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Convolution Filter
A convolution filter is a mathematical filter function (also called kernel). During image reconstruction of computed tomography scans, various types of convolution filters e.g., to smooth or to enhance edges, can be selected according to the tissue characteristics.

See also Raw Data.
Raw Data
Raw data are the values of all measured detector signals during a scan. After calibration for fluctuations in tube output and beam hardening, the attenuation properties of each x-ray signal are accounted and correlated with the ray position. From these data the CT images are reconstructed including the use of mathematical procedures like convolution filtering and back projection.
Raw data can also be used for later reconstruction of additional planes and images by using a different convolution filter, zoom reconstruction, or an alternative CT number scale.

See also Magnification, Archiving, Number of Measurements and Convolution.
Filter
Different filter types are used in medical imaging to improve image quality.
Filter materials such as aluminum, iron or copper, located in the primary x-ray beam between tube and collimator, filter out lower energetic x-rays (for example bow-tie filter). Filters partially absorb or attenuate the x-ray beam to prevent radiation overexposure. The sum of filtration consists of inherent and added filters. This filtration affects the beam energy and ability to penetrate materials.
Mathematical filter procedures are used for the convolution of the attenuation profiles and reconstruction of computed tomography (CT) images.
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 [last update: 2023-11-06 02:01:00]