A concept for holistic whole body MRI data analysis, Imiomics

• Published in: PloS one Vol. 12; no. 2; p. e0169966
• Main Authors: Strand, Robin, Malmberg, Filip, Johansson, Lars, Lind, Lars, Sundbom, Magnus, Ahlström, Håkan, Kullberg, Joel
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.02.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Body image; Body water; Computerized Image Processing; Consistency; Correlation analysis; Data analysis; Data management; Data processing; Datoriserad bildbehandling; Deformation; Diabetes Mellitus - diagnostic imaging; Elasticity; Female; Gene expression; Genomics; Humans; Hypothesis testing; Image analysis; Image detection; Image processing; Image Processing, Computer-Assisted; Image registration; Information systems; Information technology; Magnetic Resonance Imaging; Male; Medical imaging; Medicine and Health Sciences; Methods; Middle Aged; Morphology; Neoplasms - diagnostic imaging; NMR; Nuclear magnetic resonance; Pattern Recognition, Automated; Physical Sciences; Registration; Reproducibility of Results; Research and Analysis Methods; Self image; Statistical analysis; Subtraction Technique; Whole Body Imaging - methods; Sweden
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0169966

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. 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. 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. 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.