ismrmrd.bib

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@article{Campbell-Washburn2015,
	Abstract = {{MRI}-guided interventions demand high frame rate imaging, making fast imaging techniques such as spiral imaging and echo planar imaging (EPI) appealing. In this study, we implemented a real-time distortion correction framework to enable the use of these fast acquisitions for interventional MRI.},
	Author = {Campbell{-}Washburn, Adrienne E. and Xue, Hui and Lederman, Robert J. and Faranesh, Anthony Z. and Hansen, Michael S.},
	Date-Added = {2015-10-23 20:17:04 +0000},
	Date-Modified = {2015-10-23 20:17:04 +0000},
	Day = {26},
	Doi = {10.1002/mrm.25788},
	Issn = {1522-2594},
	Journal = {Magnetic resonance in medicine},
	Language = {ENG},
	Month = {Jun},
	Volume = {In press},
	Title = {Real-time distortion correction of spiral and echo planar images using the gradient system impulse response function.},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/26114951},
	Year = {2015},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/26114951},
	Bdsk-Url-2 = {http://dx.doi.org/10.1002/mrm.25788}}

@article{Vannesjo2013,
	Abstract = {This work demonstrates a fast, sensitive method of characterizing the dynamic performance of MR gradient systems. The accuracy of gradient time-courses is often compromised by field imperfections of various causes, including eddy currents and mechanical oscillations. Characterizing these perturbations is instrumental for corrections by pre-emphasis or post hoc signal processing. Herein, a gradient chain is treated as a linear time-invariant system, whose impulse response function is determined by measuring field responses to known gradient inputs. Triangular inputs are used to probe the system and response measurements are performed with a dynamic field camera consisting of NMR probes. In experiments on a whole-body MR system, it is shown that the proposed method yields impulse response functions of high temporal and spectral resolution. Besides basic properties such as bandwidth and delay, it also captures subtle features such as mechanically induced field oscillations. For validation, measured response functions were used to predict gradient field evolutions, which was achieved with an error below 0.2\%. The field camera used records responses of various spatial orders simultaneously, rendering the method suitable also for studying cross-responses and dynamic shim systems. It thus holds promise for a range of applications, including pre-emphasis optimization, quality assurance, and image reconstruction.},
	Author = {Vannesjo, Signe J. and Haeberlin, Maximilan and Kasper, Lars and Pavan, Matteo and Wilm, Bertram J. and Barmet, Christoph and Pruessmann, Klaas P.},
	Date-Added = {2015-10-23 20:15:34 +0000},
	Date-Modified = {2015-10-23 20:15:34 +0000},
	Day = {12},
	Doi = {10.1002/mrm.24263},
	Issn = {1522-2594},
	Journal = {Magnetic resonance in medicine},
	Keywords = {Sensitivity and Specificity},
	Language = {eng},
	Month = {Feb},
	Number = {2},
	Pages = {583--593},
	Title = {Gradient system characterization by impulse response measurements with a dynamic field camera.},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/22499483},
	Volume = {69},
	Year = {2013},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/22499483},
	Bdsk-Url-2 = {http://dx.doi.org/10.1002/mrm.24263}}

@article{Zwart:2014aa,
	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, 2014. {\copyright} 2014 Wiley Periodicals, Inc.},
	Author = {Zwart, Nicholas R and Pipe, James G},
	Date-Added = {2015-07-25 01:11:03 +0000},
	Date-Modified = {2015-08-13 17:00:55 +0000},
	Doi = {10.1002/mrm.25528},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Keywords = {graphical programming; reconstruction; spin simulation},
	Month = {Nov},
	Pmid = {25385670},
	Pst = {aheadofprint},
	Title = {Graphical programming interface: A development environment for {MRI} methods},
	Volume = {DOI:10.1002/mrm.25528},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/mrm.25528}}

@article{Hansen:2013aa,
	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.},
	Author = {Hansen, Michael Schacht and S{\o}rensen, Thomas Sangild},
	Date-Added = {2015-07-25 01:08:36 +0000},
	Date-Modified = {2015-07-25 01:08:36 +0000},
	Doi = {10.1002/mrm.24389},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine},
	Mesh = {Algorithms; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Programming Languages; Reproducibility of Results; Sensitivity and Specificity; Software; Software Design},
	Month = {Jun},
	Number = {6},
	Pages = {1768-76},
	Pmid = {22791598},
	Pst = {ppublish},
	Title = {Gadgetron: an open source framework for medical image reconstruction},
	Volume = {69},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/mrm.24389}}

@misc{hdf5,
	Author = {{The HDF Group}},
	Date-Added = {2015-07-23 01:25:03 +0000},
	Date-Modified = {2015-07-23 01:38:48 +0000},
	Howpublished = {\url{http://www.hdfgroup.org/HDF5/}},
	Lastchecked = {07-22-2015},
	Title = {Hierarchical Data Format, version 5},
	Url = {http://www.hdfgroup.org/HDF5/},
	Urldate = {1997-2015},
	Bdsk-Url-1 = {http://www.hdfgroup.org/HDF5/}}

@url{scipy,
	Author = {Eric Jones and Travis Oliphant and Pearu Peterson and et al.},
	Date-Added = {2015-07-23 01:22:00 +0000},
	Date-Modified = {2015-07-23 01:23:57 +0000},
	Lastchecked = {2015-07-21},
	Title = {{SciPy}: Open source scientific tools for {Python}}}

@misc{pyxb,
	Date-Added = {2015-07-23 01:21:01 +0000},
	Date-Modified = {2015-07-23 01:37:19 +0000},
	Howpublished = {\url{http://pyxb.sourceforge.net}},
	Title = {{PyXB}},
	Url = {http://pyxb.sourceforge.net},
	Bdsk-Url-1 = {http://pyxb.sourceforge.net}}

@misc{connectome,
	Date-Added = {2014-01-29 17:56:01 +0000},
	Date-Modified = {2015-08-13 16:54:04 +0000},
	Howpublished = {Human Connectome website. \url{http://www.humanconnectome.org}. Accessed August 13, 2015.},
	Title = {Human Connectome Project},
	Url = {http://www.humanconnectome.org},
	Bdsk-Url-1 = {http://www.humanconnectome.org}}

@misc{dicom,
	Date-Added = {2014-01-29 17:53:00 +0000},
	Date-Modified = {2015-08-13 16:55:05 +0000},
	Howpublished = {Medical Nema website. \url{http://medical.nema.org}. Accessed August 13, 2015.},
	Title = {{DICOM} Standard},
	Url = {http://medical.nema.org},
	Bdsk-Url-1 = {http://medical.nema.org}}

@misc{wavelab,
	Date-Added = {2014-01-29 17:52:22 +0000},
	Date-Modified = {2015-08-13 16:50:22 +0000},
	Howpublished = {WaveLab website. \url{http://statweb.stanford.edu/~wavelab}. Accessed August 13, 2015},
	Lastchecked = {August 13, 2015},
	Title = {WaveLab},
	Url = {http://statweb.stanford.edu/~wavelab},
	Bdsk-Url-1 = {http://statweb.stanford.edu/~wavelab}}

@misc{nifti,
	Date-Added = {2014-01-29 17:51:43 +0000},
	Date-Modified = {2015-08-13 16:58:34 +0000},
	Howpublished = {National Institute of Mental Health website. \url{http://nifti.nimh.nih.gov}. Accessed August 13, 2015.},
	Title = {{NIfTI} Neuroimaging Informatics Technology Initiative},
	Url = {http://nifti.nimh.nih.gov},
	Bdsk-Url-1 = {http://nifti.nimh.nih.gov}}

@misc{fits,
	Date-Added = {2014-01-29 17:50:51 +0000},
	Date-Modified = {2015-08-13 16:56:17 +0000},
	Howpublished = {NASA website. \url{http://fits.gsfc.nasa.gov/}. Accessed August 13, 2015.},
	Title = {{FITS} Astronomical Image and Table Format},
	Url = {http://fits.gsfc.nasa.gov},
	Bdsk-Url-1 = {http://fits.gsfc.nasa.gov}}

@misc{mri_unbound,
	Date-Added = {2014-01-29 17:38:02 +0000},
	Date-Modified = {2015-08-13 16:57:27 +0000},
	Howpublished = {ISMRM website. \url{http://www.ismrm.org/mri_unbound}. Accessed August 13, 2015.},
	Title = {{ISMRM MRI Unbound}},
	Url = {http://www.ismrm.org/mri_unbound},
	Bdsk-Url-1 = {http://www.ismrm.org/mri_unbound}}

@article{Peng:2011reproducible,
	Author = {Peng, Roger D and others},
	Date-Added = {2014-01-29 17:34:06 +0000},
	Date-Modified = {2014-01-29 17:34:34 +0000},
	Journal = {Science (New York, Ny)},
	Number = {6060},
	Pages = {1226--1227},
	Publisher = {NIH Public Access},
	Title = {Reproducible research in computational science},
	Volume = {334},
	Year = {2011}}

@article{Jasny:2011again,
	Author = {Jasny, Barbara R and Chin, Gilbert and Chong, Lisa and Vignieri, Sacha},
	Date-Added = {2014-01-29 17:32:34 +0000},
	Date-Modified = {2014-01-29 17:35:30 +0000},
	Journal = {Science},
	Number = {6060},
	Pages = {1225--1225},
	Publisher = {American Association for the Advancement of Science},
	Title = {Again, and again, and again{\ldots}},
	Volume = {334},
	Year = {2011}}

@article{Donoho:2006compressed,
	Author = {Donoho, David L},
	Date-Added = {2014-01-29 17:28:28 +0000},
	Date-Modified = {2014-01-29 17:28:49 +0000},
	Journal = {Information Theory, IEEE Transactions on},
	Number = {4},
	Pages = {1289--1306},
	Publisher = {IEEE},
	Title = {Compressed sensing},
	Volume = {52},
	Year = {2006}}

@book{Buckheit:1995wavelab,
	Author = {Buckheit, Jonathan B and Donoho, David L},
	Date-Added = {2014-01-28 18:20:41 +0000},
	Date-Modified = {2014-01-29 15:02:44 +0000},
	Publisher = {Springer},
	Title = {Wavelab and reproducible research},
	Year = {1995}}

@article{Kellman:2001fk,
	Abstract = {A number of different methods have been demonstrated which increase the speed of MR acquisition by decreasing the number of sequential phase encodes. The UNFOLD technique is based on time interleaving of k-space lines in sequential images and exploits the property that the outer portion of the field-of-view is relatively static. The differences in spatial sensitivity of multiple receiver coils may be exploited using SENSE or SMASH techniques to eliminate the aliased component that results from undersampling k-space. In this article, an adaptive method of sensitivity encoding is presented which incorporates both spatial and temporal filtering. Temporal filtering and spatial encoding may be combined by acquiring phase encodes in an interleaved manner. In this way the aliased components are alternating phase. The SENSE formulation is not altered by the phase of the alias artifact; however, for imperfect estimates of coil sensitivities the residual artifact will have alternating phase using this approach. This is the essence of combining temporal filtering (UNFOLD) with spatial sensitivity encoding (SENSE). Any residual artifact will be temporally frequency-shifted to the band edge and thus may be further suppressed by temporal low-pass filtering. By combining both temporal and spatial filtering a high degree of alias artifact rejection may be achieved with less stringent requirements on accuracy of coil sensitivity estimates and temporal low-pass filter selectivity than would be required using each method individually. Experimental results that demonstrate the adaptive spatiotemporal filtering method (adaptive TSENSE) with acceleration factor R = 2, for real-time nonbreath-held cardiac MR imaging during exercise induced stress are presented.},
	Author = {Kellman, P and Epstein, F H and McVeigh, E R},
	Date-Added = {2012-01-16 17:38:35 -0500},
	Date-Modified = {2012-01-16 17:43:28 -0500},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Mesh = {Algorithms; Artifacts; Exercise Test; Heart; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Sensitivity and Specificity},
	Month = {May},
	Number = {5},
	Pages = {846-52},
	Pmid = {11323811},
	Pst = {ppublish},
	Title = {Adaptive sensitivity encoding incorporating temporal filtering ({TSENSE})},
	Volume = {45},
	Year = {2001}}

@webpage{gadgetronsourceforge,
	Author = {Hansen, M S and S{\o}rensen, T. S.},
	Date-Added = {2012-01-02 18:19:56 -0500},
	Date-Modified = {2012-01-11 19:05:37 -0500},
	Title = {Gadgetron Website},
	Url = {http://gadgetron.sourceforge.net},
	Bdsk-Url-1 = {http://sourceforge.net/p/gadgetron/},
	Bdsk-Url-2 = {http://gadgetron.sourceforge.net}}

@article{Winkelmann:2007ly,
	Abstract = {In dynamic magnetic resonance imaging (MRI) studies, the motion kinetics or the contrast variability are often hard to predict, hampering an appropriate choice of the image update rate or the temporal resolution. A constant azimuthal profile spacing (111.246 degrees), based on the Golden Ratio, is investigated as optimal for image reconstruction from an arbitrary number of profiles in radial MRI. The profile order is evaluated and compared with a uniform profile distribution in terms of signal-to-noise ratio (SNR) and artifact level. The favorable characteristics of such a profile order are exemplified in two applications on healthy volunteers. First, an advanced sliding window reconstruction scheme is applied to dynamic cardiac imaging, with a reconstruction window that can be flexibly adjusted according to the extent of cardiac motion that is acceptable. Second, a contrast-enhancing k-space filter is presented that permits reconstructing an arbitrary number of images at arbitrary time points from one raw data set. The filter was utilized to depict the T1-relaxation in the brain after a single inversion prepulse. While a uniform profile distribution with a constant angle increment is optimal for a fixed and predetermined number of profiles, a profile distribution based on the Golden Ratio proved to be an appropriate solution for an arbitrary number of profiles.},
	Author = {Winkelmann, Stefanie and Schaeffter, Tobias and Koehler, Thomas and Eggers, Holger and Doessel, Olaf},
	Date-Added = {2012-01-02 17:17:17 -0500},
	Date-Modified = {2012-01-02 17:17:17 -0500},
	Doi = {10.1109/TMI.2006.885337},
	Journal = {IEEE Trans Med Imaging},
	Journal-Full = {IEEE transactions on medical imaging},
	Mesh = {Algorithms; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Magnetic Resonance Imaging; Reproducibility of Results; Sensitivity and Specificity},
	Month = {Jan},
	Number = {1},
	Pages = {68-76},
	Pmid = {17243585},
	Pst = {ppublish},
	Title = {An optimal radial profile order based on the Golden Ratio for time-resolved MRI},
	Volume = {26},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/TMI.2006.885337}}

@article{Pruessmann:2001zr,
	Abstract = {New, efficient reconstruction procedures are proposed for sensitivity encoding (SENSE) with arbitrary k-space trajectories. The presented methods combine gridding principles with so-called conjugate-gradient iteration. In this fashion, the bulk of the work of reconstruction can be performed by fast Fourier transform (FFT), reducing the complexity of data processing to the same order of magnitude as in conventional gridding reconstruction. Using the proposed method, SENSE becomes practical with nonstandard k-space trajectories, enabling considerable scan time reduction with respect to mere gradient encoding. This is illustrated by imaging simulations with spiral, radial, and random k-space patterns. Simulations were also used for investigating the convergence behavior of the proposed algorithm and its dependence on the factor by which gradient encoding is reduced. The in vivo feasibility of non-Cartesian SENSE imaging with iterative reconstruction is demonstrated by examples of brain and cardiac imaging using spiral trajectories. In brain imaging with six receiver coils, the number of spiral interleaves was reduced by factors ranging from 2 to 6. In cardiac real-time imaging with four coils, spiral SENSE permitted reducing the scan time per image from 112 ms to 56 ms, thus doubling the frame-rate.},
	Author = {Pruessmann, K P and Weiger, M and B{\"o}rnert, P and Boesiger, P},
	Date-Added = {2012-01-02 16:34:20 -0500},
	Date-Modified = {2012-01-02 16:34:20 -0500},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Mesh = {Brain; Heart; Humans; Magnetic Resonance Imaging; Mathematics; Sensitivity and Specificity},
	Month = {Oct},
	Number = {4},
	Pages = {638-51},
	Pmid = {11590639},
	Pst = {ppublish},
	Title = {Advances in sensitivity encoding with arbitrary k-space trajectories},
	Volume = {46},
	Year = {2001}}

@article{Huang:2008ys,
	Abstract = {In magnetic resonance imaging, highly parallel imaging using coil arrays with a large number of elements is an area of growing interest. With increasing channel numbers for parallel acquisition, the increased reconstruction time and extensive computer memory requirements have become significant concerns. In this work, principal component analysis (PCA) is used to develop a channel compression technique. This technique efficiently reduces the size of parallel imaging data acquired from a multichannel coil array, thereby significantly reducing the reconstruction time and computer memory requirement without undermining the benefits of multichannel coil arrays. Clinical data collected with a 32-channel cardiac coil are used in all of the experiments. The performance of the proposed method on parallel, partially acquired data, as well as fully acquired data, was evaluated. Experimental results show that the proposed method dramatically reduces the processing time without considerable degradation in the quality of reconstructed images. It is also demonstrated that this PCA technique can be used to perform intensity correction in parallel imaging applications.},
	Author = {Huang, Feng and Vijayakumar, Sathya and Li, Yu and Hertel, Sarah and Duensing, George R},
	Date-Added = {2011-12-31 17:20:57 -0500},
	Date-Modified = {2011-12-31 17:20:57 -0500},
	Doi = {10.1016/j.mri.2007.04.010},
	Journal = {Magn Reson Imaging},
	Journal-Full = {Magnetic resonance imaging},
	Mesh = {Algorithms; Computer Simulation; Heart; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging, Cine; Principal Component Analysis; Software},
	Month = {Jan},
	Number = {1},
	Pages = {133-41},
	Pmid = {17573223},
	Pst = {ppublish},
	Title = {A software channel compression technique for faster reconstruction with many channels},
	Volume = {26},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.mri.2007.04.010}}

@article{Huang:2011vn,
	Abstract = {In MRI, imaging using receiving coil arrays with a large number of elements is an area of growing interest. With increasing channel numbers for parallel acquisition, longer reconstruction times have become a significant concern. Channel reduction techniques have been proposed to reduce the processing time of channel-by-channel reconstruction algorithms. In this article, two schemes are combined to enable faster and more accurate reconstruction than existing channel reduction techniques. One scheme use two stages of channel reduction instead of one. The other scheme is to incorporate all acquired data into the final reconstruction. The combination of these two schemes is called flexible virtual coil. Applications of flexible virtual coil for partially parallel imaging, motion compensation, and compressed sensing are presented as specific examples. Theoretical analysis and experimental results demonstrate that the proposed method has a major impact in reducing computation cost in reconstruction with high-channel count coil elements. Magn Reson Med, 2011. {\copyright} 2011 Wiley-Liss, Inc.},
	Author = {Huang, Feng and Lin, Wei and Duensing, George R and Reykowski, Arne},
	Date-Added = {2011-12-31 17:20:15 -0500},
	Date-Modified = {2012-03-16 09:02:14 -0400},
	Doi = {10.1002/mrm.23048},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Month = {Mar},
	Number = {3},
	Pages = {835-43},
	Pmid = {21713980},
	Pst = {aheadofprint},
	Title = {A hybrid method for more efficient channel-by-channel reconstruction with many channels},
	Volume = {67},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/mrm.23048}}

@article{Buehrer:2007kx,
	Abstract = {Arrays with large numbers of independent coil elements are becoming increasingly available as they provide increased signal-to-noise ratios (SNRs) and improved parallel imaging performance. Processing of data from a large set of independent receive channels is, however, associated with an increased memory and computational load in reconstruction. This work addresses this problem by introducing coil array compression. The method allows one to reduce the number of datasets from independent channels by combining all or partial sets in the time domain prior to image reconstruction. It is demonstrated that array compression can be very effective depending on the size of the region of interest (ROI). Based on 2D in vivo data obtained with a 32-element phased-array coil in the heart, it is shown that the number of channels can be compressed to as few as four with only 0.3% SNR loss in an ROI encompassing the heart. With twofold parallel imaging, only a 2% loss in SNR occurred using the same compression factor.},
	Author = {Buehrer, Martin and Pruessmann, Klaas P and Boesiger, Peter and Kozerke, Sebastian},
	Date-Added = {2011-12-31 17:13:48 -0500},
	Date-Modified = {2011-12-31 17:13:48 -0500},
	Doi = {10.1002/mrm.21237},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Mesh = {Algorithms; Computer Simulation; Equipment Design; Heart; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging, Cine; Monte Carlo Method},
	Month = {Jun},
	Number = {6},
	Pages = {1131-9},
	Pmid = {17534913},
	Pst = {ppublish},
	Title = {Array compression for MRI with large coil arrays},
	Volume = {57},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/mrm.21237}}

@article{Walsh:2000uq,
	Abstract = {An adaptive implementation of the spatial matched filter and its application to the reconstruction of phased array MR imagery is described. Locally relevant array correlation statistics for the NMR signal and noise processes are derived directly from the set of complex individual coil images, in the form of sample correlation matrices. Eigen-analysis yields an optimal filter vector for the estimated signal and noise array correlation statistics. The technique enables near-optimal reconstruction of multicoil MR imagery without a-priori knowledge of the individual coil field maps or noise correlation structure. Experimental results indicate SNR performance approaching that of the optimal matched filter. Compared to the sum-of-squares technique, the RMS noise level in dark image regions is reduced by as much as the square root of N, where N is the number of coils in the array. The technique is also effective in suppressing localized motion and flow artifacts.},
	Author = {Walsh, D O and Gmitro, A F and Marcellin, M W},
	Date-Added = {2011-12-31 16:48:56 -0500},
	Date-Modified = {2011-12-31 16:49:54 -0500},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Mesh = {Artifacts; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Angiography; Magnetic Resonance Imaging; Mathematics; Movement; Thorax},
	Month = {May},
	Number = {5},
	Pages = {682-90},
	Pmid = {10800033},
	Pst = {ppublish},
	Title = {Adaptive reconstruction of phased array {MR} imagery},
	Volume = {43},
	Year = {2000}}

@article{Saybasili:2009fk,
	Abstract = {The temporal generalized autocalibrating partially parallel acquisitions (TGRAPPA) algorithm for parallel MRI was modified for real-time low latency imaging in interventional procedures using image domain, B(1)-weighted reconstruction. GRAPPA coefficients were calculated in k-space, but applied in the image domain after appropriate transformation. Convolution-like operations in k-space were thus avoided, resulting in improved reconstruction speed. Image domain GRAPPA weights were combined into composite unmixing coefficients using adaptive B(1)-map estimates and optimal noise weighting. Images were reconstructed by pixel-by-pixel multiplication in the image domain, rather than time-consuming convolution operations in k-space. Reconstruction and weight-set calculation computations were parallelized and implemented on a general-purpose multicore architecture. The weight calculation was performed asynchronously to the real-time image reconstruction using a dedicated parallel processing thread. The weight-set coefficients were computed in an adaptive manner with updates linked to changes in the imaging scan plane. In this implementation, reconstruction speed is not dependent on acceleration rate or GRAPPA kernel size.},
	Author = {Saybasili, Haris and Kellman, Peter and Griswold, Mark A and Derbyshire, J Andrew and Guttman, Michael A},
	Date-Added = {2011-12-31 16:17:24 -0500},
	Date-Modified = {2011-12-31 16:27:03 -0500},
	Doi = {10.1002/mrm.21922},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Mesh = {Algorithms; Heart; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Reproducibility of Results; Sensitivity and Specificity},
	Month = {Jun},
	Number = {6},
	Pages = {1425-33},
	Pmid = {19353673},
	Pst = {ppublish},
	Title = {{HTGRAPPA}: real-time B1-weighted image domain TGRAPPA reconstruction},
	Volume = {61},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/mrm.21922}}

@article{Goldstein:2009vn,
	Author = {Tom Goldstein and Osher, Stanley},
	Date-Added = {2011-12-21 16:55:56 -0500},
	Date-Modified = {2011-12-21 17:20:10 -0500},
	Doi = {10.1137/080725891},
	Issn = {19364954},
	Journal = {SIAM Journal on Imaging Sciences},
	Number = {2},
	Pages = {323--343},
	Title = {The Split Bregman Method for {L1}-Regularized Problems},
	Volume = {2},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1137/080725891}}

@inbook{Golub:1996kx:ConjugateGradient,
	Author = {Gene Golub and van Van Loan, Charles},
	Chapter = {10},
	Date-Added = {2011-12-21 16:50:04 -0500},
	Date-Modified = {2011-12-21 16:52:37 -0500},
	Edition = {3rd},
	Isbn = {0801854148},
	Pages = {520-531},
	Publisher = {The Johns Hopkins University Press},
	Title = {Matrix Computations (3rd Edition)},
	Year = {1996}}

@article{Jackson:1991uq,
	Author = {JI Jackson and Meyer, CH and Nishimura, DG and Macovski, A},
	Date-Added = {2011-12-21 16:34:19 -0500},
	Date-Modified = {2011-12-21 16:34:19 -0500},
	Doi = {10.1109/42.97598},
	Issn = {0278-0062},
	Journal = {Medical Imaging, IEEE Transactions on},
	Number = {3},
	Pages = {473--478},
	Title = {Selection of a convolution function for Fourier inversion using gridding [computerised tomography application]},
	Volume = {10},
	Year = {1991},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/42.97598}}

@article{FFTW05,
	Author = {Frigo, Matteo and Johnson, Steven~G.},
	Date-Added = {2011-12-21 16:26:18 -0500},
	Date-Modified = {2011-12-21 16:26:18 -0500},
	Journal = {Proceedings of the IEEE},
	Note = {Special issue on ``Program Generation, Optimization, and Platform Adaptation''},
	Number = 2,
	Pages = {216--231},
	Title = {The Design and Implementation of {FFTW3}},
	Volume = 93,
	Year = 2005}

@article{Dongarra:1990fk,
	Author = {JJ Dongarra and Du Croz, Jeremy and Hammarling, Sven and Duff, IS},
	Date-Added = {2011-12-21 14:05:59 -0500},
	Date-Modified = {2011-12-21 14:05:59 -0500},
	Doi = {10.1145/77626.79170},
	Issn = {0098-3500},
	Journal = {ACM Trans. Math. Softw.},
	Pages = {1--17},
	Title = {A set of level 3 basic linear algebra subprograms},
	Volume = {16},
	Year = {1990},
	Bdsk-Url-1 = {http://dx.doi.org/10.1145/77626.79170}}

@article{Schmidt:1993fk,
	Author = {Schmidt, Douglas C},
	Date-Added = {2011-12-18 10:44:31 -0500},
	Date-Modified = {2012-03-14 13:13:14 -0400},
	Doi = {10.1.1.42.8614},
	Journal = {Proceedings of the 11th Annual Sun Users Group Conference},
	Pages = {214-225},
	Title = {The {ADAPTIVE} Communication Environment: Object-Oriented Network Programming Components for Developing Client/Server Applications},
	Year = {1993},
	Bdsk-Url-1 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.8614},
	Bdsk-Url-2 = {http://dx.doi.org/10.1.1.42.8614}}

@article{Sorensen:2008zr,
	Abstract = {We present a fast parallel algorithm to compute the nonequispaced fast Fourier transform on commodity graphics hardware (the GPU). We focus particularly on a novel implementation of the convolution step in the transform as it was previously its most time consuming part. We describe the performance for two common sample distributions in medical imaging (radial and spiral trajectories), and for different convolution kernels as these parameters all influence the speed of the algorithm. The GPU-accelerated convolution is up to 85 times faster as our reference, the open source NFFT library on a state-of-the-art 64 bit CPU. The accuracy of the proposed GPU implementation was quantitatively evaluated at the various settings. To illustrate the applicability of the transform in medical imaging, in which it is also known as gridding, we look specifically at non-Cartesian magnetic resonance imaging and reconstruct both a numerical phantom and an in vivo cardiac image.},
	Author = {Sorensen, T S and Schaeffter, T and Noe, K O and Hansen, M S},
	Date-Added = {2011-11-20 12:46:27 -0500},
	Date-Modified = {2011-11-20 12:46:27 -0500},
	Doi = {10.1109/TMI.2007.909834},
	Journal = {IEEE Trans Med Imaging},
	Journal-Full = {IEEE transactions on medical imaging},
	Mesh = {Algorithms; Computer Graphics; Fourier Analysis; Image Enhancement; Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Time Factors},
	Month = {Apr},
	Number = {4},
	Pages = {538-47},
	Pmid = {18390350},
	Pst = {ppublish},
	Title = {Accelerating the nonequispaced fast Fourier transform on commodity graphics hardware},
	Volume = {27},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/TMI.2007.909834}}

@article{Sorensen:2009ys,
	Abstract = {A barrier to the adoption of non-Cartesian parallel magnetic resonance imaging for real-time applications has been the times required for the image reconstructions. These times have exceeded the underlying acquisition time thus preventing real-time display of the acquired images. We present a reconstruction algorithm for commodity graphics hardware (GPUs) to enable real time reconstruction of sensitivity encoded radial imaging (radial SENSE). We demonstrate that a radial profile order based on the golden ratio facilitates reconstruction from an arbitrary number of profiles. This allows the temporal resolution to be adjusted on the fly. A user adaptable regularization term is also included and, particularly for highly undersampled data, used to interactively improve the reconstruction quality. Each reconstruction is fully self-contained from the profile stream, i.e., the required coil sensitivity profiles, sampling density compensation weights, regularization terms, and noise estimates are computed in real-time from the acquisition data itself. The reconstruction implementation is verified using a steady state free precession (SSFP) pulse sequence and quantitatively evaluated. Three applications are demonstrated; real-time imaging with real-time SENSE 1) or k- t SENSE 2) reconstructions, and 3) offline reconstruction with interactive adjustment of reconstruction settings.},
	Author = {S{\o}rensen, Thomas Sangild and Atkinson, David and Schaeffter, Tobias and Hansen, Michael Schacht},
	Date-Added = {2011-11-20 12:46:25 -0500},
	Date-Modified = {2011-11-20 12:46:25 -0500},
	Doi = {10.1109/TMI.2009.2027118},
	Journal = {IEEE Trans Med Imaging},
	Journal-Full = {IEEE transactions on medical imaging},
	Mesh = {Algorithms; Computer Systems; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Magnetic Resonance Imaging; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted},
	Month = {Dec},
	Number = {12},
	Pages = {1974-85},
	Pmid = {19628452},
	Pst = {ppublish},
	Title = {Real-time reconstruction of sensitivity encoded radial magnetic resonance imaging using a graphics processing unit},
	Volume = {28},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/TMI.2009.2027118}}

@article{Lustig:2007vn,
	Abstract = {The sparsity which is implicit in MR images is exploited to significantly undersample k-space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain-for example, in terms of spatial finite-differences or their wavelet coefficients. According to the recently developed mathematical theory of compressed-sensing, images with a sparse representation can be recovered from randomly undersampled k-space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts due to random undersampling add as noise-like interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference. Incoherence is introduced by pseudo-random variable-density undersampling of phase-encodes. The reconstruction is performed by minimizing the l(1) norm of a transformed image, subject to data fidelity constraints. Examples demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin-echo brain imaging and 3D contrast enhanced angiography.},
	Author = {Lustig, Michael and Donoho, David and Pauly, John M},
	Date-Added = {2011-11-19 17:03:01 -0500},
	Date-Modified = {2011-11-19 17:09:46 -0500},
	Doi = {10.1002/mrm.21391},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Mesh = {Algorithms; Artificial Intelligence; Brain; Data Compression; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Pattern Recognition, Automated; Phantoms, Imaging; Reproducibility of Results; Sensitivity and Specificity},
	Month = {Dec},
	Number = {6},
	Pages = {1182-95},
	Pmid = {17969013},
	Pst = {ppublish},
	Title = {Sparse {MRI}: The application of compressed sensing for rapid {MR} imaging},
	Volume = {58},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/mrm.21391}}

@article{Griswold:2002kx,
	Abstract = {In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique.},
	Author = {Griswold, Mark A and Jakob, Peter M and Heidemann, Robin M and Nittka, Mathias and Jellus, Vladimir and Wang, Jianmin and Kiefer, Berthold and Haase, Axel},
	Date-Added = {2011-11-19 08:49:13 -0500},
	Date-Modified = {2011-11-19 08:55:44 -0500},
	Doi = {10.1002/mrm.10171},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Mesh = {Algorithms; Calibration; Computer Simulation; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Models, Theoretical},
	Month = {Jun},
	Number = {6},
	Pages = {1202-10},
	Pmid = {12111967},
	Pst = {ppublish},
	Title = {Generalized autocalibrating partially parallel acquisitions ({GRAPPA})},
	Volume = {47},
	Year = {2002},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/mrm.10171}}

@article{Pruessmann:1999uq,
	Abstract = {New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced. The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k-space sampling patterns. Special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density. For this case the feasibility of the proposed methods was verified both in vitro and in vivo. Scan time was reduced to one-half using a two-coil array in brain imaging. With an array of five coils double-oblique heart images were obtained in one-third of conventional scan time. Magn Reson Med 42:952-962, 1999.},
	Author = {Pruessmann, K P and Weiger, M and Scheidegger, M B and Boesiger, P},
	Date-Added = {2011-11-19 08:48:45 -0500},
	Date-Modified = {2011-11-19 08:52:15 -0500},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Mesh = {Brain; Fourier Analysis; Heart; Humans; Image Enhancement; Magnetic Resonance Imaging; Models, Theoretical; Phantoms, Imaging; Sensitivity and Specificity},
	Month = {Nov},
	Number = {5},
	Pages = {952-62},
	Pmid = {10542355},
	Pst = {ppublish},
	Title = {{SENSE}: sensitivity encoding for fast {MRI}},
	Volume = {42},
	Year = {1999}}

@article{Sodickson:1997fk,
	Abstract = {SiMultaneous Acquisition of Spatial Harmonics (SMASH) is a new fast-imaging technique that increases MR image acquisition speed by an integer factor over existing fast-imaging methods, without significant sacrifices in spatial resolution or signal-to-noise ratio. Image acquisition time is reduced by exploiting spatial information inherent in the geometry of a surface coil array to substitute for some of the phase encoding usually produced by magnetic field gradients. This allows for partially parallel image acquisitions using many of the existing fast-imaging sequences. Unlike the data combination algorithms of prior proposals for parallel imaging, SMASH reconstruction involves a small set of MR signal combinations prior to Fourier transformation, which can be advantageous for artifact handling and practical implementation. A twofold savings in image acquisition time is demonstrated here using commercial phased array coils on two different MR-imaging systems. Larger time savings factors can be expected for appropriate coil designs.},
	Author = {Sodickson, D K and Manning, W J},
	Date-Added = {2011-11-19 08:48:05 -0500},
	Date-Modified = {2011-11-19 08:52:29 -0500},
	Journal = {Magn Reson Med},
	Journal-Full = {Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine},
	Mesh = {Abdomen; Adult; Artifacts; Brain; Fourier Analysis; Humans; Image Enhancement; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Magnetics; Male; Phantoms, Imaging; Radio Waves; Sensitivity and Specificity; Thorax},
	Month = {Oct},
	Number = {4},
	Pages = {591-603},
	Pmid = {9324327},
	Pst = {ppublish},
	Title = {Simultaneous acquisition of spatial harmonics ({SMASH}): fast imaging with radiofrequency coil arrays},
	Volume = {38},
	Year = {1997}}

@article{MuthuranguRadialKTRadiology,
	Author = {Muthurangu, V. and Lurz, P. and Deanfield, J. and Taylor, A. M. and Hansen, M. S.},
	Date-Added = {2008-01-17 15:07:48 +0000},
	Date-Modified = {2008-01-17 15:10:58 +0000},
	Journal = {Radiology},
	Title = {Real-time assessment of right and left ventricular volumes and function in patients with congenital heart disease using high spatio-temporal resolution radial $k$-$t$ {SENSE}},
	Volume = {in press},
	Year = {2008}}

@article{MSHGpuSenseMRM,
	Author = {Hansen, M. S. and Atkinson, D. and Sorensen, T. S.},
	Date-Added = {2007-12-31 18:14:10 +0000},
	Date-Modified = {2012-03-14 13:14:38 -0400},
	Journal = {Magn Reson Imaging},
	Number = {3},
	Pages = {463-468},
	Title = {Cartesian {SENSE} and $k$-$t$ {SENSE} Reconstruction using Commodity Graphics Hardware},
	Volume = {59},
	Year = {2008}}

@book{lapackug,
	Address = {Philadelphia, PA},
	Author = {Anderson, E. and Bai, Z. and Bischof, C. and Blackford, S. and Demmel, J. and Dongarra, J. and Du Croz, J. and Greenbaum, A. and Hammarling, S. and McKenney, A. and Sorensen, D.},
	Edition = {Third},
	Isbn = {0-89871-447-8 (paperback)},
	Publisher = {Society for Industrial and Applied Mathematics},
	Title = {{LAPACK} Users' Guide},
	Year = {1999}}

@webpage{nsf-blas-2010,
	Added-At = {2010-06-28T20:24:31.000+0200},
	Author = {Foundation, National Science and of Energy, Department},
	Biburl = {http://www.bibsonomy.org/bibtex/2e0ac79bb29f9762a04153ba0f6fda767/mhwombat},
	Date-Modified = {2015-07-23 01:20:14 +0000},
	Groups = {public},
	Howpublished = {http://www.netlib.org/blas/},
	Interhash = {5d0dc36d01cde59d0ceed003e3bc33d1},
	Intrahash = {e0ac79bb29f9762a04153ba0f6fda767},
	Journal = {Netlib Repository at {UTK} and {ORNL}},
	Lastchecked = {2010-06-20 22:47:52},
	Timestamp = {2010-06-28T20:24:31.000+0200},
	Title = {{BLAS}},
	Url = {http://www.netlib.org/blas/},
	Username = {mhwombat},
	Year = 2010,
	Bdsk-Url-1 = {http://www.netlib.org/blas/}}