Full Paper

Graphical programming interface: A development environment for MRI methods

Corresponding Author

Nicholas R. Zwart

Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, Arizona, USA.

Correspondence to: Nicholas R. Zwart, Ph.D., Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, AZ, 85013. E-mail: [email protected]Search for more papers by this author

James G. Pipe,

James G. Pipe

Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, Arizona, USA.

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Nicholas R. Zwart,

Corresponding Author

Nicholas R. Zwart

Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, Arizona, USA.

Correspondence to: Nicholas R. Zwart, Ph.D., Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, AZ, 85013. E-mail: [email protected]Search for more papers by this author

James G. Pipe,

James G. Pipe

Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, Arizona, USA.

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First published: 10 November 2014

https://doi.org/10.1002/mrm.25528

Citations: 48

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Abstract

Purpose

To introduce a multiplatform, Python language-based, development environment called graphical programming interface for prototyping MRI techniques.

Methods

The interface allows developers to interact with their scientific algorithm prototypes visually in an event-driven environment making tasks such as parameterization, algorithm testing, data manipulation, and visualization an integrated part of the work-flow. Algorithm developers extend the built-in functionality through simple code interfaces designed to facilitate rapid implementation.

Results

This article shows several examples of algorithms developed in graphical programming interface including the non-Cartesian MR reconstruction algorithms for PROPELLER and spiral as well as spin simulation and trajectory visualization of a FLORET example.

Conclusion

The graphical programming interface framework is shown to be a versatile prototyping environment for developing numeric algorithms used in the latest MR techniques. Magn Reson Med 74:1449–1460, 2015. © 2014 Wiley Periodicals, Inc.

Supporting Information

REFERENCES

1Hansen MS, Sørensen TS. Gadgetron: an open source framework for medical image reconstruction. Magn Reson Med 2013; 69: 1768–1776.
10.1002/mrm.24389
PubMed Web of Science® Google Scholar
2Grissom WA. Web-based, platform-independent dissemination of image reconstruction & RF pulse design algorithms using the google chrome native client. In: ISMRM Workshop on Data Sampling and Image Reconstruction. Sedona, Arizona, 2013; Session 8.
Google Scholar
3Uecker M. Software toolbox & programming library for compressed sensing & parallel imaging. In: ISMRM Workshop on Data Sampling and Image Reconstruction. Sedona, Arizona, 2013; Session 8.
Google Scholar
4Vahedipour K. Codeare—common data exchange & reconstruction framework. In: ISMRM Workshop on Data Sampling and Image Reconstruction. Sedona, Arizona, 2013; Session 8.
Google Scholar
5Block KT. Prototypic setup for evaluation of a compressed-sensing technique in clinical patient studies. In: ISMRM Workshop on Data Sampling and Image Reconstruction. Sedona, Arizona, 2013; Session 8.
Google Scholar
6Zenge MO. IRecon—introducing a standardized interface into the Siemens image reconstruction environment. In: ISMRM Workshop on Data Sampling and Image Reconstruction. Sedona, Arizona, 2013; Session 8.
Google Scholar
7Zwart NR, Pipe JG. Graphical programming interface. In: ISMRM Workshop on Data Sampling and Image Reconstruction. Sedona, Arizona, 2013; Session 8.
Google Scholar
8Zwart NR, Pipe JG. Graphical programming interface. Available at: http://gpilab.com. Accessed June 24, 2014.
Google Scholar
9Upson C, Faulhaber TA, Jr, Kamins D, Laidlaw D, Schlegel D, Vroom J, Gurwitz R, Van Dam A. The application visualization system: a computational environment for scientific visualization. IEEE Comput Graph Appl 1989; 9(4): 30–42.
10.1109/38.31462
Web of Science® Google Scholar
10van Rossum G, Drake FL. The Python Language Reference Manual. Available at: http://python.org. Accessed June 25, 2014.
Google Scholar
11Sanner MF, Stoffler D, Olson AJ. ViPEr, a visual programming environment for Python. In: Proceedings of the 10th International Python conference, Alexandria, Virginia, USA, 2002. p. 103–115.
Google Scholar
12Summerfield M. Rapid GUI programming with Python and Qt: the definitive guide to PyQt programming. Ann Arbor, MI: Pearson Education, 2007.
Google Scholar
13Hunter JD. Matplotlib: A 2D graphics environment. Comput Sci Eng 2007; 9: 90–95.
10.1109/MCSE.2007.55
Web of Science® Google Scholar
14Abrahams D, Grosse-Kunstleve RW. Building hybrid systems with Boost.Python. C/C++ Users J 2003; 21(LBNL–53142).
Google Scholar
15Beazley DM. SWIG: an easy to use tool for integrating scripting languages with C and C++. In: Proceedings of the 4th USENIX Tcl/Tk workshop, Monterey, California, USA, 1996. p. 129–139.
Google Scholar
16Oliphant TE. Python for scientific computing. Comput Sci Eng 2007; 9: 10–20.
10.1109/MCSE.2007.58
CAS Web of Science® Google Scholar
17Pipe JG, Zwart NR. Spiral trajectory design: a flexible numerical algorithm and base analytical equations. Magn Reson Med 2014; 71: 278–285.
10.1002/mrm.24675
PubMed Web of Science® Google Scholar
18Pipe JG, Gibbs WN, Li Z, Karis JP, Schar M, Zwart NR. Revised motion estimation algorithm for PROPELLER MRI. Magn Reson Med 2013; 72: 430–437.
10.1002/mrm.24929
PubMed Web of Science® Google Scholar
19Pipe JG. Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging. Magn Reson Med 1999; 42: 963–969.
10.1002/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L
CAS PubMed Web of Science® Google Scholar
20Forbes KP, Pipe JG, Bird CR, Heiserman JE. PROPELLER MRI: clinical testing of a novel technique for quantification and compensation of head motion. J Magn Reson Imaging 2001; 14: 215–222.
10.1002/jmri.1176
CAS PubMed Web of Science® Google Scholar
21Jackson JI, Meyer CH, Nishimura DG, Macovski A. Selection of a convolution function for Fourier inversion using gridding [computerised tomography application]. IEEE Trans Med Imaging, 1991; 10: 473–478.
10.1109/42.97598
CAS PubMed Web of Science® Google Scholar
22Beatty PJ, Nishimura DG, Pauly JM. Rapid gridding reconstruction with a minimal oversampling ratio. IEEE Trans Med Imaging 2005; 24: 799–808.
10.1109/TMI.2005.848376
PubMed Web of Science® Google Scholar
23Frigo M, Johnson SG. The design and implementation of FFTW3. Proc IEEE 2005; 93: 216–231.
10.1109/JPROC.2004.840301
ADS Web of Science® Google Scholar
24Zwart NR, Johnson KO, Pipe JG. Efficient sample density estimation by combining gridding and an optimized kernel. Magn Reson Med 2012; 67: 701–710.
10.1002/mrm.23041
PubMed Web of Science® Google Scholar
25Johnson KO, Pipe JG. Convolution kernel design and efficient algorithm for sampling density correction. Magn Reson Med 2009; 61: 439–447.
10.1002/mrm.21840
PubMed Web of Science® Google Scholar
26Pipe JG, Menon P. Sampling density compensation in MRI: rationale and an iterative numerical solution. Magn Reson Med 1999; 41: 179–86.
10.1002/(SICI)1522-2594(199901)41:1<179::AID-MRM25>3.0.CO;2-V
CAS PubMed Web of Science® Google Scholar
27Pipe JG. Reconstructing MR images from undersampled data: data-weighting considerations. Magn Reson Med 2000; 43: 867–875.
10.1002/1522-2594(200006)43:6<867::AID-MRM13>3.0.CO;2-2
CAS PubMed Web of Science® Google Scholar
28Turley DC, Pipe JG. Distributed spirals: a new class of three-dimensional k-space trajectories. Magn Reson Med 2013; 70: 413–419.
10.1002/mrm.24475
PubMed Web of Science® Google Scholar
29Pipe JG, Zwart NR, Aboussouan EA, Robison RK, Devaraj A, Johnson KO. A new design and rationale for 3D orthogonally oversampled k-space trajectories. Magn Reson Med 2011; 66: 1303–1311.
10.1002/mrm.22918
PubMed Web of Science® Google Scholar
30Zwart NR, Wang DH, Pipe JG. Spiral CG deblurring and fat-water separation using a multi-peak fat model. In: Proceedings of the 22nd Annual Meeting of ISMRM. Milan, Italy, 2014. p. 1660.
Google Scholar
31Fletcher M, Liebscher R. PyOpenGL, the Python OpenGL binding. Available at: http://pyopengl.sourceforge.net. Accessed on June 25, 2014.
Google Scholar

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Volume74, Issue5

November 2015

Pages 1449-1460

Graphical programming interface: A development environment for MRI methods

Abstract

Purpose

Methods

Results

Conclusion

Supporting Information

REFERENCES

Citing Literature

References

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Graphical programming interface: A development environment for MRI methods

Abstract

Purpose

Methods

Results

Conclusion

Supporting Information

REFERENCES

Citing Literature

References

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