Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-319-46726-9_29
Title: Multi-input cardiac image super-resolution using convolutional neural networks
Authors: Oktay O.
Bai W.
Lee M.
Guerrero R.
Kamnitsas K.
Caballero J.
De Marvao A.
Cook S. 
O’Regan D.
Rueckert D.
Issue Date: 2016
Publisher: Springer Verlag
Citation: Oktay O., Bai W., Lee M., Guerrero R., Kamnitsas K., Caballero J., De Marvao A., Cook S., O’Regan D., Rueckert D. (2016). Multi-input cardiac image super-resolution using convolutional neural networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9902 LNCS : 246-254. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-319-46726-9_29
Abstract: 3D cardiac MR imaging enables accurate analysis of cardiac morphology and physiology. However,due to the requirements for long acquisition and breath-hold,the clinical routine is still dominated by multi-slice 2D imaging,which hamper the visualization of anatomy and quantitative measurements as relatively thick slices are acquired. As a solution,we propose a novel image super-resolution (SR) approach that is based on a residual convolutional neural network (CNN) model. It reconstructs high resolution 3D volumes from 2D image stacks for more accurate image analysis. The proposed model allows the use of multiple input data acquired from different viewing planes for improved performance. Experimental results on 1233 cardiac short and long-axis MR image stacks show that the CNN model outperforms state-of-the-art SR methods in terms of image quality while being computationally efficient. Also,we show that image segmentation and motion tracking benefits more from SR-CNN when it is used as an initial upscaling method than conventional interpolation methods for the subsequent analysis. ? Springer International Publishing AG 2016.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/150861
ISBN: 9.78E+12
ISSN: 3029743
DOI: 10.1007/978-3-319-46726-9_29
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