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

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

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...

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Bibliographic Details
Main Authors: Matthias A. Fink, Constantin Seibold, Hans-Ulrich Kauczor, Rainer Stiefelhagen, Jens Kleesiek
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Diagnostics
Subjects:
artificial intelligence
deep learning
image-to-image translation
dual-energy computed tomography
pulmonary embolism
emergency radiology
Online Access:https://www.mdpi.com/2075-4418/12/5/1224

Internet

https://www.mdpi.com/2075-4418/12/5/1224

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