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

  • Refaat E. GabrEmail author
  • Getaneh B. Tefera
  • William J. Allen
  • Amol S. Pednekar
  • Ponnada A. Narayana
Original Article

Abstract

Purpose

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

Methods

GRAPE 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.

Results

GRAPE 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.

Conclusions

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

Keywords

Graphical user interface Patient-specific imaging Advanced computing Real-time Visual programming 

Notes

Acknowledgements

This work was supported by the Clinical Translational Science Award (CTSA) Grant UL1 TR000371 from the e National Institutes of Health (NIH) National Center for Advancing Translational Sciences, and the Chair in Biomedical Engineering Endowment Funds. Stampede was generously funded by the National Science Foundation (NSF) through award ACI-1134872. We thank Xiaojun Sun for useful discussion and Vipulkumar Patel for help with the MRI experiments.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical standards

This research was approved by the Committee for the Protection of Human Subjects of the University of Texas Health Science Center at Houston.

Informed consent

Informed consent was obtained from all participants included in the study.

Supplementary material

11548_2016_1495_MOESM1_ESM.xml (21 kb)
Supplementary material 1 (xml 21 KB)

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Copyright information

© CARS 2016

Authors and Affiliations

  • Refaat E. Gabr
    • 1
    Email author
  • Getaneh B. Tefera
    • 1
  • William J. Allen
    • 2
  • Amol S. Pednekar
    • 3
  • Ponnada A. Narayana
    • 1
  1. 1.Departments of Diagnostic and Interventional ImagingUniversity of Texas Health Science Center at Houston (UTHealth)HoustonUSA
  2. 2.Texas Advanced Computing CenterUniversity of Texas at AustinAustinUSA
  3. 3.Philips HealthcareClevelandUSA

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