Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies

• Published in: Biomedical engineering online Vol. 23; no. 1; pp. 42 - 16
• Main Authors: Ahmad, Nouman, Dahlberg, Hugo, Jönsson, Hanna, Tarai, Sambit, Guggilla, Rama Krishna, Strand, Robin, Lundström, Elin, Bergström, Göran, Ahlström, Håkan, Kullberg, Joel
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.04.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Abdomen; Adipose Tissue; Analysis; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biotechnology; Body Composition; Body fat; Chronic obstructive pulmonary disease; Clinical Medicine; Computed tomography; Consistency; CT imaging; Diabetes; Diagnosis; Engineering; Evaluation; Females; Humans; Image processing; Image registration; Image segmentation; Imiomics analysis; Klinisk medicin; Liver; Lungs; Medical imaging; Metabolic diseases; Metabolic disorders; Microbiota; Muscles; Organs; Performance evaluation; Registration; Research Design; Risk factors; Spleen; Thigh; Tomography, X-Ray Computed; Vertebrae; Sweden; Image registration; Computed tomography; Body composition; Imiomics analysis
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-024-01235-x

Background Computed 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. Methods The 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. Results Our 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. Conclusion The 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.