Evidence map›Paper›PMID 38614974›Full record

ArticleBiomedical engineering online2024

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

Nouman Ahmad, Hugo Dahlberg, Hanna Jönsson, Sambit Tarai, Rama Krishna Guggilla, Robin Strand, Elin Lundström, Göran Bergström, Håkan Ahlström, Joel Kullberg

Abstract read
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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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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  7. 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 · 2024
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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Nouman AhmadRadiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden. nouman.ahmad@uu.se.
Hugo DahlbergRadiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Hanna JönssonRadiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Sambit TaraiRadiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Rama Krishna GuggillaRadiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Robin StrandRadiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Elin LundströmRadiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Göran BergströmDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Håkan AhlströmRadiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Joel KullbergRadiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.

Funding

Swedish Research Council 2019-04756
6 · The paper itself

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.

Indexed as

Body CompositionTomography, X-Ray ComputedAdipose TissueHumansLiverResearch DesignBody compositionComputed tomographyImage registrationImiomics analysis

Identifiers

PMID38614974
PMCPMC11015680

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