Ring artifacts reduction in cone-beam CT images based on independent component analysis

Chen, Yen‐Wei; Duan, Guifang; Fujita, Akitsugu; Hirooka, Ken; Ueno, Yoshio

doi:10.1109/imtc.2009.5168737

Cited by 13 publications

(10 citation statements)

References 8 publications

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“…For example, an iterative method was developed to remove the ring artifact from CT images utilizing relative total variation (TV) [26]. A method using independent component analysis was presented for suppressing the ring artifact [27]. This method divides the image into many independent components.…”

Section: Post-processing Methodsmentioning

confidence: 99%

Sparsity-based method for ring artifact elimination in computed tomography

et al. 2022

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Ring artifact elimination is one of the popular problems in computed tomography (CT). It appears in the reconstructed image in the form of bright or dark patterns of concentric circles. In this paper, based on the compressed sensing theory, we propose a method for eliminating the ring artifact during the image reconstruction. The proposed method is based on representing the projection data by a sum of two components. The first component contains ideal correct values, while the latter contains imperfect error values causing the ring artifact. We propose to minimize some sparsity-induced norms corresponding to the imperfect error components to effectively eliminate the ring artifact. In particular, we investigate the effect of using different sparse models, i.e. different sparsity-induced norms, on the accuracy of the ring artifact correction. The proposed cost function is optimized using an iterative algorithm derived from the alternative direction method of multipliers. Moreover, we propose improved versions of the proposed algorithms by incorporating a smoothing penalty function into the cost function. We also introduce angular constrained forms of the proposed algorithms by considering a special case as follows. The imperfect error values are constant over all the projection angles, as in the case where the source of ring artifact is the non-uniform sensitivity of the detector. Real data and simulation studies were performed to evaluate the proposed algorithms. Results demonstrate that the proposed algorithms with incorporating smoothing penalty and their angular constrained forms are effective in ring artifact elimination.

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Section: Post-processing Methodsmentioning

confidence: 99%

Sparsity-based method for ring artifact elimination in computed tomography

et al. 2022

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“…The measurement parameters are voltage 150 kV; current 66 μA; exposure time for each projection 0.5 s; scaled pixel size is 16.67 μm. The tomographic reconstructions were performed by correcting by the ring artefact and the beam hardening compensation parameters [16][17][18].…”

Section: Cerchiara DImentioning

confidence: 99%

Roman coins from the “Brettii and Enotri” museum: a non-destructive archaeometric study by X-ray fluorescence spectroscopy and X-ray microtomography

Smeriglio¹,

Crocco²,

Filosa³

et al. 2023

Proceedings of the 2022 IMEKO TC4 International Conference on Metrology for Archaeology and Cultural Heritage

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Two of a group of thirty coins dating between to 1st century BC and 1st century AD, found in the so-called "Grotta delle Ninfe" near Cerchiara di Calabria (Calabria, Italy), and preserved in the display case of the Brettii and Enotri Museum in Cosenza (Calabria, Italy) have been under archaeometric investigation. This paper aims to understand the oxidation processes, know constituent material and find hidden signs or inscriptions on the coins by means of X-ray fluorescence (XRF) portable spectrometer and X-ray microtomography (X-ray μ-CT) in order to make the hypothetical inscriptions readable, to know the period of manufacture and their provenance. The coins collection will be analysed in future research to make the inscriptions readable and identify the coins' provenience and typology.

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“…Artifact removal is a vital pre-processing step to remove unwanted artifacts from affecting the interpretation of the analyzed image. In recent years, many researchers have been developing artifact removal methods for the CBCT images, especially ring artifact in human abdomen, brain and dental applications [7,[15][16][17][18][19][20].…”

Section: Artifact Removalmentioning

confidence: 99%

Detection of Collaterals from Cone-Beam CT Images in Stroke

Aziz

Izhar

Asirvadam

et al. 2021

Sensors

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Collateral vessels play an important role in the restoration of blood flow to the ischemic tissues of stroke patients, and the quality of collateral flow has major impact on reducing treatment delay and increasing the success rate of reperfusion. Due to high spatial resolution and rapid scan time, advance imaging using the cone-beam computed tomography (CBCT) is gaining more attention over the conventional angiography in acute stroke diagnosis. Detecting collateral vessels from CBCT images is a challenging task due to the presence of noises and artifacts, small-size and non-uniform structure of vessels. This paper presents a technique to objectively identify collateral vessels from non-collateral vessels. In our technique, several filters are used on the CBCT images of stroke patients to remove noises and artifacts, then multiscale top-hat transformation method is implemented on the pre-processed images to further enhance the vessels. Next, we applied three types of feature extraction methods which are gray level co-occurrence matrix (GLCM), moment invariant, and shape to explore which feature is best to classify the collateral vessels. These features are then used by the support vector machine (SVM), random forest, decision tree, and K-nearest neighbors (KNN) classifiers to classify vessels. Finally, the performance of these classifiers is evaluated in terms of accuracy, sensitivity, precision, recall, F-Measure, and area under the receiver operating characteristics curve. Our results show that all classifiers achieve promising classification accuracy above 90% and able to detect the collateral and non-collateral vessels from images.

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Ring artifacts reduction in cone-beam CT images based on independent component analysis

Cited by 13 publications

References 8 publications

Sparsity-based method for ring artifact elimination in computed tomography

Sparsity-based method for ring artifact elimination in computed tomography

Roman coins from the “Brettii and Enotri” museum: a non-destructive archaeometric study by X-ray fluorescence spectroscopy and X-ray microtomography

Detection of Collaterals from Cone-Beam CT Images in Stroke

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