36,014 research outputs found
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
With the advent of convolutional neural networks~(CNN), supervised learning
methods are increasingly being used for whole brain segmentation. However, a
large, manually annotated training dataset of labeled brain images required to
train such supervised methods is frequently difficult to obtain or create. In
addition, existing training datasets are generally acquired with a homogeneous
magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such
datasets are unable to generalize on test data with different acquisition
protocols. Modern neuroimaging studies and clinical trials are necessarily
multi-center initiatives with a wide variety of acquisition protocols. Despite
stringent protocol harmonization practices, it is very difficult to standardize
the gamut of MRI imaging parameters across scanners, field strengths, receive
coils etc., that affect image contrast. In this paper we propose a CNN-based
segmentation algorithm that, in addition to being highly accurate and fast, is
also resilient to variation in the input acquisition. Our approach relies on
building approximate forward models of pulse sequences that produce a typical
test image. For a given pulse sequence, we use its forward model to generate
plausible, synthetic training examples that appear as if they were acquired in
a scanner with that pulse sequence. Sampling over a wide variety of pulse
sequences results in a wide variety of augmented training examples that help
build an image contrast invariant model. Our method trains a single CNN that
can segment input MRI images with acquisition parameters as disparate as
-weighted and -weighted contrasts with only -weighted training
data. The segmentations generated are highly accurate with state-of-the-art
results~(overall Dice overlap), with a fast run time~( 45
seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series
We propose a structured low rank matrix completion algorithm to recover a
time series of images consisting of linear combination of exponential
parameters at every pixel, from under-sampled Fourier measurements. The spatial
smoothness of these parameters is exploited along with the exponential
structure of the time series at every pixel, to derive an annihilation relation
in the domain. This annihilation relation translates into a structured
low rank matrix formed from the samples. We demonstrate the algorithm in
the parameter mapping setting and show significant improvement over state of
the art methods.Comment: 4 pages, 3 figures, accepted at ISBI 2017, Melbourne, Australi
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