138 research outputs found
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
Light propagating in tissue attains a spectrum that varies with location due
to wavelength-dependent fluence attenuation by tissue optical properties, an
effect that causes spectral corruption. Predictions of the spectral variations
of light fluence in tissue are challenging since the spatial distribution of
optical properties in tissue cannot be resolved in high resolution or with high
accuracy by current methods. Spectral corruption has fundamentally limited the
quantification accuracy of optical and optoacoustic methods and impeded the
long sought-after goal of imaging blood oxygen saturation (sO2) deep in
tissues; a critical but still unattainable target for the assessment of
oxygenation in physiological processes and disease. We discover a new principle
underlying light fluence in tissues, which describes the wavelength dependence
of light fluence as an affine function of a few reference base spectra,
independently of the specific distribution of tissue optical properties. This
finding enables the introduction of a previously undocumented concept termed
eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively
account for wavelength dependent light attenuation without explicit knowledge
of the tissue optical properties. We validate eMSOT in more than 2000
simulations and with phantom and animal measurements. We find that eMSOT can
quantitatively image tissue sO2 reaching in many occasions a better than
10-fold improved accuracy over conventional spectral optoacoustic methods.
Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor;
revealing so far unattainable tissue physiology patterns. Last, we related
eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2
images and tissue perfusion and hypoxia maps obtained by correlative
histological analysis
Quantitative photoacoustic imaging in radiative transport regime
The objective of quantitative photoacoustic tomography (QPAT) is to
reconstruct optical and thermodynamic properties of heterogeneous media from
data of absorbed energy distribution inside the media. There have been
extensive theoretical and computational studies on the inverse problem in QPAT,
however, mostly in the diffusive regime. We present in this work some numerical
reconstruction algorithms for multi-source QPAT in the radiative transport
regime with energy data collected at either single or multiple wavelengths. We
show that when the medium to be probed is non-scattering, explicit
reconstruction schemes can be derived to reconstruct the absorption and the
Gruneisen coefficients. When data at multiple wavelengths are utilized, we can
reconstruct simultaneously the absorption, scattering and Gruneisen
coefficients. We show by numerical simulations that the reconstructions are
stable.Comment: 40 pages, 13 figure
Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography
Iterative image reconstruction algorithms for optoacoustic tomography (OAT),
also known as photoacoustic tomography, have the ability to improve image
quality over analytic algorithms due to their ability to incorporate accurate
models of the imaging physics, instrument response, and measurement noise.
However, to date, there have been few reported attempts to employ advanced
iterative image reconstruction algorithms for improving image quality in
three-dimensional (3D) OAT. In this work, we implement and investigate two
iterative image reconstruction methods for use with a 3D OAT small animal
imager: namely, a penalized least-squares (PLS) method employing a quadratic
smoothness penalty and a PLS method employing a total variation norm penalty.
The reconstruction algorithms employ accurate models of the ultrasonic
transducer impulse responses. Experimental data sets are employed to compare
the performances of the iterative reconstruction algorithms to that of a 3D
filtered backprojection (FBP) algorithm. By use of quantitative measures of
image quality, we demonstrate that the iterative reconstruction algorithms can
mitigate image artifacts and preserve spatial resolution more effectively than
FBP algorithms. These features suggest that the use of advanced image
reconstruction algorithms can improve the effectiveness of 3D OAT while
reducing the amount of data required for biomedical applications
Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization
We propose a model-based image reconstruction method for photoacoustic
tomography(PAT) involving a novel form of regularization and demonstrate its
ability to recover good quality images from significantly reduced size
datasets. The regularization is constructed to suit the physical structure of
typical PAT images. We construct it by combining second-order derivatives and
intensity into a non-convex form to exploit a structural property of PAT images
that we observe: in PAT images, high intensities and high second-order
derivatives are jointly sparse. The specific form of regularization constructed
here is a modification of the form proposed for fluorescence image restoration.
This regularization is combined with a data fidelity cost, and the required
image is obtained as the minimizer of this cost. As this regularization is
non-convex, the efficiency of the minimization method is crucial in obtaining
artifact-free reconstructions. We develop a custom minimization method for
efficiently handling this non-convex minimization problem. Further, as
non-convex minimization requires a large number of iterations and the PAT
forward model in the data-fidelity term has to be applied in the iterations, we
propose a computational structure for efficient implementation of the forward
model with reduced memory requirements. We evaluate the proposed method on both
simulated and real measured data sets and compare them with a recent
reconstruction method that is based on a well-known fast iterative shrinkage
threshold algorithm (FISTA).Comment: This manuscript has been published in Journal of Instrumentatio
Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography using Radially Symmetric Expansion Functions
Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an
emerging computed biomedical imaging modality that exploits optical contrast
and ultrasonic detection principles. Iterative image reconstruction algorithms
that are based on discrete imaging models are actively being developed for OAT
due to their ability to improve image quality by incorporating accurate models
of the imaging physics, instrument response, and measurement noise. In this
work, we investigate the use of discrete imaging models based on Kaiser-Bessel
window functions for iterative image reconstruction in OAT. A closed-form
expression for the pressure produced by a Kaiser-Bessel function is calculated,
which facilitates accurate computation of the system matrix.
Computer-simulation and experimental studies are employed to demonstrate the
potential advantages of Kaiser-Bessel function-based iterative image
reconstruction in OAT
Fast Multispectral Optoacoustic Tomography (MSOT) for Dynamic Imaging of Pharmacokinetics and Biodistribution in Multiple Organs
The characterization of pharmacokinetic and biodistribution profiles is an essential step in the development process of new candidate drugs or imaging agents. Simultaneously, the assessment of organ function related to the uptake and clearance of drugs is of great importance. To this end, we demonstrate an imaging platform capable of high-rate characterization of the dynamics of fluorescent agents in multiple organs using multispectral optoacoustic tomography (MSOT). A spatial resolution of approximately 150 µm through mouse cross-sections allowed us to image blood vessels, the kidneys, the liver and the gall bladder. In particular, MSOT was employed to characterize the removal of indocyanine green from the systemic circulation and its time-resolved uptake in the liver and gallbladder. Furthermore, it was possible to track the uptake of a carboxylate dye in separate regions of the kidneys. The results demonstrate the acquisition of agent concentration metrics at rates of 10 samples per second at a single wavelength and 17 s per multispectral sample with 10 signal averages at each of 5 wavelengths. Overall, such imaging performance introduces previously undocumented capabilities of fast, high resolution in vivo imaging of the fate of optical agents for drug discovery and basic biological research
Bayesian approach to image reconstruction in photoacoustic tomography
Photoacoustic tomography is a hybrid imaging method that has a variety of biomedical applications. In photoacoustic tomography, the image reconstruction problem (inverse problem) is to resolve the initial pressure distribution from detected ultrasound waves generated within an object due to an illumination of a short light pulse. In this work, this problem is approached in Bayesian framework. Image reconstruction is investigated with numerical simulations in different detector geometries, including limited view setup, and utilizing different prior information. Furthermore, assessing the reliability of the estimates is investigated. The simulations show that the Bayesian approach can produce accurate estimates of the initial pressure distribution and uncertainty information even in a limited view setup if proper prior information is utilized
Direct Estimation of Optical Parameters From Photoacoustic Time Series in Quantitative Photoacoustic Tomography
Imaging methods applied to living organisms with emphasis on innovative approaches that use emerging technologies supported by rigorous physical and mathematical analysis and quantitative evaluation of performance.
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