TRADITIONAL POSTER - ismrm

Poster Sessions
is possible to construct an adapted random sampling pattern by using measured k-space data as a reference, which automatically ensures an appropriate
distribution of sample points for different types of scans. In this work, these sampling patterns were used in combination with regularized nonlinear inversion
for parallel imaging. This allows the use of very high acceleration factors while still yielding images with excellent image quality.
2877. Fast Non-Iterative JSENSE: From Minutes to a Few Seconds
Feng Huang 1 , Wei Lin 1 , Yu Li 1 , Arne Reykowski 1
1 Invivo Corporation, Gainesville, FL, United States
It has been shown that joint image reconstruction and sensitivity estimation in SENSE (JSENSE) can improve image reconstruction quality when
acceleration factor is high. However, existing methods for JSENSE need long reconstruction time and/or optimal termination condition, which have hindered
its clinical applicability. In this work, a fast non-iterative JSENSE technique, based on pseudo full k-space, is proposed to improve the clinical applicability
of JSENSE. Using the proposed method, the computation time for sensitivity maps could be reduced from minutes to a few seconds without degrading the
image quality.
2878. Parallel Imaging Using a 3D Stack-Of-Rings Trajectory
Holden H. Wu 1,2 , Michael Lustig 2,3 , Dwight G. Nishimura 2
1 Cardiovascular Medicine, Stanford University, Stanford, CA, United States; 2 Electrical Engineering, Stanford University, Stanford,
CA, United States; 3 Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, United States
We present an efficient parallel imaging strategy for the 3D stack-of-rings non-Cartesian trajectory to further enhance its flexible trade-offs between image
quality and scan time. Due to its distinct geometry, parallel imaging reconstruction for the 3D stack-of-rings trajectory can be decomposed directly into a
series of 2D Cartesian sub-problems, which can be solved very efficiently. Experimental results demonstrate that a 2-fold reduction in scan time can be
achieved on top of the 2-fold speedup already offered by the rings (compared to Cartesian encoding). Our approach combines the acceleration from both
non-Cartesian sampling and parallel imaging in an efficient and easily deployable algorithm.
2879. Coil-By-Coil Vs. Direct Virtual Coil (DVC) Parallel Imaging Reconstruction: An Image Quality
Comparison for Contrast-Enhanced Liver Imaging
Philip James Beatty 1 , James H. Holmes 2 , Shaorong Chang, Ersin Bayram, Jean H. Brittain 3 , Scott B.
Reeder 4
1 Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States; 2 Applied Science Laboratory, GE Healthcare,
Waukesha, WI, United States; 3 Applied Science Laboratory, GE Healthcare, Madison, WI, United States; 4 Departments of Radiology
and Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
Compared to coil-by-coil reconstructions, Direct Virtual Coil (DVC) parallel imaging reconstructions improve computational efficiency for high channel
count coil arrays by only synthesizing unacquired data for one virtual coil instead of synthesizing a separate dataset for each physical coil. In this study,
image quality is compared between coil-by-coil and DVC parallel imaging reconstructions in the context of contrast-enhanced liver imaging. Results
showed no significant difference in the image quality achieved by the two reconstruction methods.
2880. Towards a Geometry Factor for Projection Imaging with Non-Linear Gradient Fields
Jason P. Stockmann 1 , Gigi Galiana 2 , Robert Todd Constable 3
1 Biomedical Engineering, Yale University, New Haven, CT, United States; 2 Diagnostic Radiology, Yale University, New Haven, CT,
United States; 3 Diagnostic Radiology, Neurosurgery, and Biomedical Engineering, Yale University, New Haven, CT, United States
Conventional parallel imaging performance is assessed either by computing the analytical geometry factor or, if necessary, comparing the SNRs of fullysampled
and undersampled Monte Carlo reconstructions. The empirical g-factor is unsuitable, however, for methods such as O-Space imaging in which
non-linear gradients are used to obtain projections of the object. Since O-Space point spread functions are highly variable with position, the g-factor must
be corrected for voxel-size in order to distinguish intra-voxel blurring from true noise amplification. This work shows the limited utility of uncorrected
empirical g-factors for O-Space imaging and discusses how to compute the PSF for this class of non-linear projection imaging methods.
2881. Selection of Image Support Region and of an Improved Regularization for Non-Cartesian SENSE
Yoon Chung Kim 1 , Jeffrey Fessler 2 , Douglas Noll 1
1 Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States; 2 Electrical Engineering, University of Michigan,
Ann Arbor, MI, United States
Even though non-Cartesian parallel imaging has demonstrated increasing potential for an acquisition tool in MRI, there are still drawbacks such as reduced
SNR and incomplete suppression of the undersampling or aliasing artifact. In suppressing such artifacts, the selection of image support, specifying a
reconstruction region of interest is an important factor, due to the complex aliasing pattern associated with undersampling. Proper selection of image support
can improve the conditioning of the reconstruction by constraining regions that are known to be zero. In this study, we investigate how the selection of
image support region affects the performance of non-Cartesian SENSE reconstruction applied to undersampled spiral k-space data. Considering a potential
effect of the sharp edges of a conventional mask on aliasing artifact, we also applied a smoothed mask through an additional regularized term to give
smoothness to the mask edges. We tested our hypotheses on masking effects with the simulation and in-vivo human data and our results show that using a
moderate size of mask can improve the image quality and the smoothing the mask is effective in suppressing aliasing artifact. Functional MRI result also
indicates that softening function further increases the number of activated pixels and tSNR, and reduces image domain error.
2882. Variable-Density Parallel Imaging with Partially Localized Coil Sensitivities
Tolga Çukur 1 , Juan Santos 1 , John Pauly 1 , Dwight Nishimura 1
1 Department of Electrical Engineering, Stanford University, Stanford, CA, United States
PILS is a very fast reconstruction method for both Cartesian and non-Cartesian sampling; however, it can suffer from residual aliasing artifacts when
coupled with variable-density acquisitions. In this work, we propose an improved variable-FOV method that suppresses the aliasing artifacts, while