Evaluation of low-contrast detectability for iterative reconstruction in pediatric abdominal computed tomography: a phantom study

  • Nicholas RubertEmail author
  • Richard Southard
  • Susan M. Hamman
  • Ryan Robison
Original Article



Iterative reconstruction is offered by all vendors to achieve similar or better CT image quality at lower doses than images reconstructed with filtered back-projection.


The purpose of this study was to investigate the dose-reduction potential for pediatric abdominal CT imaging when using either a commercially available hybrid or a commercially available model-based iterative reconstruction algorithm from a single manufacturer.

Materials and methods

A phantom containing four low-contrast inserts and a uniform background with total attenuation equivalent to the abdomen of an average 8-year-old child was imaged on a CT scanner (IQon; Philips Healthcare, Cleveland, OH). We reconstructed images using both hybrid iterative reconstruction (iDose4) and model-based iterative reconstruction (Iterative Model Reconstruction). The four low-contrast inserts had circular cross-section with diameters of 3 mm, 5 mm, 7 mm and 10 mm and contrasts of 14 Hounsfield units (HU), 7 HU, 5 HU and 3 HU, respectively. Helical scans with identical kilovoltage (kV), pitch, rotation time, and collimation were repeated on the phantom at volume CT dose index (CTDIvol) of 2.0 milligrays (mGy), 3.0 mGy, 4.5 mGy and 6.0 mGy. We measured the contrast-to-noise ratio (CNR) in each rod across scans. Additionally, we collected sub-images containing each rod and sub-images containing the background and used them in two-alternative forced choice observer experiments with four observers (two radiologists and two physicists). We calculated the dose-reduction potential of both iterative reconstruction algorithms relative to a scan performed at 6 mGy and reconstructed with filtered back-projection.


We calculated dose-reduction potential by either matching average equal observer performance in the two-alternative forced choice experiments or matching CNR. When matching CNR, the dose-reduction potential was 34% to 45% for hybrid iterative reconstruction and 89% to 95% for model-based iterative reconstruction. When matching average observer performance, the dose-reduction potential was 9% to 30% for hybrid iterative reconstruction and 57% to 74% for model-based iterative reconstruction. The range in dose-reduction potential depended on the rod size and contrast level.


Observer performance in this phantom study indicates that the dose-reduction potential indicated by an observer study is less than that of CNR; extrapolating the results to clinical studies suggests that the dose-reduction potential would also be less.


Children Computed tomography Dose reduction Iterative reconstruction Low-contrast detectability Phantom 


Compliance with ethical standards

Conflicts of interest

Richard Southard has a financial relationship with Koninklijke Philips NV that includes a master enterprise agreement as a consultant, work with the speaker’s bureau and receipt of honoraria for his services.


  1. 1.
    Brink JA, Amis ES Jr (2010) Image Wisely: a campaign to increase awareness about adult radiation protection. Radiology 257:601–602CrossRefGoogle Scholar
  2. 2.
    Goske MJ, Applegate KE, Boylan J et al (2008) The Image Gently campaign: working together to change practice. AJR Am J Roentgenol 190:273–274CrossRefGoogle Scholar
  3. 3.
    United Nations Scientific Committee on the Effects of Atomic Radiation (2013) UNSCEAR 2013 report Vol. II. Sources, effects and risks of ionizing radiation. United Nations, New YorkGoogle Scholar
  4. 4.
    Hsieh J (2008) Adaptive statistical iterative reconstruction (white paper). GE Healthcare, WaukeshaGoogle Scholar
  5. 5.
    Beister M, Kolditz D, Kalender W (2012) Iterative reconstruction methods in X-ray CT. Phys Med 28:94–108CrossRefGoogle Scholar
  6. 6.
    Khawaja RDA, Singh S, Otrakji A et al (2015) Dose reduction in pediatric abdominal CT: use of iterative reconstruction techniques across different CT platforms. Pediatr Radiol 45:1046–1055CrossRefGoogle Scholar
  7. 7.
    Hsieh J (2015) Computed tomography: principles, design, artifacts, and recent advances, 3rd edn. SPIE Press, BellinghamCrossRefGoogle Scholar
  8. 8.
    Smith TB, Solomon J, Samei E (2018) Estimating detectability index in vivo: development and validation of an automated methodology. J Med Imaging 5:031403CrossRefGoogle Scholar
  9. 9.
    Samei E, Richard S (2015) Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology. Med Phys 42:314–323CrossRefGoogle Scholar
  10. 10.
    McCollough CH, Yu L, Kofler JM et al (2015) Degradation of CT low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels. Radiology 276:499–506CrossRefGoogle Scholar
  11. 11.
    Vaishnav JY, Jung WC, Popescu LM et al (2014) Objective assessment of image quality and dose reduction in CT iterative reconstruction. Med Phys 41:071904CrossRefGoogle Scholar
  12. 12.
    Hernandez-Giron I, Calzado A, Geleijns J et al (2014) Comparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms. Br J Radiol 87:20140014CrossRefGoogle Scholar
  13. 13.
    Yu L, Leng S, Chen L, Kofler JM et al (2013) Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms. Med Phys 40:041908CrossRefGoogle Scholar
  14. 14.
    Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276:339–357CrossRefGoogle Scholar
  15. 15.
    Noël P, Fingerle A, Renger B et al (2011) Initial performance characterization of a clinical noise-suppressing reconstruction algorithm for MDCT. AJR Am J Roentgenol 197:1404–1409CrossRefGoogle Scholar
  16. 16.
    Mehta D, Thomspon R, Morton T et al (2013) Iterative model reconstruction: simultaneously lowered computed tomography radiation dose and improved image quality. Med Phys Int 1:147–155Google Scholar
  17. 17.
    Boone JM, Strauss KJ, Cody DD et al (2011) Report of AAPM Task Group 204: size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. American Association of Physicists in Medicine, AlexandriaGoogle Scholar
  18. 18.
    Green D, Swets J (1966) Signal detection theory and psychophysics. Wiley, New YorkGoogle Scholar
  19. 19.
    Christianson O, Chen JJ, Yang Z et al (2015) An improved index of image quality for task-based performance of CT iterative reconstruction across three commercial implementations. Radiology 275:725–734CrossRefGoogle Scholar
  20. 20.
    American College of Radiology (2018) Executive summary report, Jan-Jun 2018, National Radiology Data Registry, Dose Index Registry. ACR, RestonGoogle Scholar
  21. 21.
    Ryu YJ, Choi YH, Cheon JE et al (2016) Knowledge-based iterative model reconstruction: comparative image quality and radiation dose with a pediatric computed tomography phantom. Pediatr Radiol 46:303–315CrossRefGoogle Scholar
  22. 22.
    Eck BL, Fahmi R, Brown KM et al (2015) Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction. Med Phys 42:6098–6111CrossRefGoogle Scholar
  23. 23.
    Singh S, Kaira MK, Shenoy-Bhangle AS et al (2012) Radiation dose reduction with hybrid iterative reconstruction for pediatric CT. Radiology 263:537–546CrossRefGoogle Scholar
  24. 24.
    Gay F, Pavia Y, Pierrat N et al (2014) Dose reduction with adaptive statistical iterative reconstruction for paediatric CT: phantom study and clinical experience on chest and abdomen CT. Eur Radiol 24:102–111CrossRefGoogle Scholar
  25. 25.
    Smith EA, Dillman JR, Goodsitt MM et al (2014) Model-based iterative reconstruction: effect on patient radiation dose and image quality in pediatric body CT. Radiology 270:526–534CrossRefGoogle Scholar
  26. 26.
    Lee M, Kim MJ, Han KH et al (2015) Half-dose abdominal CT with sinogram-affirmed iterative reconstruction technique in children — comparison with full-dose CT with filtered back projection. Pediatr Radiol 45:188–193CrossRefGoogle Scholar
  27. 27.
    Solomon J, Samei E (2014) Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE. Med Phys 41:091908CrossRefGoogle Scholar
  28. 28.
    Racine D, Ba AH, Ott JG et al (2016) Objective assessment of low contrast detectability in computed tomography with channelized Hotelling observer. Phys Med 32:76–83CrossRefGoogle Scholar
  29. 29.
    Tseng HW, Fan J, Kupinski MA et al (2014) Assessing image quality and dose reduction of a new X-ray computed tomography iterative reconstruction algorithm using model observers. Med Phys 41:07190Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of RadiologyPhoenix Children’s HospitalPhoenixUSA
  2. 2.College of MedicineUniversity of ArizonaPhoenixUSA
  3. 3.Section of Pediatric Radiology, C. S. Mott Children’s Hospital, Department of RadiologyUniversity of Michigan Health SystemAnn ArborUSA
  4. 4.College of Nursing and Health InnovationArizona State UniversityPhoenixUSA

Personalised recommendations