7.2 - Neural Network-Driven Image Gamma Calibration: An Innovative Approach for All-Optical Rheometer
- Event
- 17. Dresdner Sensor-Symposium 2024
2024-11-25 - 2024-11-27
Dresden - Band
- Vortrag
- Chapter
- 7. Smart Sensors
- Author(s)
- T. Wang, E. Fattahi, D. Geier, T. Becker - Technical University of Munich, Freising/D
- Pages
- 76 - 80
- DOI
- 10.5162/17dss2024/7.2
- ISBN
- 978-3-910600-04-1
- Price
- free
Abstract
When coherent light is irradiated onto a fluid sample containing particles, the interference between the scattered light paths forms granular speckles [1, 2]. In the field of optical imaging, researchers aim to reduce such speckle patterns. However, in applications such as blood flow measurement [3], surface roughness assessment [4], and optical sensing [5], speckle patterns play an important role because they carry detailed information about the sample's properties. Laser speckle rheology (LSR) uses speckle patterns to measure the viscoelastic properties of fluids [6], addressing the limitations of traditional methods such as shear rheometers, dynamic light scattering (DLS) [7], and diffusing wave spectroscopy (DWS) [8], which require large samples, direct contact, and bulky equipment. For LSR, the speckle pattern is first used to calculate the autocorrelation curve (g2(t)) to derive the mean square displacement (MSD), which is then used to determine the sample's Complex Modulus (G*(ω)) [9]. Improvements to LSR, such as the use of polarization-sensitive Monte Carlo algorithms, have improved accuracy by correcting for scattering and absorption effects, resulting in more reliable viscoelastic measurements [10]. LSR has found applications in assessing the biomechanical properties of tissues, particularly in oncology [11, 12].