Skip to main content
SpringerLink
Log in

Reduced dose CT with model-based iterative reconstruction compared to standard dose CT of the chest, abdomen, and pelvis in oncology patients: intra-individual comparison study on image quality and lesion conspicuity

  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To compare image quality and lesion conspicuity of reduced dose (RD) CT with model-based iterative reconstruction (MBIR) compared to standard dose (SD) CT in patients undergoing oncological follow-up imaging.

Methods

Forty-four cancer patients who had a staging SD CT within 12 months were prospectively included to undergo a weight-based RD CT with MBIR. Radiation dose was recorded and tissue attenuation and image noise of four tissue types were measured. Reproducibility of target lesion size measurements of up to 5 target lesions per patient were analyzed. Subjective image quality was evaluated for three readers independently utilizing 4- or 5-point Likert scales.

Results

Median radiation dose reduction was 46% using RD CT (P < 0.01). Median image noise across all measured tissue types was lower (P < 0.01) in RD CT. Subjective image quality for RD CT was higher (P < 0.01) in regard to image noise and overall image quality; however, there was no statistically significant difference regarding image sharpness (P = 0.59). There were subjectively more artifacts on RD CT (P < 0.01). Lesion conspicuity was subjectively better in RD CT (P < 0.01). Repeated target lesion size measurements were highly reproducible both on SD CT (ICC = 0.987) and RD CT (ICC = 0.97).

Conclusions

RD CT imaging with MBIR provides diagnostic imaging quality and comparable lesion conspicuity on follow-up exams while allowing dose reduction by a median of 46% compared to SD CT imaging.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Brenner DJ, Hall EJ (2007) Computed tomography—an increasing source of radiation exposure. N Engl J Med 357(22):2277–2284. doi:10.1056/NEJMra072149

    Article  CAS  PubMed  Google Scholar 

  2. Mettler FA Jr, Bhargavan M, Faulkner K, et al. (2009) Radiologic and nuclear medicine studies in the United States and worldwide: frequency, radiation dose, and comparison with other radiation sources—1950-2007. Radiology 253(2):520–531. doi:10.1148/radiol.2532082010

    Article  PubMed  Google Scholar 

  3. Chang W, Lee JM, Lee K, et al. (2013) Assessment of a model-based, iterative reconstruction algorithm (MBIR) regarding image quality and dose reduction in liver computed tomography. Invest Radiol 48(8):598–606. doi:10.1097/RLI.0b013e3182899104

    Article  CAS  PubMed  Google Scholar 

  4. Initiative to reduce unnecessary radiation exposure from medical imaging (February 2010). Silver Spring, MD

  5. Amis ES Jr, Butler PF, Applegate KE, et al. (2007) American College of Radiology white paper on radiation dose in medicine. J Am Coll Radiol 4(5):272–284. doi:10.1016/j.jacr.2007.03.002

    Article  PubMed  Google Scholar 

  6. Yu L, Bruesewitz MR, Thomas KB, et al. (2011) Optimal tube potential for radiation dose reduction in pediatric CT: principles, clinical implementations, and pitfalls. Radiographics 31(3):835–848. doi:10.1148/rg.313105079

    Article  PubMed  Google Scholar 

  7. Gunn ML, Kohr JR (2010) State of the art: technologies for computed tomography dose reduction. Emerg Radiol 17(3):209–218. doi:10.1007/s10140-009-0850-6

    Article  PubMed  Google Scholar 

  8. McCollough CH, Bruesewitz MR, Kofler JM Jr (2006) CT dose reduction and dose management tools: overview of available options. Radiographics 26(2):503–512. doi:10.1148/rg.262055138

    Article  PubMed  Google Scholar 

  9. Fleischmann D, Boas FE (2011) Computed tomography—old ideas and new technology. Eur Radiol 21(3):510–517. doi:10.1007/s00330-011-2056-z

    Article  PubMed  Google Scholar 

  10. Mitsumori LM, Shuman WP, Busey JM, Kolokythas O, Koprowicz KM (2012) Adaptive statistical iterative reconstruction versus filtered back projection in the same patient: 64 channel liver CT image quality and patient radiation dose. Eur Radiol 22(1):138–143. doi:10.1007/s00330-011-2186-3

    Article  PubMed  Google Scholar 

  11. Willemink MJ, de Jong PA, Leiner T, et al. (2013) Iterative reconstruction techniques for computed tomography Part 1: technical principles. Eur Radiol 23(6):1623–1631. doi:10.1007/s00330-012-2765-y

    Article  PubMed  Google Scholar 

  12. Prakash P, Kalra MK, Digumarthy SR, et al. (2010) Radiation dose reduction with chest computed tomography using adaptive statistical iterative reconstruction technique: initial experience. J Comput Assist Tomogr 34(1):40–45. doi:10.1097/RCT.0b013e3181b26c67

    Article  PubMed  Google Scholar 

  13. 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(2):526–534. doi:10.1148/radiol.13130362

    Article  PubMed  PubMed Central  Google Scholar 

  14. Shuman WP, Green DE, Busey JM, et al. (2013) Model-based iterative reconstruction versus adaptive statistical iterative reconstruction and filtered back projection in liver 64-MDCT: focal lesion detection, lesion conspicuity, and image noise. AJR Am J Roentgenol 200(5):1071–1076. doi:10.2214/AJR.12.8986

    Article  PubMed  PubMed Central  Google Scholar 

  15. Volders D, Bols A, Haspeslagh M, Coenegrachts K (2013) Model-based iterative reconstruction and adaptive statistical iterative reconstruction techniques in abdominal CT: comparison of image quality in the detection of colorectal liver metastases. Radiology 269(2):469–474. doi:10.1148/radiol.13130002

    Article  PubMed  Google Scholar 

  16. Kaza RK, Platt JF, Al-Hawary MM, et al. (2012) CT enterography at 80 kVp with adaptive statistical iterative reconstruction versus at 120 kVp with standard reconstruction: image quality, diagnostic adequacy, and dose reduction. AJR Am J Roentgenol 198(5):1084–1092. doi:10.2214/AJR.11.6597

    Article  PubMed  Google Scholar 

  17. Flicek KT, Hara AK, Silva AC, et al. (2010) Reducing the radiation dose for CT colonography using adaptive statistical iterative reconstruction: a pilot study. AJR Am J Roentgenol 195(1):126–131. doi:10.2214/AJR.09.3855

    Article  PubMed  Google Scholar 

  18. Sato J, Akahane M, Inano S, et al. (2012) Effect of radiation dose and adaptive statistical iterative reconstruction on image quality of pulmonary computed tomography. Jpn J Radiol 30(2):146–153. doi:10.1007/s11604-011-0026-7

    Article  PubMed  Google Scholar 

  19. Katsura M, Matsuda I, Akahane M, et al. (2012) Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique. Eur Radiol 22(8):1613–1623. doi:10.1007/s00330-012-2452-z

    Article  PubMed  Google Scholar 

  20. Ichikawa Y, Kitagawa K, Nagasawa N, Murashima S, Sakuma H (2013) CT of the chest with model-based, fully iterative reconstruction: comparison with adaptive statistical iterative reconstruction. BMC Med Imaging 13:27. doi:10.1186/1471-2342-13-27

    Article  PubMed  PubMed Central  Google Scholar 

  21. Katsura M, Matsuda I, Akahane M, et al. (2013) Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction technique. Invest Radiol 48(4):206–212. doi:10.1097/RLI.0b013e31827efc3a

    PubMed  Google Scholar 

  22. Hague CJ, Krowchuk N, Alhassan D, et al. (2014) Qualitative and quantitative assessment of smoking-related lung disease: effect of iterative reconstruction on low-dose computed tomographic examinations. J Thorac Imaging 29(6):350–356. doi:10.1097/RTI.0000000000000118

    Article  PubMed  Google Scholar 

  23. 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. Kor J Radiol 16(5):1132–1141. doi:10.3348/kjr.2015.16.5.1132

    Article  Google Scholar 

  24. Herin E, Gardavaud F, Chiaradia M, et al. (2015) Use of model-based iterative reconstruction (MBIR) in reduced-dose CT for routine follow-up of patients with malignant lymphoma: dose savings, image quality and phantom study. Eur Radiol 25(8):2362–2370. doi:10.1007/s00330-015-3656-9

    Article  PubMed  Google Scholar 

  25. Deak Z, Grimm JM, Treitl M, et al. (2013) Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study. Radiology 266(1):197–206. doi:10.1148/radiol.12112707

    Article  PubMed  Google Scholar 

  26. Pickhardt PJ, Lubner MG, Kim DH, et al. (2012) Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging. AJR Am J Roentgenol 199(6):1266–1274. doi:10.2214/AJR.12.9382

    Article  PubMed  PubMed Central  Google Scholar 

  27. Eisenhauer EA, Therasse P, Bogaerts J, et al. (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247. doi:10.1016/j.ejca.2008.10.026

    Article  CAS  PubMed  Google Scholar 

  28. Schwartz LH, Bogaerts J, Ford R, et al. (2009) Evaluation of lymph nodes with RECIST 1.1. Eur J Cancer 45(2):261–267. doi:10.1016/j.ejca.2008.10.028

    Article  CAS  PubMed  Google Scholar 

  29. Wang R, Yu W, Wu R, et al. (2012) Improved image quality in dual-energy abdominal CT: comparison of iterative reconstruction in image space and filtered back projection reconstruction. AJR Am J Roentgenol 199(2):402–406. doi:10.2214/AJR.11.7159

    Article  PubMed  Google Scholar 

  30. Karpitschka M, Augart D, Becker HC, Reiser M, Graser A (2013) Dose reduction in oncological staging multidetector CT: effect of iterative reconstruction. Br J Radiol 86(1021):20120224. doi:10.1259/bjr.20120224

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Vardhanabhuti V, Loader RJ, Mitchell GR, Riordan RD, Roobottom CA (2013) Image quality assessment of standard- and low-dose chest CT using filtered back projection, adaptive statistical iterative reconstruction, and novel model-based iterative reconstruction algorithms. AJR Am J Roentgenol 200(3):545–552. doi:10.2214/AJR.12.9424

    Article  PubMed  Google Scholar 

  32. Yasaka K, Katsura M, Akahane M, et al. (2013) Model-based iterative reconstruction for reduction of radiation dose in abdominopelvic CT: comparison to adaptive statistical iterative reconstruction. SpringerPlus 2(1):209. doi:10.1186/2193-1801-2-209

    Article  PubMed  PubMed Central  Google Scholar 

  33. European Guidelines on Quality Criteria for Computed Tomography (1999) http://www.drs.dk/guidelines/ct/quality/index.htm. Accessed January 2016

  34. Olcott EW, Shin LK, Sommer G, et al. (2014) Model-based iterative reconstruction compared to adaptive statistical iterative reconstruction and filtered back-projection in CT of the kidneys and the adjacent retroperitoneum. Acad Radiol 21(6):774–784. doi:10.1016/j.acra.2014.02.012

    Article  PubMed  Google Scholar 

  35. Nakamoto A, Kim T, Hori M, et al. (2015) Clinical evaluation of image quality and radiation dose reduction in upper abdominal computed tomography using model-based iterative reconstruction; comparison with filtered back projection and adaptive statistical iterative reconstruction. Eur J Radiol 84(9):1715–1723. doi:10.1016/j.ejrad.2015.05.027

    Article  PubMed  Google Scholar 

  36. Vardhanabhuti V, Loader R, Roobottom CA (2013) Assessment of image quality on effects of varying tube voltage and automatic tube current modulation with hybrid and pure iterative reconstruction techniques in abdominal/pelvic CT: a phantom study. Invest Radiol 48(3):167–174. doi:10.1097/RLI.0b013e31827b8f61

    Article  PubMed  Google Scholar 

  37. Solomon J, Mileto A, Nelson RC, Roy Choudhury K, Samei E (2015) Quantitative features of liver lesions, lung nodules, and renal stones at multi-detector row CT examinations: dependency on radiation dose and reconstruction algorithm. Radiology 279:185–194. doi:10.1148/radiol.2015150892

    Article  PubMed  Google Scholar 

  38. Thibault JB, Sauer KD, Bouman CA, Hsieh J (2007) A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 34(11):4526–4544

    Article  PubMed  Google Scholar 

  39. Solomon J, Mileto A, Ramirez-Giraldo JC, Samei E (2015) Diagnostic performance of an advanced modeled iterative reconstruction algorithm for low-contrast detectability with a third-generation dual-source multidetector CT scanner: potential for radiation dose reduction in a multireader study. Radiology 275(3):735–745. doi:10.1148/radiol.15142005

    Article  PubMed  Google Scholar 

  40. Ramirez-Giraldo JC, Grant KL, Raupach R (2015) ADMIRE: advanced modeled iterative reconstruction (White Paper). Siemens Healthcare

Download references

Author information

Authors and Affiliations

  1. Molecular Imaging Program at Stanford, Department of Radiology, School of Medicine, Stanford University, 300 Pasteur Drive, Room H1307, Stanford, CA, 94305-5621, USA

    Linda Nayeli Morimoto, Aya Kamaya, Isabelle Boulay-Coletta, Dominik Fleischmann, Lior Molvin & Jürgen K. Willmann

  2. Department of Radiology, Fondation Hôpital Saint-Joseph, Paris, France

    Isabelle Boulay-Coletta

  3. Department of Health, Research & Policy, Stanford University, Stanford, CA, USA

    Lu Tian

  4. Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA

    George Fisher

  5. Department of Environmental Health and Safety, Stanford University, Stanford, CA, USA

    Jia Wang

Authors
  1. Linda Nayeli Morimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aya Kamaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Isabelle Boulay-Coletta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dominik Fleischmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lior Molvin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lu Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. George Fisher
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jürgen K. Willmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jürgen K. Willmann.

Ethics declarations

Funding

No funding was received for this study.

Conflict of interest

Dominik Fleischmann, MD has received research support from Siemens Medical Solutions and General Electric HealthCare and has ownership interest in iSchemaView Inc. Lior Molvin is an imaging consultant for General Electric HealthCare. Jürgen Willmann, MD has no conflicts of interest related to current work; unrelated potential conflicts: Dr. Willmann is in the scientific advisory board of Lantheus, Bracco, and SonoVol; is consultant to Bracco; and receives grant support by Siemens, GE, Bracco, and Philips. The other 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.

Informed consent

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Morimoto, L.N., Kamaya, A., Boulay-Coletta, I. et al. Reduced dose CT with model-based iterative reconstruction compared to standard dose CT of the chest, abdomen, and pelvis in oncology patients: intra-individual comparison study on image quality and lesion conspicuity. Abdom Radiol 42, 2279–2288 (2017). https://doi.org/10.1007/s00261-017-1140-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-017-1140-5

Keywords

Search

Navigation