Advertisement

Application of ASiR in combination with noise index in the chest CT examination of preschool-age children

  • Shilong TangEmail author
  • Xianfan Liu
  • Ling He
  • Yu Zhou
  • Zhuo Cheng
CHEST RADIOLOGY
  • 19 Downloads

Abstract

Purpose

To evaluate the application of adaptive statistical iterative reconstruction (ASIR) for chest CT scans of preschool-age children.

Method

Sixty children, ages 0 to < 1, 1 to < 3, and 3 to < 6 years, who underwent CT non-contrast-enhanced and enhanced scans were included. The non-contrast-enhanced scan sequences were performed with noise indexes (NIs) of 11, 14, and 16 for the 0 to < 1, 1 to < 3, and 3 to < 6 year age groups, respectively. Collected data were reconstructed using ASIR in increments of 10%, ranging from 0 to 100%, to generate 11 image groups. The signal-to-noise ratio, image noise, and other features of images obtained using ASIR with different weights were compared and analyzed. The best weight ranges for ASIR of chest CT scans of children at different ages within the range of 0–6 years were obtained. Enhanced scan sequence: The NI default was 9, and the data were subjected to the filtered back projection reconstruction algorithm. All other scanning parameters were the same as those used in the non-contrast-enhanced scan sequence.

Results

In the 0 to < 1 year group, the image qualities were scored as 3 or above with ASIR weights of 50% for the lung window and 40% for the mediastinal window; in the 1 to < 3 year group, the image qualities were scored as 3 or above with ASIR weights of 60% for the lung window and 50% for the mediastinal window; in the 3 to < 6 year group, the image qualities were scored as 3 or above with ASIR weights of 70% for the lung window and 60% for the mediastinal window.

Conclusion

For low-dose chest CT scans of preschool-age children, application of the ASIR technique significantly improved image quality and reduced image noise. The optimum weights of image ASIR were 50%, 60%, and 70% for the lung window and 40%, 50%, and 60% for the mediastinal window for the 0 to < 1, 1 to < 3, and 3 to < 6 year groups, respectively.

Keywords

Adaptive statistical iterative reconstruction Noise index Children Chest X-ray computed tomography 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Ou P, Celermajer DS, Calcagni G, Brunelle F, Bonnet D, Sidi D (2007) Three-dimensional CT scanning: a new diagnostic modality in congenital heart disease. Heart 93(8):908–913CrossRefGoogle Scholar
  2. 2.
    Smith EA, Dillman JR, Goodsitt MM, Christodoulou EG, Keshavarzi N, Strouse PJ (2014) Model-based iterative reconstruction: effect on patient radiation dose and image quality in pediatric body CT. Radiology 270(2):526–534CrossRefGoogle Scholar
  3. 3.
    Brady SL, Moore BM, Yee BS, Kaufman RA (2014) Pediatric CT: implementation of ASIR for substantial radiation dose reduction while maintaining pre-ASIR image noise. Radiology 270:223–231CrossRefGoogle Scholar
  4. 4.
    Gervaise A, Naulet P, Henry C, Pernin M, Portron Y, Lapierre-Combes M (2014) Low-dose CT with automatic tube current modulation, adaptive statistical iterative reconstruction, and low tube voltage for the diagnosis of renal colic: impact of body mass index. AJR Am J Roentgenol 202(3):553–560CrossRefGoogle Scholar
  5. 5.
    Mayer C, Meyer M, Sedlmair M, Schoenberg SO, Henzler T (2014) Potential for radiation dose savings in abdominal and chest CT using automatic tube voltage selection in combination with automatic tube current modulation. AJR Am J Roentgenol 203(2):292–299CrossRefGoogle Scholar
  6. 6.
    Kubo T, Ohno Y, Kauczor HU, Hatabu H (2014) Radiation dose reduction in chest CT—review of available options. Eur J Radiol 83(10):1953–1961CrossRefGoogle Scholar
  7. 7.
    Chen JH, Jin EH, He W, Zhao LQ (2014) Combining automatic tube current modulation with adaptive statistical iterative reconstruction for low-dose chest CT screening. PLoS ONE 9(4):e92414CrossRefGoogle Scholar
  8. 8.
    Singh S, Kalra MK, Hsieh J, Licato PE, Do S, Pien HH, Blake MA (2010) Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques. Radiology 257(2):373–383CrossRefGoogle Scholar
  9. 9.
    American Association of Physicists in Medicine. AAPM report no. 96: the measurement, reporting, and management of radiation dose in CT. http://www.aapm.org/pubs/reports/RPT_96.pdf. Accessed 2011-01-04
  10. 10.
    Hsieh SS, Chesler DA, Fleischmann D, Pelc NJ (2016) A limit on dose reduction possible with CT reconstruction algorithms without prior knowledge of the scan subject. Med Phys 43(3):1361–1368CrossRefGoogle Scholar
  11. 11.
    Ou P, Celermajer DS, Calcagni G, Brunelle F, Bonnet D, Sidi D (2007) Three-dimensional CT scanning: a new diagnostic modality in congenital heart disease. Heart 93(8):908–913CrossRefGoogle Scholar
  12. 12.
    Young C, Taylor AM, Owens CM (2011) Paediatric cardiac computed tomography: a review of imaging techniques and radiation dose consideration. Eur Radiol 21(3):518–529CrossRefGoogle Scholar
  13. 13.
    Pearce MS, Salotti JA, Little MP, McHugh K, Lee C, Kim KP, Howe NL, Ronckers CM, Rajaraman P, Sir Craft AW, Parker L, Berrington deGonzalez A (2012) Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. Lancet 380(9840):499–505CrossRefGoogle Scholar
  14. 14.
    Widmann G, Bischel A, Stratis A, Kakar A, Bosmans H, Jacobs R, Gassner EM, Puelacher W, Pauwels R (2016) Ultralow dose dentomaxillofacial CT imaging and iterative reconstruction techniques: variability of Hounsfield units and contrast-to-noise ratio. Br J Radiol 89(1060):20151055CrossRefGoogle Scholar
  15. 15.
    Cha MJ, Jeong WK, Choi D, Kim YK, Lim S, Choi SY, Lee WJ (2016) Iterative reconstruction: comparison of techniques for reduced-dose liver computed tomography following transarterial chemoembolization for hepatocellular carcinoma. Acta Radiol 57(12):1429–1437CrossRefGoogle Scholar
  16. 16.
    Yoon HJ, Chung MJ, Hwang HS, Moon JW, Lee KS (2015) Adaptive statistical iterative reconstruction-applied ultra-low-dose CT with radiography-comparable radiation dose: usefulness for lung nodule detection. Korean J Radiol 16(5):1132–1141CrossRefGoogle Scholar
  17. 17.
    Wuest W, May M, Saake M et al (2016) Low-dose CT of the paranasal sinuses: minimizing X-ray exposure with spectral shaping. Eur Radiol 26:4155–4161CrossRefGoogle Scholar
  18. 18.
    Lell MM, May MS, Brand M et al (2015) Imaging the parasinus region with a third-generation dual-source CT and the effect of tin filtration on image quality and radiation dose. AJNR Am J Neuroradiol 36:1225–1230CrossRefGoogle Scholar
  19. 19.
    May MS, Brand M, Lell MM et al (2017) Radiation dose reduction in parasinus CT by spectral shaping. Neuroradiology 59:169–176CrossRefGoogle Scholar
  20. 20.
    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
  21. 21.
    Zhao Y, Wu Y, Zuo Z, Cheng S (2017) CT angiography of the kidney using routine CT and the latest gemstone spectral imaging combination of different noise indexes: image quality and radiationdose. Radiol Med 122(5):327–336CrossRefGoogle Scholar
  22. 22.
    Lambert J, MacKenzie JD, Cody DD, Gould R (2014) Techniques and tactics for optimizing CT dose in adults and children: state of the art and future advances. J Am Coll Radiol 11:262–266CrossRefGoogle Scholar
  23. 23.
    Phelps AS, Naeger DM, Courtier JL et al (2015) Pairwise comparison versus Likert scale for biomedical image assessment. Am J Roentgenol 204:8–14CrossRefGoogle Scholar
  24. 24.
    Giannoni CM, Guillerman RP (2015) Computed tomography for the evaluation of suspected airway foreign bodies. Clin Pediatr Emerg Med 16(4):230–234CrossRefGoogle Scholar
  25. 25.
    Kim HG, Chung YE, Lee YH et al (2015) Quantitative analysis of the effect of iterative reconstruction using a phantom: determining the appropriate blending percentage. Yonsei Med J 56:253–261CrossRefGoogle Scholar
  26. 26.
    McKnight CD, Watcharotone K, Ibrahim M, Christodoulou E, Baer AH, Parmar HA (2014) Adaptive statistical iterative reconstruction: reducing dose while preserving image quality in the pediatric head CT examination. Pediatr Radiol 44:997–1003CrossRefGoogle Scholar
  27. 27.
    Mirro AE, Brady SL, Kaufman RA (2016) Full dose-reduction potential of statistical iterative reconstruction for head CT protocols in a predominantly pediatric population. AJNR Am J Neuroradiol 37:1199–1205CrossRefGoogle Scholar
  28. 28.
    Vachha B, Brodoefel H, Wilcox C, Hackney DB, Moonis G (2013) Radiation dose reduction in soft tissue neck CT using adaptive statistical iterative reconstruction (ASIR). Eur J Radiol 82:2222–2226CrossRefGoogle Scholar

Copyright information

© Italian Society of Medical Radiology 2019

Authors and Affiliations

  • Shilong Tang
    • 1
    Email author
  • Xianfan Liu
    • 1
  • Ling He
    • 1
  • Yu Zhou
    • 1
  • Zhuo Cheng
    • 1
  1. 1.Department of Radiology of Children’s Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Key Laboratory of Pediatrics in ChongqingChongqing International Science and Technology Cooperation Center for Child Development and Critical DisordersChongqingChina

Personalised recommendations