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Jointly Optimized Deep Neural Networks to Synthesize Monoenergetic Images from Single-Energy CT Angiography for Improving Classification of Pulmonary Embolism

Fink, Matthias A. ; Seibold, Constantin ORCID iD icon 1; Kauczor, Hans-Ulrich; Stiefelhagen, Rainer ORCID iD icon 1; Kleesiek, Jens
1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT)

Abstract:

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep-learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two datasets: our institutional DE-CTPA dataset D1, comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism CT Dataset D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak signal-to-noise ratio). ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000146802
Veröffentlicht am 31.05.2022
Originalveröffentlichung
DOI: 10.3390/diagnostics12051224
Scopus
Zitationen: 4
Web of Science
Zitationen: 2
Dimensions
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2075-4418
KITopen-ID: 1000146802
Erschienen in Diagnostics
Verlag MDPI
Band 12
Heft 5
Seiten Article no: 1224
Nachgewiesen in Web of Science
Scopus
Dimensions
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