Evidence map›Paper›PMID 21500271›Full record

ArticleMagnetic resonance in medicine2011

Quantification of SPIO nanoparticles in vivo using the finite perturber method.

Jason Langley, Wei Liu, E Kay Jordan, J A Frank, Qun Zhao

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Image-guided drug delivery in lung cancer.Drug delivery and translational research · 2012
    Article
  6. Article
  7. Article
  8. 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

5 authors.

Jason LangleyDepartment of Physics and Astronomy, BioImaging Research Center (BIRC), The University of Georgia, Athens, Georgia 30602, USA.
Wei Liu
E Kay Jordan
J A Frank
Qun Zhao

Funding

Pre-clinical evaluation of Magnetically labeled Cells for Cellular MRIZIACL090007 · CLC · CLINICAL CENTER · PI FRANK, JOSEPH · 2009 to 2018
$0k
Intramural NIH HHS ZIA CL090007
6 · The paper itself

Abstract

The susceptibility gradients generated by super-paramagnetic iron oxide (SPIO) nanoparticles make them an ideal contrast agent in magnetic resonance imaging. Traditional quantification methods for SPIO nanoparticle-based contrast agents rely on either mapping T₂* values within a region or by modeling the magnetic field inhomogeneities generated by the contrast agent. In this study, a new model-based SPIO quantification method is introduced. The proposed method models magnetic field inhomogeneities by approximating regions containing SPIOs as ensembles of magnetic dipoles, referred to as the finite perturber method. The proposed method was verified using data acquired from a phantom and in vivo mouse models. The phantom consisted of an agar solution with four embedded vials, each vial containing known but different concentrations of SPIO nanoparticles. Gaussian noise was also added to the phantom data to test performance of the proposed method. The in vivo dataset was acquired using five mice, each of which was subcutaneously implanted in the flanks with 1 × 10(5) labeled and 1 × 10(6) unlabeled C6 glioma cells. For the phantom data set, the proposed algorithm was generate accurate estimations of the concentration of SPIOs. For the in vivo dataset, the method was able to give estimations of the concentration within SPIO-labeled tumors that are reasonably close to the known concentration.

Indexed as

NanoparticlesAgarAlgorithmsAnimalsFerric CompoundsGliomaMagnetic Resonance ImagingMiceModels, StatisticalModels, TheoreticalPhantoms, ImagingSensitivity and SpecificityAgarFerric Compounds

Identifiers

PMID21500271
PMCPMC3612521

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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.