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Unsharpness is the loss of spatial resolution in a radiographic image. There are generally considered to be three types of unsharpness: geometric unsharpness, motion unsharpness and photographic or system unsharpness. [1] Motion unsharpness is caused by movement of the patient, the detector or the source of X-rays, during the exposure. Movement ...
Image relating focal spot size to geometric unsharpness in projectional radiography. [2] Geometric magnification results from the detector being farther away from the X-ray source than the object. In this regard, the source-detector distance or SDD [3] is a measurement of the distance between the generator and the detector.
In optics, optical path length (OPL, denoted Λ in equations), also known as optical length or optical distance, is the length that light needs to travel through a vacuum to create the same phase difference as it would have when traveling through a given medium.
The vector projection (also known as the vector component or vector resolution) of a vector a on (or onto) a nonzero vector b is the orthogonal projection of a onto a straight line parallel to b.
Effect of an anti-scatter grid on incident beams. In medical imaging, an anti-scatter grid (also known as a Bucky-Potter grid) is a device for limiting the amount of scattered radiation reaching the detector, [1] [2] thereby improving the quality of diagnostic medical x-ray images.
Some military and expensive survey-grade civilian receivers calculate atmospheric dispersion from the different delays in the L1 and L2 frequencies, and apply a more precise correction. This can be done in civilian receivers without decrypting the P(Y) signal carried on L2, by tracking the carrier wave instead of the modulated code.
Geometric relevance: The torsion τ(s) measures the turnaround of the binormal vector. The larger the torsion is, the faster the binormal vector rotates around the axis given by the tangent vector (see graphical illustrations). In the animated figure the rotation of the binormal vector is clearly visible at the peaks of the torsion function.
Geometric constraint solving is constraint satisfaction in a computational geometry setting, which has primary applications in computer aided design. [1] A problem to be solved consists of a given set of geometric elements and a description of geometric constraints between the elements, which could be non-parametric (tangency, horizontality, coaxiality, etc) or parametric (like distance, angle ...