| CS++: Compressed Sensing & Beyond |
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Tuesday 21 April 2009 |
| Room 313BC |
16:00-18:00 |
Moderators: |
Pablo Irarrazaval and Krishna S. Nayak |
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16:00 |
377. |
Accelerating SENSE Using
Distributed Compressed Sensing |
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Dong Liang1,
Kevin f. King2, Bo Liu3,
Leslie Ying1
1Dept. of Electrical Engineering and Computer
Science, Univ. of Wisconsin-Milwaukee, Milwaukee,
WI, USA; 2Global Applied Science Lab, GE
Healthcare, Waukesha, WI, USA; 3MR
Engineering, GE Healthcare, Waukesha, WI, USA |
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Most existing methods
apply compressed sensing (CS) to parallel MRI as a
regularized SENSE reconstruction, where the
regularization function is the L1 norm of the sparse
representation. However, the CS conditions such as
incoherence are not necessarily satisfied. To
address the issue, a method is proposed which first
reconstructs a set of aliased images from all
channels simultaneously using distributed CS (DCS),
and then the final image using Cartesian SENSE. The
results on a set of eight-channel data show that the
proposed method is able to achieve a higher
reduction factor than the existing methods. |
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16:12 |
378. |
Distributed Compressed Sensing
for Accelerated MRI |
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Ricardo Otazo1,
Daniel K. Sodickson1
1Center for Biomedical Imaging, NYU School of
Medicine, New York, NY, USA |
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A framework for
combining parallel imaging with compressed sensing
is presented using the theory of distributed
compressed sensing which extends compressed sensing
to multiple sensors using the principle of joint
sparsity. We present a greedy reconstruction
algorithm named JOMP (Joint Orthogonal Matching
Pursuit) that uses intra- and inter-coil
correlations to jointly sparsify the multi-coil
image instead of sparsifying the individual images.
We show that for a sufficient number of coils, the
number of measurements required by JOMP-PMRI to
reconstruct a truly sparse image is very close to
the image sparsity level. The performance of
JOMP-PMRI with compressible images is assessed with
a simulated brain image to show feasibility of
higher accelerations with increasing number of
coils. |
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16:24 |
379. |
L1 SPIR-IT:
Autocalibrating Parallel Imaging Compressed Sensing |
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Michael Lustig1,
Marcus Alley2, Shreyas Vasanawala2,
David L. Donoho3, John Mark Pauly1
1Electrical Engineering, Stanford University,
Stanford, CA, USA; 2Radiology, Stanford
University; 3Statistics, Stanford
University |
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A detailed approach of
combining auto-calibrating parallel imaging (acPI)
with compressed sensing (CS) is presented. The
acquisition and the reconstruction are carefully
optimized to meet the requirements of both methods
in order to achieve highly accelerated robust
reconstructions. Poisson-disc sampling distribution
is used to achieve the required incoherency for CS
and uniform density for acPI. A novel L1-wavelet
penalized, iterative reconstruction (L1 SPIR-iT) is
used to enforce consistency with the calibration and
data acquisition, and in addition, joint sparsity of
the reconstructed coil images. High quality in
vivo, 5-fold accelerated reconstruction using
only 4 coils is demonstrated. |
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16:36 |
380. |
L1-Norm Regularization of Coil
Sensitivities in Non-Linear Parallel Imaging
Reconstruction |
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Carlos
Fernández-Granda1,2, Julien Sénégas3
1École des Mines, Paris, France; 2Universidad
Politécnica de Madrid, Spain; 3Philips
Research Europe, Hamburg, Germany |
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Joint estimation of the
coil sensitivities and the image in parallel imaging
can suppress aliasing more effectively than methods
based on low-resolution sensitivity estimates. We
propose a joint estimation approach related to
Compressed Sensing that exploits the sparsity of the
coil sensitivities in k-space and in a base of
Chebyshev polynomials within a greedy scheme to
solve the ill-posed reconstruction problem. In
vivo data reconstructions are presented and
compared to results obtained with Generalized SENSE
and Joint SENSE. |
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16:48 |
381. |
SPArse Reconstruction Using a
ColLEction of Bases (SPARCLE) |
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Ali Bilgin1,2,
Onur Guleryuz3, Theodore P. Trouard2,4,
Maria I. Altbach2
1Electrical and Computer Engineering,
University of Arizona, Tucson, AZ, USA; 2Dept.
of Radiology, University of Arizona, Tucson, AZ,
USA; 3Dept. of Electrical Engineering,
Polytechnic Institute of NYU, Brooklyn, NY, USA;
4Biomedical Engineering, University of
Arizona, Tucson, AZ, USA |
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We introduce a new
sparse reconstruction framework where sparsity is
enforced in a collection of bases rather than a
single one. Results indicate that this new framework
yields significantly improved reconstruction
quality. |
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17:00 |
382. |
Ultra-High Resolution 3D Upper
Airway MRI with Compressed Sensing and Parallel
Imaging |
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Yoon-Chul Kim1,
Shrikanth S. Narayanan1, Krishna S. Nayak1
1Ming Hsieh Department of Electrical
Engineering, University of Southern California, Los
Angeles, CA, USA |
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Ultra-high resolution 3D
imaging of the vocal tract can provide insight into
the shaping that occurs during complex speech
articulation. The combined use of compressed sensing
(CS) and parallel imaging is investigated to
maximize spatial resolution while maintaining
scan-time appropriate for a single sound production
task (~7 seconds). Compared to conventional
reconstructions, boundary depiction was improved by
using high-resolution phase constraints, sensitivity
encoding, and regularization based on total
variation and the l1-norm of the wavelet transform.
Eight-fold acceleration was achieved leading to
1.33x1.33x1.33 mm3 resolution and
7-second scan time. |
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17:12 |
383. |
Highly-Accelerated First-Pass
Cardiac Perfusion MRI Using Compressed Sensing and
Parallel Imaging |
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Ricardo Otazo1,
Daniel Kim1, Daniel K. Sodickson1
1Center for Biomedical Imaging, NYU School of
Medicine, New York, NY, USA |
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Compressed sensing and
parallel imaging are combined into a single joint
reconstruction paradigm named k-t Parallel-Sparse
for highly accelerated first pass cardiac perfusion
imaging. The method exploits the joint sparsity in
the sensitivity-encoded images to achieve higher
accelerations than for coil-by-coil sparsity alone,
and it does not require dynamic training data. We
demonstrate the feasibility of high in vivo
acceleration factors of 8 and 12 and assess the
effect of respiratory motion. |
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17:24 |
384. |
Motion Estimated and
Compensated Compressive Sensing Dynamic MRI Under
Field Inhomogeneity |
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Hong Jung1,
Jaeseok Park2, Jong Chul Ye3
1KAIST, Daejon, Korea; 2Yonsei
Univ. medical center, Korea; 3KAIST,
Korea |
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Recently, we proposed a
compressed sensing dynamic MR technique called k-t
FOCUSS that extends the conventional k-t BLAST/SNESE
by exploiting the sparsity of x-f signal.
Especially, we found that when a fully sampled
reference frame is available more sophisticated
prediction methods such as RIGR and motion
estimation and compensation (ME/MC) can
significantly sparsify the residual and improve the
overall reconstruction quality. Among these, ME/MC
is especially useful since it can be used for
arbitrary trajectories such as radial and spiral.
However, our extensive experiments with non-cartesian
trajectory have demonstrated that there exist
technical issues in applying the ME/MC to non-cartesian
trajectory due to the field inhomogeneities. This
paper showed that if the ME/MC is done in magnitude
image domain and the lost phase is compensated from
the current frame estimate, the field inhomogeneity
problem can be significantly alleviated.
Furthermore, we showed that the introduction of
half-pel ME/MC and intra block mode within the
estimation loop can improve the overall
reconstruction quality of compressed sensing dynamic
MRI. |
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17:36 |
385. |
Fast Relaxation Parameter
Mapping from Undersampled Data |
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Mariya Doneva1,
Christian Stehning2, Peter Börnert2,
Holger Eggers2, Alfred Mertins1
University of Luebeck, Luebeck, Germany;
2Philips Research Europe, Hamburg, Germany |
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The quantitative
assessment of MR parameters like T1, T2, ADC, etc.
requires the acquisition of multiple images of the
same anatomy, which results in long scan times.
However, these data can be described by a model with
only a few parameters and in that sense they are
highly compressible. Thus, Compressed Sensing (CS)
could be applied to accelerate the data acquisition.
In this work we introduce a model-based
reconstruction from undersampled data, which
performs simultaneous image reconstruction and
parameter mapping and demonstrate it for the example
of T1 mapping. |
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17:48 |
386. |
Quality Index for Detecting Reconstruction Errors
Without Knowing the Signal in L0-Norm
Compressed Sensing |
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Carlos A. Sing-Long1,2,
Cristian A. Tejos1,2, Pablo Irarrazaval1,2
1Departamento de Ingenieria Electrica,
Pontificia Universidad Catolica de Chile, Santiago,
R.M., Chile; 2Biomedical Imaging Center,
Pontificia Universidad Catolica de Chile, Santiago,
R.M., Chile |
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Compressed Sensing
allows reconstructing signals, if they are sparse in
some representation, from some of its Fourier
coefficients. The reconstruction conditions are
stated in terms of the support size of the signal.
Since it is generally unknown, it is impossible to
determine if there are reconstruction errors due to
high undersampling rates. Our work introduces a
modified fixed-point solver for a continuous
approximation of the l0-norm and an index
which shows high correlation with the reconstruction
error. This index does not need any a priori
information and may be used to determine if the
undersampling rate needs to be reduced. |
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