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  4. Deep learning-based rotation frequency estimation and NURD correction for IVOCT image data
 
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Deep learning-based rotation frequency estimation and NURD correction for IVOCT image data

Publikationstyp
Conference Paper
Date Issued
2020-06
Sprache
English
Author(s)
Mieling, Till Robin  
Latus, Sarah  orcid-logo
Gessert, Nils Thorben  
Lutz, Matthias  
Schlaefer, Alexander  
Institut
Medizintechnische und Intelligente Systeme E-1  
TORE-URI
http://hdl.handle.net/11420/14482
Journal
International journal of computer assisted radiology and surgery  
Start Page
162-163
Citation
34th International Congress of Computer Assisted Radiology and Surgery (CARS 2020)
Contribution to Conference
34th International Congress of Computer Assisted Radiology and Surgery, CARS 2020  
Publisher DOI
10.1007/s11548-020-02171-6
Atherosclerotic plaque in coronary arteries can lead to myocardial infarction and is one of the leading causes of death. Intravascular optical coherence tomography (IVOCT) can be used to image the affected blood vessels for assessment and treatment. However, catheter bending often causes changes in the rotation frequency of the optical probe during acquisition. The resulting non-uniform rotation distortion (NURD) artefacts complicate the image interpretation and may affect the diagnosis. Deep learning methods have been proposed to analyze IVOCT image data, including plaque detection [1] and feature extraction [2]. We present a novel approach to directly estimate the rotation frequency of the optical probe from a sequence of IVOCT images. We illustrate that this allows a proper correction of NURD artefacts
DDC Class
620: Ingenieurwissenschaften
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