Parallel Imaging
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Wednesday May 11th
Room 518AC 
16:00  18:00 
Moderators: 
R. Todd Constable and
Richard Otazo 
16:00 
478. 
WaveCAIPIRHINA: a method
for reducing gfactors in highly accelerated 3D acquisitions
Kawin Setsompop^{1,2}, Borjan A Gagoski^{3},
Johnathan Polimeni^{1,2}, and Lawrence L Wald^{1,4}
^{1}Radiology, A. A. Martinos Center for
Biomedical Imaging, MGH, Charlestown, MA, United States, ^{2}Harvard
Medical School, Boston, MA, United States, ^{3}Department
of Electrical Engineering and Computer Science, MIT,
cambridge, ma, United States, ^{4}HarvardMIT
Division of Health Sciences and Technology, MIT,
cambridge, ma, United States
Recent modifications to standard rectilinear 3D kspace
sampling trajectories have provided more robust parallel
imaging based reconstructions of highly undersampled
datasets. Here, we introduce waveCAIPIRINHA acquisition
which combines 2DCAIPIRINHA with BPE in two PE
directions, and demonstrated its associated low gfactor
penalty for highly accelerated acquisitions (Gmax=1.25
for an R=3x3 acquisition). For the reconstruction, we
propose an algorithm based on generalized SENSE but
perform in a pseudoimage domain (without gridding).
This technique can be thought of as a way to sparsify
the encoding matrix of waveCAIPIRINHA to allow it to be
divided into many small decoupled systems.

16:12 
479. 
An EigenVector Approach
to AutoCalibrating Parallel MRI, Where SENSE Meets GRAPPA
Michael Lustig^{1}, Peng Lai^{2}, Mark
Murphy^{1}, Shreyas Mark Vasanawala^{3},
Michael Elad^{4}, Jian Zhang^{5,6}, and
John Pauly^{6}
^{1}Electrical Engineering and Computer Science,
University of California Berkeley, Berkeley, CA, United
States, ^{2}ASL
West, GE Healthcare, Menlo Park, CA, United States,^{3}Radiology,
Stanford University, Stanford, CA, United States, ^{4}Computer
Science, Technion IIT, Haifa, Israel, ^{5}GE
Healthcare, ^{6}Electrical
Engineering, Stanford University, Stanford, CA, United
States
Parallel imaging techniques can be categorized roughly
into two families: explicit sensitivity based methods
like SENSE and autocalibrating methods (acPI) like
GRAPPA. In this work we finally bridge the gap between
these approaches. We present a new way to compute the
explicit sensitivity maps that are (implicitly) used by
acPI methods. These are found by Eigenvector analysis
of the kspace filtering in acPI algorithms. Our Eigen
approach performs like other acPI methods when the
prescribed FOV is smaller than the object, i.e., is not
susceptible as SENSE to FOV limitations. At the same
time, the reconstruction performs optimal calibration
and optimal reconstruction, as SENSE. Our approach can
be used to find the explicit sensitivity maps of any
acPI method from its kspace kernels.

16:24 
480. 
Multidimensional encoded
(MDE) magnetic resonance imaging
FaHsuan Lin^{1,2}, Thomas Witzel^{2},
Aapo Nummenmaa^{2,3}, Panu Vesanen^{3},
Risto J. Ilmoniemi^{3}, and John W. Belliveau^{2}
^{1}National Taiwan University, Taipei, Taiwan, ^{2}Martinos
Center, Massachusetts General Hospital, Charlestown, MA,
United States, ^{3}Department
of Biomedical Engineering and Computational Science
(BECS), Aalto University, Espoo, Finland
We propose the multidimensional encoded (MDE) MRI using
overcomplete spatial bases to achieve efficient
encoding and image reconstructions. Different from
traditional MRI using ndimensional kspace to encode an
ndimensional object, MDE suggests encoding an
ndimensional object by a pdimensional encoding space
(p > n) using spatial bases generated by the combination
of different spatial encoding magnetic fields and RF
sensitivity profiles. Preliminary results using
simultaneous multipolar SEMs in the PatLoc system and
the Ospace imaging with different center placements
(CPs) indicates the potential of further optimizing MRI
bases for a higher spatiotemporal resolution.

16:36 
481. 
KSpace Based Image
Reconstruction of MRI Data Encoded with Ambiguous Gradient
Fields
Gerrit Schultz^{1}, Daniel Gallichan^{1},
Hans Weber^{1}, Walter Witschey^{1},
Matthias Honal^{1}, Jürgen Hennig^{1},
and Maxim Zaitsev^{1}
^{1}University Medical Center Freiburg,
Freiburg, Germany
In parallel imaging, acquisition is usually accelerated
by omitting kspace lines resulting in aliased images. A
similar effect occurs when ambiguous encoding fields are
applied instead of the standard linear gradient fields.
Highly aliased images are produced when ambiguous field
encoding is combined with kspace acceleration. In this
case, calibration lines can only be acquired to
partially unfold the image. Whereas in SENSE the
aliasing artifacts from field ambiguities and from
undersampling cannot be treated separately, we show that
this is fundamentally different with kspace based
methods like GRAPPA. This interesting property of GRAPPA
is essential for kspace based image reconstructions
from acquisitions based on ambiguous field encoding.

16:48 
482. 
A performance measure for
MRI with nonlinear encoding fields
Kelvin Layton^{1,2}, Mark Morelande^{1},
Peter Mark Farrell^{1}, Bill Moran^{1},
and Leigh Andrea Johnston^{1,3}
^{1}Electrical and Electronic Engineering, The
University of Melbourne, Melbourne, Australia, ^{2}NICTA
Victorian Research Laboratory, Melbourne, Australia, ^{3}Howard
Florey Institute, Australia
Magnetic fields that varying nonlinearly across the
fieldofview have recently been employed in parallel
imaging to develop novel encoding schemes such as PatLoc
and OSpace. A result of nonlinear encoding is that the
quality of the image will vary across pixels. Since
PatLoc satisfies certain properties, an expression for
the spatially varying SNR can be derived analytically.
However, no such expression is available for other
schemes that are fundamentally different to PatLoc, such
as OSpace imaging. In this work, we develop a simple
metric to quantify the spatially varying performance,
which is computationally efficient and applicable to
arbitrary encoding schemes.

17:00 
483. 
PostCartesian
Calibrationless Parallel Imaging Reconstruction by
Structured LowRank Matrix Completion
Michael Lustig^{1}
^{1}Electrical Engineering and Computer Science,
University of California Berkeley, Berkeley, CA, United
States
An autocalibrating postCartesian parallel imaging
method is presented. It is based on structured, lowrank
matrix completion which is an extension of compressed
sensing to Matrices. The method does not require a fully
sampled autocalibration area in kspace. Instead it
jointly calibrates and reconstructs the signal from the
undersampled data alone. Results using spiral sampling
are demonstrated showing similarly good reconstruction
compared to method that use explicit calibration data.

17:12 
484. 
Rapid, Selfcalibrated
Parallel Reconstruction for Variable Density Spiral with
GROWL
Wei Lin^{1}, Peter Börnert^{2}, Feng
Huang^{1}, George R Duensing^{1}, and
Arne Reykowski^{1}
^{1}Invivo Corporation, Philips Healthcare,
Gainesville, FL, United States, ^{2}Philips
Research Europe, Hamburg, Germany
A rapid and selfcalibrated parallel imaging
reconstruction method is proposed for undersampled
variable density spiral datasets. A set of Generalized
GRAPPA for wider readout line (GROWL) operators are used
to expand each acquired spiral line into a wider spiral
band, therefore fulfilling Nyquist sampling criterion
throughout the kspace. The calibration of GROWL
operators is performed using the fully sampled central
kspace region. In vivo brain scans demonstrate that the
technique can be used either to achieve a significant
acceleration and/or to reduce offresonance artifacts
due to shortened readout duration.

17:24 
485. 
Parallel Imaging with
Nonlinear Reconstruction using Variational Penalties
Florian Knoll^{1}, Christian Clason^{2},
Kristian Bredies^{2}, Martin Uecker^{3},
and Rudolf Stollberger^{1}
^{1}Institute of Medical Engineering, Graz
University of Technology, Graz, Austria, ^{2}Institute
for Mathematics and Scientific Computing, University of
Graz, Graz, Austria,^{3}Biomedizinische NMR
Forschungs GmbH, MaxPlanckInstitut fuer
biophysikalische Chemie, Goettingen, Germany
Nonlinear inversion was recently proposed for
autocalibrated parallel imaging and shown to yield
improved reconstruction quality. In addition, it has
been shown that the aliasing arising from certain
undersampled trajectories can be removed when using
additional prior knowledge about the structure of the
solution. Nonlinear inversion can be applied to
arbitrary sampling trajectories, but the latter option
was not yet exploited for this algorithm. In this work,
it is demonstrated that nonlinear inversion can be
extended with regularization terms that make use of such
prior knowledge. The presented algorithms make use of
the iteratively regularized GaussNewton method with
additional variational constraints of total variation
and total generalized variation type. Experimental
results are presented for phantom and invivo
measurements of undersampled radial and pseudorandom
trajectories. The proposed approach yields results with
reduced noise and undersampling artifacts in all cases
when compared to conventional reconstruction with
nonlinear inversion employing standard quadratic
constraints.

17:36 
486. 
Iterative selfconsistent
magnetic resonance inverse imaging
TsungMin Huang^{1}, Thomas Witzel^{2},
WenJui Kuo^{3}, and FaHsuan Lin^{1,2}
^{1}Institute of Biomedical Engineering,
National Taiwan University, Taipei, Taiwan, ^{2}Martinos
Center, Massachusetts General Hospital, Charlestown, MA,
United States,^{3}Institute of Neuroscience,
National YangMing University, Taipei, Taiwan
Dynamic magnetic resonance inverse imaging offers an
unpredicted temporal resolution for BOLD fMRI by trading
off the spatial resolution. Previously, we found that
kspace InI (KInI) reconstruction provides a higher
spatial resolution compared with the image domain method
based on the GRAPPA formulation. Here we hypothesize
that the using SPIRiT (iterative selfconsistent
parallel imaging reconstruction) reconstruction can
further improve KInI reconstruction by adding a
constraint to ensure data consistency in kspace.
Preliminary results show that SPIRiT InI has a higher
spatial resolution and comparable detection power to KInI
in BOLD fMRI measurements.

17:48 
487. 
Derivative Encoding for
Parallel Imaging
Jun Shen^{1}
^{1}NIMH, Bethesda, Maryland, United States
A novel relationship between the partial derivatives of
k space signal acquired using multichannel receive coils
is described. It was found that the partial derivatives
of the k space signal from one coil with respect to one
direction can be expressed as a sum of partial
derivatives of signals from multiple coils with respect
to the perpendicular k space direction(s). Applications
of this partial derivatives relationship to parallel
imaging reconstruction in both k space and image domains
are demonstrated.

