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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Interference leads to systematic error in atomic absorption (AA) measurements by enhancing or diminishing the analytical signal or the background. These interferences can be grouped into three main categories: spectral interference, chemical interference, and physical interference.
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Related Experiment Video

Updated: Aug 1, 2025

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
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Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT.

Hanvit Kim, Zongyu Li, Jiye Son

    IEEE Transactions on Medical Imaging
    |April 27, 2023
    PubMed
    Summary

    This study introduces a faster, weakly supervised deep learning method for scatter estimation in quantitative SPECT imaging. It significantly reduces computation for scatter modeling, enabling accurate, patient-specific results.

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    Area of Science:

    • Medical Imaging
    • Nuclear Medicine
    • Computational Science

    Background:

    • Accurate scatter estimation is crucial for quantitative SPECT imaging, improving contrast and diagnostic accuracy.
    • Traditional Monte Carlo (MC) simulations provide accurate scatter data but are computationally intensive.
    • Existing deep learning methods require full MC simulations for ground truth data, limiting their efficiency.

    Purpose of the Study:

    • To develop a physics-guided, weakly supervised deep learning framework for rapid and accurate scatter estimation in quantitative SPECT.
    • To reduce the computational burden associated with generating training data for scatter correction.
    • To enable efficient, patient-specific scatter modeling through fine-tuning.

    Main Methods:

    • Utilized a 100x shorter MC simulation to generate weak labels for training deep neural networks.
    • Employed a physics-guided, weakly supervised approach to enhance MC simulations.
    • Implemented a fine-tuning strategy using short MC simulations for patient-specific scatter modeling.

    Main Results:

    • The weakly supervised method achieved performance comparable to supervised methods in phantom studies with substantially less labeling computation.
    • Patient-specific fine-tuning of the proposed method improved scatter estimation accuracy in clinical scans compared to supervised methods.
    • Demonstrated accurate deep scatter estimation in quantitative SPECT with reduced computational requirements.

    Conclusions:

    • Physics-guided weak supervision offers an efficient alternative for deep scatter estimation in quantitative SPECT.
    • The proposed framework significantly lowers computational costs for scatter modeling.
    • The method's capability for patient-specific fine-tuning enhances its clinical applicability.