4,067 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
Compressively characterizing high-dimensional entangled states with complementary, random filtering
The resources needed to conventionally characterize a quantum system are
overwhelmingly large for high- dimensional systems. This obstacle may be
overcome by abandoning traditional cornerstones of quantum measurement, such as
general quantum states, strong projective measurement, and assumption-free
characterization. Following this reasoning, we demonstrate an efficient
technique for characterizing high-dimensional, spatial entanglement with one
set of measurements. We recover sharp distributions with local, random
filtering of the same ensemble in momentum followed by position---something the
uncertainty principle forbids for projective measurements. Exploiting the
expectation that entangled signals are highly correlated, we use fewer than
5,000 measurements to characterize a 65, 536-dimensional state. Finally, we use
entropic inequalities to witness entanglement without a density matrix. Our
method represents the sea change unfolding in quantum measurement where methods
influenced by the information theory and signal-processing communities replace
unscalable, brute-force techniques---a progression previously followed by
classical sensing.Comment: 13 pages, 7 figure
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
ROUTING TOPOLOGY RECOVERY FOR WIRELESS SENSOR NETWORKS
Liu, Rui Ph.D., Purdue University, December 2014. Routing Topology Recovery for Wireless Sensor Networks. Major Professor: Yao Liang
Compressed Sensing - A New mode of Measurement
After introducing the concept of compressed sensing as a complementary
measurement mode to the classical Shannon-Nyquist approach, I discuss some of
the drivers, potential challenges and obstacles to its implementation. I end with a
speculative attempt to embed compressed sensing as an enabling methodology
within the emergence of data-driven discovery. As a consequence I predict the
growth of non-nomological sciences where heuristic correlations will find
applications but often bypass conventional pure basic and use-inspired basic
research stages due to the lack of verifiable hypotheses
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