Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI With Limited Training Data
| Autor: | Andreas Kofler, Christoph Kolbitsch, Tobias Schaeffter, Marc Dewey, Christian Wald |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Image quality Computer science Magnetic Resonance Imaging Cine Machine Learning (stat.ML) Iterative reconstruction Machine Learning (cs.LG) 030218 nuclear medicine & medical imaging Image (mathematics) Reduction (complexity) 03 medical and health sciences Deep Learning Imaging Three-Dimensional 0302 clinical medicine Statistics - Machine Learning FOS: Electrical engineering electronic engineering information engineering medicine Medical imaging Humans Electrical and Electronic Engineering Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Deep learning Image and Video Processing (eess.IV) Heart Magnetic resonance imaging Pattern recognition Electrical Engineering and Systems Science - Image and Video Processing Computer Science Applications Compressed sensing Undersampling Artificial intelligence business Algorithms Software |
| Zdroj: | IEEE Transactions on Medical Imaging. 39:703-717 |
| ISSN: | 1558-254X 0278-0062 |
| DOI: | 10.1109/tmi.2019.2930318 |
| Popis: | In this work we reduce undersampling artefacts in two-dimensional ($2D$) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. We train the network on $2D$ spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two $2D$ and a $3D$ Deep Learning-based post processing methods and to three iterative reconstruction methods for dynamic cardiac MRI. Our method outperforms the $2D$ spatially trained U-net and the $2D$ spatio-temporal U-net. Compared to the $3D$ spatio-temporal U-net, our method delivers comparable results, but with shorter training times and less training data. Compared to the Compressed Sensing-based methods $kt$-FOCUSS and a total variation regularised reconstruction approach, our method improves image quality with respect to all reported metrics. Further, it achieves competitive results when compared to an iterative reconstruction method based on adaptive regularization with Dictionary Learning and total variation, while only requiring a small fraction of the computational time. A persistent homology analysis demonstrates that the data manifold of the spatio-temporal domain has a lower complexity than the spatial domain and therefore, the learning of a projection-like mapping is facilitated. Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset. This makes the method particularly suitable for training a network on limited training data. Finally, in contrast to the spatial $2D$ U-net, our proposed method is shown to be naturally robust with respect to image rotation in image space and almost achieves rotation-equivariance where neither data-augmentation nor a particular network design are required. Comment: To be published in IEEE Transactions on Medical Imaging |
| Databáze: | OpenAIRE |
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