Particle Image Correlation
Particle Image Correlation - Related Videos
16:31
Echo Particle Image Velocimetry
0 Views •
Cited by 10 •
2012
An echo particle image velocimetry (EPIV) system capable of acquiring two-dimensional fields of velocity in optically opaque fluids or through optically opaque geometries is described, and validation measurements in pipe flow are reported.
11:59
High-speed Particle Image Velocimetry Near Surfaces
0 Views •
Cited by 19 •
2013
A procedure for studying transient flows near boundaries using high-resolution, high-speed particle image velocimetry (PIV) is described here. PIV is a non-intrusive measurement technique applicable to any optically accessible flow by optimizing several parameter constraints such as the image and recording properties, the laser sheet properties, and analysis algorithms.
14:21
Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation
0 Views •
Cited by 21 •
2013
A method to image ex vivo pulmonary resection specimens with optical frequency domain imaging (OFDI) and obtain precise correlation to histology is described, which is essential to developing specific OFDI interpretation criteria for pulmonary pathology. This method is applicable to other tissue types and imaging techniques to obtain precise imaging to histology correlation for accurate image interpretation and assessment. Imaging criteria established with this technique would then be...
07:53
Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows
0 Views •
Cited by 12 •
2013
Micro-particle image velocimetry (μPIV) is used to visualize paired images of micro particles seeded in blood flows which are cross-correlated to give an accurate velocity profile. Shear rate, maximum velocity, velocity profile shape, and flow rate, each of which has clinical applications, can be derived from these measurements.
02:20
Correlations
0 Views •
2020
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
FAQs
The image is divided into small interrogation regions so the method can examine local rather than only overall movement. Statistical correlation then identifies corresponding visible particles or image patterns between sequential frames. Comparing these local correspondences produces spatially resolved displacement information, which is necessary for describing nonuniform motion or deformation in biological systems.
Controlled imaging conditions and timing are essential because the method depends on matching features between sequential frames. If the visible particles or image patterns cannot be compared consistently, the resulting displacement maps may not represent the underlying movement accurately. Careful control therefore supports meaningful measurements of motion, strain, and flow.
Particle Image Correlation first provides displacement at multiple locations across the image. Those spatially distributed movements can then be used to characterize deformation as strain or movement of fluid as flow, provided the imaging conditions and timing are controlled. The resulting maps reveal how motion varies across the measured biological or engineered system.
A typical workflow begins by acquiring sequential images containing visible particles or image patterns under controlled conditions. The images are divided into interrogation regions, and statistical correlation identifies corresponding features in each region. Their measured displacements are then organized into spatial maps that describe movement, deformation, strain, or flow, depending on the application.
In bioengineering, the technique is useful when researchers need quantitative, noncontact measurements of how biological or engineered materials move and deform. Applications include characterizing cell migration, tissue deformation, biomaterial mechanics, and microscale fluid transport. These measurements can reveal responses to mechanical and environmental conditions without requiring direct contact with the system.
Particle Image Correlation supplies spatial measurements that can be compared with predictions from computational models, helping researchers evaluate whether those models represent observed biological behavior. It also supports studies of microscale fluid transport by mapping movement across image regions. Together, these uses connect experimental observations with mechanical and environmental responses in bioengineering systems.