Evidence map›Paper›PMID 27796790›Full record

ArticleInternational journal of computer assisted radiology and surgery2017

GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging.

Refaat E Gabr, Getaneh B Tefera, William J Allen, Amol S Pednekar, Ponnada A Narayana

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Platform for Automated Real-Time High Performance Analytics on Medical Image Data.IEEE journal of biomedical and health informatics · 2018
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Refaat E GabrDepartments of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA. refaat.e.gabr@uth.tmc.edu.
Getaneh B TeferaDepartments of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA.
William J AllenTexas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA.
Amol S PednekarPhilips Healthcare, Cleveland, OH, USA.
Ponnada A NarayanaDepartments of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA.

Funding

Center for Clinical and Translational Sciences (CCTS)UL1TR000371 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MCPHERSON, DAVID D · 2012 to 2016
$13.4M
NCATS NIH HHS UL1 TR000371
6 · The paper itself

Abstract

purposeWe present a platform, GRAphical Pipeline Environment (GRAPE), to facilitate the development of patient-adaptive magnetic resonance imaging (MRI) protocols.

methodsGRAPE is an open-source project implemented in the Qt C++ framework to enable graphical creation, execution, and debugging of real-time image analysis algorithms integrated with the MRI scanner. The platform provides the tools and infrastructure to design new algorithms, and build and execute an array of image analysis routines, and provides a mechanism to include existing analysis libraries, all within a graphical environment. The application of GRAPE is demonstrated in multiple MRI applications, and the software is described in detail for both the user and the developer.

resultsGRAPE was successfully used to implement and execute three applications in MRI of the brain, performed on a 3.0-T MRI scanner: (i) a multi-parametric pipeline for segmenting the brain tissue and detecting lesions in multiple sclerosis (MS), (ii) patient-specific optimization of the 3D fluid-attenuated inversion recovery MRI scan parameters to enhance the contrast of brain lesions in MS, and (iii) an algebraic image method for combining two MR images for improved lesion contrast.

conclusionsGRAPE allows graphical development and execution of image analysis algorithms for inline, real-time, and adaptive MRI applications.

Indexed as

AlgorithmsBrainComputer GraphicsHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMultiple SclerosisSoftwareUser-Computer InterfaceAdvanced computingGraphical user interfacePatient-specific imagingReal-timeVisual programming

Identifiers

PMID27796790
PMCPMC5315596

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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.