Evidence map›Paper›PMID 35785429›Full record

ArticleMagnetic resonance in medicine2022

Ultrahigh Resolution fMRI at 7T Using Radial-Cartesian TURBINE Sampling.

Nadine N Graedel, Karla L Miller, Mark Chiew

Open access · hybridAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 24% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

3 authors at 2 institutions in 1 country.

Nadine N GraedelWellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-7473-4363
Karla L MillerWellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-2511-3189
Mark ChiewWellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-6272-8783
University of Oxford · GBWellcome Centre for Human Neuroimaging · GB

Funding

Wellcome TrustWellcome Trust 202788/Z/16/ZWellcome Trust 203139/Z/16/ZWellcome Trust 203147/Z/16/Z
6 · The paper itself

Abstract

purposeWe investigate the use of TURBINE, a 3D radial-Cartesian acquisition scheme in which EPI planes are rotated about the phase-encoding axis to acquire a cylindrical k-space for high-fidelity ultrahigh isotropic resolution fMRI at 7 Tesla with minimal distortion and blurring.

methodsAn improved, completely self-navigated version of the TURBINE sampling scheme was designed for fMRI at 7 Telsa. To demonstrate the image quality and spatial specificity of the acquisition, thin-slab visual and motor BOLD fMRI at 0.67 mm isotropic resolution (16 mm slab, TRvol = 2.32 s), and 0.8 × 0.8 × 2.0 mm (whole-brain, TRvol = 2.4 s) data were acquired. To prioritize the high spatial fidelity, we employed a temporally regularized reconstruction to improve sensitivity without any spatial bias.

resultsTURBINE images provide high structural fidelity with almost no distortion, dropout, or T

conclusionTURBINE is a promising acquisition approach for high-resolution, minimally distorted fMRI at 7 Tesla and could be particularly useful for fMRI in areas of high B

Indexed as

AlgorithmsMagnetic Resonance ImagingAnisotropyBrainBrain MappingEcho-Planar ImagingImage Processing, Computer-Assisted7TfMRIhigh-resolution fMRIradial-CartesianTURBINEultrahigh-field MRI

Identifiers

PMID35785429
PMCPMC9546489
OpenAlexW4283800365

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.