Gadgetron: an open source framework for medical image reconstruction.
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This work presents a new open source framework for medical image reconstruction called the Gadgetron, which implements a flexible system for creating streaming data processing pipelines where data pass through a series of modules or “Gadgets” from raw data to reconstructed images.Abstract:
This work presents a new open source framework for medical image reconstruction called the “Gadgetron.” The framework implements a flexible system for creating streaming data processing pipelines where data pass through a series of modules or “Gadgets” from raw data to reconstructed images. The data processing pipeline is configured dynamically at run-time based on an extensible markup language configuration description. The framework promotes reuse and sharing of reconstruction modules and new Gadgets can be added to the Gadgetron framework through a plugin-like architecture without recompiling the basic framework infrastructure. Gadgets are typically implemented in C/C++, but the framework includes wrapper Gadgets that allow the user to implement new modules in the Python scripting language for rapid prototyping. In addition to the streaming framework infrastructure, the Gadgetron comes with a set of dedicated toolboxes in shared libraries for medical image reconstruction. This includes generic toolboxes for data-parallel (e.g., GPU-based) execution of compute-intensive components. The basic framework architecture is independent of medical imaging modality, but this article focuses on its application to Cartesian and non-Cartesian parallel magnetic resonance imaging. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.read more
Citations
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Journal ArticleDOI
Simultaneous multislice (SMS) imaging techniques
Markus Barth,Markus Barth,Felix A. Breuer,Peter J. Koopmans,Peter J. Koopmans,David G. Norris,David G. Norris,David G. Norris,Benedikt A. Poser +8 more
TL;DR: The relationship between classic parallel imaging techniques and SMS reconstruction methods is explored and the practical implementation of SMS imaging is described, including the acquisition of reference data, and slice cross‐talk.
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Compressed sensing for body MRI
TL;DR: An overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution, is presented.
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Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification
Peter Kellman,Michael S. Hansen,Sonia Nielles-Vallespin,Jannike Nickander,Raquel Themudo,Martin Ugander,Hui Xue +6 more
TL;DR: A dual sequence for myocardial perfusion cardiovascular magnetic resonance and AIF measurement has been optimized for quantification of myocardia blood flow and reliable perfusion mapping was demonstrated and produced estimates with low variability.
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5D whole-heart sparse MRI.
Li Feng,Simone Coppo,Davide Piccini,Davide Piccini,Jérôme Yerly,Ruth P. Lim,Pier Giorgio Masci,Matthias Stuber,Daniel K. Sodickson,Ricardo Otazo +9 more
TL;DR: A 5D whole‐heart sparse imaging framework is proposed for simultaneous assessment of myocardial function and high‐resolution cardiac and respiratory motion‐resolved whole‐ heart anatomy in a single continuous noncontrast MR scan.
Journal ArticleDOI
Non‐Cartesian parallel imaging reconstruction
TL;DR: This review will begin with an overview of non‐Cartesian k‐space trajectories and their sampling properties, followed by an in‐depth discussion of several selected non‐ Cartesian parallel imaging algorithms.
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