Deep-learning-based cardiac amyloidosis classification from early acquired pet images

The objective of the present work was to evaluate the potential of deep learning tools for characterizing the presence of cardiac amyloidosis from early acquired PET images, i.e. 15 min after [18F]-Florbetaben tracer injection. 47 subjects were included in the study: 13 patients with transthyretin-r...

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Veröffentlicht in:	The International Journal of Cardiovascular Imaging 2021-07, Vol.37 (7), p.2327-2335
Hauptverfasser:	Santarelli, Maria Filomena, Genovesi, Dario, Positano, Vincenzo, Scipioni, Michele, Vergaro, Giuseppe, Favilli, Brunella, Giorgetti, Assuero, Emdin, Michele, Landini, Luigi, Marzullo, Paolo
Format:	Artikel
Sprache:	eng
Schlagworte:	Amyloidosis Artificial neural networks Cardiac Imaging Cardiology Computed tomography Deep learning Errors Evaluation Fluorine isotopes Heart Image acquisition Image classification Imaging Injection Iterative algorithms Iterative methods Machine learning Medical imaging Medicine Medicine & Public Health Neural networks Original Paper Positron emission Radiology Tomography Transthyretin
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