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SIMULATED DOSE REDUCTION BY ADDING ARTIFICIAL NOISE TO MEASURED RAW DATA: A VALIDATION STUDY.

Söderberg, Marcus LU ; Gunnarsson, Mikael LU and Nilsson, Mats LU (2010) In Radiation Protection Dosimetry 139. p.71-77
Abstract
The purpose of this study was to verify and validate a noise simulation tool called Dose Tutor (VAMP GmbH) in terms of level and texture of the simulated noise. By adding artificial noise to measured computed tomography (CT) raw data, a scan acquired with a lower dose (mAs) than the actual one can be simulated. A homogeneous polyethylene phantom and an anthropomorphic chest phantom were scanned for different mAs levels, tube voltages, slice thicknesses and reconstruction kernels. The simulated noise levels were compared with the noise levels in real transverse slice images actually acquired with corresponding mAs values. In general, the noise comparisons showed acceptable agreement in magnitude (<20 % deviation in pixel standard... (More)
The purpose of this study was to verify and validate a noise simulation tool called Dose Tutor (VAMP GmbH) in terms of level and texture of the simulated noise. By adding artificial noise to measured computed tomography (CT) raw data, a scan acquired with a lower dose (mAs) than the actual one can be simulated. A homogeneous polyethylene phantom and an anthropomorphic chest phantom were scanned for different mAs levels, tube voltages, slice thicknesses and reconstruction kernels. The simulated noise levels were compared with the noise levels in real transverse slice images actually acquired with corresponding mAs values. In general, the noise comparisons showed acceptable agreement in magnitude (<20 % deviation in pixel standard deviation). Also, the calculated noise power spectra were similar, which indicates that the noise texture is correctly reproduced. In conclusion, this study establishes that the Dose Tutor might be a useful tool for estimating the dose reduction potential for CT protocols. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Radiation Protection Dosimetry
volume
139
pages
71 - 77
publisher
Nuclear Technology Publishing
external identifiers
  • WOS:000277738200012
  • PMID:20172933
  • Scopus:77953351550
ISSN
1742-3406
DOI
10.1093/rpd/ncq027
language
English
LU publication?
yes
id
623593af-5353-4cf6-9e5c-6f8b10f938f3 (old id 1552454)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/20172933?dopt=Abstract
date added to LUP
2010-03-03 12:48:20
date last changed
2016-10-13 04:29:40
@misc{623593af-5353-4cf6-9e5c-6f8b10f938f3,
  abstract     = {The purpose of this study was to verify and validate a noise simulation tool called Dose Tutor (VAMP GmbH) in terms of level and texture of the simulated noise. By adding artificial noise to measured computed tomography (CT) raw data, a scan acquired with a lower dose (mAs) than the actual one can be simulated. A homogeneous polyethylene phantom and an anthropomorphic chest phantom were scanned for different mAs levels, tube voltages, slice thicknesses and reconstruction kernels. The simulated noise levels were compared with the noise levels in real transverse slice images actually acquired with corresponding mAs values. In general, the noise comparisons showed acceptable agreement in magnitude (&lt;20 % deviation in pixel standard deviation). Also, the calculated noise power spectra were similar, which indicates that the noise texture is correctly reproduced. In conclusion, this study establishes that the Dose Tutor might be a useful tool for estimating the dose reduction potential for CT protocols.},
  author       = {Söderberg, Marcus and Gunnarsson, Mikael and Nilsson, Mats},
  issn         = {1742-3406},
  language     = {eng},
  pages        = {71--77},
  publisher    = {ARRAY(0xa63f298)},
  series       = {Radiation Protection Dosimetry},
  title        = {SIMULATED DOSE REDUCTION BY ADDING ARTIFICIAL NOISE TO MEASURED RAW DATA: A VALIDATION STUDY.},
  url          = {http://dx.doi.org/10.1093/rpd/ncq027},
  volume       = {139},
  year         = {2010},
}