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. 2025 Nov 20.
doi: 10.1007/s00259-025-07660-x. Online ahead of print.

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

Meghana Urs  1 , Aditya Killekar  1 , Valerie Builoff  1 , Mark Lemley  1 , Chih-Chun Wei  1 , Giselle Ramirez  1 , Paul Kavanagh  1 , Christopher Buckley  2 , Piotr J Slomka  3
Affiliations

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

Meghana Urs et al. Eur J Nucl Med Mol Imaging. .

Abstract

Purpose: Precise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in 18F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability, time-consuming, and requires significant experience. We propose a deep learning (DL) framework for automatic MC of 18F-flurpiridaz PET.

Methods: The method employs a 3D-ResNet based architecture that takes 3D PET volumes and outputs motion vectors. It was validated using 5-fold cross-validation on data from 32-sites of a Phase-III clinical trial (NCT01347710). Manual corrections from two experienced operators served as ground truth, and data augmentation using simulated vectors enhanced training robustness. The study compared the DL approach to both manual and standard non-AI automatic MC methods, assessing agreement and diagnostic accuracy using minimal segmental stress MBF and MFR.

Results: The area under the receiver operating characteristic curves (AUC) for significant CAD were comparable between DL-MC stress MBF, manual-MC stress MBF from Operators (AUC = 0.897, 0.892 and 0.889, respectively; p > 0.05), standard non-AI automatic MC (AUC = 0.877; p > 0.05) and significantly higher than No-MC (AUC = 0.835; p < 0.05). Similar findings were observed with MFR. The 95% confidence limits for agreement with the operator were ± 0.49 (mean difference = 0.00) for MFR and ± 0.24 ml/g/min (mean difference = 0.00) for stress MBF.

Conclusion: DL-MC is significantly faster but diagnostically comparable to manual-MC. The quantitative results obtained with DL-MC for stress MBF and MFR are in excellent agreement with those manually corrected by experienced operators compared to standard non-AI automatic MC in patients undergoing 18F-flurpiridaz PET-MPI.

Keywords: 18F-flurpiridaz; Deep learning; Motion correction; Myocardial blood flow; Myocardial flow reserve; PET.

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Conflict of interest statement

Declarations. Ethics approval: Institutional Review Board or ethics committee approval was obtained at each study site that participated the phase III Flurpiridaz trial (NCT01347710). The study protocol complied with the Declaration of Helsinki. Consent to participate: All patients provided written informed consent before undergoing study procedures. Consent to publish: The authors affirm that human research participants provided informed consent for publication of the images in Fig. 1. Competing interests: Dr. Piotr Slomka and Mr. Paul Kavanagh participate in software royalties for QPS software at Cedars-Sinai Medical Center. Dr. Slomka declares an equity interest in APQ Health and has received research grant support from Siemens Medical Systems and consulting fees from Synektik SA and Novo Nordisk. Dr. Buckley is an employee of GE Healthcare. The remaining authors have no relevant disclosures.

Update of

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