Evidence map›Paper›PMID 37218945›Full record

ArticleTomography (Ann Arbor, Mich.)2023

Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework.

Li-Yueh Hsu, Zara Ali, Hadi Bagheri, Fahimul Huda, Bernadette A Redd, Elizabeth C Jones

Open access · goldAbstract read
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 11% of its field
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
    Pooled it
  2. Article
  3. Propionate Induces Energy Expenditure via Browning in Mesenteric Adipose Tissue.The Journal of clinical endocrinology and metabolism · 2025
    Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Li-Yueh HsuDepartment of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA.ORCID 0000-0002-0826-7290
Zara AliDepartment of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA.ORCID 0009-0003-1830-0060
Hadi BagheriDepartment of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA.
Fahimul HudaDepartment of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA.ORCID 0000-0001-7447-2711
Bernadette A ReddDepartment of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA.
Elizabeth C JonesDepartment of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA.
National Institutes of Health Clinical Center · US

Funding

Intramural research program, Clinical Center, National Institutes of Health Z99CL999999
6 · The paper itself

Abstract

purposeReliable and objective measures of abdominal fat distribution across imaging modalities are essential for various clinical and research scenarios, such as assessing cardiometabolic disease risk due to obesity. We aimed to compare quantitative measures of subcutaneous (SAT) and visceral (VAT) adipose tissues in the abdomen between computed tomography (CT) and Dixon-based magnetic resonance (MR) images using a unified computer-assisted software framework. MATERIALS AND

methodsThis study included 21 subjects who underwent abdominal CT and Dixon MR imaging on the same day. For each subject, two matched axial CT and fat-only MR images at the L2-L3 and the L4-L5 intervertebral levels were selected for fat quantification. For each image, an outer and an inner abdominal wall regions as well as SAT and VAT pixel masks were automatically generated by our software. The computer-generated results were then inspected and corrected by an expert reader.

resultsThere were excellent agreements for both abdominal wall segmentation and adipose tissue quantification between matched CT and MR images. Pearson coefficients were 0.97 for both outer and inner region segmentation, 0.99 for SAT, and 0.97 for VAT quantification. Bland-Altman analyses indicated minimum biases in all comparisons.

conclusionWe showed that abdominal adipose tissue can be reliably quantified from both CT and Dixon MR images using a unified computer-assisted software framework. This flexible framework has a simple-to-use workflow to measure SAT and VAT from both modalities to support various clinical research applications.

Indexed as

Abdominal FatMagnetic Resonance ImagingComputersHumansSoftwareTomography, X-Ray Computedabdominal adipose tissuecomputed tomographyfat quantificationimage segmentationmagnetic resonance imaging

Identifiers

PMID37218945
PMCPMC10204377
OpenAlexW4377288831

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.