Evidence map›Paper›PMID 23387766›Full record

ArticleMedical physics2013

A comparison of radial keyhole strategies for high spatial and temporal resolution 4D contrast-enhanced MRI in small animal tumor models.

Ergys Subashi, Everett J Moding, Gary P Cofer, James R MacFall, David G Kirsch, Yi Qi, G Allan Johnson

Abstract readComparative Study
In one paragraph

Article in Medical physics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Trial
  2. Analytical corrections for BMagnetic resonance in medicine · 2024
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  8. A Spatiotemporal-Constrained Sorting Method for Motion-Robust 4D-MRI: A Feasibility Study.International journal of radiation oncology, biology, physics · 2019
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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

7 authors.

Ergys SubashiCenter for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
Everett J Moding
Gary P Cofer
James R MacFall
David G Kirsch
Yi Qi
G Allan Johnson

Funding

INTEGRATED CENTER FOR IN VIVO MICROSCOPYP41EB015897 · NIBIB · DUKE UNIVERSITY · PI JOHNSON, GEORGE ALLAN · 2012 to 2017
$7.4M
Imaging Lung Cancer in Mice To Improve Radiation TherapyK08CA114176 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI KIRSCH, DAVID GUY · 2005 to 2009
$664k
Independent Scientist Award for Radiation ResearchK02AI093866 · NIAID · DUKE UNIVERSITY · PI KIRSCH, DAVID GUY · 2011 to 2015
$501k
NCI NIH HHS K08 CA114176NIAID NIH HHS K02 AI093866NIBIB NIH HHS P41 EB015897
6 · The paper itself

Abstract

purposeDynamic contrast-enhanced (DCE) MRI has been widely used as a quantitative imaging method for monitoring tumor response to therapy. The simultaneous challenges of increasing temporal and spatial resolution in a setting where the signal from the much smaller voxel is weaker have made this MR technique difficult to implement in small-animal imaging. Existing protocols employed in preclinical DCE-MRI acquire a limited number of slices resulting in potentially lost information in the third dimension. This study describes and compares a family of four-dimensional (3D spatial + time), projection acquisition, radial keyhole-sampling strategies that support high spatial and temporal resolution.

methodsThe 4D method is based on a RF-spoiled, steady-state, gradient-recalled sequence with minimal echo time. An interleaved 3D radial trajectory with a quasi-uniform distribution of points in k-space was used for sampling temporally resolved datasets. These volumes were reconstructed with three different k-space filters encompassing a range of possible radial keyhole strategies. The effect of k-space filtering on spatial and temporal resolution was studied in a 5 mM CuSO(4) phantom consisting of a meshgrid with 350-μm spacing and in 12 tumors from three cell lines (HT-29, LoVo, MX-1) and a primary mouse sarcoma model (three tumors∕group). The time-to-peak signal intensity was used to assess the effect of the reconstruction filters on temporal resolution. As a measure of heterogeneity in the third dimension, the authors analyzed the spatial distribution of the rate of transport (K(trans)) of the contrast agent across the endothelium barrier for several different types of tumors.

resultsFour-dimensional radial keyhole imaging does not degrade the system spatial resolution. Phantom studies indicate there is a maximum 40% decrease in signal-to-noise ratio as compared to a fully sampled dataset. T1 measurements obtained with the interleaved radial technique do not differ significantly from those made with a conventional Cartesian spin-echo sequence. A bin-by-bin comparison of the distribution of the time-to-peak parameter shows that 4D radial keyhole reconstruction does not cause significant temporal blurring when a temporal resolution of 9.9 s is used for the subsamples of the keyhole data. In vivo studies reveal substantial tumor heterogeneity in the third spatial dimension that may be missed with lower resolution imaging protocols.

conclusionsVolumetric keyhole imaging with projection acquisition provides a means to increase spatiotemporal resolution and coverage over that provided by existing 2D Cartesian protocols. Furthermore, there is no difference in temporal resolution between the higher spatial resolution keyhole reconstruction and the undersampled projection data. The technique allows one to measure complex heterogeneity of kinetic parameters with isotropic, microscopic spatial resolution.

Indexed as

AnimalsCell Line, TumorFemaleHumansImaging, Three-DimensionalMagnetic Resonance ImagingMicePhantoms, ImagingSpatio-Temporal AnalysisTime Factors

Identifiers

PMID23387766
PMCPMC3562278

What OpenQuestion holds

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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.