Waveform inversion methods can produce high-resolution reconstructed sound speed images for ultrasound computed tomography; however, they are very computational expensive. Source encoding methods can reduce this computational cost by formulating the image reconstruction problem as a stochastic optimization problem. Here, we solve this optimization problem by the regularized dual averaging method instead of the more commonly used stochastic gradient descent. This new optimization method allows use of non-smooth regularization functions and treats the stochastic data fidelity term in the objective function separately from the deterministic regularization function. This allows noise to be mitigated more effectively. The method further exhibits lower variance in the estimated sound speed distributions across iterations when line search methods are employed.
Propagation-based X-ray phase-contrast tomography (XPCT) provides the opportunity to image weakly absorbing objects and is being explored actively for a variety of important pre-clinical applications. Quantitative XPCT image reconstruction methods typically involve a phase retrieval step followed by application of an image reconstruction algorithm. Most approaches to phase retrieval require either acquiring multiple images at different object-to-detector distances or introducing simplifying assumptions, such as a single-material assumption, to linearize the imaging model. In order to overcome these limitations, a non-linear image reconstruction method has been proposed previously that jointly estimates the absorption and refractive properties of an object from XPCT projection data acquired at a single propagation distance, without the need to linearize the imaging model. However, the numerical properties of the associated non-convex optimization problem remain largely unexplored. In this study, computer simulations are conducted to investigate the feasibility of the joint reconstruction problem in practice. We demonstrate that the joint reconstruction problem is ill-posed and sensitive to system inconsistencies. Particularly, the method can generate accurate refractive index images only if the object is thin and has no phase-wrapping in the data. However, we also observed that, for weakly absorbing objects, the refractive index images reconstructed by the joint reconstruction method are, in general, more accurate than those reconstructed using methods that simply ignore the object’s absorption.
The Bayesian ideal observer (IO) has been widely advocated to guide hardware optimization. However, except for special cases, computation of the IO test statistic is computationally burdensome and requires an appropriate stochastic object model that may be difficult to determine in practice. Modern reconstruction methods, referred to as sparse reconstruction methods, exploit the fact that objects of interest typically possess sparse representations and have proven to be highly effective at reconstructing images from under-sampled measurement data. Moreover, in computed imaging approaches that employ compressive sensing concepts, imaging hardware and image reconstruction are innately coupled technologies. In this work, we propose a sparsity-driven IO (SD-IO) to guide the optimization of data acquisition parameters for modern computed imaging systems. The SD-IO employs a variational Bayesian inference method to estimate the posterior distribution and calculates an approximate likelihood ratio analytically as its test statistic. Since it assumes knowledge of low-level statistical properties of the object that are related to sparsity, the SD-IO exploits the same statistical information regarding the object that is utilized by highly effective sparse image reconstruction methods. Preliminary simulation results are presented to demonstrate the feasibility of the SD-IO calculation.
Ultrasound computed tomography (USCT) holds great promise for improving the detection and management of breast cancer. Because they are based on the acoustic wave equation, waveform inversion-based reconstruction methods can produce images that possess improved spatial resolution properties over those produced by ray-based methods. However, waveform inversion methods are computationally demanding and have not been applied widely in USCT breast imaging. In this work, source encoding concepts are employed to develop an accelerated USCT reconstruction method that circumvents the large computational burden of conventional waveform inversion methods. This method, referred to as the waveform inversion with source encoding (WISE) method, encodes the measurement data using a random encoding vector and determines an estimate of the speed-of-sound distribution by solving a stochastic optimization problem by use of a stochastic gradient descent algorithm. Computer-simulation studies are conducted to demonstrate the use of the WISE method. Using a single graphics processing unit card, each iteration can be completed within 25 seconds for a 128 × 128 mm2 reconstruction region. The results suggest that the WISE method maintains the high spatial resolution of waveform inversion methods while significantly reducing the computational burden.
In this work, we introduce an improved prototype of the imaging system that combines three-dimensional optoacoustic tomography (3D-OAT) and laser ultrasound tomography slicer (2D-LUT) to obtain coregistered maps of tissue optical absorption and speed of sound (SOS). The imaging scan is performed by a 360 degree rotation of a phantom/mouse with respect to a static arc-shaped array of ultrasonic transducers. A Q-switched laser system is used to establish optoacoustic illumination pattern appropriate for deep tissue imaging with a tunable (730-840 nm) output wavelengths operated at 10 Hz pulse repetition rate. For the LUT slicer scans, the array is pivoted by 90 degrees with respect to the central transducers providing accurate registration of optoacoustic and SOS maps, the latter being reconstructed using waveform inversion with source encoding (WISE) technique. The coregistered OAT-LUT modality is validated by imaging a phantom and a live mouse. SOS maps acquired in the imaging system can be employed by an iterative optoacoustic reconstruction algorithm capable of compensating for acoustic wavefield aberrations. The most promising applications of the imaging system include 3D angiography, cancer research, and longitudinal studies of biological distributions of optoacoustic contrast agents (carbon nanotubes, metal plasmonic nanoparticles, fluorophores, etc.).
Optoacoustic tomography (OAT) is a promising imaging modality for human breast cancer imaging, with higher resolution and deeper penetration compared to other optical imaging modalities such as diffuse optical tomography or optical coherence tomography. It yields a resolution of 1 mm at depth up to 2 cm. But there is an inherent conflict between the limitations imposed on laser power and the need to adequately penetrate a substantial portion of the breast. To achieve sufficient penetration at every view angle, instead of illuminating the whole breast all at once, sometimes illumination is focused onto a small region of the breast and rotated along with the transducer array to cover the entire object. This paper evaluates the effect of this rotating partial illumination design on OAT image reconstruction. The optical process is simulated by conducting Monte Carlo simulations on a numerical phantom mimicking a real breast, with various specially designed illumination schemes. The acoustic process is simulated by incorporating the transducer's spatial impulse response. Iterative reconstruction is applied to estimate the OAT image. We conclude that rotating partial illumination introduces inconsistency into the system equation, and the degree of inconsistency determines the reconstruction quality.
Iterative image reconstruction algorithms can model complicated imaging physics, compensate for imperfect data acquisition systems, and exploit prior information regarding the object. Hence, they produce higher quality images than do analytical image reconstruction algorithms. However, three-dimensional (3D) iterative image reconstruction is computationally burdensome, which greatly hinders its use with applications requiring a large field-of-view (FOV), such as breast imaging. In this study, an improved GPU-based implementation of a numerical imaging model and its adjoint have been developed for use with general gradient-based iterative image reconstruction algorithms. Both computer simulations and experimental studies are conducted to investigate the efficiency and accuracy of the proposed implementation for optoacoustic tomography (OAT). The results suggest that the proposed implementation is more than five times faster than the previous implementation.
Conventional photoacoustic computed tomography (PACT) image reconstruction methods assume that the object and surrounding medium are described by a constant speed-of-sound (SOS) value. In order to accurately recover fine structures, SOS heterogeneities should be quantified and compensated for during PACT reconstruction. To address this problem, several groups have proposed hybrid systems that combine PACT with ultrasound computed tomography (USCT). In such systems, a SOS map is reconstructed first via USCT. Consequently, this SOS map is employed to inform the PACT reconstruction method. Additionally, the SOS map can provide structural information regarding tissue, which is complementary to the functional information from the PACT image. We propose a paradigm shift in the way that images are reconstructed in hybrid PACT-USCT imaging. Inspired by our observation that information about the SOS distribution is encoded in PACT measurements, we propose to jointly reconstruct the absorbed optical energy density and SOS distributions from a combined set of USCT and PACT measurements, thereby reducing the two reconstruction problems into one. This innovative approach has several advantages over conventional approaches in which PACT and USCT images are reconstructed independently: (1) Variations in the SOS will automatically be accounted for, optimizing PACT image quality; (2) The reconstructed PACT and USCT images will possess minimal systematic artifacts because errors in the imaging models will be optimally balanced during the joint reconstruction; (3) Due to the exploitation of information regarding the SOS distribution in the full-view PACT data, our approach will permit high-resolution reconstruction of the SOS distribution from sparse array data.
Conventional photoacoustic computed tomography (PACT) image reconstruction methods assume that the object and surrounding medium are described by a constant speed-of-sound (SOS) value. In order to accurately recover fine structures, SOS heterogeneities should be quantified and compensated for during PACT reconstruction. To address this problem, several groups have proposed hybrid systems that combine PACT with ultrasound computed tomography (USCT). In such systems, a SOS map is reconstructed first via USCT. Consequently, this SOS map is employed to inform the PACT reconstruction method. Additionally, the SOS map can provide structural information regarding tissue, which is complementary to the functional information from the PACT image. We propose a paradigm shift in the way that images are reconstructed in hybrid PACT-USCT imaging. Inspired by our observation that information about the SOS distribution is encoded in PACT measurements, we propose to jointly reconstruct the absorbed optical energy density and SOS distributions from a combined set of USCT and PACT measurements, thereby reducing the two reconstruction problems into one. This innovative approach has several advantages over conventional approaches in which PACT and USCT images are reconstructed independently: (1) Variations in the SOS will automatically be accounted for, optimizing PACT image quality; (2) The reconstructed PACT and USCT images will possess minimal systematic artifacts because errors in the imaging models will be optimally balanced during the joint reconstruction; (3) Due to the exploitation of information regarding the SOS distribution in the full-view PACT data, our approach will permit high-resolution reconstruction of the SOS distribution from sparse array data.
In order to monitor dynamic physiological events in near-real time, a variety of photoacoustic computed tomography (PACT) systems have been developed that can rapidly acquire data. Previously reported studies of dynamic PACT have employed conventional static methods to reconstruct a temporally ordered sequence of images on a frame-by-frame basis. Frame-by-frame image reconstruction (FBFIR) methods fail to exploit correlations between data frames and are known to be statistically and computationally suboptimal. In this study, a low-rank matrix estimation-based spatiotemporal image reconstruction (LRME-STIR) method is investigated for dynamic PACT applications. The LRME-STIR method is based on the observation that, in many PACT applications, the number of frames is much greater than the rank of the ideal noiseless data matrix. Using both computer-simulated and experimentally measured photoacoustic data, the performance of the LRME-STIR method is compared with that of conventional FBFIR method followed by image-domain filtering. The results demonstrate that the LRME-STIR method is not only computationally more efficient but also produces more accurate dynamic PACT images than a conventional FBFIR method followed by image-domain filtering.
In order to monitor dynamic physiological events in near-real time, a variety of photoacoustic computed tomography (PACT) systems have been developed that can rapidly acquire data. Previously reported studies of dynamic PACT have employed conventional static methods to reconstruct a temporally ordered sequence of images on a frame-by-frame basis. Frame-by-frame image reconstruction (FBFIR) methods fail to exploit correlations between data frames and are known to be statistically and computationally suboptimal. In this study, a low-rank matrix estimation-based spatio-temporal image reconstruction (LRME-STIR) method is investigated for dynamic PACT applications. The LRME-STIR method is based on the observation that, in many PACT applications, the number of frames is much greater than the rank of the ideal noiseless data matrix. Using computer-simulated photoacoustic data, the performance of the LRME-STIR method is compared with that of conventional FBFIR method. The results demonstrate that LRME-STIR method is not only computationally more efficient but also produces more accurate dynamic PACT images than a conventional FBFIR method.
An important and interesting question in photoacoustic computed tomography (PACT) is whether the absorbed optical energy density distribution, A(r), and the speed of sound distribution, c(r), can both be accurately determined from the measured photoacoustic data alone. However, in many cases c(r) is unknown or cannot be accurately estimated. Therefore, it would be practically beneficial if A(r) and c(r) can be jointly reconstructed from the measurements. In this work, we propose a reconstruction approach to the joint reconstruction of both properties in PACT.
Iterative image reconstruction algorithms for photoacoustic computed tomography (PACT) have the ability
to improve image quality over analytic algorithms due to their ability to mitigate artifacts from incomplete
data, incorporate the relevant imaging physics, and model the instrument response. In this work, Kaiser-Bessel
functions are employed as the basis functions in an iterative reconstruction algorithm for PACT. Kaiser-Bessel
functions, or blobs, can be made arbitrarily smooth (differentiable to arbitrary order), have finite spatial support,
and can be made quasi-bandlimited in the spatial Fourier domain. Closed-form solutions exist in the time-domain
and the temporal-frequency domain for the pressure signal generated by blobs.
There remains an urgent need to develop effective photoacoustic computed tomography (PACT) image recon-
struction methods for use with acoustically inhomogeneous media. Transcranial PACT brain imaging is an im-
portant example of an emerging imaging application that would benefit greatly from this. Existing approaches
to PACT image reconstruction in acoustically heterogeneous media are limited to weakly varying media, are
computationally burdensome, and/or make impractical assumptions regarding the measurement geometry. In
this work, we develop and investigate a full-wave approach to iterative image reconstruction in PACT for media
possessing inhomogeneous speed-of-sound and mass density distributions. A key contribution of the work is the
formulation of a procedure to implement a matched discrete forward and backprojection operator pair, which
facilitates the application of a wide range of modern iterative image reconstruction algorithms. This presents
the opportunity to employ application-specific regularization methods to mitigate image artifacts due to mea-
surement data incompleteness and noise. Our results establish that the proposed image reconstruction method
can effectively compensate for acoustic aberration and reduces artifacts in the reconstructed image.
Photoacoustic computed tomography (PACT), also known as optoacoustic tomography or thermoacoustic tomography,
is an emerging biomedical imaging technique that combines optical absorption contrast with ultrasound
detection principles. Recently, a novel analytic image reconstruction formula has been proposed that operates
on a data function expressed in the temporal frequency and spatial domains. The validity the formula has been
demonstrated for a two-dimensional (2D) circular measurement geometry. In this study, computer simulation
studies are conducted to validate the reconstruction formula for a three-dimensional (3D) spherical measurement
geometry. This formula provides new insights into how the spatial frequency components of the sought-after
object function can be explicitly determined by the temporal frequency components of the data function measured
with a 2D circular or 3D spherical measurement geometry in PACT. Comparing with existing Fourier
transform-based reconstruction formulas, the reconstruction formula possesses a simple structure that requires
no computation of series expansions or multi-dimensional interpolation in Fourier space.
With the increasing use of small animals for human disease studies, small-animal whole-body molecular imaging plays an important role in biomedical research. Currently, none of the existing imaging modalities can provide both anatomical and glucose molecular information, leading to higher costs of building dual-modality systems. Even with image co-registration, the spatial resolution of the molecular imaging modality is not improved. Utilizing a ring-shaped confocal photoacoustic computed tomography system, we demonstrate, for the first time, that both anatomy and glucose uptake can be imaged in a single modality. Anatomy was imaged with the endogenous hemoglobin contrast, and glucose metabolism was imaged with a near-infrared dye-labeled 2-deoxyglucose.
We report a novel small-animal whole-body imaging system called ring-shaped confocal photoacoustic computed tomography (RC-PACT). RC-PACT is based on a confocal design of free-space ring-shaped light illumination and 512-element full-ring ultrasonic array signal detection. The free-space light illumination maximizes the light delivery efficiency, and the full-ring signal detection ensures a full two-dimensional view aperture for accurate image reconstruction. Using cylindrically focused array elements, RC-PACT can image a thin cross section with 0.10 to 0.25 mm in-plane resolutions and 1.6 s/frame acquisition time. By translating the mouse along the elevational direction, RC-PACT provides a series of cross-sectional images of the brain, liver, kidneys, and bladder.
Filtered backprojection (FBP) algorithms are commonly employed for image reconstruction in optoacoustic tomography
(OAT). A limitation of FBP algorithms is that they require the measured acoustic data to be densely
sampled, which necessitates expensive ultrasound arrays that possess a large number of elements or increased
data-acquisition times if mechnical scanning is employed. Additionally, FBP algorithms are based on idealized
imaging models that do not accurately model the response of the transducers and fail to exploit the statistical
characteristics of noisy measurement data to minimize noise levels in the reconstructed images. Iterative image
reconstruction algorithms can circumvent these difficulties. However, to date, iterative reconstruction algorithms
have not been successfully applied to three-dimensional (3D) OAT. In this work we investigate the use of an
iterative image reconstruction method in 3D OAT. The large computational burden of 3D iterative image reconstruction
is circumvented by implementing the reconstrution algorithm with graphics processing units (GPUs).
The ability of the reconstruction algorithm to mitigate artifacts due to incomplete data is demonstrated.
In optoacoustic tomography (OAT), also known as photoacoustic tomography, a variety of analytic reconstruction
algorithms, such as filtered backprojection (FBP) algorithms, have been developed. Analytic algorithms are
typically computationally more efficient than iterative image reconstruction algorithms but possess disadvantages
that include the inabilty to accurately compensate for the response of the measurement system and stochastic
noise. While these shortcomings can be circumvented by use of iterative image reconstruction methods, threedimensional
(3D) iterative reconstruction is computationally burdensome. In this work, we present a novel datarestoration
method that seeks to recover an accurate estimate of the pressure data with reduced noise levels from
knowledge of the experimentally acquired transducer output data. From knowledge of the "restored" pressure
data, a computationally efficient analytic algorithm can be applied for image reconstruction. Accordingly, this
approach combines the advantages of an iterative reconstruction algorithm with the computational efficiency
of an analytic algorithm. Curvelet-based data-space restoration is demonstrated by use of computer-simulation
studies.
Optoacoustic Tomography (OAT) is an emerging hybrid imaging technique with great potential for a wide range
of biomedical imaging applications. Assuming point-like transducers, analytic algorithms are available for image
reconstruction, but they are applicable only when the measured data are densely sampled on an aperture that
encloses the object. In many cases of practical interest, however, measurements may be limited in number and are
acquired on an incomplete aperture. Total variation (TV) minimization has been proved to be a powerful tool for
limited-data reconstruction. However, most previous studies of limited-data OAT were based on an approximate
imaging model that assumed point-like transducers, which limits the improvements on the reconstructed OAT
image quality. In this work, we develop and investigate an iterative reconstruction algorithm incorporating
ultrasonic transducer properties applicable for limited-data OAT. The algorithm is based on the minimization
of the image TV subject to a data consistency condition, and is conceptually and mathematically distinct from
classic iterative reconstruction algorithms. Preliminary computer-simulation studies are conducted to investigate
the proposed algorithm. These studies reveal that the constrained, total variation minimization algorithm can
yield accurate reconstructions in many limited-data applications where classic algorithms do not perform well.
Optoacoustic Tomography (OAT) is a hybrid imaging modality that combines the advantages of both optical
imaging and ultrasound imaging techniques. Most existing reconstruction algorithms for OAT assume pointlike
transducers, which may result in conspicous image blurring and distortions in certain applications. In this
work, a new imaging model that incorporates the transducer response is employed for image reconstruction.
Computer-simulation studies demonstrate that the new reconstruction method can effectively compensate for
image resolution degradation associated with the transducer response.
Previous research correcting for variable speed of sound in photoacoustic tomography (PAT) has used a generalized radon
transform (GRT) model . In this model, the pressure is related to the optical absorption, in an acoustically inhomogeneous
medium, through integration over non-spherical isochronous surfaces. This model assumes that the path taken by acoustic
rays is linear and neglects amplitude perturbations to the measured pressure. We have derived a higher-order geometrical
acoustics (GA) expression, which takes into account the first-order effect in the amplitude of the measured signal and
higher-order perturbation to the travel times. The higher-order perturbation to travel time incorporates the effect of ray
bending. Incorrect travel times can lead to image distortion and blurring. These corrections are expected to impact image
quality and quantitative PAT. We have previously shown that
travel-time corrections in 2D suggest that perceivable differences
in the isochronous surfaces can be seen when the second-order
travel-time perturbations are taken into account with
a 10% speed of sound variation. In this work, we develop iterative image reconstruction algorithms that incorporate this
higher-order GA approximation assuming that the speed of sound map is known. We evaluate the effect of higher-order
GA approximation on image quality and accuracy.
Photoacoustic tomography (PAT), also known as thermoacoustic or optoacoustic tomography, is a hybrid imaging
modality that reconstructs the electromagnetic absorption properties of biological tissue from knowledge of
acoustic signals produced by the thermoacoustic effect. Because the propagation of acoustic signals is most
generally described by the 3D wave equation, PAT is an inherently 3D imaging modality. Due to the the limited
penetration depth of the probing electromagnetic fields and the limited availability of 3D ultrasound detector
arrays, a simplified two-dimensional (2D) PAT measurement geometry is used in many current experimental
implementations. However, in this case, when unfocused transducers are employed, the acquired data are not
sufficient to invert the 3D imaging model and ad hoc reconstruction procedures are employed. In this work
we numerically investigate 2D and 3D PAT assuming an ultrasound transducer having an anisotropic detection
response. The uncompensated effects of an anisotropic detection response on images reconstructed using a
point-detector assumption are demonstrated.
Photoacoustic tomography (PAT) is an emerging ultrasound-mediated biophotonic imaging modality that has
great potential for many biomedical imaging applications. In many practical implementations of PAT, the
photoacoustic signals are recorded over an aperture that does not enclose the object, which results in a limitedview
tomographic reconstruction problem. When conventional reconstruction algorithms are applied to limitedview
measurement data, the resulting images can contain severe image artifacts and distortions. To circumvent
such artifacts, we exploit a priori information about the locations of boundaries within the object (optical
absorption function) to improve the fidelity of the reconstructed images. Such boundary information can be
inferred, for example, from a co-registered B-mode ultrasound image or other adjunct imaging study. We develop
and implement an iterative reconstruction algorithm that exploits a priori object information in the form of
support constraints. We demonstrate that the developed iterative reconstruction algorithm produces images
with reduced artifact levels as compared to those produced by a conventional PAT reconstruction algorithm.
Photoacoustic tomography (PAT) is a hybrid imaging modality that combines the advantages of both optical
imaging and ultrasound imaging techniques. Most existing reconstruction algorithms assume the speed-of-sound
distribution within the object is homogeneous. In certain practical applications, this assumption may not be
valid and will result in conspicuous image artifacts. In this work, we investigate the possibility of simultaneously
estimating the speed-of-sound and optical absorption properties from data acquired in a PAT experiment. We
propose and numerically implement a time-domain iterative algorithm that can reconstruct both the speed-of-sound and optical absorption distribution, by use of a priori information regarding the geometry of the speed-of-sound map. Computer-simulation results are presented to demonstrate the efficacy of the proposed
reconstruction method.
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