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Updated: Jun 30, 2026

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TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion

Xueqi Guo1, Luyao Shi2, Xiongchao Chen1

  • 1Yale University, New Haven, CT 06511, USA.

Simulation and Synthesis in Medical Imaging : ... International Workshop, SASHIMI ..., Held in Conjunction with MICCAI ..., Proceedings. SASHIMI (Workshop)
|March 11, 2024
PubMed
Summary

This study introduces a new AI method, TAI-GAN, to improve motion correction in cardiac PET scans using rubidium-82 (82Rb). The AI enhances image quality and improves the accuracy of myocardial blood flow quantification.

Keywords:
cardiac PETframe conversionmotion correction

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Area of Science:

  • Nuclear Medicine
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Dynamic cardiac PET imaging with rubidium-82 (82Rb) faces challenges in inter-frame motion correction due to rapid tracer kinetics and distribution variations.
  • Conventional registration methods are often ineffective for early frames where tracer concentration is low.

Purpose of the Study:

  • To develop an advanced generative method for improved frame-wise motion correction and parametric quantification in dynamic cardiac PET.
  • To enhance the accuracy of myocardial blood flow (MBF) measurements by addressing motion artifacts in early imaging frames.

Main Methods:

  • Proposed a Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) for transforming early PET frames to a late reference frame.
  • Utilized a feature-wise linear modulation layer incorporating temporal tracer kinetics and rough cardiac segmentations for anatomical guidance.
  • Employed an all-to-one mapping strategy for frame conversion.

Main Results:

  • TAI-GAN successfully converted early 82Rb PET frames to achieve high image quality, comparable to reference frames.
  • Post-conversion, motion estimation accuracy was significantly improved.
  • Clinical myocardial blood flow (MBF) quantification demonstrated enhanced accuracy after TAI-GAN conversion compared to original frames.

Conclusions:

  • TAI-GAN effectively addresses motion correction challenges in dynamic cardiac PET, particularly for early frames.
  • The proposed method improves both image quality and quantitative accuracy of MBF.
  • This AI-driven approach offers a promising solution for robust analysis of dynamic cardiac PET data.