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Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT

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Abstract

Objectives

To optimize the slice thickness/overlap parameters for image reconstruction and to study the effect of iterative reconstruction (IR) on detectability and characterization of small non-calcified pulmonary nodules during low-dose thoracic CT.

Materials and methods

Data was obtained from computer simulations, phantom, and patient CTs. Simulations and phantom CTs were performed with 9 nodules (5, 8, and 10 mm with 100, −630, and −800 HU). Patient data were based on 11 ground glass opacities (GGO) and 9 solid nodules. For each analysis the nodules were reconstructed with filtered back projection and IR algorithms using 10 different combinations of slice thickness/overlap (0.5–5 mm). The attenuation (CT#) and the contrast to noise ratio (CNR) were measured. Spearman’s coefficient was used to correlate the error in CT# measurements and slice thickness. Paired Student’s t test was used to measure the significance of the errors.

Results

CNR measurements: CNR increases with increasing slice thickness/overlap for large nodules and peaks at 4.0/2.0 mm for smaller ones. Use of IR increases the CNR of GGOs by 60 %.

CT# measurements: Increasing slice thickness/overlap above 3.0/1.5 mm results in decreased CT# measurement accuracy.

Conclusion

Optimal detection of small pulmonary nodules requires slice thickness/overlap of 4.0/2.0 mm. Slice thickness/overlap of 2.0/2.0 mm is required for optimal nodule characterization. IR improves conspicuity of small ground glass nodules through a significant increase in nodule CNR.

Key Points

• Slice thickness/overlap affects the accuracy of pulmonary nodule detection and characterization.

• Slice thickness ≥3 mm increases the risk of misclassifying small nodules.

• Optimal nodule detection during low-dose CT requires 4.0/2.0-mm reconstructions.

• Optimal nodule characterization during low-dose CT requires 2.0/2.0-mm reconstructions.

• Iterative reconstruction improves the CNR of ground glass nodules by 60 %.

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Acknowledgements

The scientific guarantor of this publication is Dr. Narinder Paul. The authors of this manuscript declare relationships with the following companies: Toshiba Medical Systems.

This study has received funding by MITACS Accelerate. This study was partially supported by Toshiba Canada, Medical Systems Group, and MITACS accelerate. No complex statistical methods were necessary for this paper. Institutional review board approval was obtained. Written informed consent was waived by the institutional review board. Some study subjects or cohorts have been previously reported in ECR 2012. Methodology: retrospective, randomised controlled trial, performed at one institution

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Authors and Affiliations

  1. Institute of Biomaterial and Biomedical Engineering, University of Toronto, Room RS-420A, 164 College Street, Toronto, ON, Canada, M5S 3G9

    SayedMasoud Hashemi

  2. Toshiba of Canada Ltd, 191 McNabb Street, Markham, ON, Canada, L3R-8H2

    Hatem Mehrez

  3. Institute of Biomaterial and Biomedical Engineering, University of Toronto, Room MB-316A,164 College Street, Toronto, ON, Canada, M5S 3G9

    Richard S. C. Cobbold

  4. Medical Imaging, Toronto General Hospital, University Health Network, NCSB-1C562, 585 University Avenue, Toronto, ON, Canada, M5G 2 N2

    Narinder S. Paul

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  1. SayedMasoud Hashemi
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  2. Hatem Mehrez
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  4. Narinder S. Paul
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Correspondence to Narinder S. Paul.

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Hashemi, S., Mehrez, H., Cobbold, R.S.C. et al. Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT. Eur Radiol 24, 1239–1250 (2014). https://doi.org/10.1007/s00330-014-3142-9

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  • DOI: https://doi.org/10.1007/s00330-014-3142-9

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