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Application of a variable filter for presampled modulation transfer function analysis with the edge method
Author(s) -
Ryo Higashide,
Katsuhiro Ichikawa,
Hiroshi Kunitomo,
Kazuya Ohashi
Publication year - 2015
Publication title -
radiological physics and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.41
H-Index - 20
eISSN - 1865-0341
pISSN - 1865-0333
DOI - 10.1007/s12194-015-0325-2
Subject(s) - optical transfer function , noise (video) , noise reduction , filter (signal processing) , center frequency , spatial filter , enhanced data rates for gsm evolution , transfer function , robustness (evolution) , optics , acoustics , computer science , physics , band pass filter , telecommunications , artificial intelligence , computer vision , image (mathematics) , biochemistry , chemistry , electrical engineering , gene , engineering
We devised a new noise filtering method to reduce the noise in the line spread function (LSF) for presampled modulation transfer function (MTF) analysis with the edge method. A filter was designed to reduce noise effectively using a position-dependent filter controlled by the boundary frequency b for low-pass filtering, which is calculated by 1/2d (d: distance from the LSF center). In this filtering process, strong filters with very low b can be applied to regions distant from the LSF center, and the region near the LSF center can be maintained simultaneously by a correspondingly high b. Presampled MTF accuracies derived by use of the proposed method and an edge spread function (ESF)-fitting method were compared by use of simulated ESFs with and without noise, resembling a computed radiography (CR) and an indirect-type flat panel detector (FPD), respectively. In addition, the edge images of clinical CR, indirect-type FPD, and direct-type FPD systems were examined. For a simulated ESF without noise, the calculated MTFs of the variable filtering method agreed precisely with the true MTFs. The excellent noise-reduction ability of the variable filter was demonstrated for all simulated noisy ESFs and those of three clinical systems. Although the ESF-fitting method provided excellent noise reduction only for the CR-like simulated ESF with noise, its noise elimination performance could not be demonstrated due to the lesser robustness of the fitting.

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