604 research outputs found
A Multi-Grid Iterative Method for Photoacoustic Tomography
Inspired by the recent advances on minimizing nonsmooth or bound-constrained
convex functions on models using varying degrees of fidelity, we propose a line
search multigrid (MG) method for full-wave iterative image reconstruction in
photoacoustic tomography (PAT) in heterogeneous media. To compute the search
direction at each iteration, we decide between the gradient at the target
level, or alternatively an approximate error correction at a coarser level,
relying on some predefined criteria. To incorporate absorption and dispersion,
we derive the analytical adjoint directly from the first-order acoustic wave
system. The effectiveness of the proposed method is tested on a total-variation
penalized Iterative Shrinkage Thresholding algorithm (ISTA) and its accelerated
variant (FISTA), which have been used in many studies of image reconstruction
in PAT. The results show the great potential of the proposed method in
improving speed of iterative image reconstruction
A Framework for Directional and Higher-Order Reconstruction in Photoacoustic Tomography
Photoacoustic tomography is a hybrid imaging technique that combines high
optical tissue contrast with high ultrasound resolution. Direct reconstruction
methods such as filtered backprojection, time reversal and least squares suffer
from curved line artefacts and blurring, especially in case of limited angles
or strong noise. In recent years, there has been great interest in regularised
iterative methods. These methods employ prior knowledge on the image to provide
higher quality reconstructions. However, easy comparisons between regularisers
and their properties are limited, since many tomography implementations heavily
rely on the specific regulariser chosen. To overcome this bottleneck, we
present a modular reconstruction framework for photoacoustic tomography. It
enables easy comparisons between regularisers with different properties, e.g.
nonlinear, higher-order or directional. We solve the underlying minimisation
problem with an efficient first-order primal-dual algorithm. Convergence rates
are optimised by choosing an operator dependent preconditioning strategy. Our
reconstruction methods are tested on challenging 2D synthetic and experimental
data sets. They outperform direct reconstruction approaches for strong noise
levels and limited angle measurements, offering immediate benefits in terms of
acquisition time and quality. This work provides a basic platform for the
investigation of future advanced regularisation methods in photoacoustic
tomography.Comment: submitted to "Physics in Medicine and Biology". Changes from v1 to
v2: regularisation with directional wavelet has been added; new experimental
tests have been include
Photoacoustic Tomography in a Rectangular Reflecting Cavity
Almost all known image reconstruction algorithms for photoacoustic and
thermoacoustic tomography assume that the acoustic waves leave the region of
interest after a finite time. This assumption is reasonable if the reflections
from the detectors and surrounding surfaces can be neglected or filtered out
(for example, by time-gating). However, when the object is surrounded by
acoustically hard detector arrays, and/or by additional acoustic mirrors, the
acoustic waves will undergo multiple reflections. (In the absence of absorption
they would bounce around in such a reverberant cavity forever). This disallows
the use of the existing free-space reconstruction techniques. This paper
proposes a fast iterative reconstruction algorithm for measurements made at the
walls of a rectangular reverberant cavity. We prove the convergence of the
iterations under a certain sufficient condition, and demonstrate the
effectiveness and efficiency of the algorithm in numerical simulations.Comment: 21 pages, 6 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.Comment: submitted to "Physics in Medicine and Biology
An optimized ultrasound detector for photoacoustic breast tomography
Photoacoustic imaging has proven to be able to detect vascularization-driven
optical absorption contrast associated with tumors. In order to detect breast
tumors located a few centimeter deep in tissue, a sensitive ultrasound detector
is of crucial importance for photoacoustic mammography. Further, because the
expected photoacoustic frequency bandwidth (a few MHz to tens of kHz) is
inversely proportional to the dimensions of light absorbing structures (0.5 to
10+ mm), proper choices of materials and their geometries, and proper
considerations in design have to be made for optimal photoacoustic detectors.
In this study, we design and evaluate a specialized ultrasound detector for
photoacoustic mammography. Based on the required detector sensitivity and its
frequency response, a selection of active material and matching layers and
their geometries is made leading to a functional detector models. By iteration
between simulation of detector performances, fabrication and experimental
characterization of functional models an optimized implementation is made and
evaluated. The experimental results of the designed first and second functional
detectors matched with the simulations. In subsequent bare piezoelectric
samples the effect of lateral resonances was addressed and their influence
minimized by sub-dicing the samples. Consequently, using simulations, the final
optimized detector could be designed, with a center frequency of 1 MHz and a -6
dB bandwidth of ~80%. The minimum detectable pressure was measured to be 0.5
Pa, which will facilitate deeper imaging compared to the currrent systems. The
detector should be capable of detecting vascularized tumors with resolution of
1-2 mm. Further improvements by proper electrical grounding and shielding and
implementation of this design into an arrayed detector will pave the way for
clinical applications of photoacoustic mammography.Comment: Accepted for publication in Medical Physics (American Association of
Physicists in Medicine
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