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Poster Sessions<br />

accelerated parallel imaging is affected by residual artifacts, while excessive zooming sacrifices spatial coverage. A robust combination of both methods is<br />

optimized here (‘Zoomed imaging with GRAPPA’ - ZOOPPA) to provide high quality single-shot EPI human brain images with reasonable coverage and an<br />

isotropic resolution of 0.65 mm.<br />

2890. Conjugate Gradient PINOT Reconstruction with a Fast Initial Estimate<br />

Lei Hou Hamilton 1 , Benjamin Russell Hamilton 1 , David Moratal 2 , Senthil Ramamurthy 3 , Marijn Brummer 3<br />

1 School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States; 2 Universitat Politècnica<br />

de València, València, Spain; 3 Emory University, United States<br />

PINOT (Parallel Imaging and NOquist in Tandem), a fast imaging method combining SPACE-RIP and Noquist, favorably preserves edge detail at a cost of<br />

increased SNR. PINOT involves a large matrix inversion for each read-out coordinate to combine data from all frames and coils. We use iterative conjugate<br />

gradient (CG) to reduce this computational burden. An initial estimate based on the projection matrix’s structure allows CG-PINOT to converge quickly. We<br />

simulate this CG-initiated PINOT (CGi-PINOT) with phantom and in vivo studies, showing it provides better reconstructed image quality with an order of<br />

magnitude less time than direct inversion PINOT.<br />

2891. Computationally Rapid Method for Estimating SNR of Arbitrary Parallel MRI Reconstructions<br />

Curtis Nathan Wiens 1 , Shawn Joseph Kisch 2 , Jacob David Willig-Onwuachi 3 , Charles A. McKenzie 1,2<br />

1 Department of Physics and Astronomy, University of Western Ontario, London, Ontario, Canada; 2 Department of Medical<br />

Biophysics, University of Western Ontario, London, Ontario, Canada; 3 Department of Physics, Grinnell College, Grinnell, IA, United<br />

States<br />

Existing approaches for measuring parallel MRI SNR are limited because they are not applicable to all reconstructions, require significant computation time<br />

or need repeated image acquisitions. A new SNR estimation approach is proposed that is a hybrid of the two acquisition and multiple pseudo replica<br />

methods. The difference of two pseudo-images is used to estimate the noise in the acquisition. This gives a computationally rapid method of measuring SNR<br />

from a single acquisition. SNR maps using the two pseudo-image method were compared to pseudo-replica. All tests of the proposed method were on<br />

average within ±1.75%.<br />

2892. Virtual Coil Phase Determination Using Region Growing: Description and Application to Direct<br />

Virtual Coil Parallel Imaging Reconstruction<br />

Philip James Beatty 1 , Shaorong Chang 2 , Ersin Bayram 2 , Ananth Madhuranthakam 3 , Huanzhou Yu 1 , Scott<br />

B. Reeder 4 , Jean H. Brittain 5<br />

1 Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States; 2 GE Healthcare, Waukesha, WI, United States;<br />

3 Applied Science Laboratory, GE Healthcare, Boston, MA, United States; 4 Departments of Radiology and Medical Physics,<br />

University of Wisconsin-Madison, Madison, WI, United States; 5 Applied Science Laboratory, GE Healthcare, Madison, WI, United<br />

States<br />

Setting the phase of the virtual coil in the Direct Virtual Coil (DVC) reconstruction technique is both critical to achieving a high quality reconstruction and<br />

challenging, especially with high channel count arrays. In this work, a region growing approach to setting the virtual coil phase is described and evaluated in<br />

the context of the DVC technique. We demonstrate that the approach is able to generate sensible virtual coil phase even in challenging situations.<br />

2893. Random Phase Modulation of Spatial Aliasing in Temporal Domain for Dynamic MRI<br />

Yu Li 1 , Feng Huang 1 , Wei Lin 1 , Arne Reykowski 1<br />

1 Advanced Concept Development, Invivo Diagnostic Imaging, Gainesville, FL, United States<br />

In this study, we propose a new k-t space sampling trajectory for parallel dynamic MRI. This method applies random phase modulation to the spatial aliasing<br />

of images in temporal domain. As a result, the spatial aliasing induced by k-space undersampling at every time frame has a noise pattern in temporal<br />

dimension. By applying a temporal constraint that can be known from the priori knowledge of dynamic MRI data, the noise-like aliasing can be easily<br />

removed. This work uses the fMRI and cardiac imaging applications as examples to demonstrate the feasibility of the proposed method.<br />

2894. Rapid 3D Parallel Imaging of Non-Cartesian Data<br />

Nicholas Ryan Zwart 1 , James Grant Pipe 1<br />

1 Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, AZ, United States<br />

A 3D parallel imaging reconstruction technique is presented. This technique is a coil sensitivity based method used for reconstructing undersampled<br />

arbitrary 3D k-space trajectories. Iterations enforce receive b1-field and sampled data consistency without degridding/gridding operations improving the<br />

computational speed compared to similar reconstruction methods. The 3D trajectory used is Spiral Projection Imaging.<br />

2895. Improvement of Quantitative MRI Using Radial GRAPPA in Conjunction with IR-TrueFISP<br />

Martin Kunth 1 , Nicole Seiberlich 2 , Philipp Ehses 1 , Vikas Gulani 2 , Mark Griswold 2<br />

1 Experimentelle Physik V, Universitaet Wuerzburg, Wuerzburg, Germany; 2 Radiology, Case Western Reserve University, Cleveland,<br />

OH, United States<br />

While the use of IR-TrueFISP to quantify the relaxation parameters T1 and T2 and the proton density M0 has been demonstrated, these values can be<br />

difficult to quantify in species with fast relaxation because the first points along the relaxation curve are hard to assess. This abstract explores the use of the<br />

recently proposed technique through-time radial GRAPPA to reconstruct highly undersampled radial images acquired along the relaxation curve. In this<br />

way, the first few points after the inversion can be assessed and the relaxation parameters more accurately quantified.

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