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Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT

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Abstract

This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d′). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d′ for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.

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Data Availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Abbreviations

ASIR-V:

Adaptive statistical iterative reconstruction-V

AUC:

Area under the curve

DECT:

Dual-energy computed tomography

DLIR:

Deep learning image reconstruction

DLR:

Deep learning reconstruction

FBP:

Filtered back-projection

HU:

Hounsfield unit value

IR:

Iterative reconstruction

MBIR:

Model-based image reconstruction

NPS:

Noise power spectrum

ROI:

Region of interest

SECT:

Single-energy computed tomography

VMI:

Virtual monoenergetic images

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Acknowledgements

The authors would like to express their gratitude to Dr. Zhen Pan for her assistance in image quality assessment, and Dr. Shiqi Mao for his advice on data visualization.

Funding

This work was supported by National Natural Science Foundation of China (82271934, 82101986), Yangfan Project of Science and Technology Commission of Shanghai Municipality (22YF1442400, 20YF1427200), Shanghai Science and Technology Commission Science and Technology Innovation Action Clinical Innovation Field (18411953000), Medicine and Engineering Combination Project of Shanghai Jiao Tong University (YG2021QN08, YG2019ZDB09), Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine (TRKYRC-XX202204, TRGG202101, TRYJ2021JC06, 2020TRYJ(LB)06, 2020TRYJ(JC)07), Guangci Innovative Technology Launch Plan of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (2022–13).

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Author notes

  1. Jingyu Zhong and Hailin Shen contributed equally to this work.

Authors and Affiliations

  1. Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Changning District, No. 1111 Xianxia Road, Shanghai, 200336, China

    Jingyu Zhong, Yue Xing, Yangfan Hu, Xiang Ge, Defang Ding, Zhenming Jiang & Weiwu Yao

  2. Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, 215028, China

    Hailin Shen

  3. Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

    Yong Chen & Yihan Xia

  4. Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK

    Xiaomeng Shi

  5. Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China

    Wei Lu

  6. Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China

    Jianying Li

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  1. Jingyu Zhong
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jingyu Zhong, Hailin Shen, Yong Chen, Yihan Xia, Yue Xing, Yangfan Hu, Xiang Ge, Defang Ding, and Zhenming Jiang. The first draft of the manuscript was written by Jingyu Zhong and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Weiwu Yao.

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Ethics Approval

Institutional Review Board approval was not required because of the nature of our study, which was a phantom study.

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Written informed consent was not required for this study because of the nature of our study, which was a phantom study.

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Consent to publish was not required because of the nature of our study, which was a phantom study.

Competing Interests

Mr. Wei Lu and Dr. Jianying Li are employees of GE Healthcare. However, they neither had access nor control on the data acquisition and analysis. All other authors of this manuscript have no relevant financial or non-financial interests to disclose.

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Zhong, J., Shen, H., Chen, Y. et al. Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT. J Digit Imaging 36, 1390–1407 (2023). https://doi.org/10.1007/s10278-023-00806-z

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