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Dynamic frame-by-frame motion correction for 18F-flurpiridaz PET-MPI using convolution neural network.

Meghana Urs1, Aditya Killekar1, Valerie Builoff1

  • 1Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, 6500 Wilshire Blvd, Los Angeles, CA, 90048, USA.

European Journal of Nuclear Medicine and Molecular Imaging
|November 19, 2025
PubMed
Summary

Deep learning motion correction (DL-MC) offers a faster and diagnostically equivalent alternative to manual methods for 18F-flurpiridaz PET myocardial blood flow (MBF) and flow reserve (MFR) quantification. This automated approach achieves excellent agreement with expert manual corrections, improving efficiency in cardiac PET imaging.

Keywords:
18F-flurpiridazDeep learningMotion correctionMyocardial blood flowMyocardial flow reservePET

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Area of Science:

  • Nuclear Medicine
  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine

Background:

  • Accurate quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR) using 18F-flurpiridaz PET is crucial for diagnosing coronary artery disease (CAD).
  • Motion artifacts in PET imaging significantly impact the precision of MBF and MFR quantification.
  • Current manual motion correction (MC) methods are time-consuming, operator-dependent, and require extensive expertise, leading to inter-observer variability.

Purpose of the Study:

  • To develop and validate a deep learning (DL) framework for automated motion correction (MC) in 18F-flurpiridaz PET imaging.
  • To compare the diagnostic performance and quantitative agreement of DL-based MC with manual MC and standard non-AI automatic MC methods.

Main Methods:

  • A 3D-ResNet architecture was utilized to generate motion vectors from 3D PET volumes.
  • The DL framework was trained and validated using data from a Phase-III clinical trial (NCT01347710), with manual corrections by experienced operators serving as ground truth.
  • Data augmentation with simulated motion vectors enhanced the robustness of the DL model. Performance was evaluated against manual MC and standard non-AI automatic MC techniques.

Main Results:

  • The area under the ROC curve (AUC) for detecting significant CAD was comparable between DL-MC (0.897), manual MC (0.892, 0.889), and superior to no MC (0.835).
  • DL-MC demonstrated diagnostic accuracy comparable to standard non-AI automatic MC (AUC 0.877).
  • Quantitative analysis showed excellent agreement for MFR (95% confidence limits ±0.49) and stress MBF (±0.24 ml/g/min) between DL-MC and manual MC.

Conclusions:

  • Deep learning-based motion correction (DL-MC) provides a significantly faster alternative to manual MC for 18F-flurpiridaz PET.
  • DL-MC achieves diagnostic performance comparable to manual MC and superior to no MC in assessing significant CAD.
  • The quantitative MBF and MFR results from DL-MC show excellent agreement with expert manual corrections, making it a reliable tool for PET-MPI.