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

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

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Context-sensitive super-resolution for fast fetal magnetic resonance imaging. / McDonagh, Steven; Hou, Benjamin; Alansary, Amir; Oktay, Ozan; Kamnitsas, Konstantinos; Rutherford, Mary; Hajnal, Jo; Kainz, Bernhard.

Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment - 5th International Workshop, CMMI 2017 2nd International Workshop, RAMBO 2017 and 1st International Workshop, SWITCH 2017 Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10555 LNCS Springer Verlag, 2017. p. 116-126 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10555 LNCS).

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Harvard

McDonagh, S, Hou, B, Alansary, A, Oktay, O, Kamnitsas, K, Rutherford, M, Hajnal, J & Kainz, B 2017, Context-sensitive super-resolution for fast fetal magnetic resonance imaging. in Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment - 5th International Workshop, CMMI 2017 2nd International Workshop, RAMBO 2017 and 1st International Workshop, SWITCH 2017 Held in Conjunction with MICCAI 2017, Proceedings. vol. 10555 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10555 LNCS, Springer Verlag, pp. 116-126, 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, Canada, 14/09/2017. https://doi.org/10.1007/978-3-319-67564-0_12

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 Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment - 5th International Workshop, CMMI 2017 2nd International Workshop, RAMBO 2017 and 1st International Workshop, SWITCH 2017 Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10555 LNCS, pp. 116-126). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10555 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67564-0_12

Vancouver

McDonagh S, Hou B, Alansary A, Oktay O, Kamnitsas K, Rutherford M et al. Context-sensitive super-resolution for fast fetal magnetic resonance imaging. In Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment - 5th International Workshop, CMMI 2017 2nd International Workshop, RAMBO 2017 and 1st International Workshop, SWITCH 2017 Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10555 LNCS. Springer Verlag. 2017. p. 116-126. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-67564-0_12

Author

McDonagh, Steven ; Hou, Benjamin ; Alansary, Amir ; Oktay, Ozan ; Kamnitsas, Konstantinos ; Rutherford, Mary ; Hajnal, Jo ; Kainz, Bernhard. / Context-sensitive super-resolution for fast fetal magnetic resonance imaging. Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment - 5th International Workshop, CMMI 2017 2nd International Workshop, RAMBO 2017 and 1st International Workshop, SWITCH 2017 Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10555 LNCS Springer Verlag, 2017. pp. 116-126 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex Download

@inbook{1e8200dc8b3f40c19b7f5330981917bd,
title = "Context-sensitive super-resolution for fast fetal magnetic resonance imaging",
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.",
author = "Steven McDonagh and Benjamin Hou and Amir Alansary and Ozan Oktay and Konstantinos Kamnitsas and Mary Rutherford and Jo Hajnal and Bernhard Kainz",
year = "2017",
month = "9",
day = "9",
doi = "10.1007/978-3-319-67564-0_12",
language = "English",
isbn = "9783319675633",
volume = "10555 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "116--126",
booktitle = "Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment - 5th International Workshop, CMMI 2017 2nd International Workshop, RAMBO 2017 and 1st International Workshop, SWITCH 2017 Held in Conjunction with MICCAI 2017, Proceedings",
address = "Germany",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Context-sensitive super-resolution for fast fetal magnetic resonance imaging

AU - McDonagh, Steven

AU - Hou, Benjamin

AU - Alansary, Amir

AU - Oktay, Ozan

AU - Kamnitsas, Konstantinos

AU - Rutherford, Mary

AU - Hajnal, Jo

AU - Kainz, Bernhard

PY - 2017/9/9

Y1 - 2017/9/9

N2 - 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.

AB - 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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U2 - 10.1007/978-3-319-67564-0_12

DO - 10.1007/978-3-319-67564-0_12

M3 - Other chapter contribution

AN - SCOPUS:85029581532

SN - 9783319675633

VL - 10555 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment - 5th International Workshop, CMMI 2017 2nd International Workshop, RAMBO 2017 and 1st International Workshop, SWITCH 2017 Held in Conjunction with MICCAI 2017, Proceedings

PB - Springer Verlag

ER -

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