SDART: An algorithm for discrete tomography from noisy projections

Bleichrodt, Folkert; Tabak, Frank; Batenburg, Kees Joost

doi:10.1016/j.cviu.2014.06.002

Cited by 15 publications

(9 citation statements)

References 16 publications

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“…In comparison with DART, our curve-based representation and regularization is more successful in handling the hight noise levels. The performance of DART would presumably improve had we used the more robust versions SDART [24] and TVR-DART [25] which were not available in the ASTRA toolbox.…”

Section: Resultsmentioning

confidence: 99%

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Computing segmentations directly from x-ray projection data via parametric deformable curves

Dahl¹,

Dahl²,

Hansen³

2017

Meas. Sci. Technol.

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We describe an efficient algorithm that computes a segmented reconstruction directly from x-ray projection data. Our algorithm uses a parametric curve to define the segmentation. Unlike similar approaches which are based on level-sets, our method avoids a pixel or voxel grid; hence the number of unknowns is reduced to the set of points that define the curve, and attenuation coefficients of the segments. Our current implementation uses a simple closed curve and is capable of separating one object from the background. However, our basic algorithm can be applied to an arbitrary topology and multiple objects corresponding to different attenuation coefficients in the reconstruction. Through systematic tests we demonstrate a high robustness to the noise, and an excellent performance under a small number of projections.

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Section: Resultsmentioning

confidence: 99%

“…Also related to our approach is discrete tomography [1] and its variants [24] and [25], where the pixel/voxel values are restricted to a small number of discrete values, each corresponding to a different phase in the object.…”

Section: Related Workmentioning

confidence: 99%

Computing segmentations directly from x-ray projection data via parametric deformable curves

Dahl¹,

Dahl²,

Hansen³

2017

Meas. Sci. Technol.

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“…The DART algorithm has demonstrated to obtain higher image quality reconstructions with limited projections and angles compared to standard reconstruction methods. Numerous successive studies have improved the DART algorithm, which include automatic parameter estimation (PDM-DART [1] and TVR-DART [18]), multi-resolution reconstruction (MDART [8]), relaxing voxel constraints (SDART [6]) and adaptive boundary reconstructions (ADART [13]). Nevertheless, a key limitation of DART is that it can only improve reconstruction quality if the number of different materials in the object is relatively small.…”

Section: Introductionmentioning

confidence: 99%

A Multi-channel DART Algorithm

Zeegers

Lucka

Batenburg

2018

Lecture Notes in Computer Science

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Tomography deals with the reconstruction of objects from their projections, acquired along a range of angles. Discrete tomography is concerned with objects that consist of a small number of materials, which makes it possible to compute accurate reconstructions from highly limited projection data. For cases where the allowed intensity values in the reconstruction are known a priori, the discrete algebraic reconstruction technique (DART) has shown to yield accurate reconstructions from few projections. However, a key limitation is that the benefit of DART diminishes as the number of different materials increases. Many tomographic imaging techniques can simultaneously record tomographic data at multiple channels, each corresponding to a different weighting of the materials in the object. Whenever projection data from more than one channel is available, this additional information can potentially be exploited by the reconstruction algorithm. In this paper we present Multi-Channel DART (MC-DART), which deals effectively with multi-channel data. This class of algorithms is a generalization of DART to multiple channels and combines the information for each separate channelreconstruction in a multi-channel segmentation step. We demonstrate that in a range of simulation experiments, MC-DART is capable of producing more accurate reconstructions compared to single-channel DART.

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“…Recently, the Discrete Algebraic Reconstruction Technique (DART) was reported and shown to be capable of calculating very high quality reconstructions using very few projections (Batenburg & Sijbers, 2011;Batenburg et al, 2010). The DART method with total variation regularization (Zhuge et al, 2016(Zhuge et al, , 2017 and soft constraints (Bleichrodt et al, 2014;van Aarle et al, 2012) was proposed to achieve high-quality reconstructions. However, two types of prior knowledge are still required in order to obtain high-quality reconstructions with the DART method.…”

Section: Introductionmentioning

confidence: 99%

A modified discrete tomography for improving the reconstruction of unknown multi-gray-level material in the `missing wedge' situation

Jian-hong

Liang

Guan

et al. 2018

J Synchrotron Radiat

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Full angular rotational projections cannot always be acquired in tomographic reconstructions because of the limited space in the experimental setup, leading to the 'missing wedge' situation. In this paper, a recovering 'missing wedge' discrete algebraic reconstruction technique algorithm (rmwDART) has been proposed to solve the 'missing wedge' problem and improve the quality of the three-dimensional reconstruction without prior knowledge of the material component's number or the material's values. By using oversegmentation, boundary extraction and mathematical morphological operations, 'missing wedge' artifact areas can be located. Then, in the iteration process, by updating the located areas and regions, high-quality reconstructions can be obtained from the simulations, and the reconstructed images based on the rmwDART algorithm can be obtained from soft X-ray nano-computed tomography experiments. The results showed that there is the potential for discrete tomography.

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SDART: An algorithm for discrete tomography from noisy projections

Cited by 15 publications

References 16 publications

Computing segmentations directly from x-ray projection data via parametric deformable curves

Computing segmentations directly from x-ray projection data via parametric deformable curves

A Multi-channel DART Algorithm

A modified discrete tomography for improving the reconstruction of unknown multi-gray-level material in the `missing wedge' situation

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