Automatic motion correction for myocardial blood flow estimation improves diagnostic performance for coronary artery disease in 18F-flurpiridaz PET-MPI

Valerie Builoff; Cathleen Huang; Keiichiro Kuronuma; Chih-Chun Wei; Hidesato Fujito; Yuka Otaki; Serge D Van Kriekinge; Paul Kavanagh; Mark Lemley; Mark C Hyun; Marcelo Di Carli; Daniel S Berman; Piotr J Slomka

doi:10.1016/j.nuclcard.2024.102072

Automatic motion correction for myocardial blood flow estimation improves diagnostic performance for coronary artery disease in ¹⁸F-flurpiridaz PET-MPI

J Nucl Cardiol. 2024 Nov 5:102072. doi: 10.1016/j.nuclcard.2024.102072. Online ahead of print.

Affiliations

¹ Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA.
² Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA; Department of Cardiology, Nihon University, Tokyo, Japan.
³ Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA; Department of Cardiology, Nihon University Itabashi Hospital, Tokyo, Japan.
⁴ Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA; Department of Radiology, Sakakibara Heart Institute, Tokyo, Japan.
⁵ Division of Cardiovascular Medicine, Department of Medicine, Heart and Vascular Center Brigham and Women's Hospital, Harvard Medical School Boston MA.
⁶ Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA. Electronic address: Piotr.Slomka@cshs.org.

PMID: 39510243
DOI: 10.1016/j.nuclcard.2024.102072

Abstract

Background: Motion correction (MC) is critical for accurate quantification of myocardial blood flow (MBF) and flow reserve (MFR) from ¹⁸F-flurpiridaz PET myocardial perfusion imaging (MPI). However, manual correction is time consuming and introduces inter-observer variability. We aimed to validate an automatic MC algorithm for ¹⁸F-flurpiridaz PET-MPI in terms of diagnostic performance for predicting coronary artery disease (CAD).

Methods: In total, 231 patients who underwent invasive coronary angiography and rest/pharmacologic stress ¹⁸F-flurpiridaz PET-MPI from phase III Flurpiridaz trial (NCT01347710) were enrolled. For manual MC, two operators (Reader 1 and Reader 2) shifted each frame's images in three directions. The automatic MC algorithm, initially developed for ⁸²Rb-chloride PET-MPI, was optimized for ¹⁸F-flurpiridaz. Diagnostic performance was compared using minimal segmental MBF/MFR with and without MC to predict CAD ≥70% stenosis by angiography.

Results: Manual MC took 10 minutes per case (both stress and rest) on average, while automatic MC required <17 seconds. The area under the receiver operating characteristic curves (AUCs) for significant CAD using minimal segmental MBF were comparable between automatic and manual MC (AUC=0.877 automatic, AUC=0.888 Reader 1 and AUC=0.892 Reader 2; all p>0.05). AUCs of minimal segmental MBF with manual and automatic MC were significantly higher than without MC (p<0.05 for both). Similar findings were observed with minimal segmental MFR.

Conclusions: Automatic MC can be performed rapidly, with diagnostic performance for predicting obstructive CAD comparable to manual MC. This method could be utilized for analysis of MBF/MFR in patients undergoing ¹⁸F-flurpiridaz PET-MPI.

Keywords: (18)F-flurpiridaz; PET; motion correction; myocardial blood flow; myocardial flow reserve; myocardial perfusion imaging.