Category: Optoacoustic tomography

source: © 2019  IEEE Transactions on Medical Imaging. 

In optoacoustic tomography, negatively focused detectors have been shown to improve the tangential image resolution without sacrificing sensitivity. Since no exact inversion formulae exist for optoacoustic image reconstruction with negatively focused detectors, image reconstruction in such cases is based on using the virtual-detector approximation, in which it is assumed that the response of the negatively focused detector is identical, up to a constant time delay, to that of a point-like detector positioned in the detector’s center of curvature. In this work, we analyze the response of negatively focused spherical ultrasound detectors in three dimensions and demonstrate how their properties affect the optoacoustic reconstruction. Our analysis sheds new light on commonly reported experimental reconstruction artifacts in optoacoustic systems that employ negatively focused detectors. Based on our analysis, we introduce a simple correction to the virtual-detector approximation that significantly enhances image contrast and reduces artifacts.  [Read more…]

Fig. (a) The geometry of the negative acoustic lens studied with full
acoustic simulations. The speed of sound of the surrounding medium and lens
material were 1500 m/s and 2757 m/s. respectively. (b) The detected acoustic
signals obtained when the lens was acoustically matched to the surrounding
medium (solid-blue curve) and when its acoustic impedance was 1.xx times that
of the surrounding medium (dashed-red curve), leading to internal reflections
in the lens structure. The reflection from the detection surface was 50% of the
pressure signal. 

Gilad Drozdov, Ahiad Levi, Amir Rosenthal.
IEEE Transactions on Medical Imaging.DOI: 10.1109/TMI.2019.2897588

 

source: © 2018 Optical Society of America

The simplicity and computational efficiency of back-projection formulae have made them a popular choice in optoacoustic tomography. Nonetheless, exact back-projection formulae exist for only a small set of tomographic problems. This limitation is overcome by algebraic algorithms, but at the cost of higher numerical complexity. In this paper, we present a generic algebraic framework for calculating back-projection operators in optoacoustic tomography. We demonstrate our approach in a two-dimensional optoacoustic-tomography example and show that once the algebraic back-projection operator has been found, it achieves a comparable run time to that of the conventional back-projection algorithm, but with the superior image quality of algebraic methods.[Read More…]

Fig. 1 (a) The grid of the image and detector locations used for calculating the model matrix?. The image is divided into ??×?? square pixels with a pixel area of ???? and the acoustic signals are sampled at ?? positions over a line with a distance of?? between them. (b) The image grid on which the projection operator ? is calculated. Here, only a single back-projection is calculated, and the number of pixels in the x directions is increased to ??=??+??−1.

Amir Rosenthal, “Algebraic determination of back-projection operators for optoacoustic tomography,” Biomed. Opt. Express 9, 5173-5193 (2018)

source:© 2017 IEEE Transactions on Medical Imaging

In optoacoustic tomography, negatively focused transducers may be used for improving the tangential image resolution while preserving a high signal-to-noise ratio. Commonly, image reconstruction in such scenarios is facilitated by the use of the virtual-detector approach. Although the validity of this approach has been experimentally verified, it is based on an approximation whose effect on optoacoustic image reconstruction has not yet been studied. In this paper, we analyze the response of negatively focused acoustic detectors in 2D in both time and frequency domains. Based on this analysis, tradeoffs between the detector size, curvature, and sensitivity are formulated. In addition, our analysis reveals the geometrical underpinning of the virtual-detector approximation and quantifies its deviation from the exact solution. The error involved in the virtual-detector approximation is studied in image reconstruction simulations and its effect on image quality is shown. The theoretical tools developed in this work may be used in the design of new optoacoustic detection geometries as well as for improved image reconstruction.
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Schematic illustration of (a,b) tangential and (c,d) non-tangential impact between of an impinging acoustic wave on a convex detector. (a,c) An illustration of the intersection of the acoustic wavefront with the detector surface and (b,d) of the corresponding phase factor exp[ikL(θ)] . The figure illustrates the conclusion from the SPM analysis given in (16) and (17): In the case of tangential intersection L∼θ2 and the integration over exp[ikL(θ)] in (15) is not cancelled out, whereas in the case on non-tangential intersection L∼θ which causes nullification of exp[ikL(θ)] under integration.

G. Drozdov and A. Rosenthal “Analysis of Negatively Focused Ultrasound Detectors in Optoacoustic Tomography,” accepted to Transactions on Medical Imaging ( Volume: 36 , Issue: 1 , Jan. 2017 )

source: © 2016 J. of Biomedical Optics

In optoacoustic tomography, detectors with relatively large areas are often employed to achieve high detection sensitivity. However, spatial-averaging effects over large detector areas may lead to attenuation of high acoustic frequencies and, subsequently, loss of fine features in the reconstructed image. Model-based reconstruction algorithms improve image resolution in such cases by correcting for the effect of the detector’s aperture on the detected signals. However, the incorporation of the detector’s geometry in the optoacoustic model leads to a significant increase of the model matrix memory cost, which hinders the application of inversion and analysis tools such as singular value decomposition (SVD). We demonstrate the use of the wavelet-packet framework for optoacoustic systems with finite-aperture detectors. The decomposition of the model matrix in the wavelet-packet domain leads to sufficiently smaller model matrices on which SVD may be applied. Using this methodology over an order of magnitude reduction in inversion time is demonstrated for numerically generated and experimental data. Additionally, our framework is demonstrated for the analysis of inversion stability and reveals a new, nonmonotonic dependency of the system condition number on the detector size. Thus, the proposed framework may assist in choosing the optimal detector size in future optoacoustic systems.[Read more…]

Fig.Optoacoustic reconstructions of a mouse’s head from limited view (180 deg) experimental data obtained using (a) BP, (b) IMMI, (c) IMMI-FAD, and (d) GWP-IMMI-FAD.

Yiyong Han, Vasilis Ntziachristos, Amir Rosenthal. J. of Biomedical Optics, 21(1), 016002 (2016). https://doi.org/10.1117/1.JBO.21.1.016002

source: © 2016 IEEE Transactions on Medical Imaging

The concept of sparsity is extensively exploited in the fields of data acquisition and image processing, contributing to better signal-to-noise and spatio-temporal performance of the various imaging methods. In the field of optoacoustic tomography, the image reconstruction problem is often characterized by computationally extensive inversion of very large datasets, for instance when acquiring volumetric multispectral data with high temporal resolution. In this article we seek to accelerate accurate model-based optoacoustic inversions by identifying various sources of sparsity in the forward and inverse models as well as in the single- and multi-frame representation of the projection data. These sources of sparsity are revealed through appropriate transformations in the signal, model and image domains and are subsequently exploited for expediting image reconstruction. The sparsity-based inversion scheme was tested with experimental data, offering reconstruction speed enhancement by a factor of 40 to 700 times as compared with the conventional iterative model-based inversions while preserving similar image quality. The demonstrated results pave the way for achieving real-time performance of model-based reconstruction in multi-dimensional optoacoustic imaging.[Read more…..]

Fig.3d maximum intensity projections of the volumetric dataset showing a 3d angiogram of a human finger obtained by cross-sectional scan in the z direction. (a) Photograph of the finger and reconstructions obtained with (b) LSQR-15, (c) WP-o, and (d) PCA-wp-T are shown.

Christian Lutzweiler, Stratis Tzoumas, Amir Rosenthal, Vasilis Ntziachristos, Daniel Razansky. Published in: IEEE Transactions on Medical Imaging ( Volume: 35 , Issue: 2 , Feb. 2016 ).

source:© 2015 IEEE Signal Processing Magazine

Light offers a range of interactions with tissue that give rise to an extensive list of methods to sense physical, chemical, or biological processes. Combined with using safe and nonionizing radiation, optical imaging is considered as a fundamental tool in the biomedical sciences.
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Fig. 1 Principles of optical and optoacoustic tomography. (a) Themorelastic expansion of an optically absorbing object (black circle) within tissue (blue circle) upon illumination by pulsed laser beams. The object expands and contracts, due to temperature variation, and releases the absorbed energy as pressure waves (dotted circles). (b) Typical time-resolved optoacoustic signal detected using an ultrasound sensor. (c) A reconstructed transversal optoacoustic image of the abdominal region of a mouse, using a two-dimensional (2-D) circular measurement system geometry,. (d) The principles of fluorescence, as electrons are excited to higher energy levels upon absorbing photons. Fluorescence photons are then emitted as the excited electrons vibrationally relax to their base states. (e) Fluorescence image acquired with a CCD camera from the dorsal side of a mouse. (f) A three-dimensional (3-D) image of a pancreatic tumor model reconstructed with concurrent X-ray CT and fluorescence molecular tomography (FMT-XCT), in 360° transillumination geometry.

P. Mohajerani, S. Tzoumas, A. Rosenthal, and V. Ntziachristos “Optical and optoacoustic model-based tomography: theory and current challenges for deep tissue imaging of optical contrast,” IEEE Signal Processing Magazine, Vol. 32, pp. 88-100 (2015).

source: © 2015  Medical physics & American Association of Physicists in Medicine

Purpose:
With recent advancement in hardware of optoacoustic imaging systems, highly detailed cross‐sectional images may be acquired at a single laser shot, thus eliminating motion artifacts. Nonetheless, other sources of artifacts remain due to signal distortion or out‐of‐plane signals. The purpose of image reconstruction algorithms is to obtain the most accurate images from noisy, distorted projection data.

Methods:
In this paper, the authors use the model‐based approach for acoustic inversion, combined with a sparsity‐based inversion procedure. Specifically, a cost function is used that includes the L1 norm of the image in sparse representation and a total variation (TV) term. The optimization problem is solved by a numerically efficient implementation of a nonlinear gradient descent algorithm. TV–L1 model‐based inversion is tested in the cross section geometry for numerically generated data as well as for in vivo experimental data from an adult mouse.

Results:
In all cases, model‐based TV–L1 inversion showed a better performance over the conventional Tikhonov regularization, TV inversion, and L1 inversion. In the numerical examples, the images reconstructed with TV–L1 inversion were quantitatively more similar to the originating images. In the experimental examples, TV–L1 inversion yielded sharper images and weaker streak artifact.

Conclusions:
The results herein show that TV–L1 inversion is capable of improving the quality of highly detailed, multiscale optoacoustic images obtained in vivo using cross‐sectional imaging systems. As a result of its high fidelity, model‐based TV–L1 inversion may be considered as the new standard for image reconstruction in cross‐sectional imaging. [Read more……]

Fig. Experimental data reconstructions in almost-completed-view with (a) Tik–Lap, (b) TV, (c) L1, and (d) TV–L1; (e)–(h) zoomed images in the dash line rectangle region of (a), (d), (c), and (d); (i) comparison of FWHM of the vessel along the dashed lines in (e)–(h).

Yiyong Han, Stratis Tzoumas, Antonio Nunes, Vasilis Ntziachristos, Amir Rosenthal.  Medical physics.