Photoacoustic computed tomography (PACT) is a hybrid technique that combines optical excitation and ultrasonic detection to provide high resolution images in deep tissues. In the image reconstruction, a constant speed of sound (SOS) is normally assumed. This assumption, however, is often not strictly satisfied in deep tissue imaging, due to acoustic heterogeneities within the object and between the object and coupling medium. If these heterogeneities are not accounted for, they will cause distortions and artifacts in the reconstructed images. In this paper, we incorporated ultrasonic computed tomography (USCT), which measures the SOS distribution within the object, into our full-ring array PACT system. Without the need for ultrasonic transmitting electronics, USCT was performed using the same laser beam as for PACT measurement. By scanning the laser beam on the array surface, we can sequentially fire different elements. As a first demonstration of the system, we studied the effect of acoustic heterogeneities on photoacoustic vascular imaging. We verified that constant SOS is a reasonable approximation when the SOS variation is small. When the variation is large, distortion will be observed in the periphery of the object, especially in the tangential direction.
Photoacoustic computed tomography (PACT) holds great promise for transcranial brain imaging. However, the strong reflection, scattering and attenuation of acoustic waves in the skull present significant challenges to developing this method. We report on a systematic computer-simulation study of transcranial brain imaging using PACT. The goal of this study was to identify an effective imaging system design that can be translated for clinical use. The propagation of photoacoustic waves through a model skull was studied by use of an elastic finite-difference time-domain (FDTD) method. The acoustic radiation pattern from a photoacoustic source just beneath the skull was observed with a ring transducer array that was level with the source. The observed radiation pattern was found to contain stronger contributions from waves that were converted to shear waves in skull than longitudinal waves that did not undergo mode conversion. Images reconstructed from the pressure data that contain shear wave components possess better resolution than images reconstructed from the data that only contain the longitudinal wave signals. These observations revealed that the detection system should be designed to capture photoacoustic signals that travel through the skull in the form of shear waves as well as in the form of longitudinal waves. A preliminary investigation on the effect of the presence of absorption in the skull is also reported. This study provides an insight into the wave phenomena in transcranial PACT imaging, as well as a concrete detection design strategy that mitigates the degraded resolution of reconstructed images.
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.
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.
A challenge in photoacoustic tomography (PAT) brain imaging is to compensate for aberrations in the measured photoacoustic data due to their propagation through the skull. By use of information regarding the skull morphology and composition obtained from adjunct x-ray computed tomography image data, we developed a subject-specific imaging model that accounts for such aberrations. A time-reversal-based reconstruction algorithm was employed with this model for image reconstruction. The image reconstruction methodology was evaluated in experimental studies involving phantoms and monkey heads. The results establish that our reconstruction methodology can effectively compensate for skull-induced acoustic aberrations and improve image fidelity in transcranial PAT.
We report an investigation of image reconstruction in photoacoustic tomography for objects that possess heterogeneous material and acoustic attenuation distributions. When the object contains materials, such as bone and soft-tissue, that are modeled using power law attenuation models with distinct exponents, we demonstrate that the effects of acoustic attenuation due to the most strongly attenuating material can be compensated for if the attenuation of the other less attenuating material(s) are neglected. Experiments with phantom objects are presented to validated our findings.
A photoacoustic tomography (PAT) system using a virtual point ultrasonic transducer was developed for transcranial
imaging of monkey brains. The virtual point transducer provided a 10 times greater field-of-view (FOV) than finiteaperture
unfocused transducers, which enables large primate imaging. The cerebral cortex of a monkey brain was
accurately mapped transcranially, through up to two skulls ranging from 4 to 8 mm in thickness. The mass density and
speed of sound distributions of the skull were estimated from adjunct X-ray CT image data and utilized with a timereversal
algorithm to mitigate artifacts in the reconstructed image due to acoustic aberration. The oxygenation saturation
(sO2) in blood phantoms through a monkey skull was also imaged and quantified, with results consistent with
measurements by a gas analyzer. The oxygenation saturation (sO2) in blood phantoms through a monkey skull was also
imaged and quantified, with results consistent with measurements by a gas analyzer. Our experimental results
demonstrate that PAT can overcome the optical and ultrasound attenuation of a relatively thick skull, and the imaging
aberration caused by skull can be corrected to a great extent.
Optoacoustic tomography (OAT) is an emerging ultrasound-mediated biophotonic imaging modality that has exciting
potential for many biomedical imaging applications. There is great interest in conducting B-mode ultrasound and OAT
imaging studies for breast cancer detection using a common transducer. In this situation, the range of tomographic view
angles is limited, which can result in distortions in the reconstructed OAT image if conventional reconstruction
algorithms are applied to limited-view measurement data. In this work, we investigate an image reconstruction method
that utilizes information regarding target boundaries to improve the quality of the reconstructed OAT images. This is
accomplished by developing boundary-constrained image reconstruction algorithm for OAT based on Bayesian image
reconstruction theory. The computer-simulation studies demonstrate that the Bayesian approach can effectively reduce
the artifact and noise levels and preserve the edges in reconstructed limited-view OAT images as compared to those
produced by a conventional OAT reconstruction algorithm.
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