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Accuracy of deep learning-based attenuation correction in 99mTc-GSA SPECT/CT hepatic imaging.

M Miyai1, R Fukui2, M Nakashima3

  • 1Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama-Shi, Okayama 700-8558, Japan; Department of Radiology, Kawasaki Medical School General Medical Center, 2-6-1 Nakasange, Kita-ku, Okayama-shi, Okayama 700-8505, Japan.

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Deep learning generates pseudo CT images for SPECT attenuation correction, matching conventional CTAC accuracy. This method reduces patient radiation exposure by eliminating CT scans.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Accurate radioactive distribution assessment in SPECT requires attenuation correction (AC).
  • Computed tomography-based AC (CTAC) is accurate but exposes patients to radiation.
  • Deep learning offers a potential solution to reduce radiation exposure during SPECT/CT imaging.

Purpose of the Study:

  • To generate pseudo CT images for AC from non-AC SPECT images using deep learning.
  • To evaluate the efficacy of deep learning-based AC in 99mTc-labeled galactosyl human serum albumin SPECT/CT imaging.
  • To compare deep learning-based AC with conventional CTAC and uncorrected SPECT.

Main Methods:

  • Cycle-consistent generative network (CycleGAN) was employed to create pseudo CT images.
  • SPECT images were reconstructed using three methods: without AC (SPECTNC), with conventional CTAC (SPECTCTAC), and with deep learning-based AC (SPECTGAN).
  • Accuracy was assessed via total liver count, structural similarity index (SSIM), and coefficient of variation (%CV) for uniformity.

Main Results:

  • SPECTGAN showed significantly improved total liver counts compared to SPECTNC, with a ~7% difference from SPECTCTAC.
  • Both SPECTCTAC and SPECTGAN demonstrated significantly lower %CV than SPECTNC, indicating improved uniformity.
  • Mean SSIM values for SPECTCTAC and SPECTGAN were high (0.985 and 0.977, respectively), indicating strong image similarity.

Conclusions:

  • Deep learning-based AC achieved accuracy comparable to conventional CTAC.
  • The proposed method, using only non-AC SPECT images, significantly reduces patient radiation exposure by eliminating the need for CT scans.
  • This approach holds great potential for improving SPECT/CT imaging safety and accessibility.