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Context-sensitive super-resolution for fast fetal magnetic resonance imaging

Mcdonagh S, Hou B, Alansary A, Oktay O, Kamnitsas K, Rutherford M, Hajnal JV, Kainz B (2017)

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Publication Type: Conference contribution

Publication year: 2017

Journal

Publisher: Springer Verlag

Book Volume: 10555 LNCS

Pages Range: 116-126

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Quebec City, QC, CAN

ISBN: 9783319675633

DOI: 10.1007/978-3-319-67564-0_12

Abstract

3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but low-resolution image acquisition and highly detailed but slow image acquisition. Fast imaging is required for targets that move to avoid motion artefacts. This is in particular difficult for fetal MRI. Spatially independent upsampling techniques, which are the state-of-the-art to address this problem, are error prone and disregard contextual information. In this paper we propose a context-sensitive upsampling method based on a residual convolutional neural network model that learns organ specific appearance and adopts semantically to input data allowing for the generation of high resolution images with sharp edges and fine scale detail. By making contextual decisions about appearance and shape, present in different parts of an image, we gain a maximum of structural detail at a similar contrast as provided by high-resolution data. We experiment on 145 fetal scans and show that our approach yields an increased PSNR of 1.25 dB when applied to under-sampled fetal data cf. baseline upsampling. Furthermore, our method yields an increased PSNR of 1.73 dB when utilizing under-sampled fetal data to perform brain volume reconstruction on motion corrupted captured data.

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How to cite

APA:

Mcdonagh, S., Hou, B., Alansary, A., Oktay, O., Kamnitsas, K., Rutherford, M.,... Kainz, B. (2017). Context-sensitive super-resolution for fast fetal magnetic resonance imaging. In M. Jorge Cardoso, Tal Arbel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 116-126). Quebec City, QC, CAN: Springer Verlag.

MLA:

Mcdonagh, Steven, et al. "Context-sensitive super-resolution for fast fetal magnetic resonance imaging." Proceedings of the 5th International Workshop on Computational Methods for Molecular Imaging, CMMI 2017, 2nd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017 and 1st International Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. M. Jorge Cardoso, Tal Arbel, Springer Verlag, 2017. 116-126.

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