Compensation of ring artefacts in synchrotron tomographic images

Boin, Mirko; Haibel, Astrid

doi:10.1364/oe.14.012071

Cited by 105 publications

(83 citation statements)

References 6 publications

(9 reference statements)

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“…An apparent improvement in image contrast and higher signal-to-noise ratio is observed. Ring artifacts [9], which are due to the noisy points on the scintillator and the detector chip, can be observed in both reconstructed images as non-uniform band structures. The existence of the ring artefacts can severely inhibit the segmentation of the individual rod after structural reconstruction.…”

Section: Tomography Reconstructionmentioning

confidence: 99%

Fast synchrotron X-ray tomography study of the rod packing structures

Zhang¹,

Xia²,

Sun³

et al. 2013

AIP Conference Proceedings

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Section: Tomography Reconstructionmentioning

confidence: 99%

Fast synchrotron X-ray tomography study of the rod packing structures

Zhang¹,

Xia²,

Sun³

et al. 2013

AIP Conference Proceedings

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“…These residual ring artifacts may appear due to the no linear response of the detector elements with the incident X-ray flux, which can change between acquisitions. Among the number of methods that have been presented [22,[41][42][43][44][45][46][47][48][49][50], we propose a correction algorithm that works on the projection data before reconstruction, as it can be efficiently included in the correction/reconstruction pipe-line.…”

Section: Ring Artifact Correctionmentioning

confidence: 99%

“…We propose the following approach, following the same idea as in [43,45,47,51]: we divide each oblique sinogram in parts corresponding to a subset of projection angles and correct each part independently. For each part, a low-pass version is obtained by applying a 4-pixel median filter in the radial direction (kernel size was derived experimentally and depends on the expected thickness of the ring artifacts).…”

Section: Ring Artifact Correctionmentioning

confidence: 99%

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Abella

Vaquero

Sisniega

et al. 2012

Computer Methods and Programs in Biomedicine

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Cone-beam t r a c tMost small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. (FDK). Besides the implementation of the reconstruction algorithm itself, in order to design a real system it is necessary to take into account numerous issues so as to obtain the best quality images from the acquired data. This work presents a comprehensive, novel software architecture for small-animal CT scanners based on cone-beam geometry with circular scanning trajectory. The proposed architecture covers all the steps from the system calibration to the volume reconstruction and conversion into Hounsfield units. It includes an efficient implementation of an FDK-based reconstruction algorithm that takes advantage of system symmetries and allows for parallel reconstruction using a multiprocessor computer. Strategies for calibration and artifact correction are discussed to justify the strategies adopted. New procedures for multi-bed misalignment, beam-hardening, and Housfield units calibration are proposed. Experiments with phantoms and real data showed the suitability of the proposed software architecture for an X-ray small animal CT based on cone-beam geometry. IntroductionMany small animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory [1][2][3][4]. This configuration presents advantages over other alternatives used in clinical and preclinical applications: reduction of acquisition time, large axial field of view (FOV) without geometrical distortions, and optimization of radiated dose [5]. proposed by Feldkamp et al. (FDK) [6] are still widely used for solving the 3D reconstruction task because of their straightforward implementation and computational efficiency [4]. Almost every aspect of the reconstruction process has been studied: there is literature on algorithm variations for different trajectories [7,8], optimizations using graphic processing units (GPUs) [9][10][11][12][13][14][15], strategies to reduce cone beam artifacts [16,17], study of consistency conditions [18], optimization of the back-projection step [19], etc. However, in a real practical system, the implementation of a reconstruction algorithm core such as FDK is just an initial step of the process, and there 1

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“…(i) In Boin & Haibel (2006) the mean values yðiÞ for each column of a sinogram p n ðiÞ are found, the moving average filter is applied to the values found in order to replace yðiÞ by y s ðiÞ, which is the average value of 2N þ 1 neighbouring values, then the sinogram is normalized by the formula p (b) Suppression is carried out after the image has been reconstructed (see, for example, Sijbers & Postnov, 2004;Yang et al, 2008). In Sijbers & Postnov (2004) a reconstructed image is transformed into polar coordinates, where a set of homogeneous rows is detected.…”

Section: Figurementioning

confidence: 99%

Improved tomographic reconstructions using adaptive time-dependent intensity normalization

Titarenko

Withers

et al. 2010

J Synchrotron Radiat

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The first processing step in synchrotron-based micro-tomography is the normalization of the projection images against the background, also referred to as a white field. Owing to time-dependent variations in illumination and defects in detection sensitivity, the white field is different from the projection background. In this case standard normalization methods introduce ring and wave artefacts into the resulting three-dimensional reconstruction. In this paper the authors propose a new adaptive technique accounting for these variations and allowing one to obtain cleaner normalized data and to suppress ring and wave artefacts. The background is modelled by the product of two timedependent terms representing the illumination and detection stages. These terms are written as unknown functions, one scaled and shifted along a fixed direction (describing the illumination term) and one translated by an unknown two-dimensional vector (describing the detection term). The proposed method is applied to two sets (a stem Salix variegata and a zebrafish Danio rerio) acquired at the parallel beam of the micro-tomography station 2-BM at the Advanced Photon Source showing significant reductions in both ring and wave artefacts. In principle the method could be used to correct for time-dependent phenomena that affect other tomographic imaging geometries such as cone beam laboratory X-ray computed tomography.

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Compensation of ring artefacts in synchrotron tomographic images

Cited by 105 publications

References 6 publications

Fast synchrotron X-ray tomography study of the rod packing structures

Fast synchrotron X-ray tomography study of the rod packing structures

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Improved tomographic reconstructions using adaptive time-dependent intensity normalization

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