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Attenuation correction using deep learning for brain perfusion SPECT images

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Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Objective

Non-uniform attenuation correction using computed tomography (CT) improves the image quality and quantification of single-photon emission computed tomography (SPECT). However, it is not widely used because it requires a SPECT/CT scanner. This study constructs a convolutional neural network (CNN) to generate attenuation-corrected SPECT images directly from non-attenuation-corrected SPECT images.

Methods

We constructed an auto-encoder (AE) using a CNN to correct the attenuation in brain perfusion SPECT images. SPECT image datasets of 270 (44,528 slices including augmentation), 60 (5002 slices), and 30 (2558 slices) cases were used for training, validation, and testing, respectively. The acquired projection data were reconstructed in three patterns: uniform attenuation correction using Chang’s method (Chang-AC), non-uniform attenuation correction using CT (CT-AC), and no attenuation correction (No-AC). The AE learned an end-to-end mapping between the No-AC and CT-AC images. The No-AC images in the test dataset were loaded into the trained AE, which generated images simulating the CT-AC images as output. The generated SPECT images were employed as attenuation-corrected images using the AE (AE-AC). The accuracy of the AE-AC images was evaluated in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity metric (SSIM). The intensities of the AE-AC and CT-AC images were compared by voxel-by-voxel and region-by-region analysis.

Results

The PSNRs of the AE-AC and Chang-AC images, compared using CT-AC images, were 62.2, and 57.9, and their SSIM values were 0.9995 and 0.9985, respectively. The AE-AC and CT-AC images were visually and statistically in good agreement.

Conclusions

The proposed AE-AC method yields highly accurate attenuation-corrected brain perfusion SPECT images.

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Acknowledgements

The authors thank Takeshi Hara and Hiroshi Fujita from the Graduate School of Medicine Gifu University for technical support in constructing the deep learning network.

We thank Saad Anis, Ph.D., from Edanz Group (https://en-author-services.edanzgroup.com/ac) for editing a draft of this manuscript.

Funding

The authors declare that they received no funding.

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Authors and Affiliations

  1. Radiology Center, Kindai University Hospital, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan

    Kenta Sakaguchi & Shuhei Yoshida

  2. Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osaka, 589-8511, Japan

    Kenta Sakaguchi, Hayato Kaida & Kazunari Ishii

  3. Department of Radiology, Faculty of Medicine, Kindai University, Osaka, 589-8511, Japan

    Hayato Kaida & Kazunari Ishii

Authors
  1. Kenta Sakaguchi
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  2. Hayato Kaida
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  3. Shuhei Yoshida
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  4. Kazunari Ishii
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Correspondence to Kenta Sakaguchi.

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The authors declare that they have no competing interests.

Ethics approval and consent to participate

The institutional review board at our institution approved this retrospective study (code: 31-042). The requirement of obtaining informed consent of patients was waived owing to the retrospective nature of the study, but we provided a means for patients to opt out on our hospital website.

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Sakaguchi, K., Kaida, H., Yoshida, S. et al. Attenuation correction using deep learning for brain perfusion SPECT images. Ann Nucl Med 35, 589–599 (2021). https://doi.org/10.1007/s12149-021-01600-z

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  • DOI: https://doi.org/10.1007/s12149-021-01600-z

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