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European Radiology

, Volume 17, Issue 7, pp 1885–1891 | Cite as

The effect of dose reduction and feasibility of edge-preserving noise reduction on the detection of liver lesions using MSCT

  • Johannes Wessling
  • Rainer Esseling
  • Rainer Raupach
  • Stefanie Fockenberg
  • Nani Osada
  • Joachim Gerß
  • Walter Heindel
  • Roman Fischbach
Computer Tomography

Abstract

The purpose of this study was to assess the effect of dose reduction and the potential of noise reduction filters on image quality and the detection of liver lesions using MSCT. Twenty-nine patients with a total of 40 liver lesions underwent 16-slice CT (120 kV; 180 mAs). Virtual noise was added to CT raw datasets simulating effective mAs levels of 155, 130, 105, 80, 55, 30 and 10 mAs. All datasets were post-processed with an edge-preserving noise-reduction filter (ANR-3D), yielding a total of 15 datasets per patient. Ten radiologists performed independent evaluations of image quality, the presence of liver lesions and diagnostic confidence. Quantitative noise and contrast-to-noise ratios (CNR) were obtained. Superior image quality (P < 0.02), reduction of image noise (P < 0.001) and the increase of lesion-to-liver CNR (P < 0.001) were observed in images processed with the ANR-3D filter. Sensitivity for lesion detection remained unchanged down to 105 mAs (CTDIw 6.6 mGy) without filter and 80 mAs (CTDIw 5.1 mGy) with ANR-3D. Confidence was rated significantly higher for datasets reconstructed with ANR-3D. The use of a noise-reducing, but edge-preserving filter (ANR-3D) is a promising option to reduce further the radiation dose in liver CT.

Keywords

Multi-slice CT Radiation dose Liver CT 

References

  1. 1.
    UNSCEAR 2000 (2000) The United Nations Scientific Committee on the Effects of Atomic Radiation. Health Phys 79(3):314CrossRefGoogle Scholar
  2. 2.
    Diederich S, Thomas M, Semik M et al (2004) Screening for early lung cancer with low-dose spiral computed tomography: results of annual follow-up examinations in asymptomatic smokers. Eur Radiol 14(4):691–702PubMedCrossRefGoogle Scholar
  3. 3.
    Wessling J, Fischbach R, Meier N et al (2003) CT colonography: Protocol optimization with multi-detector row CT-Study in an anthropomorphic colon phantom. Radiology 228:753–759PubMedCrossRefGoogle Scholar
  4. 4.
    Jensch S, van Gelder RE, Venema HW et al (2006) Effective radiation doses in CT colonography: results of an inventory among research institutions. Eur Radiol 16(5):981–987PubMedCrossRefGoogle Scholar
  5. 5.
    Yu L, Pan X, La Riviere P, Pelizzari C, Pan T (2002) A novel algorithm for CT image reconstruction with enhanced noise properties (abstract). Radiology 225(P):255Google Scholar
  6. 6.
    Baum U, Anders K, Steinbichler G et al (2004) Improvement of image quality of multislice spiral CT scans of the head and neck region using a raw data-based multidimensional adaptive filtering (MAF) technique. Eur Radiol 14(10):1873–1881PubMedCrossRefGoogle Scholar
  7. 7.
    Kalra MK, Maher MM, Sahani DV et al (2003) Low-dose CT of the abdomen: evaluation of image improvement with use of noise reduction filters—pilot study. Radiology 228:251–256PubMedCrossRefGoogle Scholar
  8. 8.
    Frush DP, Slack CC, Hollingsworth CL et al (2002) Computer-simulated radiation dose reduction for abdominal multidetector CT of pediatric patients. AJR Am J Roentgenol 179:1107–1113PubMedGoogle Scholar
  9. 9.
    Leidecker C, Fuchs T, Kachelriess M, Schaller S, Kalender W (2002) Comparison of different methods for adding virtual noise to measured raw data in order to estimate the dose reduction potential for clinical protocols in CT (abstract). Radiology 225(P):592Google Scholar
  10. 10.
    Kalender WA, Schmidt B, Zankl M, Schmidt MA (1999) PC program for estimating organdose and effective dose values in computed tomography. Eur Radiol 9:555–562PubMedCrossRefGoogle Scholar
  11. 11.
    Wormanns D, Ludwig K, Beyer F et al (2005) Detection of pulmonary nodules at multirow-detector CT: effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest CT. Eur Radiol 15:14–22PubMedCrossRefGoogle Scholar
  12. 12.
    Cohnen M, Fischer H, Hamacher J et al (2000) CT of the head by use of reduced current and kilovoltage: relationship between image quality and dose reduction. AJNR 21:1654–1660PubMedGoogle Scholar
  13. 13.
    Iannaccone R, Laghi A, Catalano C et al (2003) Feasibility of ultra-low-dose multislice CT colonography for the detection of colorectal lesions: preliminary experience. Eur Radiol 13(6):1297–1302PubMedGoogle Scholar
  14. 14.
    Diederich S, Lenzen H, Windmann R et al (1999) Pulmonary nodules: experimental and clinical studies at low-dose CT. Radiology 213:289–298PubMedGoogle Scholar
  15. 15.
    Greess H, Nomayr A, Wolf H et al (2002) Dose reduction in CT examination of children by an attenuation-based on-line modulation of tube current (CARE Dose). Eur Radiol 12(6):1571–1576PubMedCrossRefGoogle Scholar
  16. 16.
    Toth TL (2002) Dose reduction opportunities for CT scanners. Pediatr Radiol 32:261–267PubMedCrossRefGoogle Scholar
  17. 17.
    Itoh S, Koyama S, Ikeda M et al (2001) Further reduction of radiation dose in helical CT for lung cancer screening using small tube current and a newly designed filter. J Thorac Imaging 16:81–88PubMedCrossRefGoogle Scholar
  18. 18.
    Kalra MK, Prasad S, Saini S et al (2002) Clinical comparison of standard-dose and 50% reduced-dose abdominal CT: effect on image quality. Am J Roentgenol 179(5):1101–1106Google Scholar
  19. 19.
    Kalra MK, Maher MM, Blake MA et al (2004) Detection and characterization of lesions on low-radiation-dose abdominal CT images postprocessed with noise reduction filters. Radiology 232:791–797PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Johannes Wessling
    • 1
  • Rainer Esseling
    • 1
  • Rainer Raupach
    • 3
  • Stefanie Fockenberg
    • 1
  • Nani Osada
    • 2
  • Joachim Gerß
    • 2
  • Walter Heindel
    • 1
  • Roman Fischbach
    • 1
  1. 1.Department of Clinical RadiologyUniversity of MuensterMuensterGermany
  2. 2.Department of Medical Informatics and BiomathematicsUniversity of MuensterMuensterGermany
  3. 3.Siemens Medical SolutionsForchheimGermany

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