Imperial College London


Faculty of EngineeringDepartment of Computing

Head of Department of Computing



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BibTex format

author = {Oktay, O and Bai, W and Lee, M and Guerrero, R and Kamnitsas, K and Caballero, J and De, Marvao A and Cook, S and O’Regan, D and Rueckert, D},
doi = {10.1007/978-3-319-46726-9_29},
pages = {246--254},
title = {Multi-input cardiac image super-resolution using convolutional neural networks},
url = {},
year = {2016}

RIS format (EndNote, RefMan)

AB - © Springer International Publishing AG 2016. 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.
AU - Oktay,O
AU - Bai,W
AU - Lee,M
AU - Guerrero,R
AU - Kamnitsas,K
AU - Caballero,J
AU - De,Marvao A
AU - Cook,S
AU - O’Regan,D
AU - Rueckert,D
DO - 10.1007/978-3-319-46726-9_29
EP - 254
PY - 2016///
SN - 0302-9743
SP - 246
TI - Multi-input cardiac image super-resolution using convolutional neural networks
UR -
ER -