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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Related Experiment Video

Updated: Mar 24, 2026

Multi-timescale Microscopy Methods for the Characterization of Fluorescently-labeled Microbubbles for Ultrasound-Triggered Drug Release

Multi-timescale Microscopy Methods for the Characterization of Fluorescently-labeled Microbubbles for Ultrasound-Triggered Drug Release

Published on: June 12, 2021

Analysis of the Uncertainty in Microbubble Characterization.

Caroline Harfield¹, Christopher R Fury², Gianluca Memoli³

¹Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Oxford, UK.

Ultrasound in Medicine & Biology

|March 20, 2016

Summary

Experimental uncertainties significantly impact microbubble characterization for quantitative imaging. Even small variations in microbubble radius measurements can lead to substantial errors in estimated coating parameters, affecting predictive modeling.

Keywords:

Characterization Experimental error Microbubbles Modelling Quantitative imaging Ultrasound contrast agent Uncertainty

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Area of Science:

Biomedical Engineering
Acoustic Imaging
Materials Science

Background:

Microbubble contrast agents are vital for quantitative imaging like perfusion and blood pressure measurement.
Microbubble response to ultrasound is highly sensitive to size, coating, and environmental factors.
Experimental uncertainties can limit the reliability of microbubble characterization for predictive modeling.

Purpose of the Study:

To quantify uncertainties in microbubble characterization experiments.
To assess the influence of these uncertainties on measured microbubble characteristics.
To improve the design and data interpretation of microbubble characterization studies.

Main Methods:

Numerical simulations of microbubble dynamics were employed.
Model data was fitted to simulation results to estimate microbubble coating parameters.
Experimental parameter uncertainties were systematically varied to gauge their impact.

Main Results:

Uncertainty in microbubble radius measurements (e.g., optical microscopy) can cause ~20% variation in estimated coating parameters.
Variations in experimental parameters significantly influence derived microbubble characteristics.
The study quantifies the impact of common experimental uncertainties.

Conclusions:

Microbubble characterization experiments must account for inherent uncertainties.
Data from microbubble characterization studies should be interpreted with an understanding of potential errors.
Addressing these uncertainties is crucial for accurate quantitative imaging applications.