Evidence map›Paper›PMID 33089962›Full record

ArticleHuman brain mapping2021

Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting.

Shohei Fujita, Guido Buonincontri, Matteo Cencini, Issei Fukunaga, Naoyuki Takei, Rolf F Schulte, Akifumi Hagiwara, Wataru Uchida, Masaaki Hori, Koji Kamagata and 2 more

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.6field-weighted citation impact, top 14% 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

15 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  3. Deep Learning-based Hierarchical Brain Segmentation with Preliminary Analysis of the Repeatability and Reproducibility.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
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  12. MR Imaging in the 21st Century: Technical Innovation over the First Two Decades.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022
    Review
  13. Article
  14. Automated design of pulse sequences for magnetic resonance fingerprinting using physics-inspired optimization.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  15. 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

12 authors at 6 institutions in 4 countries.

Shohei FujitaDepartment of Radiology, Juntendo University, Tokyo, Japan.ORCID 0000-0003-4276-6226
Guido BuonincontriImago7 Foundation, Pisa, Italy.
Matteo CenciniImago7 Foundation, Pisa, Italy.
Issei FukunagaDepartment of Radiology, Juntendo University, Tokyo, Japan.
Naoyuki TakeiMR Applications and Workflow, GE Healthcare, Tokyo, Japan.
Rolf F SchulteGE Healthcare, Munich, Germany.
Akifumi HagiwaraDepartment of Radiology, Juntendo University, Tokyo, Japan.ORCID 0000-0001-5277-3249
Wataru UchidaDepartment of Radiology, Juntendo University, Tokyo, Japan.
Masaaki HoriDepartment of Radiology, Toho University Omori Medical Center, Tokyo, Japan.
Koji KamagataDepartment of Radiology, Juntendo University, Tokyo, Japan.ORCID 0000-0001-5028-218X
Osamu AbeDepartment of Radiology, The University of Tokyo, Tokyo, Japan.
Shigeki AokiDepartment of Radiology, Juntendo University, Tokyo, Japan.
Juntendo University · JPFondazione Stella Maris · ITGeneral Electric (Spain) · ESSiemens Healthineers (Germany) · DEThe University of Tokyo · JPToho University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Three-dimensional (3D) Magnetic resonance fingerprinting (MRF) permits whole-brain volumetric quantification of T1 and T2 relaxation values, potentially replacing conventional T1-weighted structural imaging for common brain imaging analysis. The aim of this study was to evaluate the repeatability and reproducibility of 3D MRF in evaluating brain cortical thickness and subcortical volumetric analysis in healthy volunteers using conventional 3D T1-weighted images as a reference standard. Scan-rescan tests of both 3D MRF and conventional 3D fast spoiled gradient recalled echo (FSPGR) were performed. For each sequence, the regional cortical thickness and volume of the subcortical structures were measured using standard automatic brain segmentation software. Repeatability and reproducibility were assessed using the within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), and mean percent difference and ICC, respectively. The wCV and ICC of cortical thickness were similar across all regions with both 3D MRF and FSPGR. The percent relative difference in cortical thickness between 3D MRF and FSPGR across all regions was 8.0 ± 3.2%. The wCV and ICC of the volume of subcortical structures across all structures were similar between 3D MRF and FSPGR. The percent relative difference in the volume of subcortical structures between 3D MRF and FSPGR across all structures was 7.1 ± 3.6%. 3D MRF measurements of human brain cortical thickness and subcortical volumes are highly repeatable, and consistent with measurements taken on conventional 3D T1-weighted images. A slight, consistent bias was evident between the two, and thus careful attention is required when combining data from MRF and conventional acquisitions.

Indexed as

Brain Cortical ThicknessAdultAgedBrainFemaleHumansImaging, Three-DimensionalMagnetic Resonance ImagingMaleMiddle AgedOrgan SizeReproducibility of ResultsYoung Adulthuman brain imagingmagnetic resonance fingerprintingmorphometryrelaxometryrepeatabilityreproducibility

Identifiers

PMID33089962
PMCPMC7775993
OpenAlexW3093997667

What OpenQuestion 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 OpenQuestion graph.