Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal
Tänzer M, Yook SH, Ferreira P, Yang G, Rueckert D, Nielles-Vallespin S (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13593 LNCS
Pages Range: 123-132
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Singapore, SGP
ISBN: 9783031234422
DOI: 10.1007/978-3-031-23443-9_12
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 architectural properties would benefit cardiac DTI acceleration the most. In this work, we compare the effect of several input types (magnitude images vs complex images), multiple dimensionalities (2D vs 3D operations), and multiple input types (single slice vs multi-slice) on the performance of a model trained to remove artefacts caused by a simultaneous multi-slice (SMS) acquisition. Despite our initial intuition, our experiments show that, for a fixed number of parameters, simpler 2D real-valued models outperform their more advanced 3D or complex counterparts. The best performance is although obtained by a real-valued model trained using both the magnitude and phase components of the acquired data. We believe this behaviour to be due to real-valued models making better use of the lower number of parameters, and to 3D models not being able to exploit the spatial information because of the low SMS acceleration factor used in our experiments.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Tänzer, M., Yook, S.H., Ferreira, P., Yang, G., Rueckert, D., & Nielles-Vallespin, S. (2022). Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal. In Oscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 123-132). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.
MLA:
Tänzer, Michael, et al. "Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal." Proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, SGP Ed. Oscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang, Springer Science and Business Media Deutschland GmbH, 2022. 123-132.
BibTeX: Download