Refaat E Gabr1, Getaneh B Tefera2, William J Allen3, Amol S Pednekar4, Ponnada A Narayana2. 1. Departments of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA. refaat.e.gabr@uth.tmc.edu. 2. Departments of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA. 3. Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA. 4. Philips Healthcare, Cleveland, OH, USA.
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.
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.
Authors: Zahra Karimaghaloo; Mohak Shah; Simon J Francis; Douglas L Arnold; D Louis Collins; Tal Arbel Journal: IEEE Trans Med Imaging Date: 2012-02-03 Impact factor: 10.048
Authors: Balasrinivasa Rao Sajja; Sushmita Datta; Renjie He; Meghana Mehta; Rakesh K Gupta; Jerry S Wolinsky; Ponnada A Narayana Journal: Ann Biomed Eng Date: 2006-03-09 Impact factor: 3.934
Authors: Brian B Avants; Nicholas J Tustison; Gang Song; Philip A Cook; Arno Klein; James C Gee Journal: Neuroimage Date: 2010-09-17 Impact factor: 6.556
Authors: William J Allen; Refaat E Gabr; Getaneh B Tefera; Amol S Pednekar; Matthew W Vaughn; Ponnada A Narayana Journal: IEEE J Biomed Health Inform Date: 2018-03 Impact factor: 5.772