13,628 research outputs found
Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing
Current 3D photoacoustic tomography (PAT) systems offer either high image
quality or high frame rates but are not able to deliver high spatial and
temporal resolution simultaneously, which limits their ability to image dynamic
processes in living tissue. A particular example is the planar Fabry-Perot (FP)
scanner, which yields high-resolution images but takes several minutes to
sequentially map the photoacoustic field on the sensor plane, point-by-point.
However, as the spatio-temporal complexity of many absorbing tissue structures
is rather low, the data recorded in such a conventional, regularly sampled
fashion is often highly redundant. We demonstrate that combining variational
image reconstruction methods using spatial sparsity constraints with the
development of novel PAT acquisition systems capable of sub-sampling the
acoustic wave field can dramatically increase the acquisition speed while
maintaining a good spatial resolution: First, we describe and model two general
spatial sub-sampling schemes. Then, we discuss how to implement them using the
FP scanner and demonstrate the potential of these novel compressed sensing PAT
devices through simulated data from a realistic numerical phantom and through
measured data from a dynamic experimental phantom as well as from in-vivo
experiments. Our results show that images with good spatial resolution and
contrast can be obtained from highly sub-sampled PAT data if variational image
reconstruction methods that describe the tissues structures with suitable
sparsity-constraints are used. In particular, we examine the use of total
variation regularization enhanced by Bregman iterations. These novel
reconstruction strategies offer new opportunities to dramatically increase the
acquisition speed of PAT scanners that employ point-by-point sequential
scanning as well as reducing the channel count of parallelized schemes that use
detector arrays.Comment: submitted to "Physics in Medicine and Biology
Green compressive sampling reconstruction in IoT networks
In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks
Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation
A crucial limitation of current high-resolution 3D photoacoustic tomography
(PAT) devices that employ sequential scanning is their long acquisition time.
In previous work, we demonstrated how to use compressed sensing techniques to
improve upon this: images with good spatial resolution and contrast can be
obtained from suitably sub-sampled PAT data acquired by novel acoustic scanning
systems if sparsity-constrained image reconstruction techniques such as total
variation regularization are used. Now, we show how a further increase of image
quality can be achieved for imaging dynamic processes in living tissue (4D
PAT). The key idea is to exploit the additional temporal redundancy of the data
by coupling the previously used spatial image reconstruction models with
sparsity-constrained motion estimation models. While simulated data from a
two-dimensional numerical phantom will be used to illustrate the main
properties of this recently developed
joint-image-reconstruction-and-motion-estimation framework, measured data from
a dynamic experimental phantom will also be used to demonstrate their potential
for challenging, large-scale, real-world, three-dimensional scenarios. The
latter only becomes feasible if a carefully designed combination of tailored
optimization schemes is employed, which we describe and examine in more detail
Localization of Sound Sources in a Room with One Microphone
Estimation of the location of sound sources is usually done using microphone
arrays. Such settings provide an environment where we know the difference
between the received signals among different microphones in the terms of phase
or attenuation, which enables localization of the sound sources. In our
solution we exploit the properties of the room transfer function in order to
localize a sound source inside a room with only one microphone. The shape of
the room and the position of the microphone are assumed to be known. The design
guidelines and limitations of the sensing matrix are given. Implementation is
based on the sparsity in the terms of voxels in a room that are occupied by a
source. What is especially interesting about our solution is that we provide
localization of the sound sources not only in the horizontal plane, but in the
terms of the 3D coordinates inside the room
Compressive Matched-Field Processing
Source localization by matched-field processing (MFP) generally involves
solving a number of computationally intensive partial differential equations.
This paper introduces a technique that mitigates this computational workload by
"compressing" these computations. Drawing on key concepts from the recently
developed field of compressed sensing, it shows how a low-dimensional proxy for
the Green's function can be constructed by backpropagating a small set of
random receiver vectors. Then, the source can be located by performing a number
of "short" correlations between this proxy and the projection of the recorded
acoustic data in the compressed space. Numerical experiments in a Pekeris ocean
waveguide are presented which demonstrate that this compressed version of MFP
is as effective as traditional MFP even when the compression is significant.
The results are particularly promising in the broadband regime where using as
few as two random backpropagations per frequency performs almost as well as the
traditional broadband MFP, but with the added benefit of generic applicability.
That is, the computationally intensive backpropagations may be computed offline
independently from the received signals, and may be reused to locate any source
within the search grid area
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Recent advances in deep learning for tomographic reconstructions have shown
great potential to create accurate and high quality images with a considerable
speed-up. In this work we present a deep neural network that is specifically
designed to provide high resolution 3D images from restricted photoacoustic
measurements. The network is designed to represent an iterative scheme and
incorporates gradient information of the data fit to compensate for limited
view artefacts. Due to the high complexity of the photoacoustic forward
operator, we separate training and computation of the gradient information. A
suitable prior for the desired image structures is learned as part of the
training. The resulting network is trained and tested on a set of segmented
vessels from lung CT scans and then applied to in-vivo photoacoustic
measurement data
Deep learning versus -minimization for compressed sensing photoacoustic tomography
We investigate compressed sensing (CS) techniques for reducing the number of
measurements in photoacoustic tomography (PAT). High resolution imaging from CS
data requires particular image reconstruction algorithms. The most established
reconstruction techniques for that purpose use sparsity and
-minimization. Recently, deep learning appeared as a new paradigm for
CS and other inverse problems. In this paper, we compare a recently invented
joint -minimization algorithm with two deep learning methods, namely a
residual network and an approximate nullspace network. We present numerical
results showing that all developed techniques perform well for deterministic
sparse measurements as well as for random Bernoulli measurements. For the
deterministic sampling, deep learning shows more accurate results, whereas for
Bernoulli measurements the -minimization algorithm performs best.
Comparing the implemented deep learning approaches, we show that the nullspace
network uniformly outperforms the residual network in terms of the mean squared
error (MSE).Comment: This work has been presented at the Joint Photoacoustics Session with
the 2018 IEEE International Ultrasonics Symposium Kobe, October 22-25, 201
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