Feasibility of low-dose CT with spectral shaping and third-generation iterative reconstruction in evaluating interstitial lung diseases associated with connective tissue disease: an intra-individual comparison study

  • Xiaoli Xu
  • Xin SuiEmail author
  • Lan Song
  • Yao Huang
  • Yingqian Ge
  • Zhengyu JinEmail author
  • Wei SongEmail author



To investigate the feasibility of low-dose CT (LDCT) with tin filtration and third-generation iterative reconstruction (IR) in evaluating interstitial lung diseases associated with connective tissue disease (CTD-ILD).


Fifty-three consecutive adult patients with CTD-ILD underwent regular-dose chest CT (RDCT) at 110 kVp followed by LDCT with tin-filtered 100 kVp. RDCT was reconstructed with filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE); LDCT was reconstructed with ADMIRE. Image noise, streak artifact, image quality, and visualization of normal and abnormal CT features were evaluated and compared among RDCT-ADMIRE, RDCT-FBP, and LDCT-ADMIRE groups.


The mean radiation dose of LDCT was reduced to 20% of RDCT. Objective image noise of RDCT-ADMIRE (38.08 ± 6.37 HU), LDCT-ADMIRE (51.68 ± 9.06 HU), and RDCT-FBP (62.09 ± 10.95 HU) increased progressively (p < 0.001 in any two pairs). RDCT-ADMIRE significantly improved subjective image noise, streak artifact, and overall image quality compared with RDCT-FBP and LDCT-ADMIRE (all p < 0.001), while no significant difference was noted between the latter two groups. All abnormal lung structures were better scored in RDCT-ADMIRE compared with those in RDCT-FBP (all p < 0.001). LDCT-ADMIRE was inferior to RDCT-FBP in visualizing peripheral bronchi and vessels as well as reticulation (all p < 0.001); other normal and abnormal structures were similar between the two groups.


LDCT with tin filtration and third-generation IR was applicable in evaluating ILD lesions of CTD. Image quality was significantly improved after applying ADMIRE algorithm to CT protocols.

Key Points

Optimization of CT radiation dose is a clinical concern in patients with connective tissue disease.

Spectral shaping and third-generation iterative reconstruction emerge as promising techniques in reducing radiation dose and acquiring desired image quality of CTD-ILD patients.

The third-generation iterative reconstruction algorithm can optimize visualization of ILD patterns in low-dose CT.


X-ray computed tomography Connective tissue disease Interstitial lung disease Image reconstruction Radiation dosage 



Advanced modeled iterative reconstruction




Computed tomography


Connective tissue disease


Interstitial lung diseases associated with connective tissue disease


Volume CT dose index


Dose-length product


Effective radiation dose


Filtered back projection


Ground-glass opacities


High-resolution computed tomography


Interstitial lung disease


Iterative reconstruction




Low-dose CT


Regular-dose chest CT


Sinogram-affirmed iterative reconstruction


Signal-to-noise ratio


Size-specific dose estimates



This study was supported by the National Public Welfare Basic Scientific Research Project (2017PT32004).

Compliance with ethical standards


The scientific guarantor of this publication is Zhengyu Jin.

Conflict of interest

Yingqian Ge is an employee of Siemens. She had no control on the study raw data and analysis.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

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

Ethical approval

Institutional Review Board approval of Peking Union Medical College Hospital was obtained.


• retrospective

• observational study

• performed at one institution


  1. 1.
    Vij R, Strek ME (2013) Diagnosis and treatment of connective tissue disease-associated interstitial lung disease. Chest 143:814–824PubMedPubMedCentralGoogle Scholar
  2. 2.
    Walsh SL, Sverzellati N, Devaraj A, Keir GJ, Wells AU, Hansell DM (2014) Connective tissue disease related fibrotic lung disease: high resolution computed tomographic and pulmonary function indices as prognostic determinants. Thorax 69:216–222PubMedGoogle Scholar
  3. 3.
    Fischer A, Lee JS, Cottin V (2015) Interstitial lung disease evaluation: detecting connective tissue disease. Respiration 90:177–184PubMedGoogle Scholar
  4. 4.
    Goh NS, Desai SR, Veeraraghavan S et al (2008) Interstitial lung disease in systemic sclerosis: a simple staging system. Am J Respir Crit Care Med 177:1248–1254PubMedGoogle Scholar
  5. 5.
    Mathai SC, Danoff SK (2016) Management of interstitial lung disease associated with connective tissue disease. BMJ 352:h6819PubMedGoogle Scholar
  6. 6.
    Bankier AA, Tack D (2010) Dose reduction strategies for thoracic multidetector computed tomography: background, current issues, and recommendations. J Thorac Imaging 25:278–288PubMedGoogle Scholar
  7. 7.
    Baumueller S, Winklehner A, Karlo C et al (2012) Low-dose CT of the lung: potential value of iterative reconstructions. Eur Radiol 22:2597–2606PubMedGoogle Scholar
  8. 8.
    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 24:817–826PubMedGoogle Scholar
  9. 9.
    McCollough CH, Bruesewitz MR, Kofler JM Jr (2006) CT dose reduction and dose management tools: overview of available options. Radiographics 26:503–512PubMedGoogle Scholar
  10. 10.
    Ohno Y, Koyama H, Yoshikawa T, Seki S (2015) State-of-the-art imaging of the lung for connective tissue disease (CTD). Curr Rheumatol Rep 17:69PubMedGoogle Scholar
  11. 11.
    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 279:297–305PubMedGoogle Scholar
  12. 12.
    Katsura M, Sato J, Akahane M, Mise Y, Sumida K, Abe O (2017) Effects of pure and hybrid iterative reconstruction algorithms on high-resolution computed tomography in the evaluation of interstitial lung disease. Eur J Radiol 93:243–251PubMedGoogle Scholar
  13. 13.
    Gordic S, Morsbach F, Schmidt B et al (2014) Ultralow-dose chest computed tomography for pulmonary nodule detection: first performance evaluation of single energy scanning with spectral shaping. Invest Radiol 49:465–473PubMedGoogle Scholar
  14. 14.
    Newell JD Jr, Fuld MK, Allmendinger T et al (2015) Very low-dose (0.15 mGy) chest CT protocols using the COPDGene 2 test object and a third-generation dual-source CT scanner with corresponding third-generation iterative reconstruction software. Invest Radiol 50:40–45Google Scholar
  15. 15.
    Haubenreisser H, Meyer M, Sudarski S, Allmendinger T, Schoenberg SO, Henzler T (2015) Unenhanced third-generation dual-source chest CT using a tin filter for spectral shaping at 100kVp. Eur J Radiol 84:1608–1613PubMedGoogle Scholar
  16. 16.
    Martini K, Barth BK, Nguyen-Kim TD, Baumueller S, Alkadhi H, Frauenfelder T (2016) Evaluation of pulmonary nodules and infection on chest CT with radiation dose equivalent to chest radiography: prospective intra-individual comparison study to standard dose CT. Eur J Radiol 85:360–365PubMedGoogle Scholar
  17. 17.
    Messerli M, Ottilinger T, Warschkow R et al (2017) Emphysema quantification and lung volumetry in chest X-ray equivalent ultralow dose CT - intra-individual comparison with standard dose CT. Eur J Radiol 91:1–9PubMedGoogle Scholar
  18. 18.
    Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246:697–722PubMedGoogle Scholar
  19. 19.
    Studler U, Gluecker T, Bongartz G, Roth J, Steinbrich W (2005) Image quality from high-resolution CT of the lung: comparison of axial scans and of sections reconstructed from volumetric data acquired using MDCT. AJR Am J Roentgenol 185:602–607PubMedGoogle Scholar
  20. 20.
    The measurement, reporting, and management of radiation dose in CT: AAPM report no. 96. 2008. (Accessed 23 Feb 2015, at
  21. 21.
    Christner JA, Braun NN, Jacobsen MC, Carter RE, Kofler JM, McCollough CH (2012) Size-specific dose estimates for adult patients at CT of the torso. Radiology 265:841–847PubMedGoogle Scholar
  22. 22.
    AAPM Task Group 204 (2011) Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. Report of AAPM Task Group 204Google Scholar
  23. 23.
    Pan X, Sidky EY, Vannier M (2009) Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Probl 25:1230009PubMedPubMedCentralGoogle Scholar
  24. 24.
    Khawaja RD, Singh S, Otrakji A et al (2015) Dose reduction in pediatric abdominal CT: use of iterative reconstruction techniques across different CT platforms. Pediatr Radiol 45:1046–1055PubMedGoogle Scholar
  25. 25.
    Braun FM, Johnson TR, Sommer WH, Thierfelder KM, Meinel FG (2015) Chest CT using spectral filtration: radiation dose, image quality, and spectrum of clinical utility. Eur Radiol 25:1598–1606PubMedGoogle Scholar
  26. 26.
    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 276:499–506PubMedPubMedCentralGoogle Scholar
  27. 27.
    Christe A, Charimo-Torrente J, Roychoudhury K, Vock P, Roos JE (2013) Accuracy of low-dose computed tomography (CT) for detecting and characterizing the most common CT-patterns of pulmonary disease. Eur J Radiol 82:e142–e150PubMedGoogle Scholar
  28. 28.
    Walsh SL, Calandriello L, Sverzellati N, Wells AU, Hansell DM (2016) Interobserver agreement for the ATS/ERS/JRS/ALAT criteria for a UIP pattern on CT. Thorax 71:45–51PubMedGoogle Scholar
  29. 29.
    Watadani T, Sakai F, Johkoh T et al (2013) Interobserver variability in the CT assessment of honeycombing in the lungs. Radiology 266:936–944PubMedGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Radiology, Peking Union Medical College HospitalChinese Academy of Medical SciencesBeijingChina
  2. 2.Siemens ChinaBeijingChina

Personalised recommendations