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Title [sv]
Storskalig medicinsk bildanalys för detaljerade studier av orsaker och konsekvenser av kroppssammansättning i relation till hjärt-kärlsjukdom
Title [en]
Large-scale medical image analysis for detailed studies of causes and consequences of body composition in relation to cardiovascular disease
Abstract [sv]
Vi står inför en global epidemi av övervikt och hjärt-kärlsjukdom. För att kunna utveckla nya behandlingsmetoder och förebyggande strategier behöver vi förbättra vår förståelse av de underliggande biologiska mekanismerna. Både den totala fettmassan och dess fördelning i kroppen påverkar risken för hjärt- kärlsjukdom. Onormal fettinlagring i vävnader, som till exempel lever och muskler, är extra negativt för hjärta och kärl.  Medicinsk avbildning med magnetresonanstomografi (MRI) och datortomografi (CT) kan användas för detaljerade mätningar av fysiologiska egenskaper (som tex vävnadsvolym och fetthalt) i hela kroppen. Dessa är viktiga för utveckling av sjukdomar i hjärta och kärl. MRI och CT-bilder består av miljontals millimeter-stora tredimensionella bildelement (voxlar).  Syftet med projektet är att utveckla och applicera bildanalystekniker för att studera orsaker till, och konsekvenser av, variationer i människans kroppssammansättning i relation till hjärt-kärlsjukdom. Arbetet delas upp i två delar: teknikutveckling och medicinsk applikation.  Vi kommer att utveckla nya bildanalystekniker som möjliggör olika unika sätt att analysera sambanden mellan den detaljerade bildinformationen och annan insamlad medicinsk information (som tex genetisk eller klinisk information from tex blodprover eller levnadsvanor). Teknikerna kommer möjliggöra helt unika medicinska studier som tidigare inte var möjliga att genomföra.  Vi kommer att genomföra en serie unika medicinska studier där samband studeras i hela eller i specifika områden i kroppen på den högsta detaljnivå som hittills åstadkommits. Detta kommer att ge ny och viktig kunskap och sjukdomsförståelse. Storskaliga internationella studier (med bilder från över 68 000 kvinnor och män i olika åldrar) kommer att användas. Vi kommer tex att kunna jämföra hur bilderna från personer som drabbas av hjärt-och kärlsjukdom eller dess riskfaktorer skiljer från de som inte drabbas.
Abstract [en]
We are facing a global epidemic of obesity and related cardiovascular complications. Improved understanding of underlying mechanisms is crucial to optimize intervention strategies. Both total fat mass, its distribution in the body, and fat infiltration of, for example, liver and muscle are linked to cardiovascular risk. Magnetic resonance imaging (MRI) and computed tomography (CT) can measure this physiological information (tissue volume and fat content) in millions of voxels throughout the whole body. We have experience and new ideas for multiple advanced image analysis techniques based on image registration and machine learning that allow unique integrated analysis of medical image data and non-image (pheno- and genotype) data.The overall aim of this project is to develop and apply these image analysis techniques to improve our understanding of human body composition and its causes and consequences in relation to cardiovascular disease.Specific aims include:1) Development of multiple novel approaches including tailored deep regression, cohort saliency analysis, and causality imaging for both MRI and CT.2) Application to multiple large-scale cohort studies (n>86,000) including detailed studies of cardiovascular disease, and type 2 diabetes. 3) Studies of genetic data in relation to image data for detailed analysis of causes and consequences of body composition.We anticipate multiple important findings that may serve as a springboard for novel intervention strategies.
Publications (1 of 1) Show all publications
Utkueri, Y., Lundström, E., Ahlström, H., Öfverstedt, J. & Kullberg, J. (2026). A method for tissue-mask supported whole-body image registration in the UK Biobank. Scientific Reports, 16(1), Article ID 19383.
Open this publication in new window or tab >>A method for tissue-mask supported whole-body image registration in the UK Biobank
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2026 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 16, no 1, article id 19383Article in journal (Refereed) Published
Abstract [en]

The UK Biobank is a large-scale study collecting whole-body MR imaging and non-imaging health data. Robust and accurate inter-subject image registration of these whole-body MR images would enable their body-wide spatial standardization, and region-/voxel-wise correlation analysis of non-imaging data with image-derived parameters (e.g., tissue volume or fat content).

We propose a sex-stratified inter-subject whole-body MR image registration approach that uses subcutaneous adipose tissue- and muscle-masks from the state-of-the-art VIBESegmentator method to augment intensity-based graph-cut registration. The proposed method (that we refer to as mask-supported) was evaluated on a subset of 4000 subjects by comparing it to an intensity-only method as well as two previously published registration methods, uniGradICON and MIRTK. The evaluation consisted of a comparison of Jacobian Determinant (JD) folding frequency, Dice scores, and voxel-wise label error frequency calculated from the 71 VIBESegmentator masks. The 40 masks from MRSegmentator and 50 masks from TotalSegmentator were also used for independent Dice score evaluations. Additionally, voxel-wise correlation between age and each of fat content and tissue volume was studied to exemplify the usefulness for medical research.

The proposed method showed 7percentage points (pp) / 11pp lower frequency of JD folding for males / females when compared to the intensity-based method. The mask-supported method exhibited a mean Dice score of 0.773 / 0.744 across the cohort when evaluated on all VIBESegmentator masks, excluding the two used in the registration, for males / females, respectively. In comparison to the intensity-only registration, the mean values were 6 pp higher for both sexes, and the label error frequency was decreased in most tissue regions. These differences were 9pp / 8pp against uniGradICON and 12pp / 13pp against MIRTK. The mask-supported method achieved a mean Dice score of 0.736 / 0.676 when evaluated with MRSegmentator and 0.683/ 0.617 when evaluated with TotalSegmentator, showing an increase that ranged between 0.7pp and 12pp from the other three methods. Using the proposed method, the age-correlation maps were less noisy and showed higher anatomical alignment.

In conclusion, the image registration method using two tissue masks improves whole-body registration of UK Biobank images.

Place, publisher, year, edition, pages
Springer Nature, 2026
Keywords
Whole body MRI, Image registration, Segmentation
National Category
Radiology and Medical Imaging
Identifiers
urn:nbn:se:uu:diva-591687 (URN)10.1038/s41598-026-58409-x (DOI)001808087800015 ()42332145 (PubMedID)2-s2.0-105042512485 (Scopus ID)
Funder
Uppsala UniversitySwedish Heart Lung Foundation, 20240402Swedish Research CouncilEXODIAB - Excellence of Diabetes Research in Sweden
Available from: 2026-06-23 Created: 2026-06-23 Last updated: 2026-07-09Bibliographically approved
Strand, Robin
Principal InvestigatorKullberg, Joel
Ahlström, Håkan
Larsson, Susanna
Fall, Tove
Lind, Lars
Bergström, Göran
Coordinating organisation
Uppsala University
Funder
Period
2024-01-01 - 2027-12-31
National Category
Medical Image Processing
Identifiers
DiVA, id: project:9034Project, id: 2023-03607_VR

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