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Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study

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

Abstract

Objectives

To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications.

Methods

Acquisitions on phantoms were performed at 5 dose levels (CTDIvol: 13/11/9/6/1.8 mGy). Raw data were reconstructed using level 4 of iDose4 (i4) and 3 levels of AI-DLR (Smoother/Smooth/Standard). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d′) were computed: d′ modelled detection of a liver metastasis (LM) and hepatocellular carcinoma at portal (HCCp) and arterial (HCCa) phases. Image quality was subjectively assessed on an anthropomorphic phantom by 2 radiologists.

Results

From Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability (d′) of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d′ values were higher using the Smoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level.

Conclusion

Use of the Smooth and Smoother levels of AI-DLR reduces the image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality.

Key Points

• Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm.

• The Smooth and Smoother levels of AI-DLR reduced the image noise and improved the detectability of lesions and spatial resolution for standard and low-dose levels.

• The Smooth level seems the best compromise between the lowest dose level and adequate image quality.

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Abbreviations

AI-DLR:

Artificial intelligence deep-learning reconstruction

CNN:

Convolutional neural network

CT:

Computed tomography

FBP:

Filtered back projection

HCCa:

Hepatocellular carcinoma at the arterial phase

HCCp:

Hepatocellular carcinoma at the portal phase

IR:

Iterative reconstruction

LM:

Liver metastasis

NPS:

Noise power spectrum

ROI:

Region of interest

TTF:

Task-based transfer function

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Acknowledgements

We thank T. Sawyers and Dr H. de Forges, for their help in revising the manuscript.

Funding

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

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Authors and Affiliations

  1. Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, Bd. Prof Robert Debré, 30029, Nîmes Cedex 9, France

    Joël Greffier, Quentin Durand, Julien Frandon, Maeliss Loisy, Fabien de Oliveira, Jean-Paul Beregi & Djamel Dabli

  2. University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, 7 Avenue Jean Capelle O, 69100, Villeurbanne, France

    Salim Si-Mohamed

  3. Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, 59 Boulevard Pinel, 69500, Bron, France

    Salim Si-Mohamed

Authors
  1. Joël Greffier
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  2. Quentin Durand
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  3. Julien Frandon
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  4. Salim Si-Mohamed
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  5. Maeliss Loisy
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  6. Fabien de Oliveira
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  7. Jean-Paul Beregi
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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

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.

Joël Greffier is a member of the European Radiology Editorial Board. He has not taken part in the review or selection process of this 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 this study is performed on phantoms.

Ethical approval

Institutional Review Board approval was not required because this study was performed on phantoms.

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Greffier, J., Durand, Q., Frandon, J. et al. Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study. Eur Radiol 33, 699–710 (2023). https://doi.org/10.1007/s00330-022-09003-y

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  • DOI: https://doi.org/10.1007/s00330-022-09003-y

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