Automated quantitative Rb-82 3D PET/CT myocardial perfusion imaging: normal limits and correlation with invasive

Ryo Nakazato1, Daniel S Berman, Damini Dey

  • 1Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

Abstract

Insights

Automated 3D Rubidium-82 (Rb-82) PET/CT myocardial perfusion imaging (MPI) establishes normal limits and demonstrates high accuracy for detecting coronary artery disease (CAD). This quantitative analysis aids in diagnosing obstructive CAD with QPET software.

Area of Science:

  • Nuclear Cardiology
  • Cardiovascular Imaging
  • Quantitative Analysis

Background:

  • Myocardial perfusion imaging (MPI) using 3D Rubidium-82 (Rb-82) PET/CT is a key diagnostic tool.
  • Automated quantification methods require established normal limits for accurate interpretation.
  • Coronary artery disease (CAD) diagnosis relies on precise assessment of myocardial blood flow.

Purpose of the Study:

  • To establish normal limits for 3D Rb-82 PET/CT MPI.
  • To evaluate the diagnostic accuracy of automated quantification using QPET software.
  • To assess the software's ability to detect obstructive coronary artery disease.

Main Methods:

  • Studied 125 patients with suspected CAD and 42 with low likelihood (LLk) of CAD undergoing Rb-82 PET/CT MPI.
  • Derived normal perfusion and function limits from LLk patients.
  • Utilized QPET software for automated quantification of total perfusion deficit (TPD) at rest and stress.

Main Results:

  • No significant differences in relative perfusion between males and females across 17 segments.
  • Area under the ROC curve for CAD detection was 0.86 for ≥50% and ≥70% stenosis.
  • Sensitivity/specificity for ≥50% stenosis: 86%/86%; for ≥70% stenosis: 93%/77%.

Conclusions:

  • Normal limits for 3D Rb-82 PET/CT analysis with QPET software have been successfully established.
  • Fully automated quantification of myocardial perfusion PET data demonstrates high diagnostic accuracy.
  • This automated approach is effective for detecting obstructive CAD.