| Application: | 14237, Application of Imiomics to whole-body MRI data for creation and application of a human imaging atlas |
| Title: | Identifying Morphological Indicators of Aging With Neural Networks on Large-Scale Whole-Body MRI |
| Size: | 512805 B |
| Archived: | 15 Dec 2020 |
| Stability: | Accruing |
| Personal: | Contains individual-level data |
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Notes
A wealth of information is contained in images obtained by whole-body magnetic resonance imaging (MRI). Studying the link between the imaged anatomy and properties known from outside sources has the potential to give new insights into the underlying factors that manifest themselves in individual human morphology. In this work we investigate the expression of age-related changes in the whole-body image. A large dataset of about 32,000 subjects scanned from neck to knee and aged 44-82 years from the UK Biobank study was used for a machine-based analysis. We trained a convolutional neural network based on the VGG16 architecture to predict the age of a given subject based on image data from these scans.Application 14237
Application of Imiomics to whole-body MRI data for creation and application of a human imaging atlas
We are facing a global epidemic of obesity and related cardiovascular complications. This calls for novel intervention strategies where improved understanding of the underlying mechanisms is key. Both total fat mass and its distribution throughout the body have been linked to development of cardiovascular disease. Whole-body medical imaging using magnetic resonance (MRI) can sample anatomical information such as tissue volume and fat content, i.e. parameters relevant for cardiovascular studies, at the millimetre scale using millions of small 3D-elements (voxels). We have developed and validated a novel technology, Imiomics, which enables whole body voxel-wise correlations with non-imaging data, e.g. genotypes and disease phenotypes. Imiomics thereby allows innovative types of whole body composition studies. During the Imiomics analysis, whole-body images are registered/deformed to a common coordinate system/geometry. This allows statistical analysis, in the whole-body region, such as creation of a `mean person` (atlas) and studies of deviations from that mean person. This also allows integration of imaging and non-imaging data as whole-body `correlation- images` to for example blood parameters can be made. The aim of this project is to determine causes and consequences of variation in human body composition. This goal will be achieved by 1) applying Imiomics to build a Human Imaging Atlas using whole-body imaging data from large-scale cohort studies 2) genome-wide body-wide studies of body composition 3) assessment of the association of body composition with cardiovascular disease, including ischemic stroke and myocardial infarction and cardiovascular risk factors, including type 2 diabetes, hypertension, and dyslipidemia 4) Mendelian Randomization studies of causal effects. This interdisciplinary project addresses several fundamental questions related to causes and consequences of variation in body composition. This will improve our understanding of the underlying mechanisms of cardiovascular disorders and accelerate development of prevention, diagnosis and treatments for cardiovascular complications. Furthermore, the resources created by this project are anticipated to open up new avenues for research within the obesity-field.
| Lead investigator: | Dr Joel Kullberg |
| Lead institution: | Uppsala University |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3073 | Identifying Morphological Indicators of Aging With Neural Networks on Large-Scale Whole-Body MRI | Taro Langner (+4) | 2019 | IEEE Transactions on Medical Imaging |
Enabling scientific discoveries that improve human health