478 research outputs found
Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation
Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24–31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method
On Iterative Algorithms for Quantitative Photoacoustic Tomography in the Radiative Transport Regime
In this paper, we describe the numerical reconstruction method for
quantitative photoacoustic tomography (QPAT) based on the radiative transfer
equation (RTE), which models light propagation more accurately than diffusion
approximation (DA). We investigate the reconstruction of absorption coefficient
and/or scattering coefficient of biological tissues. Given the scattering
coefficient, an improved fixed-point iterative method is proposed to retrieve
the absorption coefficient for its cheap computational cost. And we prove the
convergence. To retrieve two coefficients simultaneously, Barzilai-Borwein (BB)
method is applied. Since the reconstruction of optical coefficients involves
the solution of original and adjoint RTEs in the framework of optimization, an
efficient solver with high accuracy is improved from~\cite{Gao}. Simulation
experiments illustrate that the improved fixed-point iterative method and the
BB method are the comparative methods for QPAT in two cases.Comment: 21 pages, 44 figure
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
Image Enhancement and Noise Reduction Using Modified Delay-Multiply-and-Sum Beamformer: Application to Medical Photoacoustic Imaging
Photoacoustic imaging (PAI) is an emerging biomedical imaging modality
capable of providing both high contrast and high resolution of optical and
UltraSound (US) imaging. When a short duration laser pulse illuminates the
tissue as a target of imaging, tissue induces US waves and detected waves can
be used to reconstruct optical absorption distribution. Since receiving part of
PA consists of US waves, a large number of beamforming algorithms in US imaging
can be applied on PA imaging. Delay-and-Sum (DAS) is the most common
beamforming algorithm in US imaging. However, make use of DAS beamformer leads
to low resolution images and large scale of off-axis signals contribution. To
address these problems a new paradigm namely Delay-Multiply-and-Sum (DMAS),
which was used as a reconstruction algorithm in confocal microwave imaging for
breast cancer detection, was introduced for US imaging. Consequently, DMAS was
used in PA imaging systems and it was shown this algorithm results in
resolution enhancement and sidelobe degrading. However, in presence of high
level of noise the reconstructed image still suffers from high contribution of
noise. In this paper, a modified version of DMAS beamforming algorithm is
proposed based on DAS inside DMAS formula expansion. The quantitative and
qualitative results show that proposed method results in more noise reduction
and resolution enhancement in expense of contrast degrading. For the
simulation, two-point target, along with lateral variation in two depths of
imaging are employed and it is evaluated under high level of noise in imaging
medium. Proposed algorithm in compare to DMAS, results in reduction of lateral
valley for about 19 dB followed by more distinguished two-point target.
Moreover, levels of sidelobe are reduced for about 25 dB.Comment: This paper was accepted and presented at Iranian Conference on
Electrical Engineering (ICEE) 201
Joint Image Reconstruction and Segmentation Using the Potts Model
We propose a new algorithmic approach to the non-smooth and non-convex Potts
problem (also called piecewise-constant Mumford-Shah problem) for inverse
imaging problems. We derive a suitable splitting into specific subproblems that
can all be solved efficiently. Our method does not require a priori knowledge
on the gray levels nor on the number of segments of the reconstruction.
Further, it avoids anisotropic artifacts such as geometric staircasing. We
demonstrate the suitability of our method for joint image reconstruction and
segmentation. We focus on Radon data, where we in particular consider limited
data situations. For instance, our method is able to recover all segments of
the Shepp-Logan phantom from angular views only. We illustrate the
practical applicability on a real PET dataset. As further applications, we
consider spherical Radon data as well as blurred data
Distributed Compressive Sensing Algorithm for Photoacoustic Tomography
Biomedical imaging techniques are playing an essential role in diagnosing different kinds of diseases, which always motivates the search for improving their sensitivity and accuracy. Photoacoustic Tomography (PAT) is one of the most powerful techniques. PAT has many advantages as it is less expensive and faster than Magnetic Resonance Imaging (MRI). It combines the advantages of optical imaging and ultrasound imaging as it provides high contrast, high penetration, and high-resolution images for biological tissues. Also, it uses non-ionizing radiation which is very safe for human health. The main challenge in PAT is that human tissues can be exposed only to a limited amount of radiation, so a full-view of PAT requires many transducers and a great number of measurements. This thesis aims to develop an efficient reconstruction algorithm of Photoacoustic (PA) images that uses a few number of transducers, a few number of measurements, and offers low computational complexity while maintaining a high quality of recovered images.
The proposed reconstruction algorithm depends on the Compressive Sensing (CS) theory which is a signal processing technique that is capable of forming a full view PAT images (under certain prerequisites) with a few number of measurements. The proposed algorithm solves the CS problem using a distributed and parallel implementation of the Alternating Direction Method of Multipliers (ADMM). ADMM is a well-known method for solving convex optimization problems. A group of local processors that work in parallel with one global processor is used to form the images. The iterative algorithm of ADMM is distributed over local processors in such a way perfect reconstruction of images is possible.
Simulation results show that the proposed algorithm is powerful and successful in reconstructing different kinds of PA images with very high quality and significantly reduced computational complexity. Reducing the computational complexity is reflected in a much lower reconstruction time. Also, the algorithm requires lower cost and shorter acquisition time since the CS theory is used which allows the recovery of images from a few number of samples and sensors. Although the idea of distributed ADMM has been introduced before in literature but to the best of our knowledge, this is the first work to apply distributed ADMM method in recovering photoacoustic images by distributing the iterative algorithm among multiple processors working in parallel
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
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