A directional TV based ring artifact reduction method

Salehjahromi, Morteza; Wang, Qian; Gjesteby, Lars; Harrison, Daniel; Wang, Ge; Yu, Hengyong

doi:10.1117/12.2513037

Cited by 4 publications

(9 citation statements)

References 11 publications

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“…The cause of non-uniformity in photon-counting detector is complicated. Generally, the relationship between the incident and measured photon counts can be depicted like this [30]:…”

Section: Methods and Materials A Problem Statementmentioning

confidence: 99%

“…Wu et al proposed a ring artefacts removal method based on TV-Stokes and unidirectional total variation model [29]. Salehjahromi et al proposed an iterative reconstruction method with a directional total variation regularization term for ring artefacts removal [30]. However, most of these methods seem to oversimplify the problem or impose strong assumptions.…”

Section: Introductionmentioning

confidence: 99%

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Removing Ring Artefacts for Photon-Counting Detectors Using Neural Networks in Different Domains

Fang

Chen

2020

IEEE Access

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The development of energy-resolving photon-counting detectors provides a new approach for obtaining spectral information in computed tomography. However, the responses of different photon counting detector pixels can be inconsistent, which will always cause stripe artefacts in projection domain and concentric ring artefacts in image domain. Traditional ring artifacts processing methods are mostly based on averaging and filtering. In this paper, we propose to use deep learning methods for ring artifacts removal respectively in image domain, projection domain and the polar coordinate system. Besides, by incorporating reconstruction process into neural networks, we unite the information from image domain and projection domain for ring artifacts removal under the framework of deep learning for the first time. A traditional ring artifacts removal method, which is based on wavelet and Fourier transform, is implemented for comparison. Quantitative analysis is performed on simulation and experimental results and it shows that deep learning based methods are promising in solving the problem of non-uniformity correction for photon-counting detectors. INDEX TERMS Ring artefacts removal, deep learning methods, photon counting detectors, spectral CT.

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“…The cause of non-uniformity in photon-counting detector is complicated. Generally, the relationship between the incident and measured photon counts can be depicted like this [30]:…”

Section: Methods and Materials A Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Removing Ring Artefacts for Photon-Counting Detectors Using Neural Networks in Different Domains

Fang

Chen

2020

IEEE Access

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“…In order to penalize the ring artifacts, we suggest placing the semi‐minor axis of the ellipses along with the tangential vector of the ring artifacts. Figure (b) shows the directions of these ellipses with respect to the rings . Thus, neighbor pixels along the radius directions are utilized with higher weights than other neighboring pixels.…”

Section: Methodsmentioning

confidence: 99%

A new iterative algorithm for ring artifact reduction in CT using ring total variation

Salehjahromi

Wang

Zhang³

et al. 2019

Medical Physics

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Purpose: In computed tomography (CT), miscalibrated or imperfect detector elements produce stripe artifacts in the sinogram. The stripe artifacts in Radon space are responsible for concentric ring artifacts in the reconstructed images. In this work, a novel optimization model is proposed to remove the ring artifacts in an iterative reconstruction procedure. Method: In the proposed optimization model, a novel ring total variation (RTV) regularization is developed to penalize the ring artifacts in the image domain. Moreover, to correct the sinogram, a new correcting vector is proposed to compensate for malfunctioning of detectors in the projection domain. The optimization problem is solved by using the alternating minimization scheme (AMS). In each iteration, the fidelity term along with the RTV regularization is solved using the alternating direction method of multipliers (ADMM) to find the image, and then the correcting coefficient vector is updated for certain detectors according to the obtained image. Because the sinogram and the image are simultaneously updated, the proposed method basically performs in both image and sinogram domains. Results: The proposed method is evaluated using both simulated and physical phantom datasets containing different ring artifact patterns. In the simulated datasets, the Shepp-Logan phantom, a real chest scan image and a noisy low-contrast phantom are considered for the performance evaluation of our method. We compare the quantitative root mean square error (RMSE) and structural similarity (SSIM) results of our algorithm with wavelet-Fourier sinogram filtering method by Munch et al., the ring artifact reduction method by Brun et al., and the TV-based ring correction method by Paleo and Mirone. Our proposed method is also evaluated using a physical phantom dataset where strong ring artifacts are manifest due to the miscalibration of a large number of detectors. Our proposed method outperforms the competing methods in terms of both qualitative and quantitative evaluation results. Conclusion: The experimental results in both simulated and physical phantom datasets show that the proposed method achieves the state-of-the-art ring artifact reduction performance in terms of RMSE, SSIM, and subjective visual quality.

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“…We end this section by mentioning that both the model of Aggrawal et al [1] and that of Salehjahromi et al [22] may be viewed as special cases of the hierarchical model (14). Specifically, the model of Aggrawal et al [1] corresponds to the assumption that the prior on v is a Gamma distribution and that ω = 1 and w = 0.…”

Section: Bayesian Hierarchical Modelmentioning

confidence: 99%

“…Preprocessing methods work by manipulating or filtering the projection images prior to the reconstruction [17,26], and postprocessing methods are designed to remove or reduce rings in the image domain after the reconstruction step [20,23,27,28]. The extended reconstruction models either implicitly or explicitly include the detector response as an unknown quantity to be estimated along with the attenuation function [1,19,22,24]. A common trait of all the aforementioned approaches is that they are oblivious to intensity drift.…”

Section: Introductionmentioning

confidence: 99%

A statistical reconstruction model for absorption CT with source uncertainty ^*

Bangsgaard¹,

Andersen²

2021

Inverse Problems

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Reconstruction methods for computed tomography are often based on the assumption that the source intensity and the detector response are known and static. In practice, however, both are unknown and must be estimated. An estimate of the combined source intensity and detector response is typically obtained by acquiring a number of so-called flat-field measurements, but this approach is oblivious to intensity drift, e.g., due to source instabilities, vibrating beamline components, etc. Discrepancies between the estimated response and true response can lead to severe artifacts in the reconstruction, especially for dose-and/or time-limited experiments. We propose a new, extended reconstruction model that jointly estimates the reconstruction, the detector response, and the (possibly time-varying) source intensity. We demonstrate through simulated experiments that the proposed reconstruction model leads to reconstructions with significantly reduced artifacts.

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A directional TV based ring artifact reduction method

Cited by 4 publications

References 11 publications

Removing Ring Artefacts for Photon-Counting Detectors Using Neural Networks in Different Domains

Removing Ring Artefacts for Photon-Counting Detectors Using Neural Networks in Different Domains

A new iterative algorithm for ring artifact reduction in CT using ring total variation

A statistical reconstruction model for absorption CT with source uncertainty ^*

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A directional TV based ring artifact reduction method

Cited by 4 publications

References 11 publications

Removing Ring Artefacts for Photon-Counting Detectors Using Neural Networks in Different Domains

Removing Ring Artefacts for Photon-Counting Detectors Using Neural Networks in Different Domains

A new iterative algorithm for ring artifact reduction in CT using ring total variation

A statistical reconstruction model for absorption CT with source uncertainty *

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A statistical reconstruction model for absorption CT with source uncertainty ^*