11,474 research outputs found
Mismatch and resolution in compressive imaging
Highly coherent sensing matrices arise in discretization of continuum
problems such as radar and medical imaging when the grid spacing is below the
Rayleigh threshold as well as in using highly coherent, redundant dictionaries
as sparsifying operators. Algorithms (BOMP, BLOOMP) based on techniques of band
exclusion and local optimization are proposed to enhance Orthogonal Matching
Pursuit (OMP) and deal with such coherent sensing matrices. BOMP and BLOOMP
have provably performance guarantee of reconstructing sparse, widely separated
objects {\em independent} of the redundancy and have a sparsity constraint and
computational cost similar to OMP's. Numerical study demonstrates the
effectiveness of BLOOMP for compressed sensing with highly coherent, redundant
sensing matrices.Comment: Figure 5 revise
Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization
We present a reconstruction method involving maximum-likelihood expectation
maximization (MLEM) to model Poisson noise as applied to fluorescence molecular
tomography (FMT). MLEM is initialized with the output from a sparse
reconstruction-based approach, which performs truncated singular value
decomposition-based preconditioning followed by fast iterative
shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation
for this approach is that sparsity information could be accounted for within
the initialization, while MLEM would accurately model Poisson noise in the FMT
system. Simulation experiments show the proposed method significantly improves
images qualitatively and quantitatively. The method results in over 20 times
faster convergence compared to uniformly initialized MLEM and improves
robustness to noise compared to pure sparse reconstruction. We also
theoretically justify the ability of the proposed approach to reduce noise in
the background region compared to pure sparse reconstruction. Overall, these
results provide strong evidence to model Poisson noise in FMT reconstruction
and for application of the proposed reconstruction framework to FMT imaging
Sparsity-driven sparse-aperture ultrasound imaging
We propose an image formation algorithm for ultrasound imaging based on sparsity-driven regularization functionals. We consider data collected by synthetic transducer arrays, with the primary motivating application being nondestructive evaluation. Our framework involves the use of a physical optics-based forward model of the observation process; the formulation of an optimization problem for image formation; and the solution of that problem through efficient numerical algorithms. Our sparsity-driven, model-based approach achieves the preservation of physical features while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse observation apertures. We demonstrate the effectiveness of our imaging strategy on real ultrasound data
Exploiting flow dynamics for super-resolution in contrast-enhanced ultrasound
Ultrasound localization microscopy offers new radiation-free diagnostic tools
for vascular imaging deep within the tissue. Sequential localization of echoes
returned from inert microbubbles with low-concentration within the bloodstream
reveal the vasculature with capillary resolution. Despite its high spatial
resolution, low microbubble concentrations dictate the acquisition of tens of
thousands of images, over the course of several seconds to tens of seconds, to
produce a single super-resolved image. %since each echo is required to be well
separated from adjacent microbubbles. Such long acquisition times and stringent
constraints on microbubble concentration are undesirable in many clinical
scenarios. To address these restrictions, sparsity-based approaches have
recently been developed. These methods reduce the total acquisition time
dramatically, while maintaining good spatial resolution in settings with
considerable microbubble overlap. %Yet, non of the reported methods exploit the
fact that microbubbles actually flow within the bloodstream. % to improve
recovery. Here, we further improve sparsity-based super-resolution ultrasound
imaging by exploiting the inherent flow of microbubbles and utilize their
motion kinematics. While doing so, we also provide quantitative measurements of
microbubble velocities. Our method relies on simultaneous tracking and
super-localization of individual microbubbles in a frame-by-frame manner, and
as such, may be suitable for real-time implementation. We demonstrate the
effectiveness of the proposed approach on both simulations and {\it in-vivo}
contrast enhanced human prostate scans, acquired with a clinically approved
scanner.Comment: 11 pages, 9 figure
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
The availability of large-scale annotated image datasets and recent advances
in supervised deep learning methods enable the end-to-end derivation of
representative image features that can impact a variety of image analysis
problems. Such supervised approaches, however, are difficult to implement in
the medical domain where large volumes of labelled data are difficult to obtain
due to the complexity of manual annotation and inter- and intra-observer
variability in label assignment. We propose a new convolutional sparse kernel
network (CSKN), which is a hierarchical unsupervised feature learning framework
that addresses the challenge of learning representative visual features in
medical image analysis domains where there is a lack of annotated training
data. Our framework has three contributions: (i) We extend kernel learning to
identify and represent invariant features across image sub-patches in an
unsupervised manner. (ii) We initialise our kernel learning with a layer-wise
pre-training scheme that leverages the sparsity inherent in medical images to
extract initial discriminative features. (iii) We adapt a multi-scale spatial
pyramid pooling (SPP) framework to capture subtle geometric differences between
learned visual features. We evaluated our framework in medical image retrieval
and classification on three public datasets. Our results show that our CSKN had
better accuracy when compared to other conventional unsupervised methods and
comparable accuracy to methods that used state-of-the-art supervised
convolutional neural networks (CNNs). Our findings indicate that our
unsupervised CSKN provides an opportunity to leverage unannotated big data in
medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional
Sparse Kernel Network for Unsupervised Medical Image Analysis'). The
manuscript is available from following link
(https://doi.org/10.1016/j.media.2019.06.005
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