Ultralow-dose CT with knowledge-based iterative model reconstruction (IMR) in evaluation of pulmonary tuberculosis: comparison of radiation dose and image quality

  • Chenggong Yan
  • Chunyi Liang
  • Jun Xu
  • Yuankui Wu
  • Wei Xiong
  • Huan Zheng
  • Yikai XuEmail author



To evaluate the image quality of ultralow-dose computed tomography (ULDCT) reconstructed with knowledge-based iterative model reconstruction (IMR) in patients with pulmonary tuberculosis (TB).


This IRB-approved prospective study enrolled 59 consecutive patients (mean age, 43.9 ± 16.6 years; F:M 18:41) with known or suspected pulmonary TB. Patients underwent a low-dose CT (LDCT) using automatic tube current modulation followed by an ULDCT using fixed tube current. Raw image data were reconstructed with filtered-back projection (FBP), hybrid iterative reconstruction (iDose), and IMR. Objective measurements including CT attenuation, image noise, and contrast-to-noise ratio (CNR) were assessed and compared using repeated-measures analysis of variance. Overall image quality and visualization of normal and pathological findings were subjectively scored on a five-point scale. Radiation output and subjective scores were compared by the paired Student t test and Wilcoxon signed-rank test, respectively.


Compared with FBP and iDose, IMR yielded significantly lower noise and higher CNR values at both dose levels (p < 0.01). Subjective ratings for pathological findings including centrilobular nodules, consolidation, tree-in-bud, and cavity were significantly better with ULDCT IMR images than those with LDCT iDose images (p < 0.01), but blurred edges were observed. With IMR implementation, a 59% reduction of the mean effective dose was achieved with ULDCT (0.28 ± 0.02 mSv) compared with LDCT (0.69 ± 0.15 mSv) without impairing image quality (p < 0.001).


IMR offers considerable noise reduction and improvement in image quality for patients with pulmonary TB undergoing chest ULDCT at an effective dose of 0.28 mSv.

Key Points

• Radiation dose is a major concern for tuberculosis patients requiring repeated follow-up CT.

• IMR allows substantial radiation dose reduction in chest CT without compromising image quality.

• ULDCT reconstructed with IMR allows accurate depiction of CT features of pulmonary tuberculosis.


Tomography, X-ray computed Infection Thorax Pulmonary tuberculosis Radiation dosage 



Contrast-to-noise ratio


Volume CT dose index


Dose-length product


Dose right index


Effective dose


Filtered-back projection


Figure of merit


Ground-glass opacity


Iterative model reconstruction


Iterative reconstruction


Low-dose CT


Region of interest




Ultralow-dose CT



The authors would like to thank Dr. Yan Jiang from the Philips Healthcare for providing technical support.


This study has received funding by the National Key Research and Development Program of China (grant 2016YFC0107104) and the Science and Technology Planning Project of Guangdong Province, China (grant 2015B010131011).

Compliance with ethical standards


The scientific guarantor of this publication is Prof. Yikai Xu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


• prospective

• observational

• performed at one institution


  1. 1.
    Zumla A, George A, Sharma V et al (2015) The WHO 2014 global tuberculosis report--further to go. Lancet Glob Health 1:e10–e12Google Scholar
  2. 2.
    Atun R, Weil DE, Eang MT, Mwakyusa D (2010) Health-system strengthening and tuberculosis control. Lancet 9732:2169–2178CrossRefGoogle Scholar
  3. 3.
    Wormanns D (2012) Radiological imaging of pulmonary tuberculosis. Radiologe 2:173–184CrossRefGoogle Scholar
  4. 4.
    Hara AK, Wellnitz CV, Paden RG, Pavlicek W, Sahani DV (2013) Reducing body CT radiation dose: beyond just changing the numbers. AJR Am J Roentgenol 1:33–40CrossRefGoogle Scholar
  5. 5.
    Berlin SC, Weinert DM, Vasavada PS et al (2015) Successful dose reduction using reduced tube voltage with hybrid iterative reconstruction in pediatric abdominal CT. AJR Am J Roentgenol 2:392–399CrossRefGoogle Scholar
  6. 6.
    Laqmani A, Veldhoen S, Dulz S et al (2016) Reduced-dose abdominopelvic CT using hybrid iterative reconstruction in suspected left-sided colonic diverticulitis. Eur Radiol 1:216–224CrossRefGoogle Scholar
  7. 7.
    Khawaja RD, Singh S, Blake M et al (2015) Ultralow-dose abdominal computed tomography: comparison of 2 iterative reconstruction techniques in a prospective clinical study. J Comput Assist Tomogr 4:489–498CrossRefGoogle Scholar
  8. 8.
    Den Harder AM, Willemink MJ, van Hamersvelt RW et al (2016) Effect of radiation dose reduction and iterative reconstruction on computer-aided detection of pulmonary nodules: intra-individual comparison. Eur J Radiol 2:346–351CrossRefGoogle Scholar
  9. 9.
    Iyama Y, Nakaura T, Iyama A et al (2017) Feasibility of iterative model reconstruction for unenhanced lumbar CT. Radiology 1:153–160CrossRefGoogle Scholar
  10. 10.
    Sauter A, Koehler T, Brendel B et al (2018) CT pulmonary angiography: dose reduction via a next generation iterative reconstruction algorithm. Acta Radiol.
  11. 11.
    Yan C, Xu J, Liang C et al (2018) Radiation dose reduction by using CT with iterative model reconstruction in patients with pulmonary invasive fungal infection. Radiology 1:285–292CrossRefGoogle Scholar
  12. 12.
    Laqmani A, Avanesov M, Butscheidt S et al (2016) Comparison of image quality and visibility of normal and abnormal findings at submillisievert chest CT using filtered back projection, iterative model reconstruction (IMR) and iDose(4). Eur J Radiol 11:1971–1979CrossRefGoogle Scholar
  13. 13.
    Pontana F, Billard AS, Duhamel A et al (2016) Effect of iterative reconstruction on the detection of systemic sclerosis-related interstitial lung disease: clinical experience in 55 patients. Radiology 1:297–305CrossRefGoogle Scholar
  14. 14.
    Skoura E, Zumla A, Bomanji J (2015) Imaging in tuberculosis. Int J Infect Dis 32:87–93CrossRefGoogle Scholar
  15. 15.
    Yeh JJ, Yu JK, Teng WB et al (2012) High-resolution CT for identify patients with smear-positive, active pulmonary tuberculosis. Eur J Radiol 1:195–201CrossRefGoogle Scholar
  16. 16.
    Yan C, Tan X, Wei Q et al (2015) Lung MRI of invasive fungal infection at 3 Tesla: evaluation of five different pulse sequences and comparison with multidetector computed tomography (MDCT). Eur Radiol 2:550–557CrossRefGoogle Scholar
  17. 17.
    Mileto A, Zamora DA, Alessio AM et al (2018) CT detectability of small low-contrast hypoattenuating focal lesions: iterative reconstructions versus filtered back projection. Radiology 2:443–454CrossRefGoogle Scholar
  18. 18.
    Zhang M, Qi W, Sun Y, Jiang Y, Liu X, Hong N (2017) Screening for lung cancer using sub-millisievert chest CT with iterative reconstruction algorithm: image quality and nodule detectability. Br J Radiol 1090:20170658Google Scholar
  19. 19.
    Pourjabbar S, Singh S, Kulkarni N et al (2015) Dose reduction for chest CT: comparison of two iterative reconstruction techniques. Acta Radiol 6:688–695CrossRefGoogle Scholar
  20. 20.
    Hata A, Yanagawa M, Kikuchi N, Honda O, Tomiyama N (2018) Pulmonary emphysema quantification on ultra-low-dose computed tomography using model-based iterative reconstruction with or without lung setting. J Comput Assist Tomogr 5:760–766CrossRefGoogle Scholar
  21. 21.
    Neroladaki A, Botsikas D, Boudabbous S, Becker CD, Montet X (2013) Computed tomography of the chest with model-based iterative reconstruction using a radiation exposure similar to chest X-ray examination: preliminary observations. Eur Radiol 2:360–366CrossRefGoogle Scholar
  22. 22.
    Park HJ, Lee JM, Park SB, Lee JB, Jeong YK, Yoon JH (2016) Comparison of knowledge-based iterative model reconstruction and hybrid reconstruction techniques for liver CT evaluation of hypervascular hepatocellular carcinoma. J Comput Assist Tomogr 6:863–871CrossRefGoogle Scholar
  23. 23.
    Padole A, Singh S, Ackman JB et al (2014) Submillisievert chest CT with filtered back projection and iterative reconstruction techniques. AJR Am J Roentgenol 4:772–781CrossRefGoogle Scholar
  24. 24.
    Lee SW, Kim Y, Shim SS et al (2014) Image quality assessment of ultra low-dose chest CT using sinogram-affirmed iterative reconstruction. Eur Radiol 4:817–826CrossRefGoogle Scholar
  25. 25.
    Vardhanabhuti V, Pang CL, Tenant S, Taylor J, Hyde C, Roobottom C (2017) Prospective intra-individual comparison of standard dose versus reduced-dose thoracic CT using hybrid and pure iterative reconstruction in a follow-up cohort of pulmonary nodules-effect of detectability of pulmonary nodules with lowering dose based on nodule size, type and body mass index. Eur J Radiol 91:130–141CrossRefGoogle Scholar
  26. 26.
    de Margerie-Mellon C, de Bazelaire C, Montlahuc C et al (2016) Reducing radiation dose at chest CT: comparison among model-based type iterative reconstruction, hybrid iterative reconstruction, and filtered Back projection. Acad Radiol 10:1246–1254CrossRefGoogle Scholar
  27. 27.
    Khawaja RD, Singh S, Gilman M et al (2014) Computed tomography (CT) of the chest at less than 1 mSv: an ongoing prospective clinical trial of chest CT at submillisievert radiation doses with iterative model image reconstruction and iDose4 technique. J Comput Assist Tomogr 4:613–619CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Chenggong Yan
    • 1
  • Chunyi Liang
    • 1
  • Jun Xu
    • 2
  • Yuankui Wu
    • 1
  • Wei Xiong
    • 1
  • Huan Zheng
    • 1
  • Yikai Xu
    • 1
    Email author
  1. 1.Department of Medical Imaging Center, Nanfang HospitalSouthern Medical UniversityGuangzhouPeople’s Republic of China
  2. 2.Department of Hematology, Nanfang HospitalSouthern Medical UniversityGuangzhouPeople’s Republic of China

Personalised recommendations