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Low contrast detectability and spatial resolution with model-based Iterative reconstructions of MDCT images: a phantom and cadaveric study

  • Computed Tomography
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Abstract

Objectives

To compare image quality [low contrast (LC) detectability, noise, contrast-to-noise (CNR) and spatial resolution (SR)] of MDCT images reconstructed with an iterative reconstruction (IR) algorithm and a filtered back projection (FBP) algorithm.

Methods

The experimental study was performed on a 256-slice MDCT. LC detectability, noise, CNR and SR were measured on a Catphan phantom scanned with decreasing doses (48.8 down to 0.7 mGy) and parameters typical of a chest CT examination. Images were reconstructed with FBP and a model-based IR algorithm. Additionally, human chest cadavers were scanned and reconstructed using the same technical parameters. Images were analyzed to illustrate the phantom results.

Results

LC detectability and noise were statistically significantly different between the techniques, supporting model-based IR algorithm (p < 0.0001). At low doses, the noise in FBP images only enabled SR measurements of high contrast objects. The superior CNR of model-based IR algorithm enabled lower dose measurements, which showed that SR was dose and contrast dependent. Cadaver images reconstructed with model-based IR illustrated that visibility and delineation of anatomical structure edges could be deteriorated at low doses.

Conclusion

Model-based IR improved LC detectability and enabled dose reduction. At low dose, SR became dose and contrast dependent.

Key Points

• Model- based Iterative Reconstruction improves detectability of low contrast object.

• With model- based Iterative Reconstruction, spatial resolution is dose and contrast dependent.

• Model-based Iterative Reconstruction algorithms enable improved IQ combined with dose-reduction possibilities.

• Improvement of SR and LC detectability on the same IMR data set would reduce reconstructions.

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Acknowledgments

A poster has been presented based on these results at the ECR 2015. The scientific guarantor of this publication is Pr Emmanuel COCHE.The authors of this manuscript declare relationships with the following companies: Alain Vlassenbroek is an employee of Philips Healthcare. The authors state that this work has not received any funding. One of the authors has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Methodology: experimental, performed at one institution.

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Authors and Affiliations

  1. Department of Radiology and Medical Imaging, Cliniques Universitaires Saint Luc, Université Catholique de Louvain, Avenue Hippocrate 10, 1200, Brussels, Belgium

    Domitille Millon & Emmanuel E. Coche

  2. Philips Healthcare, Rue des deux gares 80, 1070, Brussels, Belgium

    Alain Vlassenbroek

  3. Statistics Unit, King Albert II Cancer Institute, Université Catholique de Louvain, Avenue Hippocrate 10, 1200, Brussels, Belgium

    Aline G. Van Maanen & Samantha E. Cambier

Authors
  1. Domitille Millon
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  2. Alain Vlassenbroek
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  3. Aline G. Van Maanen
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  4. Samantha E. Cambier
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  5. Emmanuel E. Coche
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Correspondence to Domitille Millon.

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Millon, D., Vlassenbroek, A., Van Maanen, A.G. et al. Low contrast detectability and spatial resolution with model-based Iterative reconstructions of MDCT images: a phantom and cadaveric study. Eur Radiol 27, 927–937 (2017). https://doi.org/10.1007/s00330-016-4444-x

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  • DOI: https://doi.org/10.1007/s00330-016-4444-x

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