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Deep learning-based attenuation correction method in 99mTc-GSA SPECT/CT hepatic imaging: a phantom study

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Abstract

This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECTNC) for AC in 99mTc-galactosyl human albumin diethylenetriamine pentaacetic acid (GSA) scintigraphy and to reduce patient dosage. A cycle-consistent generative network (CycleGAN) model was used to generate pseudo-CT images. The training datasets comprised approximately 850 liver phantom images obtained from SPECTNC and real CT images. The training datasets were then input to CycleGAN, and pseudo-CT images were output. SPECT images with real-time CT attenuation correction (SPECTCTAC) and pseudo-CT attenuation correction (SPECTGAN) were acquired. The difference in liver volume between real CT and pseudo-CT images was evaluated. Total counts and uniformity were then used to evaluate the effects of AC. Additionally, the similarity coefficients of SPECTCTAC and SPECTGAN were assessed using a structural similarity (SSIM) index. The pseudo-CT images produced a lower liver volume than the real CT images. SPECTCTAC exhibited a higher total count than SPECTNC and SPECTGAN, which were approximately 60% and 7% lower, respectively. The uniformities of SPECTCTAC and SPECTGAN were better than those of SPECTNC. The mean SSIM value for SPECTCTAC and SPECTGAN was 0.97. We proposed a deep learning-based AC approach to generate pseudo-CT images from SPECTNC images in 99mTc-GSA scintigraphy. SPECTGAN with AC using pseudo-CT images was similar to SPECTCTAC, demonstrating the possibility of SPECT/CT examination with reduced exposure to radiation.

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Authors and Affiliations

  1. Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-Shi, Okayama, 700-8558, Japan

    Masahiro Miyai

  2. Department of Radiology, Kawasaki Medical School General Medical Center, 2-6-1 Nakasange, Kita-Ku, Okayama-shi, Okayama, 700-8505, Japan

    Masahiro Miyai

  3. Department of Radiological Technology, Faculty of Health Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-shi, Okayama, 700-8558, Japan

    Ryohei Fukui & Sachiko Goto

  4. Division of Radiological Technology, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-shi, Okayama, 700-8558, Japan

    Masahiro Nakashima

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  1. Masahiro Miyai
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  2. Ryohei Fukui
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  3. Masahiro Nakashima
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  4. Sachiko Goto
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Correspondence to Masahiro Miyai.

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Miyai, M., Fukui, R., Nakashima, M. et al. Deep learning-based attenuation correction method in 99mTc-GSA SPECT/CT hepatic imaging: a phantom study. Radiol Phys Technol 17, 165–175 (2024). https://doi.org/10.1007/s12194-023-00762-x

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  • DOI: https://doi.org/10.1007/s12194-023-00762-x

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