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Bayesian image quality transfer

Tanno, R; Ghosh, A; Grussu, F; Kaden, E; Criminisi, A; Alexander, DC; (2016) Bayesian image quality transfer. In: Medical Image Computing and Computer-Assisted Intervention. MICCAI 2016:19th International Conference. Proceedings, Part II. (pp. pp. 265-273). Springer International Publishing Green open access

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Abstract

Image quality transfer (IQT) aims to enhance clinical images of relatively low quality by learning and propagating high-quality structural information from expensive or rare data sets. However,the original framework gives no indication of confidence in its output,which is a significant barrier to adoption in clinical practice and downstream processing. In this article,we present a general Bayesian extension of IQT which enables efficient and accurate quantification of uncertainty,providing users with an essential prediction of the accuracy of enhanced images. We demonstrate the efficacy of the uncertainty quantification through super-resolution of diffusion tensor images of healthy and pathological brains. In addition,the new method displays improved performance over the original IQT and standard interpolation techniques in both reconstruction accuracy and robustness to anomalies in input images.

Type: Proceedings paper
Title: Bayesian image quality transfer
Event: 19th International Conference
ISBN-13: 978-3-319-46722-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-46723-8_31
Publisher version: http://dx.doi.org/10.1007/978-3-319-46723-8_31
Language: English
Additional information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46723-8.
Keywords: Image Processing and Computer Vision; Pattern Recognition Computer Graphics; Artificial Intelligence; Robotics; Imaging; Radiology; Health Informatics
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Neurosciences Dept
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1531208
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