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  • Iterative reconstruction algorithms have been recently introduced into X-ray computed tomography imaging. Enabling patient dose reduction by up to 70% without affecting image quality they deserve attention; therefore properties of noise present in iteratively reconstructed data should be examined and compared to the images reconstructed by conventionally used filtered back projection. Instead of evaluating noise in imaged phantoms or small homogeneous regions of interest in real patient data, a methodology for assessing the noise in full extent of real patient data and in diverse tissues is presented in this paper. The methodology is based on segmentation of basic tissues, subtraction of images reconstructed by different algorithms and computation of standard deviation and radial one-dimensional noise power spectra. Tissue segmentation naturally introduces errors into estimation of noise power spectra; therefore, magnitude of segmentation error is examined and is considered to be acceptable for estima
  • Iterative reconstruction algorithms have been recently introduced into X-ray computed tomography imaging. Enabling patient dose reduction by up to 70% without affecting image quality they deserve attention; therefore properties of noise present in iteratively reconstructed data should be examined and compared to the images reconstructed by conventionally used filtered back projection. Instead of evaluating noise in imaged phantoms or small homogeneous regions of interest in real patient data, a methodology for assessing the noise in full extent of real patient data and in diverse tissues is presented in this paper. The methodology is based on segmentation of basic tissues, subtraction of images reconstructed by different algorithms and computation of standard deviation and radial one-dimensional noise power spectra. Tissue segmentation naturally introduces errors into estimation of noise power spectra; therefore, magnitude of segmentation error is examined and is considered to be acceptable for estima (en)
Title
  • Methodology for estimation of tissue noise power spectra in iteratively reconstructed MDCT data
  • Methodology for estimation of tissue noise power spectra in iteratively reconstructed MDCT data (en)
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  • Methodology for estimation of tissue noise power spectra in iteratively reconstructed MDCT data
  • Methodology for estimation of tissue noise power spectra in iteratively reconstructed MDCT data (en)
skos:notation
  • RIV/00216305:26220/13:PU105140!RIV14-MSM-26220___
http://linked.open...avai/riv/aktivita
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  • S
http://linked.open...vai/riv/dodaniDat
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  • 87590
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/13:PU105140
http://linked.open...riv/jazykVysledku
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  • X-ray computed tomography, iterative reconstruction, dose reduction, image quality, noise power spectra (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [411D52E1F234]
http://linked.open...v/mistoKonaniAkce
  • Plzeň
http://linked.open...i/riv/mistoVydani
  • Neuveden
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  • WSCG 2013 - Full Papers Proceedings
http://linked.open...in/vavai/riv/obor
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http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Jan, Jiří
  • Walek, Petr
  • Ouředníček, Petr
  • Skotáková, Jarmila
  • Jíra, Igor
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • Neuveden
https://schema.org/isbn
  • 978-80-86943-74-9
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  • 26220
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