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Machine learning in cardiac CT image reconstruction
Citation Link: https://doi.org/10.15480/882.2906
Publikationstyp
Doctoral Thesis
Date Issued
2020-05
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2020-03-03
Institut
TORE-DOI
TORE-URI
Citation
Zuerst veröff.: ISBN 978-3-8440-7353-9, Shaker, 2020
This dissertation focuses on the removal of cardiac CT imaging artifacts caused by motion and metal implants. A combination of model-based data synthesis and subsequent data-driven learning of image enhancement methods is proposed. Forward models for virtual artifact generation are developed by incorporating prior knowledge about the cardiac anatomy and CT imaging physics. They form the counterpart of resulting learning-based backward models, which achieve significant reduction of artifacts during testing on real data.
Subjects
Cardiac Computed Tomography
Image Reconstruction
Artifact Quantification
Motion Compensation
Metal Artifact Removal
Machine Learning
Convolutional Neural Network
DDC Class
570: Biowissenschaften, Biologie
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