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A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods
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Deep learning-based attenuation correction method in 99mTc-GSA SPECT/CT hepatic imaging: a phantom study.

Masahiro Miyai1,2, Ryohei Fukui3, Masahiro Nakashima4

  • 1Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-Shi, Okayama, 700-8558, Japan. miya0210@hp.kawasaki-m.ac.jp.

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A novel deep learning method generates pseudo-CT images from SPECT scans for attenuation correction. This approach, SPECT-GAN, offers similar results to traditional SPECT/CT, potentially reducing radiation exposure in 99mTc-GSA scintigraphy.

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99mTc-GSAAttenuation correctionDeep learningPseudo-CT imageSingle-photon emission computed tomography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Nuclear Medicine

Background:

  • Accurate attenuation correction (AC) is crucial for quantitative SPECT imaging.
  • Traditional SPECT/CT requires a separate CT scan, increasing radiation dose and scan time.
  • 99mTc-galactosyl human albumin diethylenetriamine pentaacetic acid (GSA) scintigraphy is used for liver imaging.

Purpose of the Study:

  • To evaluate a deep learning-based AC method using pseudo-CT images generated from non-AC SPECT (SPECTNC).
  • To assess the feasibility of reducing patient radiation dosage in 99mTc-GSA scintigraphy.
  • To compare the performance of pseudo-CT AC (SPECTGAN) with conventional CT AC (SPECTCTAC).

Main Methods:

  • A Cycle-consistent Generative Adversarial Network (CycleGAN) model was employed to generate pseudo-CT images from SPECTNC.
  • Training involved approximately 850 liver phantom SPECTNC and real CT image pairs.
  • SPECTGAN and SPECTCTAC images were acquired and compared using liver volume, total counts, uniformity, and structural similarity index (SSIM).

Main Results:

  • Pseudo-CT images generated by CycleGAN showed slightly lower liver volumes compared to real CT.
  • SPECTCTAC had higher total counts than SPECTNC and SPECTGAN (SPECTGAN was ~7% lower).
  • Both SPECTCTAC and SPECTGAN demonstrated improved uniformity over SPECTNC, with a mean SSIM of 0.97 between SPECTCTAC and SPECTGAN.

Conclusions:

  • The proposed deep learning approach effectively generates pseudo-CT images for AC in 99mTc-GSA SPECT.
  • SPECTGAN provides comparable results to SPECTCTAC, indicating its potential for accurate attenuation correction.
  • This method enables SPECT/CT examinations with potentially reduced radiation exposure for patients.