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A general framework and review of scatter correction methods in x-ray
cone-beam computerized tomography. Part 1: Scatter compensation approaches
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radiation in cone-beam volume CT implies severe degradation of CTimages by quantification errors, artifacts, and noise increase, scatter suppression is one of
the main issues related to image quality in
CBCTimaging. The aim of this review is to structurize the variety of
suppression methods, to analyze the common structure, and to develop a general framework
correction procedures. In general, scatter suppression combines hardware techniques of scatter rejection and software
methods of scatter
correction. The authors emphasize that scatter correction procedures consist of the main
estimation (by measurement or mathematical modeling) and scatter compensation
(deterministic or statistical methods). The framework comprises most scatter correction approaches
and its validity also goes beyond transmission CT. Before the advent of cone-beam CT, a lot of papers on
correction approaches in x-ray radiography, mammography, emission tomography, and in
had been published. The opportunity to avail from research in those other fields of
imaging has not yet been sufficiently exploited. Therefore additional
references are included when ever it seems pertinent. Scatter estimation and
compensation are typically intertwined in iterative procedures. It makes sense to
recognize iterative approaches in the light of the concept of self-consistency. The
importance of incorporating scatter compensation approaches into a statistical framework for
minimization has to be underscored. Signal and noise propagation analysis is presented. A main result is
the preservation of differential-signal-to-noise-ratio (dSNR) in CT projection data by
correction. The objective of scatter compensation methods is the restoration of quantitative
accuracy and a balance between low-contrast restoration and noise reduction. In a synopsis
section, the different deterministic and statistical methods are discussed with respect to
their properties and applications. The current paper is focused on scatter compensation
algorithms. The multitude of scatter estimation models will be dealt with in a separate paper.
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