10:30 
544. 
Fast MR
Parameter Mapping Using KT PCA
Frederike Hermi Petzschner^{1,2}, Irene Paola Garcia
Ponce^{3}, Martin Blaimer^{4}, Peter M.
Jakob^{3}, Felix A. Breuer^{4
1}LudwigMaximilians University,
Institute of Clinical Neurosciences, Munich, Bavaria,
Germany; ^{2}Bernstein Center for Computational
Neurosciences, Munich, Germany, Germany; ^{3}University
of Würzburg, Experimental Physics 5, Germany; ^{4}Research
Center Magnetic Resonance Bavaria, Germany
In this work, kt PCA is
demonstrated to be a promising acceleration technique for MR
relaxation measurements, since the dynamics along the
relaxation curve can be described by only a small number of
principal components. Invivo IRTrueFISP experiments for
quantitative T1, T2 & M0 parameter mapping acquired with up
to 8fold acceleration by using the kt PCA concept are
presented. 



10:42 
545. 
kT Group
Sparse Reconstruction Method for Dynamic Compressed MRI
Muhammad Usman^{1}, Claudia Prieto^{1},
Tobias Schaeffter^{1}, Philip G. Batchelor^{1
1}King's College London, London,
United Kingdom
Up to now, besides sparsity,
the standard compressed sensing methods used in MR do not
exploit any other prior information about the underlying
signal. In general, the MR data in its sparse representation
always exhibits some structure. As an example, for dynamic
cardiac MR data, the signal support in its sparse
representation (xf space) is always in compact form. In
this work, exploiting the structural properties of sparse
representation, we propose a new formulation titled ‘kt
group sparse compressed sensing’. This formulation
introduces a constraint that forces a group structure in
sparse representation of the reconstructed signal. The kt
group sparse reconstruction achieves much higher temporal
and spatial resolution than the standard L1 method at high
acceleration factors (9fold acceleration). 



10:54 
546. 
Parallel
Imaging Technique Using Localized Gradients (PatLoc)
Reconstruction Using Compressed Sensing (CS)
FaHsuan Lin^{1}, Panu Vesanen^{2}, Thomas
Witzel, Risto Ilmoniemi, Juergen Hennig^{3
1}A. A. Martinos
Center, Charlestown, MA, United States; ^{2}Helsinki
University of Technology, Helsinki, Finland; ^{3}University
Hospital Freiburg, Freiburg, Germany
The parallel imaging
technique using localized gradients (PatLoc) system has the
degree of freedom to encode spatial information using
multiple surface gradient coils. Previous PatLoc
reconstructions focused on acquisitions at moderate
accelerations. Compressed sensing (CS) is the emerging
theory to achieve imaging acceleration beyond the Nyquist
limit if the image has a sparse representation and the data
can be acquired randomly and reconstructed nonlinearly. Here
we apply CS to PatLoc image reconstruction to achieve
further accelerated image reconstruction. Specifically, we
compare the reconstructions between PatLoc and traditional
linear gradient systems at acceleration rates in an
underdetermined system. 



11:06 
547. 
Designing
KSpace Trajectories for Simultaneous Encoding with Linear
and PatLoc Gradients
Daniel
Gallichan^{1}, Gerrit Schultz^{1}, Jürgen
Hennig^{1}, Maxim Zaitsev^{1}
^{1}University
Hospital Freiburg, Freiburg, Germany
Recent work has shown that MR
imaging can be performed using nonlinear encoding gradients
(PatLoc). Here we investigate the possibilities of combining
nonlinear encoding gradients with simultaneous use of the
conventional linear gradients. We introduce the concept of a
'local kspace' to compare trajectories, as well as
presenting a combination of a splitradial 4D trajectory
which is able to exploit the advantages of varying spatial
resolution across the FoV whilst retaining control over the
resolution in the centre. 



11:18 
548. 
A
TimeEfficient SubSampling Strategy to Homogenise
Resolution in PatLoc Imaging
Hans
Weber^{1}, Daniel Gallichan^{1}, Gerrit
Schultz^{1}, Jürgen Hennig^{1}, Maxim
Zaitsev^{1
1}University Hospital Freiburg,
Dept. of Diagnostic Radiology, Medical Physics, Freiburg,
Germany
Varying spatial resolution is
one of the characteristic properties of MR imaging when
using nonlinear gradient fields for spatial encoding, as
realised by PatLoc. In the particular configuration of two
orthogonal quadrupolar encoding fields, voxel size is
inversely proportional to the distance to the FOV centre. In
this work we present an iterative reconstruction method for
subsampled PatLoc data that improves the local resolution
at the centre and leads to shorter scan times for equivalent
central resolution recovery. The method is demonstrated on
simulated and experimentally acquired data. 



11:30 
549. 
An
Assessment of OSpace Imaging Robustness to Local Field
Inhomogeneities
Jason P. Stockmann^{1},
R. Todd Constable^{2}
^{1}Biomedical
Engineering, Yale University, New Haven, CT, United States;
^{2}Diagnostic Radiology, Neurosurgery, and
Biomedical Engineering, Yale University, New Haven, CT,
United States
OSpace imaging permits
highlyaccelerated acquisitions using nonlinear gradients
to extract extra spatial encoding from surface coil profiles
as compared with linear gradients. For accurate
reconstruction to occur, however, the curvilinear frequency
contours created by the gradients must intersect one another
at the appropriate locations, making the technique
potentially vulnerable to local field inhomogeneity, such as
the susceptibility gradients arising in the head near the
sinuses. This work shows that with appropriate
regularization, OSpace imaging is robust to typical levels
of field inhomogeneity. Field inhomogeneity is shown to
manifest itself as noiselike artifacts throughout the FOV
rather than gross geometric distortion. 



11:42 
550. 
Highly
Accelerated Multislice Parallel Imaging: Cartesian Vs Radial
Stephen R. Yutzy^{1},
Nicole Seiberlich^{2}, Jeffrey L. Duerk^{1,2},
Mark A. Griswold^{2}
^{1}Biomedical
Engineering, Case Western Reserve University, Cleveland, OH,
United States; ^{2}Radiology, University Hospitals
of Cleveland and Case Western Reserve University, Cleveland,
OH, United States
Multiband imaging allows for
multiple simultaneously acquired slices, thus giving an SNR
benefit over conventional slice selection without potential
artifacts from secondary phase encoding. While methods have
been shown that can separate the slides using parallel
imaging for Cartesian trajectories, these methods are not
compatible with nonCartesian sampling. Here we demonstrate
the possibility of reconstructing two simultaneously
acquired radial slices using an acquisition/reconstruction
method known as radial CAIPIRINHA. We show that this method
is capable of higher accelerations than possible with
comparable Cartesian trajectories. 



11:54 
551. 
Blipped
CAIPIRHINA for Simultaneous MultiSlice EPI with Reduced
GFactor Penalty
Kawin Setsompop^{1,2},
B. A. Gagoski^{3}, J. Polimeni^{1,2}, T. Witzel^{1},
V. J. Wedeen^{1,2}, L. L. Wald^{1,2}
^{1}Radiology, A.
A. Martinos Center for Biomedical Imaging, Massachusetts
General Hospital, Charlestown, MA, United States; ^{2}Harvard
Medical School, Boston, MA, United States; ^{3}EECS,
Massachusetts Institute of Technology, Cambridge, MA, United
States
The acquisition of
simultaneous slices in EPI has the potential to increase the
temporal sampling rate of fMRI or the number of diffusion
directions obtained per unit time in diffusion imaging. In
this work, we introduced a blipped CAIPIRINHA technique
applicable to EPI acquisition and demonstrated its
associated low gfactor penalty and 3x acceleration of the
slices per second of acquisition. 3x sliceaccelerated SEEPI
was acquired with retain SNR of close to unity. The 3x
blipped CAIPIRINHA was also combined with 2x Simultaneous
Image Refocusing (SIR) acquisition to create 6 simultaneous
multislice GEEPI acquisition with low gfactor penalty. 



12:06 
552. 
SNR
Quantification with PhasedArray Coils and Parallel Imaging
for 3DFSE
Charles Qingchuan Li^{1},
Weitian Chen^{2}, Philip J. Beatty^{2}, Anja
C. Brau^{2}, Brian A. Hargreaves^{1}, Reed
F. Busse^{3}, Garry E. Gold^{1}
^{1}Radiology,
Stanford University, Stanford, CA, United States; ^{2}Global
Applied Science Laboratory, GE Healthcare, Menlo Park, CA,
United States; ^{3}Global Applied Science
Laboratory, GE Healthcare, Madison, WI, United States
Current clinical MRI
techniques often employ parallel imaging, partial Fourier
and multicoil acquisition to decrease scan time while
maintaining image quality. To aid in image quality
assessment, image noise statistics can be measured by
reconstructing noiseonly acquisitions through an identical
linear pipeline as signal data, which may involve signal
datadependent steps such as parallel imaging, partial
Fourier homodyne and multichannel reconstructions. In this
study it was shown that SNR and CNR measurements performed
in 146 clinical knee MRIs using this quantification method
significantly differ from the measurements obtained using
the traditional foreground and background volume of interest
approach. 



12:18 
553. 
A
Mathematical Model Toward Quantitative Assessment of
Parallel Imaging Reconstruction
Yu Li^{1}, Feng
Huang^{1}, Wei Lin^{1}, Arne Reykowski^{1}
^{1}Advanced
Concept Development, Invivo Diagnostic Imaging, Gainesville,
FL, United States
In this work, we propose a
mathematical model that gives explicit representations for
three different types of errors in parallel imaging
reconstruction. These errors have different patterns in
image space and affect the image quality in different
fashions. This model offers a tool to extensively
investigate how to quantitatively assess imaging quality
beyond signal to noise ratio. Based on the proposed model,
practical reconstruction techniques can be developed to
suppress three types of errors to different degrees for
improved overall imaging performance. 



