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Published in: European Journal of Nuclear Medicine and Molecular Imaging 4/2023

19-11-2022 | Angiography | Short Communication

Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial

Authors: Tomoe Hagio, Jonathan B. Moody, Alexis Poitrasson-Rivière, Jennifer M. Renaud, Lora Pierce, Christopher Buckley, Edward P. Ficaro, Venkatesh L. Murthy

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 4/2023

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Abstract

Purpose

Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without attenuation correction. Deep learning-based attenuation corrected (DLAC) polar maps improved diagnostic accuracy for detection of coronary artery disease (CAD) beyond non-attenuation-corrected (NAC) polar maps in a large single center study. However, the generalizability of this approach to other institutions with different scanner models and protocols is uncertain. In this study, we evaluated the diagnostic performance of DLAC compared to NAC for detection of CAD as defined by invasive coronary angiography (ICA) in a large multi-center trial.

Methods

During the phase 3 flurpiridaz multi-center diagnostic clinical trial, conducted over 74 international sites, patients with known or suspected CAD who were referred for a clinically indicated ICA were enrolled. Using receiver operating characteristic (ROC) analysis, we evaluated the detectability of obstructive CAD, defined by quantitative coronary angiography by a core laboratory, using total perfusion deficit (TPD) as an integrated measure of defect extent and severity on DLAC polar maps compared to NAC polar maps. This was also compared against the visual scoring of three expert core lab readers.

Results

Out of 755 patients, 722 (69% male) had evaluable SPECT and ICA for this study. ROC analysis demonstrated significant improvement in detecting per-patient obstructive CAD with DLAC over NAC with area under the curve (AUC) of 0.752 (95% CI: 0.711–0.792) for DLAC compared to 0.717 (0.675–0.759) for NAC (p value = 0.016). Compared to the consensus of expert readers AUC = 0.743 (0.701–0.784), DLAC was comparable (p value = 0.913), whereas NAC underperformed (p value = 0.051).

Conclusion

DL-based attenuation correction improves diagnostic performance of SPECT MPI for detecting CAD in data from a large multi-center clinical trial regardless of SPECT camera model or protocol.

Trial registration

A Phase 3 Multi-center Study to Assess PET Imaging of Flurpiridaz F 18 Injection in Patients With CAD, ClinicalTrials.gov Identifier: NCT01347710, registered on 4 May 2011. https://​clinicaltrials.​gov/​ct2/​show/​study/​NCT01347710
Appendix
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Metadata
Title
Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial
Authors
Tomoe Hagio
Jonathan B. Moody
Alexis Poitrasson-Rivière
Jennifer M. Renaud
Lora Pierce
Christopher Buckley
Edward P. Ficaro
Venkatesh L. Murthy
Publication date
19-11-2022
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 4/2023
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-022-06045-8

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