Evidence map›Paper›PMID 38156527›Full record

ArticleMagnetic resonance in medicine2024

Maximizing SNR per unit time in diffusion MRI with multiband T-Hex spirals.

Maria Engel, Lars Mueller, André Döring, Maryam Afzali, Derek K Jones

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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.

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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

5 authors.

Maria EngelCardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.ORCID 0000-0002-0143-1908
Lars MuellerCardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.ORCID 0000-0002-9605-9183
André DöringCardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.ORCID 0000-0002-2096-5374
Maryam AfzaliCardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.ORCID 0000-0003-3378-0878
Derek K JonesCardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.ORCID 0000-0003-4409-8049

Funding

Wellcome TrustWellcome Trust 096646/Z/11/ZWellcome Trust 104943/Z/14/ZWellcome Trust 202962Wellcome Trust 219536/Z/19/Z
6 · The paper itself

Abstract

purposeThe characterization of tissue microstructure using diffusion MRI (dMRI) signals is rapidly evolving, with increasing sophistication of signal representations and microstructure models. However, this progress often requires signals to be acquired with very high b-values (e.g., b > 30 ms/μm

methodsSpiral readouts, multiband imaging, and sampling on tilted hexagonal grids (T-Hex) are combined and implemented on a 3T MRI system with ultra-strong gradients. Image reconstruction is performed through an iterative cg-SENSE algorithm incorporating static off-resonance distributions and field dynamics as measured with an NMR field camera. Additionally, T-Hex multiband is combined with a more conventional EPI-readout and compared with state-of-the-art blipped-CAIPIRINHA sampling. The advantage of the proposed approach is furthermore investigated for clinically available gradient performance and diffusion kurtosis imaging.

resultsHigh fidelity in vivo images with b-values up to 40 ms/μm

conclusionThe demonstrated gains hold promise for the widespread dissemination of advanced microstructural scans, especially in clinical populations.

Indexed as

Diffusion Magnetic Resonance ImagingMagnetic Resonance ImagingAlgorithmsBrainDiffusion Tensor ImagingImage Processing, Computer-Assisteddiffusion MRImagnetic field monitoringmultibandsimultaneous multi-slicespiral imaging

Identifiers

PMID38156527
PMCPMC10953427

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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.