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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...
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AI-based automatic patient positioning in a digital-BGO PET/CT scanner: efficacy and impact.

John A Kennedy1,2, Tala Palchan-Hazan3, Zohar Keidar3,4

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Summary

Artificial intelligence (AI) in digital positron emission tomography/x-ray CT (PET/CT) scanners significantly reduces patient off-centering. This AI-driven auto-positioning improves image quality and optimizes radiation exposure for better patient care.

Keywords:
Image qualityPET/CTPatient off-centeringRadiation exposure

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Healthcare

Background:

  • A new digital solid-state positron emission tomography/x-ray CT (PET/CT) scanner utilizes bismuth germanate (BGO) scintillators and an AI system for automatic patient positioning.
  • The study evaluated the efficacy of this AI-based auto-positioning system in achieving isocentric patient placement and its effects on image quality and radiation dose.

Purpose of the Study:

  • To assess the performance of AI-based automatic patient positioning in a digital-BGO PET/CT scanner.
  • To compare the AI auto-positioning system with manual positioning using a digital-LYSO PET/CT scanner.
  • To evaluate the impact of AI auto-positioning on patient off-centering, CT image quality, and radiation exposure (DLP).

Main Methods:

  • A retrospective analysis compared 432 digital-BGO PET/CT studies with AI auto-positioning to 343 digital-LYSO PET/CT studies with manual positioning.
  • Patient off-centering was measured using automated CT slice analysis, with phantom studies validating the measurement accuracy (<1.6 mm error).
  • Image noise (coefficient of variation in liver) and radiation exposure (DLP) were quantified and correlated with off-centering distance.

Main Results:

  • AI auto-positioning significantly reduced patient off-centering compared to manual positioning, especially after recalibration of the digital-BGO system (27% vs. 49-55% of studies with >2.5cm off-centering).
  • Patient off-centering showed a mean bias of 1.92 ± 1.79 cm posteriorly for the digital-BGO system.
  • Increased off-centering correlated with decreased CT image quality (3.2% noise increase per cm) and increased radiation exposure (144 Gy·cm increase per cm anterior off-centering).

Conclusions:

  • AI-based automatic patient positioning in digital-BGO PET/CT scanners substantially decreases patient off-centering.
  • This improved positioning enhances CT image quality and ensures appropriate radiation dose management.
  • The AI system demonstrates significant potential for optimizing PET/CT procedures.