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A concept for holistic whole body MRI data analysis, Imiomics
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.ORCID iD: 0000-0001-7764-1787
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med AB, BioVenture Hub, Molndal, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cardiovascular epidemiology.
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2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 2, e0169966Article in journal (Refereed) Published
Abstract [en]

Purpose: To present and evaluate a whole-body image analysis concept, Imiomics (imaging omics) and an image registration method that enables Imiomics analyses by deforming all image data to a common coordinate system, so that the information in each voxel can be compared between persons or within a person over time and integrated with non-imaging data.

Methods: The presented image registration method utilizes relative elasticity constraints of different tissue obtained from whole-body water-fat MRI. The registration method is evaluated by inverse consistency and Dice coefficients and the Imiomics concept is evaluated by example analyses of importance for metabolic research using non-imaging parameters where we know what to expect. The example analyses include whole body imaging atlas creation, anomaly detection, and cross-sectional and longitudinal analysis.

Results: The image registration method evaluation on 128 subjects shows low inverse consistency errors and high Dice coefficients. Also, the statistical atlas with fat content intensity values shows low standard deviation values, indicating successful deformations to the common coordinate system. The example analyses show expected associations and correlations which agree with explicit measurements, and thereby illustrate the usefulness of the proposed Imiomics concept.

Conclusions: The registration method is well-suited for Imiomics analyses, which enable analyses of relationships to non-imaging data, e.g. clinical data, in new types of holistic targeted and untargeted big-data analysis.

Place, publisher, year, edition, pages
2017. Vol. 12, no 2, e0169966
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-316830DOI: 10.1371/journal.pone.0169966ISI: 000395934400002PubMedID: 28241015OAI: oai:DiVA.org:uu-316830DiVA: diva2:1079106
Available from: 2017-02-27 Created: 2017-03-07 Last updated: 2017-11-29Bibliographically approved

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Strand, RobinMalmberg, FilipJohansson, LarsLind, LarsSundbom, MagnusAhlström, HåkanKullberg, Joel

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