Optoacoustic Reconstruction Research Articles

Signal domain adaptation network for limited-view optoacoustic tomography

Optoacoustic (OA) imaging is based on optical excitation of biological tissues with nanosecond-duration laser pulses and detection of ultrasound (US) waves generated by thermoelastic expansion following light absorption. The image quality and fidelity of OA images critically depend on the extent of tomographic coverage provided by the US detector arrays. However, full tomographic coverage is not always possible due to experimental constraints. One major challenge concerns an efficient integration between OA and pulse-echo US measurements using the same transducer array. A common approach toward the hybridization consists in using standard linear transducer arrays, which readily results in arc-type artifacts and distorted shapes in OA images due to the limited angular coverage. Deep learning methods have been proposed to mitigate limited-view artifacts in OA reconstructions by mapping artifactual to artifact-free (ground truth) images. However, acquisition of ground truth data with full angular coverage is not always possible, particularly when using handheld probes in a clinical setting. Deep learning methods operating in the image domain are then commonly based on networks trained on simulated data. This approach is yet incapable of transferring the learned features between two domains, which results in poor performance on experimental data. Here, we propose a signal domain adaptation network (SDAN) consisting of i) a domain adaptation network to reduce the domain gap between simulated and experimental signals and ii) a sides prediction network to complement the missing signals in limited-view OA datasets acquired from a human forearm by means of a handheld linear transducer array. The proposed method showed improved performance in reducing limited-view artifacts without the need for ground truth signals from full tomographic acquisitions.

Disentangling the Frequency Content in Optoacoustics.

Signals acquired by optoacoustic tomography systems have broadband frequency content that encodes information about structures on different physical scales. Concurrent processing and rendering of such broadband signals may result in images with poor contrast and fidelity due to a bias towards low frequency contributions from larger structures. This problem cannot be addressed by filtering different frequency bands and reconstructing them individually, as this procedure leads to artefacts due to its incompatibility with the entangled frequency content of signals generated by structures of different sizes. Here we introduce frequency-band model-based (fbMB) reconstruction to separate frequency-band-specific optoacoustic image components during image formation, thereby enabling structures of all sizes to be rendered with high fidelity. In order to disentangle the overlapping frequency content of image components, fbMB uses soft priors to achieve an optimal trade-off between localization of the components in frequency bands and their structural integrity. We demonstrate that fbMB produces optoacoustic images with improved contrast and fidelity, which reveal anatomical structures in in vivo images of mice in unprecedented detail. These enhancements further improve the accuracy of spectral unmixing in small vasculature. By offering a precise treatment of the frequency components of optoacoustic signals, fbMB improves the quality, accuracy, and quantification of optoacoustic images and provides a method of choice for optoacoustic reconstructions.

Weighted model-based optoacoustic reconstruction for partial-view geometries.

Acoustic heterogeneities in biological samples are known to cause artifacts in tomographic optoacoustic (photoacoustic) image reconstruction. A statistical weighted model-based reconstruction approach was previously introduced to mitigate such artifacts. However, this approach does not reliably provide high-quality reconstructions for partial-view imaging systems, which are common in preclinical and clinical optoacoustics. In this article, the capability of the weighted model-based algorithm is extended to generate optoacoustic reconstructions with less distortions for partial-view geometry data. This is achieved by manipulating the weighting scheme based on the detector geometry. Using partial-view optoacoustic tomography data from a tissue-mimicking phantom containing a strong acoustic reflector, tumors grafted onto mice, and a mouse brain with intact skull, the proposed partial-view-corrected weighted model-based algorithm is shown to mitigate reflection artifacts in reconstructed images without distorting structures or boundaries, compared with both conventional model-based and the weighted model-based algorithms. It is also demonstrated that the partial-view-corrected weighted model-based algorithm has the additional advantage of suppressing streaking artifacts due to the partial-view geometry itself in the presence of a very strong optoacoustic chromophore. Due to its enhanced performance, the partial-view-corrected weighted model-based algorithm may prove useful for improving the quality of partial-view multispectral optoacoustic tomography, leading to enhanced visualization of functional parameters such as tissue oxygenation.

Застосування нечіткої логіки при пошуку оптимального фільтру в задачах оптоакустики

Denoising is an important step in the early stage of signal preprocessing in optoacoustic applications. The efficiency of such modern noise removal methods as wavelet or curvlet filtering depends significantly on the numerical combinations and forms of wavelet transform parameters, and the multidimensional extension of such filters is rather non-trivial. These issues are serious obstacle for using of these highly effective filters in the tasks of optoacoustic reconstruction, especially in real laboratorial or medical practice. The objective of our study was to find the optimal filter, convenient for use in laboratorian and medical practice, when the types of noise are a priori unknown, and the filter settings should not take much time. In the offered work spatial filters which have only one parameter of adjustment - the size of a window are considered. Three-dimensional extensions of such well-established denoising techniques, as mean filter, median filter, their adaptive variants (Wiener spatial filter and modified median filter), as well as iterative truncated arithmetic mean filter were analyzed. The proposed filters were tested on a test set that contains versions of Shepp-Logan's three-dimensional phantom with mixtures of Gaussian and alpha-stable noise, as well as speckle noise. The identification of the best filter for simultaneous suppression of these types of interference was carried out using the theory of fuzzy sets. In our tests, a modified median filter and an iterative truncated arithmetic mean filter were rated as the best choice when the goal is to minimize aberrations when noise is not known a priory.

The Impulse Response of Negatively Focused Spherical Ultrasound Detectors and Its Effect on Tomographic Optoacoustic Reconstruction.

In optoacoustic tomography, negatively focused detectors have been shown to improve the tangential image resolution without sacrificing sensitivity. Since no exact inversion formulas 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 paper, 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.

Transmission\u2013reflection optoacoustic ultrasound (TROPUS) computed tomography of small animals

Rapid progress in the development of multispectral optoacoustic tomography techniques has enabled unprecedented insights into biological dynamics and molecular processes in vivo and noninvasively at penetration and spatiotemporal scales not covered by modern optical microscopy methods. Ultrasound imaging provides highly complementary information on elastic and functional tissue properties and further aids in enhancing optoacoustic image quality. We devised the first hybrid transmission–reflection optoacoustic ultrasound (TROPUS) small animal imaging platform that combines optoacoustic tomography with both reflection- and transmission-mode ultrasound computed tomography. The system features full-view cross-sectional tomographic imaging geometry for concomitant noninvasive mapping of the absorbed optical energy, acoustic reflectivity, speed of sound, and acoustic attenuation in whole live mice with submillimeter resolution and unrivaled image quality. Graphics-processing unit (GPU)-based algorithms employing spatial compounding and bent-ray-tracing iterative reconstruction were further developed to attain real-time rendering of ultrasound tomography images in the full-ring acquisition geometry. In vivo mouse imaging experiments revealed fine details on the organ parenchyma, vascularization, tissue reflectivity, density, and stiffness. We further used the speed of sound maps retrieved by the transmission ultrasound tomography to improve optoacoustic reconstructions via two-compartment modeling. The newly developed synergistic multimodal combination offers unmatched capabilities for imaging multiple tissue properties and biomarkers with high resolution, penetration, and contrast.

Effects of small variations of speed of sound in optoacoustic tomographic imaging.

Speed of sound difference in the imaged object and surrounding coupling medium may reduce the resolution and overall quality of optoacoustic tomographic reconstructions obtained by assuming a uniform acoustic medium. In this work, the authors investigate the effects of acoustic heterogeneities and discuss potential benefits of accounting for those during the reconstruction procedure. The time shift of optoacoustic signals in an acoustically heterogeneous medium is studied theoretically by comparing different continuous and discrete wave propagation models. A modification of filtered back-projection reconstruction is subsequently implemented by considering a straight acoustic rays model for ultrasound propagation. The results obtained with this reconstruction procedure are compared numerically and experimentally to those obtained assuming a heuristically fitted uniform speed of sound in both full-view and limited-view optoacoustic tomography scenarios. The theoretical analysis showcases that the errors in the time-of-flight of the signals predicted by considering the straight acoustic rays model tend to be generally small. When using this model for reconstructing simulated data, the resulting images accurately represent the theoretical ones. On the other hand, significant deviations in the location of the absorbing structures are found when using a uniform speed of sound assumption. The experimental results obtained with tissue-mimicking phantoms and a mouse postmortem are found to be consistent with the numerical simulations. Accurate analysis of effects of small speed of sound variations demonstrates that accounting for differences in the speed of sound allows improving optoacoustic reconstruction results in realistic imaging scenarios involving acoustic heterogeneities in tissues and surrounding media.

Expediting model-based optoacoustic reconstructions with tomographic symmetries

Image quantification in optoacoustic tomography implies the use of accurate forward models of excitation, propagation, and detection of optoacoustic signals while inversions with high spatial resolution usually involve very large matrices, leading to unreasonably long computation times. The development of fast and memory efficient model-based approaches represents then an important challenge to advance on the quantitative and dynamic imaging capabilities of tomographic optoacoustic imaging. Herein, a method for simplification and acceleration of model-based inversions, relying on inherent symmetries present in common tomographic acquisition geometries, has been introduced. The method is showcased for the case of cylindrical symmetries by using polar image discretization of the time-domain optoacoustic forward model combined with efficient storage and inversion strategies. The suggested methodology is shown to render fast and accurate model-based inversions in both numerical simulations and post mortem small animal experiments. In case of a full-view detection scheme, the memory requirements are reduced by one order of magnitude while high-resolution reconstructions are achieved at video rate. By considering the rotational symmetry present in many tomographic optoacoustic imaging systems, the proposed methodology allows exploiting the advantages of model-based algorithms with feasible computational requirements and fast reconstruction times, so that its convenience and general applicability in optoacoustic imaging systems with tomographic symmetries is anticipated.

Weighted model-based optoacoustic reconstruction in acoustic scattering media

Model-based optoacoustic inversion methods are capable of eliminating image artefacts associated with the widely adopted back-projection reconstruction algorithms. Yet, significant image artefacts might also occur due to reflections and scattering of optoacoustically-induced waves from strongly acoustically-mismatched areas in tissues. Herein, we modify the model-based reconstruction methodology to incorporate statistically-based weighting in order to minimize these artefacts. The method is compared with another weighting procedure termed half-image reconstruction, yielding generally better results. The statistically-based weighting is subsequently verified experimentally, attaining quality improvement of the optoacoustic image reconstructions in the presence of acoustic mismatches in tissue phantoms and small animals ex-vivo.

Acceleration of Optoacoustic Model-Based Reconstruction Using Angular Image Discretization

Optoacoustic tomography has recently demonstrated powerful performance in small animal imaging and initial clinical trials in terms of the high spatial resolution, versatile contrast, and dynamic imaging capabilities it can provide. Yet, the current optoacoustic image reconstruction methods are usually based on inaccurate forward modelling approaches or otherwise demand a high computational cost, which imposes certain practical limitations and hinders image quantification. Herein, we introduce a new method for accelerating optoacoustic reconstructions, based on angular image discretization of the forward model solution. The method is particularly suitable for accurate image reconstruction with arbitrary meshes and space-dependent resolution, while it can also readily account for small speed of sound variations without compromising the calculation speed. It is further anticipated that the new approach will greatly facilitate development of high performance 3-D optoacoustic reconstruction methods.

The effects of acoustic attenuation in optoacoustic signals

In this paper, it is demonstrated that the effects of acoustic attenuation may play a significant role in establishing the quality of tomographic optoacoustic reconstructions. Accordingly, spatially dependent reduction of signal amplitude leads to quantification errors in the reconstructed distribution of the optical absorption coefficient while signal broadening causes loss of image resolution. Here we propose a correction algorithm for accounting for attenuation effects, which is applicable in both the time and frequency domains. It is further investigated which part of the optoacoustic signal spectrum is practically affected by those effects in realistic imaging scenarios. The validity and benefits of the suggested modelling and correction approaches are experimentally validated in phantom measurements.

Model‐based optoacoustic inversion with arbitrary‐shape detectors

Optoacoustic imaging enables mapping the optical absorption of biological tissue using optical excitation and acoustic detection. Although most image-reconstruction algorithms are based on the assumption of a detector with an isotropic sensitivity, the geometry of the detector often leads to a response with spatially dependent magnitude and bandwidth. This effect may lead to attenuation or distortion in the recorded signal and, consequently, in the reconstructed image. Herein, an accurate numerical method for simulating the spatially dependent response of an arbitrary-shape acoustic transducer is presented. The method is based on an analytical solution obtained for a two-dimensional line detector. The calculated response is incorporated in the forward model matrix of an optoacoustic imaging setup using temporal convolution, and image reconstruction is performed by inverting the matrix relation. The method was numerically and experimentally demonstrated in two dimensions for both flat and focused transducers and compared to the spatial-convolution method. In forward simulations, the developed method did not suffer from the numerical errors exhibited by the spatial-convolution method. In reconstruction simulations and experiments, the use of both temporal-convolution and spatial-convolution methods lead to an enhancement in resolution compared to a reconstruction with a point detector model. However, because of its higher modeling accuracy, the temporal-convolution method achieved a noise figure approximated three times lower than the spatial-convolution method. The demonstrated performance of the spatial-convolution method shows it is a powerful tool for reducing reconstruction artifacts originating from the detector finite size and improving the quality of optoacoustic reconstructions. Furthermore, the method may be used for assessing new system designs. Specifically, detectors with nonstandard shapes may be investigated.

Statistical optoacoustic image reconstruction using a-priori knowledge on the location of acoustic distortions

Strong reflection and scattering effects, arising at the boundaries of acoustically mismatched areas in living organisms, such as bones, lungs, and other air cavities, may introduce severe image artifacts into optoacoustic reconstructions. Yet, in many cases, an a priori knowledge on the location of strongly mismatched areas is available, either based on general anatomical knowledge or using other imaging modalities. In this letter, we suggest a statistical optoacoustic image reconstruction method, which uses a priori knowledge on the location of acoustic distortions in order to improve image quality and quantification. Significant improvements are showcased experimentally on tissue mimicking phantoms of different complexities.