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Related Experiment Videos

Additive noise properties of active matrix flat-panel imagers.

M Maolinbay1, Y El-Mohri, L E Antonuk

  • 1Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor 48109, USA. manat@umich.edu

Medical Physics
|September 13, 2000
PubMed
Summary

This study models additive noise in active matrix flat-panel imagers (AMFPIs), detailing noise sources like TFT thermal and photodiode shot noise. Model predictions closely matched experimental results for AMFPI noise characterization.

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Area of Science:

  • Medical Imaging Physics
  • Electronic Engineering
  • Semiconductor Device Physics

Background:

  • Active matrix flat-panel imagers (AMFPIs) are crucial for indirect detection imaging.
  • Understanding additive noise is essential for optimizing AMFPI performance.
  • Existing models may not fully capture the complex noise contributions within AMFPIs.

Purpose of the Study:

  • To develop a comprehensive theoretical model for additive noise in AMFPIs.
  • To empirically validate the model by measuring various noise components.
  • To identify the dominant noise sources across different operating conditions.

Main Methods:

  • Developed an equivalent-noise-circuit model for AMFPIs.
  • Identified and categorized noise components: pixel, data line thermal, preamplifier, and digitization noise.

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  • Conducted experimental measurements on a prototype AMFPI and discrete components.

    • Main Results:

    • Model predictions for total additive noise, pixel noise, preamplifier noise, and data line thermal noise agreed well with measurements.
    • Pixel noise is dominated by photodiode/TFT shot and 1/f noise for frame times > 1s.
    • TFT thermal noise becomes dominant at shorter frame times, validating its inclusion in the model.

    Conclusions:

    • The developed theoretical model accurately represents additive noise in AMFPIs.
    • The model successfully incorporates key noise contributors, including TFT thermal noise.
    • Experimental validation confirms the model's predictive power for AMFPI noise analysis.