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Related Concept Videos

Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...

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Related Experiment Video

Updated: May 16, 2026

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
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Development of AI-based dopamine transporter (DAT) image generation technique using early phase [18F]-FP-CIT PET

Changhwan Sung1, Jungsu S Oh1, Sun Young Chae2

  • 1Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Plos One
|May 14, 2026
PubMed
Summary

A deep learning model successfully generates dopamine transporter (DAT) images from early [18F]-FP-CIT PET scans. The generated images show high similarity to real delayed images, enabling comparable diagnostic performance for conditions like parkinsonism.

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Published on: October 26, 2018

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Nuclear Medicine

Background:

  • Dopamine transporter (DAT) imaging using [18F]-FP-CIT PET is crucial for diagnosing neurodegenerative diseases.
  • Generating delayed-phase DAT images from early-phase scans could optimize imaging protocols and reduce patient burden.

Purpose of the Study:

  • To develop and validate a deep learning model for generating delayed-phase DAT images from early-phase [18F]-FP-CIT PET scans.
  • To assess the quantitative and visual performance of the generated DAT images compared to real delayed-phase images.

Main Methods:

  • A conditional generative adversarial network was trained on 477 dual-phase [18F]-FP-CIT PET scans.
  • The model generated delayed-phase images from early-phase scans, using adjacent slices to predict the central slice.
  • Validation was performed on internal and independent prospective datasets, comparing striatal binding ratios (SNBRs) and diagnostic performance.

Main Results:

  • Generated DAT images exhibited high similarity to real delayed-phase images.
  • Strong correlations were observed in SNBRs between real and generated images (R = 0.93 and 0.90).
  • Diagnostic performance for detecting abnormality and degenerative parkinsonism was comparable between real and generated images, particularly in the internal validation set.

Conclusions:

  • The developed deep learning model effectively generates DAT images from early PET scans.
  • The generated images demonstrate satisfactory quantitative and visual performance.
  • This approach holds potential for improving the efficiency of DAT imaging in clinical practice.