Evidence map›Paper›PMID 36750150›Full record

ArticleNeuroImage2023

Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration.

William Warner, Marco Palombo, Renata Cruz, Ross Callaghan, Noam Shemesh, Derek K Jones, Flavio Dell'Acqua, Andrada Ianus, Ivana Drobnjak

Open access · goldAbstract read
In one paragraph

Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.4field-weighted citation impact, top 7% 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

12 citing papers in PubMed, 15 citations in OpenAlex.

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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

9 authors at 4 institutions in 2 countries.

William WarnerCentre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom.
Marco PalomboCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom.
Renata CruzChampalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Ross CallaghanAINOSTICS Ltd., Manchester, United Kingdom.
Noam ShemeshChampalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Derek K JonesCardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.
Flavio Dell'AcquaNatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
Andrada IanusChampalimaud Research, Champalimaud Foundation, Lisbon, Portugal. Electronic address: andrada.ianus@neuro.fchampalimaud.org.
Ivana DrobnjakCentre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom. Electronic address: i.drobnjak@ucl.ac.uk.
Champalimaud Foundation · PTCardiff University · GBUniversity College London · GBKing's College London · GB

Funding

Medical Research Council MR/T020296/1Wellcome Trust 096646Wellcome Trust 104943
6 · The paper itself

Abstract

Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.

Indexed as

AxonsDiffusion Magnetic Resonance ImagingAnimalsComputer SimulationImage Processing, Computer-AssistedRatsAxon diameterDiffusionMicrostructureOptimizationSpinal cordTDR

Identifiers

PMID36750150
PMCPMC7615244
OpenAlexW4319265418

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.