ArticleBiomedical engineering online2024
Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies.
Article in Biomedical engineering online, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Validation of voxel-based computed tomography liver fat quantification for assessing incident type 2 diabetes risk in a health check-up population.Quantitative imaging in medicine and surgery · 2026Article
- A method for tissue-mask supported whole-body image registration in the UK Biobank.Scientific reports · 2026Article
- Interpretable predictions from whole-body FDG-PET/CT using parameters associated with clinical outcome.Communications medicine · 2026Article
- A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.Scientific reports · 2026Article
- Automated Body Composition Analysis Using DAFS Express on 2D MRI Slices at L3 Vertebral Level.Journal of imaging informatics in medicine · 2026Article
- Development and validation of AI-based automatic segmentation and measurement of thymus on chest CT scans.BMC medical imaging · 2025Article
- Methodology for pediatric head computed tomography image segmentation and volumetric calculation using a tablet computer and stylus pen.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
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Abstract
backgroundComputed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data.
methodsThe current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies.
resultsOur findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information.
conclusionThe developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.
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