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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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Thermal drift correction method for laboratory nanocomputed tomography based on global mixed evaluation.

Mengnan Liu, Yu Han, Xiaoqi Xi

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    |October 14, 2022
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    Summary

    This study introduces a new method for correcting artifacts in nano computed tomography (nanoCT) imaging. The novel global mixed evaluation (GME) approach significantly improves drift estimation accuracy without needing correction phantoms.

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    Area of Science:

    • Materials Science
    • Imaging Science
    • Nanotechnology

    Background:

    • Nanocomputed tomography (nanoCT) enables non-destructive 3D imaging of nanomaterials.
    • Artifacts from X-ray source drift and thermal expansion necessitate correction methods.
    • Existing drift correction methods using sparse projections are limited by noise and brightness variations, impacting feature-based algorithms like LPM and RANSAC.

    Purpose of the Study:

    • To develop a precise drift estimation method for nanoCT without correction phantoms.
    • To introduce a novel evaluation criterion for projection alignment.
    • To enhance the accuracy of nanoCT imaging by mitigating artifacts.

    Main Methods:

    • Proposed a rough-to-refined correction framework utilizing global mixed evaluation (GME).
    • Designed GME, combining structural similarity (SSIM) and average phase difference (APD), as a new projection alignment criterion.
    • Implemented an outlier elimination strategy within the GME optimization for drift estimation.

    Main Results:

    • The GME criterion demonstrated significantly improved accuracy in simulated 2D projection alignment.
    • GME achieved 14x and 12x higher accuracy compared to LPM and RANSAC, respectively.
    • The proposed method was successfully validated in real-world 3D nanoCT imaging experiments.

    Conclusions:

    • The GME-based correction framework offers a precise and effective solution for nanoCT drift correction.
    • This method overcomes limitations of existing techniques, enhancing imaging fidelity without phantoms.
    • The validated approach holds promise for advancing nanoCT applications in materials science and beyond.