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Titel:

Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal

Dokumenttyp:
Proceedings Paper
Autor(en):
Tanzer, Michael; Yook, Sea Hee; Ferreira, Pedro; Yang, Guang; Rueckert, Daniel; Nielles-Vallespin, Sonia
Abstract:
As diffusion tensor imaging (DTI) gains popularity in cardiac imaging due to its unique ability to non-invasively assess the cardiac microstructure, deep learning-based Artificial Intelligence is becoming a crucial tool in mitigating some of its drawbacks, such as the long scan times. As it often happens in fast-paced research environments, a lot of emphasis has been put on showing the capability of deep learning while often not enough time has been spent investigating what input and architectur...     »
Zeitschriftentitel:
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv
Jahr:
2022
Band / Volume:
13593
Seitenangaben Beitrag:
123-132
Volltext / DOI:
doi:10.1007/978-3-031-23443-9_12
Print-ISSN:
0302-9743
TUM Einrichtung:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
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