Evidence map›Paper›PMID 40739192›Full record

ArticleScientific reports2025

Assessment of a diffusion phantom for quality assurance in brain microstructure diffusion MRI studies.

Mattia Ricchi, Aaron Axford, Jordan McGing, Ayaka Shinozaki, Kylie Yeung, Rebecca Mills, Fulvio Zaccagna, Damian J Tyler, Claudia Testa, James T Grist

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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
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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

10 authors.

Mattia RicchiDepartment of Computer Sciences, University of Pisa, Pisa, Italy.
Aaron AxfordOxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, UK.
Jordan McGingOxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, UK.
Ayaka ShinozakiOxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, UK.
Kylie YeungOxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, UK.
Rebecca MillsOxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, UK.
Fulvio ZaccagnaDepartment of Radiology, Cambridge University Hospitals, Cambridge, UK.
Damian J TylerOxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, UK.
Claudia TestaNational Institute of Nuclear Physics (INFN), Bologna Division, Bologna, Italy.
James T GristOxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, UK. james.grist@cardiov.ox.ac.uk.

Funding

British Heart Foundation FS/19/18/34252
6 · The paper itself

Abstract

Diffusion-weighted imaging (DWI) is a key contrast mechanism in MRI which allows for the assessment of microstructural properties of brain tissues by measuring the displacement of water molecules. Several diffusion models, including the tensor (DTI), kurtosis (DKI), and neurite orientation dispersion and density imaging (NODDI), are commonly used in both research and clinical practice. However, there is currently no standardized method for validating the stability and repeatability of these models over time. This study evaluates the use of a DTI phantom as a standard reference for diffusion MRI model validation. The phantom, along with four healthy volunteers, was scanned repeatedly on different days to assess repeatability and stability. The acquired data were fitted to the diffusion models, with repeatability assessed in the phantom using the coefficient of variation (CoV), while stability in vivo was assessed using the repeatability coefficient (RC). The phantom was consecutively scanned eight times to investigate the impact of gradient coil heating on measurement consistency. Results showed that the phantom provided a highly reproducible reference, with CoVs below 5% across repeated and consecutive acquisitions, confirming the robustness of the diffusion models. In vivo, the low RCs indicated that the models remained stable over time, despite potential physiological variability. This study highlights the essential role of phantoms in diffusion MRI research, providing a reference framework for model validation. Future research will expand on this work to a multi-center study to assess inter-scanner variability, potentially incorporating the phantom into calibration protocols to standardize diffusion MRI measurements across different MRI systems.

Indexed as

BrainDiffusion Magnetic Resonance ImagingPhantoms, ImagingAdultFemaleHumansMaleReproducibility of ResultsDiffusion modellingDWI/DTI/DKINODDIPhantomValidation

Identifiers

PMID40739192
PMCPMC12311061

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