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Holographic video captures microstructures

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Rebecca C. Jernigan, [email protected]

Holograms often are thought of as the stuff of science fiction, but they actually are part of our daily lives. They decorate toys and serve as security features on credit cards, and holographic microscopy can be used to investigate the properties of tiny structures.

Taking the latter a step further, researchers at New York University, led by physics professor David G. Grier, have developed a method that records 3-D movies of microscopic systems by using holographic video.

Initially, their plan was to get a closer look at the microscopic 3-D structures created with computer-generated optical trapping. The structures were too small to examine visually but too large to view with high-resolution light microscopy. The researchers also needed to characterize the optical properties of their chemically synthesized creations.

A winding road

They believed that holographic microscopy, which had already proved able to reconstruct the 3-D light field responsible for creating the holographic image, could solve their problem. After demonstrating that the technique could track their colloidal building blocks, even when stacked atop one another, they began to look at applying the Lorenz-Mie theory – which defines the patterns caused when light is scattered by a spherical object – to the scattered light to measure the refractive index of the individual particles.

SNHolovid_Fig1.jpg

Researchers have developed an in-line holographic video microscope that uses a collimated laser beam to illuminate a sample. The light scattered by the sample interferes with the unscattered light around it, enabling use of the Lorenz-Mie theory to gain additional information from the sample. Images courtesy of David G. Grier.

Grier explained that the scattering pattern predicted by the theory “depends on the position of the particle in three dimensions – its size, shape and orientation – and its index of refraction, assuming that the particle is made of a homogeneous material.” The scientists attempted to find just the final number but discovered that the formula was much too sensitive to the particle’s location.

Fitting images for both the refractive index and the size still did not produce the required information, however, and they were faced with rewriting the software to consider a total of five adjustable parameters (when more than two or three typically do not produce usable data).

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“My group would never have undertaken fitting images to Lorenz-Mie theory if we’d known that there would be so many free parameters. Such highly unconstrained fits almost never converge to a meaningful answer,” Grier said. In this case, however, all of the details converged to provide the precise information they were looking for. After their algorithm was put through a set of independent tests for verification, they discovered that the process could measure a particle’s 3-D location with nanometer precision, its refractive index to within one part per thousand and its radius to within one nanometer.

Some of this information had not been available by other means, yet here it was – and all at once.

Making of the movie

To turn the process into 3-D holographic videos of the particles, the researchers recorded video images with a conventional camera. They analyzed the video frame by frame, using a “thing recognition” algorithm to identify the holographs of individual particles in each image. They used Monte Carlo techniques to estimate the position and properties of each object, with the estimates serving as inputs to the nonlinear fits in the predictions of the Lorenz-Mie theory.

SNHolovid_Fig2.jpg
Using holographic particle-image velocimetry, investigators measured the 3-D trajectory of 500 colloidal spheres as they moved down a microfluidic channel.

The scientists are using the graphical processing units of high-end computer graphics cards to perform the computationally intensive fits, enabling them to follow a single particle in near-real time and to characterize its properties simultaneously.

They have used this process in several applications, including fundamental research into statistical physics and probing the micromechanical properties of bacterial biofilms, and for high-resolution 3-D flow visualization. The technique also could have applications in medical diagnostics, perhaps creating single-particle, label-free, bead-based systems that would enable miniaturized medical tests.

Published: October 2009
Glossary
algorithm
A precisely defined series of steps that describes how a computer performs a task.
label-free
Label-free refers to a technique or method that does not require the use of additional labels, tags, or markers to detect or identify specific components or entities. In various scientific and technological applications, labeling often involves attaching fluorescent dyes, radioactive isotopes, or other markers to molecules, cells, or structures of interest. However, in label-free approaches, detection or analysis is performed without the need for these additional labels. Common applications...
light field
The term light field refers to the spatial distribution of light rays traveling in all directions through a given space. It includes information about the intensity and direction of light rays at every point in a scene. In the context of imaging and photography, the light field is a comprehensive description of the light entering a camera or other optical system. Key points about light field include: Directional information: Unlike traditional photographs that capture only the total light...
microscopic
Characteristic of an object so small in size or so fine in structure that it cannot be seen by the unaided eye. A microscopic object may be rendered visible when examined under a microscope.
algorithmbacterial biofilmsbead-basedBiophotonicscolloidal building blockscolloidal spheresDavid G. Grierdefenseflow visualizationGrierhologramsholographic videolabel-freelight fieldLorenz-Miemedical diagnosticsmicromechanical propertiesmicroscopicMicroscopyMonte CarloNew York UniversityNews & Featuresnonlinear fitsOptical trappingRebecca C. Jerniganrefractive indexsingle-particlestatistical physicsthing recognitionthree-dimensionalLasers

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