189 research outputs found
Auto-Calibration and Biconvex Compressive Sensing with Applications to Parallel MRI
We study an auto-calibration problem in which a transform-sparse signal is
compressive-sensed by multiple sensors in parallel with unknown sensing
parameters. The problem has an important application in pMRI reconstruction,
where explicit coil calibrations are often difficult and costly to achieve in
practice, but nevertheless a fundamental requirement for high-precision
reconstructions. Most auto-calibrated strategies result in reconstruction that
corresponds to solving a challenging biconvex optimization problem. We
transform the auto-calibrated parallel sensing as a convex optimization problem
using the idea of `lifting'. By exploiting sparsity structures in the signal
and the redundancy introduced by multiple sensors, we solve a mixed-norm
minimization problem to recover the underlying signal and the sensing
parameters simultaneously. Robust and stable recovery guarantees are derived in
the presence of noise and sparsity deficiencies in the signals. For the pMRI
application, our method provides a theoretically guaranteed approach to
self-calibrated parallel imaging to accelerate MRI acquisitions under
appropriate assumptions. Developments in MRI are discussed, and numerical
simulations using the analytical phantom and simulated coil sensitives are
presented to support our theoretical results.Comment: Keywords: Self-calibration, Compressive sensing, Convex optimization,
Random matrices, Parallel MR
Compressive PCA for Low-Rank Matrices on Graphs
We introduce a novel framework for an approxi- mate recovery of data matrices
which are low-rank on graphs, from sampled measurements. The rows and columns
of such matrices belong to the span of the first few eigenvectors of the graphs
constructed between their rows and columns. We leverage this property to
recover the non-linear low-rank structures efficiently from sampled data
measurements, with a low cost (linear in n). First, a Resrtricted Isometry
Property (RIP) condition is introduced for efficient uniform sampling of the
rows and columns of such matrices based on the cumulative coherence of graph
eigenvectors. Secondly, a state-of-the-art fast low-rank recovery method is
suggested for the sampled data. Finally, several efficient, parallel and
parameter-free decoders are presented along with their theoretical analysis for
decoding the low-rank and cluster indicators for the full data matrix. Thus, we
overcome the computational limitations of the standard linear low-rank recovery
methods for big datasets. Our method can also be seen as a major step towards
efficient recovery of non- linear low-rank structures. For a matrix of size n X
p, on a single core machine, our method gains a speed up of over Robust
Principal Component Analysis (RPCA), where k << p is the subspace dimension.
Numerically, we can recover a low-rank matrix of size 10304 X 1000, 100 times
faster than Robust PCA
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