Low-rank flat-field correction for artifact reduction in spectral computed tomography

Bangsgaard, Katrine O.; Burca, Genoveva; Ametova, Evelina; Andersen, Martin S.; Jørgensen, Jakob Sauer

doi:10.1080/27690911.2023.2176000

Applied Mathematics in Science and Engineering

2023

DOI: 10.1080/27690911.2023.2176000

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Low-rank flat-field correction for artifact reduction in spectral computed tomography

Katrine O. Bangsgaard

Genoveva Burca

Evelina Ametova

et al.

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2024

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Cited by 3 publications

References 32 publications

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Mitigation of DMM-induced stripe patterns in synchrotron X-ray radiography through dynamic tilting

Eddah,

Markötter,

Mieller

et al. 2024

J Synchrotron Rad

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In synchrotron X-ray radiography, achieving high image resolution and an optimal signal-to-noise ratio (SNR) is crucial for the subsequent accurate image analysis. Traditional methods often struggle to balance these two parameters, especially in situ applications where rapid data acquisition is essential to capture specific dynamic processes. For quantitative image data analysis, using monochromatic X-rays is essential. A double multilayer monochromator (DMM) is successfully used for this aim at the BAMline, BESSY II (Helmholtz Zentrum Berlin, Germany). However, such DMMs are prone to producing an unstable horizontal stripe pattern. Such an unstable pattern renders proper signal normalization difficult and thereby causes a reduction of the SNR. We introduce a novel approach to enhance SNR while preserving resolution: dynamic tilting of the DMM. By adjusting the orientation of the DMM during the acquisition of radiographic projections, we optimize the X-ray imaging quality, thereby enhancing the SNR. The corresponding shift of the projection during this movement is corrected in post-processing. The latter correction allows a good resolution to be preserved. This dynamic tilting technique enables the homogenization of the beam profile and thereby effectively reduces noise while maintaining high resolution. We demonstrate that data captured using this proposed technique can be seamlessly integrated into the existing radiographic data workflow, as it does not need hardware modifications to classical X-ray imaging beamline setups. This facilitates further image analysis and processing using established methods.

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Mitigation of DMM-induced stripe patterns in synchrotron X-ray radiography through dynamic tilting

Eddah,

Markötter,

Mieller

et al. 2024

J Synchrotron Rad

View full text Add to dashboard Cite

show abstract

Adaptive automated sinogram normalization for ring artifacts suppression in CT

Kazimirov,

Polevoy,

Ingacheva

et al. 2024

Opt. Express

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Ring artifacts pose a major barrier to obtaining precise reconstruction in computed tomography (CT). The presence of ring artifacts complicates the use of automatic means of processing CT reconstruction results, such as segmentation, correction of geometric shapes, alignment of reconstructed volumes. Although there are numerous efficient methods for suppressing ring artifacts, many of them appear to be manual. Along with this, a large proportion of the automatic methods cope unsatisfactorily with the target task while requiring computational capacity. The current work introduces a projection data preprocessing method for suppressing ring artifacts that constitutes a compromise among the outlined aspects – automaticity, high efficiency and computational speed. Derived as the automation of the classical sinogram normalization method, the proposed method specific advantages consist in adaptability in relation to the filtered sinograms and the edge-preservation property proven within the experiments on both synthetic and real CT data. Concerning the challenging open-access data, the method has performed superior quality comparable to that of the advanced methods: it has demonstrated 70.4% ring artifacts suppression percentage (RASP) quality metric. In application to our real laboratory CT data, the proposed method allowed us to gain significant refinement of the reconstruction quality which has not been surpassed by a range of compared manual ring artifacts suppression methods.

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Time varying flat field correction of X-ray microtomography with an improved deep-learning method

Du,

Xu,

Xue

et al. 2024

Opt. Express

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In X-ray microtomography, the flat field image is usually needed to normalize the collected sample projections. Owing to the high brightness of the synchrotron radiation facility, dynamic CT imaging of in-situ or in-operando processes is broadly employed for the investigation of three-dimensional microstructure evolution. However, the fast, continuous data acquisition and the heavy, bulky in-situ devices usually prevent the easy collection of accurate flat field images, which means that conventional flat field correction is hard to efficiently correct the artefacts of X-ray microtomography. We report a deep-learning-based artefact correction method for X-ray microtomography, which uses flat field generated from each CT projection by an improved pix2pixHD model. Experimental results demonstrated that the proposed method has a significant advantage over the conventional method and available deep-learning-based flat field correction method for the flat field correction of projection images. The CT imaging results show that the proposed method efficiently reduces the systematic error during the intensity normalization process, and the CT reconstruction is improved significantly. Therefore, the method developed in this paper is applicable for the flat field correction of dynamic CT. Furthermore, experiments with a set of low Z material samples verified the generalization of the deep-learning-based method for a variety of samples never used for network training. In conclusion, the method developed in this paper is practicable for the flat field correction of in-situ CT imaging of dynamic processes and is also applicable to new samples as long as the neural network model is effectively trained.

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Low-rank flat-field correction for artifact reduction in spectral computed tomography

Cited by 3 publications

References 32 publications

Mitigation of DMM-induced stripe patterns in synchrotron X-ray radiography through dynamic tilting

Mitigation of DMM-induced stripe patterns in synchrotron X-ray radiography through dynamic tilting

Adaptive automated sinogram normalization for ring artifacts suppression in CT

Time varying flat field correction of X-ray microtomography with an improved deep-learning method

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