Frangi, AF, Taylor, ZA orcid.org/0000-0002-0718-1663 and Gooya, A (2016) Precision Imaging: more descriptive, predictive and integrative imaging. Medical Image Analysis, 33. pp. 27-32. ISSN 1361-8415
Abstract
Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological model-based imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright (c) 2016 Elsevier B. V. All rights reserved. This is an author produced version of a paper published in Medical Image Analysis. Uploaded in accordance with the publisher's self-archiving policy |
Keywords: | Precision Imaging; Precision Medicine; Image-based modelling; Model-based imaging; Phenomenological modelling; Mechanistic modelling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Aug 2018 10:29 |
Last Modified: | 10 Aug 2018 10:29 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.media.2016.06.024 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134403 |