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Related Experiment Video

Updated: Apr 1, 2026

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately
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Rapid sphere sizing using a Bayesian analysis of reciprocal space imaging data.

K Ziovas1, A J Sederman1, C Gehin-Delval2

  • 1Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK.

Journal of Colloid and Interface Science
|October 7, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian statistical model to accurately measure sphere size distribution in concentrated dispersed systems using magnetic resonance imaging (MRI) and X-ray micro-tomography (X-ray μCT). The method rapidly quantifies particle sizes in model and food foam samples.

Keywords:
Bayesian analysisBubblesColloidsDispersionsFoamsNuclear magnetic resonanceSize distributionX-ray

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Area of Science:

  • Physics
  • Materials Science
  • Chemical Engineering

Background:

  • Dispersed systems are crucial in industries like petroleum, pharmaceuticals, and food.
  • Non-invasive measurement of physical properties, especially dispersed phase size distribution, is vital for concentrated systems.
  • Traditional methods like microscopy and scattering have limitations with concentrated or complex systems.

Purpose of the Study:

  • To develop and validate a Bayesian statistical model for extracting sphere size distribution (SSD).
  • To apply the model to reciprocal space data from 1D magnetic resonance imaging (MRI) and 2D X-ray micro-tomographic (X-ray μCT).
  • To assess the method's accuracy and speed for both model systems and real-world samples like food foams.

Main Methods:

  • Utilized reciprocal space data from 1D MRI and 2D X-ray μCT.
  • Employed a Bayesian statistical model for data analysis.
  • Validated the technique using glass spheres in xanthan gels (45μm–850μm) and air/water food foams (160μm–400μm).

Main Results:

  • Successfully estimated sphere size distribution (SSD) from both MRI and X-ray μCT data.
  • Achieved high accuracy for a wide range of sphere sizes in model systems.
  • Demonstrated rapid analysis, with estimations completed in as little as two seconds.
  • Successfully applied the method to air/water foam samples.

Conclusions:

  • The Bayesian statistical model provides an accurate and rapid method for determining SSD in concentrated dispersed systems.
  • This technique overcomes limitations of traditional methods, offering a valuable tool for industrial applications.
  • The study validates the model's efficacy on both model systems and complex food foams.