Evidence map›Paper›PMID 33237013›Full record

ReviewMagnetic resonance imaging clinics of North America2021

High-resolution Structural Magnetic Resonance Imaging and Quantitative Susceptibility Mapping.

Vivek Yedavalli, Phillip DiGiacomo, Elizabeth Tong, Michael Zeineh

Abstract readReview
In one paragraph

Review in Magnetic resonance imaging clinics of North America, 2021. 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
–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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Iron and Alzheimer's Disease: From Pathology to Imaging.Frontiers in human neuroscience · 2022
    Review
  6. Review
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

4 authors.

Vivek YedavalliDepartment of Radiology, Stanford University, 300 Pasteur Drive, Room S047, Stanford, CA 94305-5105, USA; Division of Neuroradiology, Johns Hopkins University, 600 N. Wolfe St. B-112 D, Baltimore, MD 21287, USA.
Phillip DiGiacomoDepartment of Bioengineering, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA.
Elizabeth TongDepartment of Radiology, 300 Pasteur Drive, Room S031, Stanford, CA 94305-5105, USA.
Michael ZeinehDepartment of Radiology, Stanford University, Lucas Center for Imaging, Room P271, 1201 Welch Road, Stanford, CA 94305-5488, USA. Electronic address: mzeineh@stanford.edu.

Funding

Iron as an Imaging Biomarker for Inflammation in ADR01AG061120 · NIA · STANFORD UNIVERSITY · PI ZEINEH, MICHAEL M · 2019 to 2023
$3.5M
NIA NIH HHS R01 AG061120
6 · The paper itself

Abstract

High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.

Indexed as

BrainHumansMagnetic Resonance ImagingNeurodegenerative DiseasesHigh-resolution 7 THippocampusMidbrainMotion correctionNeurodegenerative diseasesNeuropsychiatric diseasesQuantitative susceptibility mapping

Identifiers

PMID33237013
PMCPMC7886008

What OpenQuestion holds

Textmetadata
LicenceTDM
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.