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Automated dynamic motion correction using normalized gradient fields for 82rubidium PET myocardial blood flow

Benjamin C Lee1, Jonathan B Moody1, Alexis Poitrasson-Rivière1

  • 1INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 48108, USA.

Journal of Nuclear Cardiology : Official Publication of the American Society of Nuclear Cardiology
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PubMed
Summary

An automated algorithm corrects patient motion in dynamic PET scans, improving the accuracy of myocardial blood flow and flow reserve measurements. This technique enhances diagnostic precision for cardiovascular conditions.

Keywords:
Myocardial perfusion imaging: PETcoronary blood flowcoronary flow reserveimage artifactsmotion correction

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Area of Science:

  • Cardiovascular Imaging
  • Nuclear Medicine
  • Medical Physics

Background:

  • Patient motion during dynamic PET imaging causes misalignment of left ventricular volumes-of-interest (VOIs).
  • This misalignment leads to inaccurate quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR).
  • Conventional registration algorithms struggle with motion correction in early blood-phase frames.

Purpose of the Study:

  • To develop and validate an image-based, 3D-automated motion-correction algorithm for dynamic PET myocardial perfusion imaging.
  • The algorithm aims to correct translational motion across the entire dynamic sequence, especially during the critical first minute post-injection.
  • To improve the accuracy of MBF and MFR quantification affected by patient motion.

Main Methods:

  • Studied 225 patients undergoing dynamic rubidium-82 chloride (82Rb) PET imaging.
  • Developed an automated algorithm using normalized gradient fields and a signed distance function to register dynamic frames.
  • Compared automated motion-correction results to manual correction by three physician readers.

Main Results:

  • Automated motion-correction shifts closely matched manual shifts (within 5 mm).
  • Excellent linear agreement (R=0.99) was observed between automated and manual global MBF values.
  • 90% of global MBF and 87% of global MFR values were brought into agreement with manual correction, with significant improvements in the RCA territory.

Conclusions:

  • An automated, image-based motion-correction algorithm effectively corrects translational motion in dynamic PET sequences.
  • The algorithm demonstrates comparable performance to manual correction, reducing bias and variance in MBF and MFR quantification.
  • This technique significantly improves the accuracy of myocardial perfusion quantification, particularly in specific coronary territories like the RCA.