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Development of an individual display optimization system based on deep convolutional neural network transition learning for somatostatin receptor scintigraphy

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Abstract

Somatostatin receptor scintigraphy (SRS) is an essential examination for the diagnosis of neuroendocrine tumors (NETs). This study developed a method to individually optimize the display of whole-body SRS images using a deep convolutional neural network (DCNN) reconstructed by transfer learning of a DCNN constructed using Gallium-67 (67Ga) images. The initial DCNN was constructed using U-Net to optimize the display of 67Ga images (493 cases/986 images), and a DCNN with transposed weight coefficients was reconstructed for the optimization of whole-body SRS images (133 cases/266 images). A DCNN was constructed for each observer using reference display conditions estimated in advance. Furthermore, to eliminate information loss in the original image, a grayscale linear process is performed based on the DCNN output image to obtain the final linearly corrected DCNN (LcDCNN) image. To verify the usefulness of the proposed method, an observer study using a paired-comparison method was conducted on the original, reference, and LcDCNN images of 15 cases with 30 images. The paired comparison method showed that in most cases (29/30), the LcDCNN images were significantly superior to the original images in terms of display conditions. When comparing the LcDCNN and reference images, the number of LcDCNN and reference images that were superior to each other in the display condition was 17 and 13, respectively, and in both cases, 6 of these images showed statistically significant differences. The optimized SRS images obtained using the proposed method, while reflecting the observer's preference, were superior to the conventional manually adjusted images.

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Acknowledgements

We would like to thank Editage (www.editage.jp) for the English language editing.

Funding

PDRadiopharma Inc.

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Authors and Affiliations

  1. Division of Radiology, Yamaguchi University Hospital, 1-1-1 Minamikogushi, Yamaguchi, 755-8505, Japan

    Shun Matsumoto, Yuki Nakahara, Teppei Yonezawa & Yuto Nakamura

  2. Graduate School of Health Sciences, Kumamoto University, 4-24-1 Kuhonji, Kumamoto, 862-0976, Japan

    Shun Matsumoto

  3. Department of Radiology, Yamaguchi University School of Medicine, 1-1-1 Minamikogushi, Yamaguchi, 755-8505, Japan

    Masahiro Tanabe & Mayumi Higashi

  4. Faculty of Life Sciences, Kumamoto University, 4-24-1 Kuhonji, Kumamoto, 862-0976, Japan

    Junji Shiraishi

Authors
  1. Shun Matsumoto
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  2. Yuki Nakahara
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  3. Teppei Yonezawa
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  4. Yuto Nakamura
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  5. Masahiro Tanabe
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  6. Mayumi Higashi
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  7. Junji Shiraishi
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Correspondence to Shun Matsumoto.

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Conflict of interest

Co-investigator Junji Shiraishi was supported in part by a research grant from PD Radiopharma Inc.

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Ethical approval for case use was obtained from the institutional review board (IRB) of both the authors’ institutions.

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Matsumoto, S., Nakahara, Y., Yonezawa, T. et al. Development of an individual display optimization system based on deep convolutional neural network transition learning for somatostatin receptor scintigraphy. Radiol Phys Technol 17, 195–206 (2024). https://doi.org/10.1007/s12194-023-00766-7

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  • DOI: https://doi.org/10.1007/s12194-023-00766-7

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