Speckle Contrast Flowmeter Calibration for Noise Correction
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Solution Overview
Problem
Laser speckle imaging (LSI) is highly susceptible to noise from various sources, affecting the accuracy of flow measurements, particularly in biomedical applications like blood flow analysis, due to its reliance on standard deviation between pixels, which can be influenced by sensor noise, laser coherence, and ambient light.
Innovation Solution
A calibration method using known samples with known particle characteristics to correct for noise and non-flow related signals in speckle contrast measurements, employing a processor and memory system to correlate and interpolate or extrapolate contrast measurements, thereby reducing errors caused by noise and ambient light.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laser speckle imaging is used to measure flow rate, then flow measurement capability is provided, but measurement precision deteriorates due to noise from sensor, shot noise, dark sensor noise, and ambient light
Solution Approach 1:
The patent applies preliminary action by performing calibration measurements before actual flow measurements. The system captures images of a calibration sample with known flow characteristics under the same lighting and sensor conditions, then uses these pre-acquired calibration data to correct subsequent measurements. This preliminary calibration step establishes a reference that accounts for all noise sources present in the measurement environment.
Solution Approach 2:
The patent converts harmful noise factors into beneficial calibration information. By deliberately measuring a calibration sample with known characteristics under the same noisy conditions as actual measurements, the system captures the noise signature and uses it to correct future measurements. The previously harmful noise becomes a quantifiable correction factor that improves measurement accuracy.
2Measurement precision
If speckle contrast is calculated using standard deviation between pixels, then flow rate information can be extracted, but measurement precision worsens due to susceptibility to sensor noise and ambient light
Solution Approach 1:
The patent implements feedback by using calibration measurements to correct actual measurements. The system calculates speckle contrast for both the calibration sample and the target sample, then uses the known characteristics of the calibration sample to determine correction factors. These correction factors are applied to the target sample measurements, creating a feedback loop that compensates for noise and improves accuracy.
Solution Approach 2:
The calibration sample acts as an intermediary between the noisy measurement environment and the final flow rate calculation. By measuring the calibration sample with known characteristics under the same conditions, the system creates a reference that mediates the relationship between the noisy speckle contrast measurements and the actual flow rate, enabling accurate correction.
3Measurement precision
If calibration measurements are performed using known samples, then measurement accuracy is improved, but device complexity increases due to additional calibration procedures and data processing
Solution Approach 1:
The patent applies copying by creating a digital reference model of the measurement system's noise characteristics through calibration measurements. Instead of physically modifying the system or adding complex hardware, the method copies the noise signature into calibration data that can be stored and applied computationally. This virtual copy allows for accurate correction without adding physical complexity.
Solution Approach 2:
The patent uses parameter changes by transforming the calibration measurements into correction parameters that are applied to the actual measurements. The system changes the state of the data from raw speckle contrast values to corrected flow rate values by applying parameters derived from the calibration sample's known characteristics, simplifying the overall process through mathematical transformation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of particle motion characterization, specifically in determining blood flow rates by eliminating noise-related errors, leading to more reliable measurements of blood flow and pulsatile waveforms.
Implementation Method 1
a light source configured to emit light such that the light scatters within a sample
Implementation Method 2
light interrogates a sample and randomly interferes on the image sensor, producing a signature 'speckle' pattern
Implementation Method 3
a photo-sensitive detector having one or more light-sensitive pixel elements configured to receive at least some of the scattered light
Data Source
AI summary
Disclosed herein are systems, methods, and devices for calibrating contrast measurements from laser speckle imaging systems to accurately determine unknown particle motion characteristics, such as flow rate. The calibration stores to memory calibration data, which may include a set of measurements from samples with known particle characteristics and/or estimates of noise, including the effects on contrast arising from undesired signals unrelated to the unknown particle motion characteristics. The calibration data may be accessed and used to correct an empirical measurement of contrast and/or interpolate a value of the unknown particle motion characteristic. The system may include a light source, photodetector, processor, and memory, which can be combined into a single device, such as a wearable device, for providing calibrated flow measurements. The device may be used, for example, to measure blood flow, cardiac output, and heart rate, and can be used to amplify the pulsatile signal.