Publication detail

Methodology for estimation of tissue noise power spectra in iteratively reconstructed MDCT data

WALEK, P. JAN, J. OUŘEDNÍČEK, P. SKOTÁKOVÁ, J. JÍRA, I.

Original Title

Methodology for estimation of tissue noise power spectra in iteratively reconstructed MDCT data

Czech Title

Metodologie pro odhad šumového výkonového spektra iterativně rekonstruovaných MDCT obrazových dat

English Title

Methodology for estimation of tissue noise power spectra in iteratively reconstructed MDCT data

Type

conference paper

Language

en

Original Abstract

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 estimation of noise power spectra in soft tissue and bones. As a result of this study it can be concluded that iDose4 hybrid iterative reconstruction algorithm effectively reduces noise in multidetector X-ray computed tomography (MDCT) data. The MDCT noise has naturally different characteristics in diverse tissues; thus it is object dependent and phantom studies are therefore unable to reflect its whole complexity.

Czech abstract

Iterativní rekonstrukční metody byly nedávno zavedeny jako součást zobrazování pomocí rentgenové výpočetní tomografie. Jejich hlavním přínosem je možnost redukce pacientské dávky až o 70% bez ztráty obrazové kvality. Měly by tedy být prozkoumány vlastnosti iterativně rekonstruovaných obrazů z hlediska obsahu šumu a porovnány s doposud používaným rekonstrukčním algoritmem filtrované zpětné projekce. Práce prezentovaná v tomto příspěvku směřuje k hodnocení a analýze šumu v celém rozsahu reálných pacientských dat a navíc separátně v různých typech tkání, což představuje inovativní přístup v porovnání s doposud publikovanými metodami. Metodologie je založená na segmentaci základních tkáňových struktur, subtrakci obrazů, výpočtu směrodatné odchylky a radiálního jednorozměrného šumového výkonového spektra. Segmentace tkání zavádí chyby do odhadu šumového výkonového spektra, tyto chyby jsou kvantifikovány a je navržen postup jejich eliminace. Výstupem této studie je potvrzení předpokladu, že hybridní iterativní metoda iDose4 je schopna efektivně redukovat úroveň šumu v CT datech. Dále bylo potvrzeno, že vlastnosti šumu jsou proměnlivé v závislosti na snímané tkáni.

English abstract

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 estimation of noise power spectra in soft tissue and bones. As a result of this study it can be concluded that iDose4 hybrid iterative reconstruction algorithm effectively reduces noise in multidetector X-ray computed tomography (MDCT) data. The MDCT noise has naturally different characteristics in diverse tissues; thus it is object dependent and phantom studies are therefore unable to reflect its whole complexity.

Keywords

X-ray computed tomography, iterative reconstruction, dose reduction, image quality, noise power spectra

RIV year

2013

Released

20.06.2013

ISBN

978-80-86943-74-9

Book

WSCG 2013 - Full Papers Proceedings

Pages from

243

Pages to

252

Pages count

10

BibTex


@inproceedings{BUT101657,
  author="Petr {Walek} and Jiří {Jan} and Petr {Ouředníček} and Jarmila {Skotáková} and Igor {Jíra}",
  title="Methodology for estimation of tissue noise power spectra in iteratively reconstructed MDCT data",
  annote="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 estimation of noise power spectra in soft tissue and bones. As a result of this study it can be concluded that iDose4 hybrid iterative reconstruction algorithm effectively reduces noise in multidetector X-ray computed tomography (MDCT) data. The MDCT noise has naturally different characteristics in diverse tissues; thus it is object dependent and phantom studies are therefore unable to reflect its whole complexity.",
  booktitle="WSCG 2013 - Full Papers Proceedings",
  chapter="101657",
  howpublished="online",
  year="2013",
  month="june",
  pages="243--252",
  type="conference paper"
}