated with a convolution model to account for detector secondary radiations combined with a forced-detection scheme to speed-up the Monte Carlo simulation without loss of accuracy. We more specifically assess to what extent a 2D description of the flat-panel detector would be sufficient for the forward model (i.e., the image formation process) of an iterative correction algorithm, both in terms of energy and incidence angle of incoming photons. Results show that both object and detector secondary radiations have to be considered in CBCT. The correction algorithm iteratively compensates for the secondary radiations and the beam hardening in object space. Preliminary results on tomographic acquisitions demonstrate a quantitative improvement on the first iteration.
KEYWORDS: Sensors, Monte Carlo methods, Luminescence, Convolution, Photons, Algorithm development, Detection and tracking algorithms, Image processing, Machine vision, Current controlled current source
Accurate quantitative reconstruction in kV cone-beam computed tomography (CBCT) is challenged by the presence of secondary radiations (scattering, fluorescence and bremsstrahlung photons) coming from the object and from the flat-panel detector itself. This paper presents a simulation study of the CBCT imaging chain as a first step towards the development of a comprehensive correction algorithm. A layer model of the detector is built in a Monte Carlo environment in order to help localizing and analyzing the secondary radiations. The contribution of these events to the final image is estimated with a forced-detection scheme to speed-up the Monte Carlo simulation without loss of accuracy. We more specifically assess to what extent a 2D description of the flat-panel detector would be sufficient for the forward model (i.e. the image formation process) of an iterative correction algorithm, both in terms of energy and incidence angle of incoming photons. A convolution model to account for detector secondary radiations is presented and validated. Results show that both object and detector secondary radiations have to be considered in CBCT.
KEYWORDS: Image resolution, Super resolution, Signal to noise ratio, Sensors, Modulation transfer functions, X-ray imaging, X-rays, Detection and tracking algorithms, Algorithm development, Reconstruction algorithms
The resolution of digitized images is linked to the detector array pixel size. Aliasing effects result from a non- adequation between the detector sampling and the signal bandwidths. The aim of this study is to develop a super- resolution algorithm for X-ray images. Our technique uses controlled horizontal and vertical subpixel shifts. Generalized sampling theorem of Papoulis, based on a multichannel approach, is the theoretical justification for the recovery of a high resolution image thanks to a set of low resolution ones. A higher resolution image is recovered by a minimization of a quadratic criterion. An iterative relaxation method is used to compute the minimum. To regularize, a priori data about the signal are introduced in order to fight against noise effects. Because of the opposite effects of regularization and super-resolution an adapted regularization that preserves discontinuities has to be used. Results obtained show that our algorithm recovers high frequency components on X-ray images without noise amplification. An analysis of real acquisitions in terms of modulation transfer function (MTF) shows that we obtain, thanks to this method, a 'virtual' detector better than a low resolution one, and equivalent to a real high resolution one.
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