Estimation of geometrical parameters and collimator evaluation for cone beam tomography

Gullberg, Grant T.; Tsui, B.M.W.; Crawford, Carl R.; Ballard, J. Glen; Hagius, John T.

doi:10.1118/1.596505

Cited by 127 publications

(91 citation statements)

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“…Then we have (2) where (3) Thus, the seven geometric parameters R FD , R FI , u 0 , υ 0 , ϕ, σ, and η are required to completely describe the cone beam CT geometry, which means that with precise measurement of these seven parameters, the exact three-dimensional geometric structure of the object can be reconstructed from the projection data.…”

Section: A Geometry Definition and System Parametersmentioning

confidence: 99%

A geometric calibration method for cone beam CT systems

et al. 2006

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Cone beam CT systems are being deployed in large numbers for small animal imaging, dental imaging, and other specialty applications. A new high-precision method for cone beam CT system calibration is presented in this paper. It uses multiple projection images acquired from rotating pointlike objects (metal ball bearings) and the angle information generated from the rotating gantry system is also used. It is assumed that the whole system has a mechanically stable rotation center and that the detector does not have severe out-of-plane rotation (< 2°). Simple geometrical relationships between the orbital paths of individual BBs and five system parameters were derived. Computer simulations were employed to validate the accuracy of this method in the presence of noise. Equal or higher accuracy was achieved compared with previous methods. This method was implemented for the geometrical calibration of both a micro CT scanner and a breast CT scanner. The reconstructed tomographic images demonstrated that the proposed method is robust and easy to implement with high precision.

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Section: A Geometry Definition and System Parametersmentioning

confidence: 99%

A geometric calibration method for cone beam CT systems

et al. 2006

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“…Effects of yshifts are not clearly visible either. Many methods for the estimation of geometrical parameters of cone-beam scanners have been proposed since 1990 [20, [26][27][28][29][30]. These techniques have generally fallen into two categories: those based on iterative nonlinear optimization [26,27] and those based on the direct solution of geometric equations [20,28,29,31].…”

Section: Misalignment Correction In Single Bed Acquisitionsmentioning

confidence: 99%

“…The second type has become favored in recent years because of superior performance and ease of implementation. One important result in [26] was to show that the distance detector-object only plays the role of a magnifying factor in the reconstruction. This can be estimated a posteriori if one knows the distance between some landmarks, such as two point objects in the reconstruction, similarly to what is explained in Section 4.1.…”

Section: Misalignment Correction In Single Bed Acquisitionsmentioning

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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“…We do not discuss the estimation of the misalignment since many approaches have been proposed in literature, e.g., reference-or registration-based calibration methods, where a calibration object is used to evaluate the misalignment (also called "phantom-based" calibration methods [11], [12], [13]), and in reference-less algorithms without using special calibration specimen (also called "self-calibration" methods [14], [15]). Moreover, as shown in [16,5], if prior information about the object is available, registration parameters can be learned during the reconstruction, by minimizing the likelihood function via gradient steps over a differentiable registration operator.…”

Section: Incorporation Of Misalignmentmentioning

confidence: 99%

Differential SART for sub-Nyquist tomographic reconstruction in presence of misalignments

Roemer

Grosmann

Schoen

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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-In this paper we study tomographic reconstruction methods in the case that prior knowledge about the object is available. In particular, we consider the case that a reference object that is similar in shape and orientation is available, which is very common in non-destructive testing applications. We demonstrate that a differential version of existing reconstruction methods can easily be derived which reconstructs only the deviation between test and reference object. Since this difference volume is significantly more sparse, the differential reconstruction can be implemented very efficiently. We also discuss the case where knowledge about the misalignment between test and reference object is available, in which case the efficiency of the differential reconstruction can be improved even further. The resulting algorithm is faster, more accurate, and less sensitive to the choice of the step size parameters and regularization than state of the art reconstruction methods.

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Estimation of geometrical parameters and collimator evaluation for cone beam tomography

Cited by 127 publications

References 0 publications

A geometric calibration method for cone beam CT systems

A geometric calibration method for cone beam CT systems

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

Differential SART for sub-Nyquist tomographic reconstruction in presence of misalignments

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