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Deep learning image reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of image quality and radiation dose in a phantom study

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To compare the image quality and radiation dose of a deep learning image reconstruction (DLIR) algorithm compared with iterative reconstruction (IR) and filtered back projection (FBP) at different tube voltages and tube currents.

Materials and methods

A customized body phantom was scanned at different tube voltages (120, 100, and 80 kVp) with different tube currents (200, 100, and 60 mA). The CT datasets were reconstructed with FBP, hybrid IR (30% and 50%), and DLIR (low, medium, and high levels). The reference image was set as an image taken with FBP at 120 kVp/200 mA. The image noise, contrast-to-noise ratio (CNR), sharpness, artifacts, and overall image quality were assessed in each scan both qualitatively and quantitatively. The radiation dose was also evaluated with the volume CT dose index (CTDIvol) for each dose scan.

Results

In qualitative and quantitative analyses, compared with reference images, low-dose CT with DLIR significantly reduced the noise and artifacts and improved the overall image quality, even with decreased sharpness (p < 0.05). Despite the reduction of image sharpness, low-dose CT with DLIR could maintain the image quality comparable to routine-dose CT with FBP, especially when using the medium strength level.

Conclusion

The new DLIR algorithm reduced noise and artifacts and improved overall image quality, compared to FBP and hybrid IR. Despite reduced image sharpness in CT images of DLIR algorithms, low-dose CT with DLIR seems to have an overall greater potential for dose optimization.

Key Points

• Using deep learning image reconstruction (DLIR) algorithms, image quality was maintained even with a radiation dose reduced by approximately 70%.

• DLIR algorithms yielded lower image noise, higher contrast-to-noise ratios, and higher overall image quality than FBP and hybrid IR, both subjectively and objectively.

• DLIR algorithms can provide a better image quality, much better than FBP and even better than hybrid IR, while facilitating a reduction in radiation dose.

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Abbreviations

ASIR:

Adaptive statistical iterative reconstruction

CNR:

Contrast-to-noise ratio

DCNN:

Deep convolutional neural network

DLIR:

Deep learning image reconstruction

FBP:

Filtered back projection

HU:

Hounsfield unit

IR:

Iiterative reconstruction

mGy:

Milligrays

ROI:

Regions of interest

SSIM:

Structural similarity

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Acknowledgements

This work was supported by the Soonchunhyang University Research Fund. The authors thank Kyoung-A Um for their technical support.

Funding

This work was supported by the Soonchunhyang University Research Fund.

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Authors and Affiliations

  1. Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, 170 Jomaru-ro, Bucheon, 14584, Republic of Korea

    Hye Joo Park, Seo-Youn Choi, Ji Eun Lee, Sanghyeok Lim, Min Hee Lee, Boem Ha Yi & Jang Gyu Cha

  2. Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro Gangnam-gu, Seoul,, 06351, Republic of Korea

    Ji Hye Min

  3. Institue of Public Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea

    Bora Lee

  4. Department of Statistics, Chung-Ang University, Seoul, Republic of Korea

    Bora Lee

  5. GE Healthcare Co., Ltd, 416 Hangang-daero, Jung-gu, Seoul,, 04637, Republic of Korea

    Yunsub Jung

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  1. Hye Joo Park
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Corresponding author

Correspondence to Seo-Youn Choi.

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Guarantor

The scientific guarantor of this publication is Seo-Youn Choi, M.D. PhD..

Conflict of interest

One of the authors (Yunsub Jung) is an employee of GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors is a statistician (Bora Lee, PhD, of medical statistics with 15 years of experience and majored in statistics) kindly provided statistical advice for this manuscript.

Informed consent

This study was performed with a phantom not human patients or animal; institutional review board (IRB) approval was not required and informed consent was not needed as well.

Ethical approval

This study was performed with a phantom not human patients or animal; institutional review board (IRB) approval was not required and informed consent was not needed as well.

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Supplementary Information

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330_2021_8459_MOESM1_ESM.docx

Supplementary file1 Note.—FBP = filtered back projection, ASIR-V = adaptive statistical iterative reconstruction V, DLIR-L = low-level deep learning imaging reconstruction, DLIR-M = medium-level deep learning image reconstruction, DLIR-H = high-level deep learning imaging reconstruction, CNR = contrast-to-noise ratio, SSIM = structural similarity.(DOCX 24 KB)

*Blur metrics is the index representing image sharpness. †SSIM is the index indicating overall image quality.

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Park, H.J., Choi, SY., Lee, J.E. et al. Deep learning image reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of image quality and radiation dose in a phantom study. Eur Radiol 32, 3974–3984 (2022). https://doi.org/10.1007/s00330-021-08459-8

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  • DOI: https://doi.org/10.1007/s00330-021-08459-8

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