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

Autor:	Builoff V; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA., Huang C; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA., Kuronuma K; 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., Wei CC; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA., Fujito H; 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., Otaki Y; 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., Van Kriekinge SD; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA., Kavanagh P; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA., Lemley M; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA., Hyun MC; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA., Di Carli M; Division of Cardiovascular Medicine, Department of Medicine, Heart and Vascular Center Brigham and Women's Hospital, Harvard Medical School Boston MA., Berman DS; Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA., Slomka PJ; 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.
Jazyk:	angličtina
Zdroj:	Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology [J Nucl Cardiol] 2024 Nov 05, pp. 102072. Date of Electronic Publication: 2024 Nov 05.
DOI:	10.1016/j.nuclcard.2024.102072
Abstrakt:	Background: Motion correction (MC) is critical for accurate quantification of myocardial blood flow (MBF) and flow reserve (MFR) from 18 F-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 18 F-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 18 F-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 82 Rb-chloride PET-MPI, was optimized for 18 F-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 18 F-flurpiridaz PET-MPI. Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Piotr J. Slomka reports financial support was provided by National Heart Lung and Blood Institute. Marcelo Di Carli reports a relationship with Gilead Sciences Inc that includes: funding grants. Marcelo Di Carli reports a relationship with Amgen Inc that includes: funding grants. Marcelo Di Carli reports a relationship with MedTrace that includes: consulting or advisory. Piotr J Slomka reports a relationship with Siemens Medical Solutions USA Inc that includes: funding grants. Piotr J Slomka reports a relationship with Synektik that includes: consulting or advisory. Daniel Berman reports a relationship with GE Healthcare that includes: consulting or advisory. Piotr J. Slomka has patent QPET software with royalties paid to Cedars-Sinai Medical Center. Daniel Berman has patent QPET software with royalties paid to Cedars-Sinai Medical Center. Paul Kavanagh has patent QPET Software with royalties paid to Cedars-Sinai Medical Center. The remaining authors have no relevant disclosures. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 American Society of Nuclear Cardiology. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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