To study the distribution of cells in the surface layer of articular cartilage, rabbit hip and knee specimens were stained with silver and studied by scanning electron microscopy (SEM). The cartilage was treated en bloc using the Gomori methenamine silver technique, which stains the nuclei of exposed cells with reduced silver. The intact surface was then studied with a binocular microscope and SEM in the backscatter mode Only those cells within 30 microns of the surface stained, permitting that population to be imaged selectively. Depressions in the surface were related to groups of cells in clusters or rows bounded by collagen fibers. This study demonstrates the effectiveness of backscatter imaging in the study of chondrocytes. The relationship between surface contours and underlying cells is more complex than previously described.
Structured light illumination (SLI) systems can measure a target's two-dimensional height profile. High-quality imaging in SLI systems is typically dependent upon well-focused optical systems that can be difficult to achieve due to the nonlinear nature of defocus. We present the quad target method (QTM), a simple and fast alignment technique that linearizes defocus for SLI projectors. This linear response provides a clear zero crossing along the optical axis to minimize any focus ambiguity and maximize projector focus quality. QTM creates the linearization by sampling the field at two locations along the optical axis using a quad target, which is a diffuse target with two different heights in opposing quadrants. To maximize feedback speed, QTM can estimate the current focus position using only a single image by measuring the contrast of projected sinusoidal fringes on the quad target. QTM was tested using a commercial SLI system and compared with a published phase shifting reconstruction technique. The nonlinear defocus behavior was efficiently linearized as evidenced by focus estimation accuracies over a broad range of QTM variables.
A scanning laser acoustic microscope (SLAM) is described. The SLAM achieves a resolution of about 10 μm and is capable of imaging subsurface features at depths up to several millimeters, using appropriate reconstruction algorithms. The various components of the apparatus, both acousto-optical and electronic, are described. A major feature of the SLAM is a digital data-acquisition system capable of storing and processing the complex acoustic field in images with 256×256-pixel resolution. An array processor enables most floating-point operations to be completed in a few seconds. Examples of acoustic images obtained from a test sample are presented.
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