Evidence map›Paper›PMID 37710346›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2023

A whole-body diffusion MRI normal atlas: development, evaluation and initial use.

Therese Sjöholm, Sambit Tarai, Filip Malmberg, Robin Strand, Alexander Korenyushkin, Gunilla Enblad, Håkan Ahlström, Joel Kullberg

Abstract read
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

The trial behind it

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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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Therese SjöholmDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Sambit TaraiDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Filip MalmbergDepartment of Information Technology, Uppsala University, Uppsala, Sweden.
Robin StrandDepartment of Information Technology, Uppsala University, Uppsala, Sweden.
Alexander KorenyushkinAntaros Medical AB, Mölndal, Sweden.
Gunilla EnbladDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Håkan AhlströmDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Joel KullbergDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden. Joel.Kullberg@radiol.uu.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStatistical atlases can provide population-based descriptions of healthy volunteers and/or patients and can be used for region- and voxel-based analysis. This work aims to develop whole-body diffusion atlases of healthy volunteers scanned at 1.5T and 3T. Further aims include evaluating the atlases by establishing whole-body Apparent Diffusion Coefficient (ADC) values of healthy tissues and including healthy tissue deviations in an automated tumour segmentation task.

methodsMulti-station whole-body Diffusion Weighted Imaging (DWI) and water-fat Magnetic Resonance Imaging (MRI) of healthy volunteers (n = 45) were acquired at 1.5T (n = 38) and/or 3T (n = 29), with test-retest imaging for five subjects per scanner. Using deformable image registration, whole-body MRI data was registered and composed into normal atlases. Healthy tissue ADC

resultsSex- and Body Mass Index (BMI)-stratified whole-body high b-value DWI and ADC normal atlases were created at 1.5T and 3T. %RC of healthy tissue ADC

conclusionsWhole-body DWI and ADC normal atlases were created at 1.5T and 3T, and applied in whole-body voxel-wise analyses.

Indexed as

Diffusion Magnetic Resonance ImagingMagnetic Resonance ImagingBenchmarkingFemaleHumansLiverMaleWhole Body ImagingADCAutomated segmentationLymphomaNormal atlasPrecisionVoxel-wise analysisWhole-body DWI

Identifiers

PMID37710346
PMCPMC10503210

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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 OpenQuestion graph.