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Erschienen in: Journal of Nuclear Cardiology 4/2016

01.08.2016 | Original Article

Accurate analysis and visualization of cardiac 11C-PIB uptake in amyloidosis with semiautomatic software

verfasst von: Tanja Kero, MD, Lars Lindsjö, Jens Sörensen, MD, PhD, Mark Lubberink, PhD

Erschienen in: Journal of Nuclear Cardiology | Ausgabe 4/2016

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Abstract

Background

11C-PIB PET is a promising non-invasive diagnostic tool for cardiac amyloidosis. Semiautomatic analysis of PET data is now available but it is not known how accurate these methods are for amyloid imaging. The aim of this study was to evaluate the feasibility of one semiautomatic software tool for analysis and visualization of 11C-PIB left ventricular retention index (RI) in cardiac amyloidosis.

Methods and results

Patients with systemic amyloidosis and cardiac involvement (n = 10) and healthy controls (n = 5) were investigated with dynamic 11C-PIB PET. Two observers analyzed the PET studies with semiautomatic software to calculate the left ventricular RI of 11C-PIB and to create parametric images. The mean RI at 15-25 min from the semiautomatic analysis was compared with RI based on manual analysis and showed comparable values (0.056 vs 0.054 min−1 for amyloidosis patients and 0.024 vs 0.025 min−1 in healthy controls; P = .78) and the correlation was excellent (r = 0.98). Inter-reader reproducibility also was excellent (intraclass correlation coefficient, ICC > 0.98). Parametric polarmaps and histograms made visual separation of amyloidosis patients and healthy controls fast and simple.

Conclusion

Accurate semiautomatic analysis of cardiac 11C-PIB RI in amyloidosis patients is feasible. Parametric polarmaps and histograms make visual interpretation fast and simple.
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Metadaten
Titel
Accurate analysis and visualization of cardiac 11C-PIB uptake in amyloidosis with semiautomatic software
verfasst von
Tanja Kero, MD
Lars Lindsjö
Jens Sörensen, MD, PhD
Mark Lubberink, PhD
Publikationsdatum
01.08.2016
Verlag
Springer US
Erschienen in
Journal of Nuclear Cardiology / Ausgabe 4/2016
Print ISSN: 1071-3581
Elektronische ISSN: 1532-6551
DOI
https://doi.org/10.1007/s12350-015-0149-9

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