Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks
Open accessAbstract
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.
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The proposed image SR technique extends previous work and is robust against slice misalignments.
“(III) An image SR technique that extends previous work [34] and that is robust against slice misalignments;”
From Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation (2018) · cites this paper as [34]
The proposed SR approach is computationally more efficient than the state-of-the-art SR-CNN model because feature extraction is performed in the low-dimensional image space.
“our approach is computationally more efficient than the state-of-the-art SR-CNN model [34] as the feature extraction is performed in the low-dimensional image space.”
From Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation (2018) · cites this paper as [34]
Existing state-of-the-art CNN approaches for segmentation and image enhancement perform poorly when input data is not self-consistent.
“As can be seen in Fig. 1, the existing state-of-the-art convolutional neural network (CNN) approaches for segmentation [44], [11] and image enhancement [34] tasks perform poorly when the input data is not self-consistent for the analysis.”
From Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation (2018) · cites this paper as [34]
The proposed SR model generates image features in the LR image grid rather than early upsampling, which reduces memory and computation requirements significantly.
“It provides two main advantages over the state-of-the-art medical image SR model proposed in [34]: (I) the network generates image features in the LR image grid rather than early upsampling of the features, which reduces memory and computation requirements significantly.”
From Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation (2018) · cites this paper as [34]
The ACNN-SR model is computationally more efficient in run-time than the SR-CNN model by a factor of 5.
“In addition to the increased image quality, the ACNN-SR model is computationally more efficient in terms of run-time in comparison to the SR-CNN model [34] by a factor of 5 .”
From Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation (2018) · cites this paper as [34]
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105 · see citing works
SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK
BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1994) · Series in Machine Perception and Artificial Intelligence · doi:10.1142/9789812797926_0003 · Cited by 2,350 · see citing works
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M. A., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2837502 · Cited by 2,431 · see citing works
Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2743464 · Cited by 751 · see citing works
“Similar to the SR framework described in [34], our proposed SR model learns a mapping function : X → Y to estimate a high-resolution image ŷr = (x ; θr ) where θ r denotes the model parameters such as convolution kernels and batchnormalisation statistics.”
BackgroundMethodInfluentialSelf-citationMazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554 · see citing works
Duan, J., Bello, G., Schlemper, J., Bai, W., Dawes, T. J. W., Biffi, C., de Marvao, A., Doumoud, G., O'Regan, D. P., & Rueckert, D. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2894322 · Cited by 227 · see citing works
Self-citationZeng, K., Zheng, H., Cai, C., Yang, Y., Zhang, K., & Chen, Z. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.06.010 · Cited by 161 · see citing works
- Authors
- Oktay, O., Bai, W., Lee, M., Guerrero, R., Kamnitsas, K., Caballero, J., de Marvao, A., Cook, S., O’Regan, D., & Rueckert, D.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Oktay, Ozan
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 246-254
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2526558307
- Cited by
- 246
- References
- 3
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 15 · 2023: 24 · 2022: 28 · 2021: 40 · 2020: 32 · 2019: 39 · 2018: 27 · 2017: 21 · 2016: 1
- FWCI
- 10.18
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion, cc-by-nc)
- National University of Singapore (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/K030523/1, EP/P001009/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- 3D cardiac MRI, multi-input convolutional neural network, residual convolutional network, image segmentation, motion tracking
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