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.
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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.
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This research was approved by the Committee for the Protection of Human Subjects of the University of Texas Health Science Center at Houston.
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Informed consent was obtained from all participants included in the study.
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In the original publication of this article the given name of Amol S. Pednekar has been published incorrectly; this error has now been corrected.
An erratum to this article is available at http://dx.doi.org/10.1007/s11548-016-1508-y.
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Gabr, R.E., Tefera, G.B., Allen, W.J. et al. GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging. Int J CARS 12, 449–457 (2017). https://doi.org/10.1007/s11548-016-1495-z
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DOI: https://doi.org/10.1007/s11548-016-1495-z