Fluid Transport Imaging
Fluid Transport Imaging - Related Videos
02:41
In Vivo Imaging of Cerebrospinal Fluid Transport in a Mouse Brain Using Fluorescence Macroscopy
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2025
Source: Sweeney, A. M., et al. In Vivo Imaging of Cerebrospinal Fluid Transport through the Intact Mouse Skull using Fluorescence Macroscopy. J. Vis. Exp. (2019)This video demonstrates the process of visualizing cerebrospinal fluid (CSF) flow in a mouse brain using fluorescence imaging. A fluorescent tracer is injected into the cisterna magna (CM), allowing its movement with CSF to be tracked through the subarachnoid and perivascular spaces. Time-lapse imaging captures the tracer's journey,...
06:22
In Vivo Imaging of Cerebrospinal Fluid Transport through the Intact Mouse Skull using Fluorescence Macroscopy
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Cited by 27 •
2019
Transcranial optical imaging allows wide-field imaging of cerebrospinal fluid transport in the cortex of live mice through an intact skull.
12:13
Single-Molecule Imaging of Nuclear Transport
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2010
Single molecule microscopy approch provided novel insights into nuclear transport.
06:48
Fluid-cell Raman Spectroscopy for operando Studies of Reaction and Transport Phenomena during Silicate Glass Corrosion
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Cited by 1 •
2025
Fluid-cell Raman Spectroscopy (FCRS) enables in operando observations of reaction and transport phenomena during the aqueous corrosion of silicate glasses at a microscopic level, at elevated temperatures, and in real-time. Without interrupting ongoing processes, FCRS provides information about reaction mechanisms, kinetics, and transport processes.
09:24
Expanding the Toolkit for In Vivo Imaging of Axonal Transport
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Cited by 16 •
2021
Using transgenic fluorescent mice, detailed protocols are described to assess in vivo axonal transport of signaling endosomes and mitochondria within motor and sensory axons of the intact sciatic nerve in live animals.
FAQs
The measured signal provides a basis for linking observed transport behavior to physical flow properties. Tracking visible tracers or suspended particles can reveal movement through a channel or porous structure, while interfaces and concentration changes can show boundaries, mixing, or dispersion. These observations allow researchers to map pathways, estimate velocity, and locate regions where transport becomes restricted or irregular.
Each signal highlights a different aspect of transport. Tracers and suspended particles make movement through a fluid observable, interfaces reveal the position or evolution of fluid boundaries, and concentration changes expose redistribution within the system. Selecting among these signals helps investigators examine pathways, mixing, dispersion, leakage, or barriers according to the behavior being studied.
Imaging can expose spatial variations that a single flow measurement might miss. Researchers can identify preferred pathways, leakage, mixing zones, dispersed material, and barriers that interrupt transport. This is especially useful in porous materials, biological systems, and engineered devices, where local irregularities may determine how effectively mass, momentum, or heat moves through the system.
A study generally begins by selecting a visible tracer, suspended particle, interface, or concentration signal that represents the transport of interest. Researchers then observe it with optical or other sensors, use the recorded changes to map pathways and quantify movement, and examine the results for velocity, mixing, dispersion, leakage, or barriers. The measurements can subsequently be compared with computational predictions.
Engineering applications include microfluidic systems, heat exchangers, filtration devices, pipelines, and porous structures. In these settings, imaging can show whether fluid follows intended pathways, mixes as expected, leaks, or encounters transport barriers. The resulting measurements support device design and evaluation by connecting observed fluid behavior with performance involving mass, momentum, or heat transfer.
Measured transport patterns provide experimental evidence for evaluating computational models. Agreement or disagreement between observed pathways, velocities, mixing, dispersion, and leakage can reveal whether a model represents the system adequately. In engineering studies, this feedback helps identify flow irregularities and supports improved control of processes and devices that depend on predictable fluid transport.