Super-Resolution for Computed Tomography Based on Discrete Tomography

Aarle, Wim van; Batenburg, Kees Joost; Gompel, Gert Van; Casteele, Elke Van de; Sijbers, Jan

doi:10.1109/tip.2013.2297025

Cited by 34 publications

(19 citation statements)

References 30 publications

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“…In this case, instead, Joseph's method wouldn't be able to associate some voxels to the related detector pixels, but various oversampling techniques exist to solve this problem, like the super-sampling method used in [8] for their super-resolution application.…”

Section: Super-sampled Projection Modelmentioning

confidence: 99%

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An Orientation-space Super Sampling Technique for Six-dimensional Diffraction Contrast Tomography

Viganò

Batenburg

Ludwig

2016

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Abstract. Diffraction contrast tomography (DCT) is an X-ray full-field imaging technique that allows for the non-destructive three-dimensional investigation of polycrystalline materials and the determination of the physical and morphological properties of their crystallographic domains, called grains. This task is considered more and more challenging with the increasing intra-granular deformation, also known as orientation-spread. The recent introduction of a sixdimensional reconstruction framework in DCT (6D-DCT) has proven to be able to address the intra-granular crystal orientation for moderately deformed materials. The approach used in 6D-DCT, which is an extended sampling of the six-dimensional combined position-orientation space, has a linear scaling between the number of sampled orientations, which determine the orientation-space resolution of the problem, and computer memory usage. As a result, the reconstruction of more deformed materials is limited by their high resource requirements from a memory and computational point of view, which can easily become too demanding for the currently available computer technologies. In this article we propose a super-sampling method for the orientation-space representation of the six-dimensional DCT framework that enables the reconstruction of more deformed cases by reducing the impact on system memory, at the expense of longer reconstruction times. The use of super-sampling can further improve the quality and accuracy of the reconstructions, especially in cases where memory restrictions force us to adapt to inadequate (undersampled) orientation-space sampling.

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Section: Super-sampled Projection Modelmentioning

confidence: 99%

“…It is in fact easier to start from the back-projection super-sampling technique presented in [8], that is based on a sampling approach, and model a similar type of orientation super-sampling.…”

Section: Super-sampled Projection Modelmentioning

confidence: 99%

An Orientation-space Super Sampling Technique for Six-dimensional Diffraction Contrast Tomography

Viganò

Batenburg

Ludwig

2016

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“…Although Computed tomography imaging (CT) does have drawbacks, such as non-real time and electromagnetic radiation, it is employed in our work due to its advantages. CT images biological tissues clearly, provides absolute positions of needles and tissues, and is not strongly affected by the presence of needles [14] . The focus of this paper is how to reconstruct the needle shape and estimate the tip attitude via CT images.…”

Section: Introductionmentioning

confidence: 99%

Shape reconstruction and attitude estimation of bevel-tip needle via CT-guidance

Huo

Zhao

Han

et al. 2015

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Abstract-The bevel-tip flexible needle is an improvement of the traditional rigid needle and has the potential to reduce traumas, improve the controllability and reach multi-targets in one insertion. The needle steering robot system has the ability to deal with complex puncture environment and to improve the puncture precision, but it requires a precise feedback of needle tip's position and attitude. Computerized tomography (CT) is a widely available imaging system that provides precise and clear images. Unfortunately, CT is not a real-time system and it is impossible to fetch the attitude of bevel-tip needle from CT image. A new method is proposed to estimate the attitude, in which the unscented kalman filter is employed. We extract the positions of the needle in CT images, reconstruct the needle shape, calculate the control quantity according to the insert distance and then estimate the attitude of the needle tip. Simulations and experiments demonstrate that the method proposed is effective. Implementation of shape reconstruction and attitude estimation of the bevel-tip needle in biological tissue is a significant step toward the precise control of bevel-tip needles.

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“…Meanwhile, DART or variations of DART [11]–[14] have been successfully applied in electron tomography [2], [15], micro-CT [16], [17] and magnetic resonance imaging (MRI) [18]. However, being an iterative reconstruction algorithm, DART suffers from long computation times, which limits its use for in applications where computation time is important.…”

Section: Introductionmentioning

confidence: 99%

A Multiresolution Approach to Discrete Tomography Using DART

2014

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In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant speed-up, reduce missing wedge artefacts and improve feature reconstruction in the object compared with DART within the same time, making its use with large datasets more feasible.

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Super-Resolution for Computed Tomography Based on Discrete Tomography

Cited by 34 publications

References 30 publications

An Orientation-space Super Sampling Technique for Six-dimensional Diffraction Contrast Tomography

An Orientation-space Super Sampling Technique for Six-dimensional Diffraction Contrast Tomography

Shape reconstruction and attitude estimation of bevel-tip needle via CT-guidance

A Multiresolution Approach to Discrete Tomography Using DART

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