194 research outputs found
Full field inversion in photoacoustic tomography with variable sound speed
Recently, a novel measurement setup has been introduced to photoacoustic
tomography, that collects data in the form of projections of the full 3D
acoustic pressure distribution at a certain time instant. Existing imaging
algorithms for this kind of data assume a constant speed of sound. This
assumption is not always met in practice and thus leads to erroneous
reconstructions. In this paper, we present a two-step reconstruction method for
full field detection photoacoustic tomography that takes variable speed of
sound into account. In the first step, by applying the inverse Radon transform,
the pressure distribution at the measurement time is reconstructed point-wise
from the projection data. In the second step, one solves a final time wave
inversion problem where the initial pressure distribution is recovered from the
known pressure distribution at the measurement time. For the latter problem, we
derive an iterative solution approach, compute the required adjoint operator,
and show its uniqueness and stability
Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast
Ultrasound Tomography has seen a revival of interest in the past decade,
especially for breast imaging, due to improvements in both ultrasound and
computing hardware. In particular, three-dimensional ultrasound tomography, a
fully tomographic method in which the medium to be imaged is surrounded by
ultrasound transducers, has become feasible. In this paper, a comprehensive
derivation and study of a robust framework for large-scale bent-ray ultrasound
tomography in 3D for a hemispherical detector array is presented. Two
ray-tracing approaches are derived and compared. More significantly, the
problem of linking the rays between emitters and receivers, which is
challenging in 3D due to the high number of degrees of freedom for the
trajectory of rays, is analysed both as a minimisation and as a root-finding
problem. The ray-linking problem is parameterised for a convex detection
surface and three robust, accurate, and efficient ray-linking algorithms are
formulated and demonstrated. To stabilise these methods, novel
adaptive-smoothing approaches are proposed that control the conditioning of the
update matrices to ensure accurate linking. The nonlinear UST problem of
estimating the sound speed was recast as a series of linearised subproblems,
each solved using the above algorithms and within a steepest descent scheme.
The whole imaging algorithm was demonstrated to be robust and accurate on
realistic data simulated using a full-wave acoustic model and an anatomical
breast phantom, and incorporating the errors due to time-of-flight picking that
would be present with measured data. This method can used to provide a
low-artefact, quantitatively accurate, 3D sound speed maps. In addition to
being useful in their own right, such 3D sound speed maps can be used to
initialise full-wave inversion methods, or as an input to photoacoustic
tomography reconstructions
Image reconstruction in transcranial photoacoustic computed tomography of the brain
Photoacoustic computed tomography (PACT) holds great promise for transcranial brain imaging. However, the strong reflection, scattering, attenuation, and mode-conversion of photoacoustic waves in the skull pose serious challenges to establishing the method. The lack of an appropriate model of solid media in conventional PACT imaging models, which are based on the canonical scalar wave equation, causes a significant model mismatch in the presence of the skull and thus results in deteriorated reconstructed images. The goal of this study was to develop an image reconstruction algorithm that accurately models the skull and thereby ameliorates the quality of reconstructed images. The propagation of photoacoustic waves through the skull was modeled by a viscoelastic stress tensor wave equation, which was subsequently discretized by use of a staggered grid fourth-order finite-difference time-domain (FDTD) method. The matched adjoint of the FDTD-based wave propagation operator was derived for implementing a back-projection operator. Systematic computer simulations were conducted to demonstrate the effectiveness of the back-projection operator for reconstructing images in a realistic three-dimensional PACT brain imaging system. The results suggest that the proposed algorithm can successfully reconstruct images from transcranially-measured pressure data and readily be translated to clinical PACT brain imaging applications
A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation
In an inhomogeneously illuminated photoacoustic image, important information
like vascular geometry is not readily available when only the initial pressure
is reconstructed. To obtain the desired information, algorithms for image
segmentation are often applied as a post-processing step. In this work, we
propose to jointly acquire the photoacoustic reconstruction and segmentation,
by modifying a recently developed partially learned algorithm based on a
convolutional neural network. We investigate the stability of the algorithm
against changes in initial pressures and photoacoustic system settings. These
insights are used to develop an algorithm that is robust to input and system
settings. Our approach can easily be applied to other imaging modalities and
can be modified to perform other high-level tasks different from segmentation.
The method is validated on challenging synthetic and experimental photoacoustic
tomography data in limited angle and limited view scenarios. It is
computationally less expensive than classical iterative methods and enables
higher quality reconstructions and segmentations than state-of-the-art learned
and non-learned methods.Comment: "copyright 2019 IEEE. Personal use of this material is permitted.
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