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CT iterative reconstruction algorithms: a task-based image quality assessment

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Abstract

Purpose

To assess the dose performance in terms of image quality of filtered back projection (FBP) and two generations of iterative reconstruction (IR) algorithms developed by the most common CT vendors.

Materials and methods

We used four CT systems equipped with a hybrid/statistical IR (H/SIR) and a full/partial/advanced model-based IR (MBIR) algorithms. Acquisitions were performed on an ACR phantom at five dose levels. Raw data were reconstructed using a standard soft tissue kernel for FBP and one iterative level of the two IR algorithm generations. The noise power spectrum (NPS) and the task-based transfer function (TTF) were computed. A detectability index (d′) was computed to model the detection task of a large mass in the liver (large feature; 120 HU and 25-mm diameter) and a small calcification (small feature; 500 HU and 1.5-mm diameter).

Results

With H/SIR, the highest values of d′ for both features were found for Siemens, then for Canon and the lowest values for Philips and GE. For the large feature, potential dose reductions with MBIR compared with H/SIR were − 35% for GE, − 62% for Philips, and − 13% for Siemens; for the small feature, corresponding reductions were − 45%, − 78%, and − 14%, respectively. With the Canon system, a potential dose reduction of − 32% was observed only for the small feature with MBIR compared with the H/SIR algorithm. For the large feature, the dose increased by 100%.

Conclusion

This multivendor comparison of several versions of IR algorithms allowed to compare the different evolution within each vendor. The use of d′ is highly adapted and robust for an optimization process.

Key Points

• The performance of four CT systems was evaluated by using imQuest software to assess noise characteristic, spatial resolution, and lesion detection.

• Two task functions were defined to model the detection task of a large mass in the liver and a small calcification.

• The advantage of task-based image quality assessment for radiologists is that it does not include only complicated metrics, but also clinically meaningful image quality.

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Abbreviations

CTDI:

CT dose index

CTDIvol :

Volume CT dose index

ESF:

Edge-spread function

FBP:

Filtered back projection

H/SIR:

Hybrid or statistical iterative reconstruction

IR:

Iterative reconstruction

LSF:

Line-spread function

MBIR:

Full or advanced or partial model-based iterative reconstruction

NPS:

Noise power spectrum

TTF:

Task-based transfer function

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Acknowledgments

We are deeply grateful to Dr. J. Solomon for support regarding the use of imQuest software. We would like to thank Pr H. Rousseau and Dr. J.M. Teissier for giving us permission to use their measurement results.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiology, CHU Nîmes, Medical Imaging Group Nîmes, University of Montpellier, EA 2415, Bd Prof Robert Debré, 30029, Nîmes Cedex 9, France

    J. Greffier, J. Frandon, A. Larbi, J. P. Beregi & F. Pereira

Authors
  1. J. Greffier
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  2. J. Frandon
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  3. A. Larbi
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  4. J. P. Beregi
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  5. F. Pereira
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Corresponding author

Correspondence to J. Greffier.

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Guarantor

The scientific guarantor of this publication is Jean Paul Beregi.

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 not required for this study because it’s a phantom study.

Ethical approval

Institutional Review Board approval was not required because it’s a phantom study.

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• experimental

• multicenter study

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Greffier, J., Frandon, J., Larbi, A. et al. CT iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol 30, 487–500 (2020). https://doi.org/10.1007/s00330-019-06359-6

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  • DOI: https://doi.org/10.1007/s00330-019-06359-6

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