Application-driven MRI: Joint reconstruction and segmentation from undersampled MRI data

Caballero J, Bai W, Price AN, Rueckert D, Hajnal JV (2014)

Publication Type: Conference contribution

Publication year: 2014

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 8673 LNCS

Pages Range: 106-113

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

Event location: USA

ISBN: 9783319104034

DOI: 10.1007/978-3-319-10404-1_14

Abstract

Medical image segmentation has traditionally been regarded as a separate process from image acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these first stages of the imaging pipeline. Adopting an integrated acquisition-reconstruction- segmentation process can provide a more efficient and accurate solution. In this paper we propose a joint segmentation and reconstruction algorithm for undersampled magnetic resonance data. Merging a reconstructive patch-based sparse modelling and a discriminative Gaussian mixture modelling can produce images with enhanced edge information ultimately improving their segmentation. © 2014 Springer International Publishing.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Guy's and St Thomas' (NHS Foundation Trust) GB United Kingdom (GB)

How to cite

APA:

Caballero, J., Bai, W., Price, A.N., Rueckert, D., & Hajnal, J.V. (2014). Application-driven MRI: Joint reconstruction and segmentation from undersampled MRI data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 106-113). USA: Springer Verlag.

MLA:

Caballero, Jose, et al. "Application-driven MRI: Joint reconstruction and segmentation from undersampled MRI data." Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, USA Springer Verlag, 2014. 106-113.

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