A concept for holistic whole body MRI data analysis, Imiomics.

Area of Science:

• Medical Imaging
• Radiomics
• Big Data Analytics

Background:

• Whole-body imaging generates vast datasets.
• Integrating imaging with clinical data presents challenges.
• Standardized analysis across individuals and time is needed.

Purpose of the Study:

• Introduce and evaluate Imiomics (imaging-omics), a whole-body image analysis concept.
• Present and validate an image registration method for Imiomics.
• Enable comparison and integration of imaging and non-imaging data.

Main Methods:

• Developed a whole-body image registration method using water-fat MRI elasticity constraints.
• Evaluated registration using inverse consistency and Dice coefficients.
• Assessed Imiomics through metabolic research examples: atlas creation, anomaly detection, and longitudinal analysis.

Main Results:

• Registration method demonstrated high accuracy (low inverse consistency, high Dice coefficients) in 128 subjects.
• Statistical atlas showed low standard deviation, confirming successful registration.
• Example analyses confirmed expected associations, validating the Imiomics concept.

Conclusions:

• The registration method is suitable for Imiomics.
• Imiomics facilitates holistic, targeted, and untargeted big-data analyses.
• Enables novel analysis of relationships between imaging and clinical data.