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Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To assess the impact on image quality and dose reduction of a new deep learning image reconstruction (DLIR) algorithm compared with a hybrid iterative reconstruction (IR) algorithm.

Methods

Data acquisitions were performed at seven dose levels (CTDIvol : 15/10/7.5/5/2.5/1/0.5 mGy) using a standard phantom designed for image quality assessment. Raw data were reconstructed using the filtered back projection (FBP), two levels of IR (ASiR-V50% (AV50); ASiR-V100% (AV100)), and three levels of DLIR (TrueFidelity™ low, medium, high). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index (d′) was computed to model a large mass in the liver, a small calcification, and a small subtle lesion with low contrast.

Results

NPS peaks were higher with AV50 than with all DLIR levels and only higher with DLIR-H than with AV100. The average NPS spatial frequencies were higher with DLIR than with IR. For all DLIR levels, TTF50% obtained with DLIR was higher than that with IR. d′ was higher with DLIR than with AV50 but lower with DLIR-L and DLIR-M than with AV100. d′ values were higher with DLIR-H than with AV100 for the small low-contrast lesion (10 ± 4%) and in the same range for the other simulated lesions.

Conclusions

New DLIR algorithm reduced noise and improved spatial resolution and detectability without changing the noise texture. Images obtained with DLIR seem to indicate a greater potential for dose optimization than those with hybrid IR.

Key Points

• This study assessed the impact on image quality and radiation dose of a new deep learning image reconstruction (DLIR) algorithm as compared with hybrid iterative reconstruction (IR) algorithm.

• The new DLIR algorithm reduced noise and improved spatial resolution and detectability without perceived alteration of the texture, commonly reported with IR.

• As compared with IR, DLIR seems to open further possibility of dose optimization.

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Abbreviations

AiCE:

Advanced intelligent Clear-IQ Engine

AV or ASiR-V:

Next generation of adaptative statistical iterative reconstruction algorithm

CTDIvol :

Volume CT dose index

DLIR:

Deep learning image reconstruction

DNN:

Deep neural network

FBP:

Filtered back projection

IQ:

Image quality

IR:

Iterative reconstruction

MBIR:

Model-based image reconstruction

NPS:

Noise power spectrum

TCM:

Tube current modulation

TTF:

Task-based transfer function

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Acknowledgments

We thank the Centre Cardiologique du Nord for giving us the permission to use their measurement results. We thank S. Kabani for her help in editing the manuscript.

Funding

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

Author information

Authors and Affiliations

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

    Joël Greffier, Aymeric Hamard, Fabricio Pereira, Jean Paul Beregi & Julien Frandon

  2. Department of Medical Physics, CHU Nimes, Univ Montpellier, Montpellier, France

    Joël Greffier & Corinne Barrau

  3. GE Healthcare, Buc, France

    Hugo Pasquier

Authors
  1. Joël Greffier
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  2. Aymeric Hamard
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  3. Fabricio Pereira
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  4. Corinne Barrau
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  5. Hugo Pasquier
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  6. Jean Paul Beregi
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  7. Julien Frandon
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Corresponding author

Correspondence to Joël Greffier.

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Guarantor

The scientific guarantor of this publication is Jean Paul Beregi.

Conflict of interest

One author declares relationship with the following company: GE Healthcare. Hugo Pasquier is a GE Healthcare employee. However, he neither had access nor control on the phantom data acquisition and analysis.

All other 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 is a study phantom.

Ethical approval

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

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

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Greffier, J., Hamard, A., Pereira, F. et al. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol 30, 3951–3959 (2020). https://doi.org/10.1007/s00330-020-06724-w

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  • DOI: https://doi.org/10.1007/s00330-020-06724-w

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