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Physical characteristics of deep learning-based image processing software in computed tomography: a phantom study

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Abstract

Purpose

This study aimed to assess the image characteristics of deep-learning-based image processing software (DLIP; FCT PixelShine, FUJIFILM, Tokyo, Japan) and compare it with filtered back projection (FBP), model-based iterative reconstruction (MBIR), and deep-learning-based reconstruction (DLR).

Methods

This phantom study assessed the object-specific spatial resolution (task-based transfer function [TTF]), noise characteristics (noise power spectrum [NPS]), and low-contrast detectability (low-contrast object-specific contrast-to-noise ratio [CNRLO]) at three different output doses (standard: 10 mGy; low: 3.9 mGy; ultralow: 2.0 mGy). The processing strength of DLIPFBP with A1, A4, and A9 was compared with those of FBP, MBIR, and DLR.

Result

The standard dose with high-contrast TTFs of DLIPFBP exceeded that of FBP. Low-contrast TTFs were comparable to or lower than that of FBP. The NPS peak frequency (fP) of DLIPFBP shifts to low spatial frequencies of up to 8.6% at ultralow doses compared to the standard FBP dose. MBIR shifted the most fP compared to FBP—a marked shift of up to 49%. DLIPFBP showed a CNRLO equal to or greater than that of DLR in standard or low doses. In contrast, the CNRLO of the DLIPFBP was equal to or lower than that of the DLR in ultralow doses.

Conclusion

DLIPFBP reduced image noise while maintaining a resolution similar to commercially available MBIR and DLR. The slight spatial frequency shift of fP in DLIPFBP contributed to the noise texture degradation suppression. The NPS suppression in the low spatial frequency range effectively improved the low-contrast detectability.

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Data availability

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

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Acknowledgements

This study was supported as a joint research project by FUJIFILM Corporation. A.U. is currently receiving a grant (JSPS KAKENHI Grant No. 22K15834). The authors are grateful to the radiological diagnosis staff of the National Cancer Center Hospital who supported this study.

Funding

This work was supported by FUJIFILM Corporation as a joint research project. Atsushi Urikura is currently receiving a grant (JSPS KAKENHI Grant No. 22K15834).

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Authors and Affiliations

  1. Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan

    Seiya Sato, Atsushi Urikura, Makoto Mimatsu, Yuta Miyamae & Toshihiro Ishihara

  2. Clinical Product Specialist Marketing Group, FUJIFILM Corporation, 7-3, Akasaka 9-Chome Minato-Ku, Tokyo, Japan

    Yuji Jibiki & Mami Yamashita

Authors
  1. Seiya Sato
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by SS, AU, and MM. The first draft of the manuscript was written by SS and AU and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Atsushi Urikura.

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Conflict of interest

Yuji Jibiki and Mami Yamashita are employees of FUJIFILM Corporation. The authors have no relevant financial or nonfinancial interests to disclose.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Sato, S., Urikura, A., Mimatsu, M. et al. Physical characteristics of deep learning-based image processing software in computed tomography: a phantom study. Phys Eng Sci Med 46, 1713–1721 (2023). https://doi.org/10.1007/s13246-023-01331-7

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