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Estimating subjective evaluation of low-contrast resolution using convolutional neural networks

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Abstract

To develop a convolutional neural network-based method for the subjective evaluation of computed tomography (CT) images having low-contrast resolution due to imaging conditions and nonlinear image processing. Four radiological technologists visually evaluated CT images that were reconstructed using three nonlinear noise reduction processes (AIDR 3D, AIDR 3D Enhanced, AiCE) on a CT system manufactured by CANON. The visual evaluation consisted of two items: low contrast detectability (score: 0–9) and texture pattern (score: 1–5). Four AI models with different convolutional and max pooling layers were constructed and trained on pairs of CANON CT images and average visual assessment scores of four radiological technologists. CANON CT images not used for training were used to evaluate prediction performance. In addition, CT images scanned with a SIEMENS CT system were input to each AI model for external validation. The mean absolute error and correlation coefficients were used as evaluation metrics. Our proposed AI model can evaluate low-contrast detectability and texture patterns with high accuracy, which varies with the dose administered and the nonlinear noise reduction process. The proposed AI model is also expected to be suitable for upcoming reconstruction algorithms that will be released in the future.

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Data availability

The datasets during and/or analyzed during the current study available from the corresponding author on reasonable request.

Code availability

The codes we write during the current study available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank Mr. Matsumoto for useful discussions. We would like to express the deepest appreciation to Mr. Mori and Mr. Yamamoto, department of radiology, Toyama university hospital. We would like to thank Editage (www.editage.com) for English language editing.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Department of Radiology, Fujita Health University Hospital, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake City, Aichi, 470-1192, Japan

    Yujiro Doi

  2. Graduate School of Medical Science, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake City, Aichi, 470-1192, Japan

    Atsushi Teramoto, Ayumi Yamada, Masanao Kobayashi & Kuniaki Saito

  3. Faculty of Engineering, Gifu University, 1‑1 Yanagido, Gifu‑City, Gifu, 501‑1194, Japan

    Hiroshi Fujita

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  1. Yujiro Doi
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  4. Masanao Kobayashi
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  5. Kuniaki Saito
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Correspondence to Atsushi Teramoto.

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This study was supported in part by the research grant from A-Line corporation, Osaka, Japan.

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Doi, Y., Teramoto, A., Yamada, A. et al. Estimating subjective evaluation of low-contrast resolution using convolutional neural networks. Phys Eng Sci Med 44, 1285–1296 (2021). https://doi.org/10.1007/s13246-021-01062-7

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  • DOI: https://doi.org/10.1007/s13246-021-01062-7

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