Optoacoustic imaging and tomography: Reconstruction approaches and outstanding challenges in image performance and quantification.

This paper comprehensively reviews the emerging topic of optoacoustic imaging from the image reconstruction and quantification perspective. Optoacoustic imaging combines highly attractive features, including rich contrast and high versatility in sensing diverse biological targets, excellent spatial resolution not compromised by light scattering, and relatively low cost of implementation. Yet, living objects present a complex target for optoacoustic imaging due to the presence of a highly heterogeneous tissue background in the form of strong spatial variations of scattering and absorption. Extracting quantified information on the actual distribution of tissue chromophores and other biomarkers constitutes therefore a challenging problem. Image quantification is further compromised by some frequently-used approximated inversion formulae. In this review, the currently available optoacoustic image reconstruction and quantification approaches are assessed, including back-projection and model-based inversion algorithms, sparse signal representation, wavelet-based approaches, methods for reduction of acoustic artifacts as well as multi-spectral methods for visualization of tissue bio-markers. Applicability of the different methodologies is further analyzed in the context of real-life performance in small animal and clinical in-vivo imaging scenarios

Cross-sectional optoacoustic tomographic reconstructions in a polar grid.

A commonly employed optoacoustic configuration utilizes an array of cylindrically-focused transducers around the sample to selectively image it cross-sectionally. For this geometry, model-based reconstruction procedures have showcased improved performance over common back-projection formulae. The forward model is expressed as a linear operator (model-matrix) mapping the optical absorption in a grid to the resulting measured wavefield. Herein, a model based on a time-domain discretization in a polar grid is introduced, which allows efficient storage of the model-matrix due to rotational symmetries, thus leading to a fast and memory efficient inversion. Good performance of the method is showcased numerically and experimentally

Expediting model-based optoacoustic reconstructions with tomographic symmetries.

Purpose: 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.Methods: 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.Results: 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.Conclusions: 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

Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation.

One of the major challenges in dynamic multispectral optoacoustic imaging is its relatively low signal-to-noise ratio which often requires repetitive signal acquisition and averaging, thus limiting imaging rate. The development of denoising methods which prevent the need for signal averaging in time presents an important goal for advancing the dynamic capabilities of the technology. Methods: In this paper, a denoising method is developed for multispectral optoacoustic imaging which exploits the implicit sparsity of multispectral optoacoustic signals both in space and in spectrum. Noise suppression is achieved by applying thresholding on a combined wavelet-Karhunen–Loève representation in which multispectral optoacoustic signals appear particularly sparse. The method is based on inherent characteristics of multispectral optoacoustic signals of tissues, offering promise for general application in different incarnations of multispectral optoacoustic systems. Results: The performance of the proposed method is demonstrated on mouse images acquired in vivo for two common additive noise sources: time-varying parasitic signals and white noise. In both cases, the proposed method shows considerable improvement in image quality in comparison to previously published denoising strategies that do not consider multispectral information. Conclusions: The suggested denoising methodology can achieve noise suppression with minimal signal loss and considerably outperforms previously proposed denoising strategies, holding promise for advancing the dynamic capabilities of multispectral optoacoustic imaging while retaining image quality. &nbsp

Image reconstruction in cross-sectional optoacoustic tomography based on non-negative constrained model-based inversion.

In optoacoustic tomography, images representing the light absorption distribution are reconstructed from the measured acoustic pressure waves at several locations around the imaged sample. Most reconstruction algorithms typically yield negative absorption values due to modelling inaccuracies and imperfect measurement conditions. Those negative optical absorption values have no physical meaning and their presence hinders image quantification and interpretation of biological information. We investigate herein the performance of optimization methods that impose non-negative constraints in model-based optoacoustic inversion. Specifically, we analyze the effects of the non-negative restrictions on image quality and accuracy as compared to the unconstrained approach. An efficient algorithm based on the projected quasi-Newton scheme and the limitedmemory Broyden-Fletcher-Goldfarb-Shannon method is used for the non-negative constrained inversion. We showcase that imposing non-negative constraints in model-based reconstruction leads to a quality increase in cross-sectional tomographic optoacoustic images.CCC

High-throughput sparsity-based inversion scheme for optoacoustic tomography.

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

Real-time optoacoustic tomography of indocyanine green perfusion in human vasculature.

We interrogated whether optoacoustic tomography could be employed to study blood flow kinetics and bio-distribution of injected fluorescent agents in humans. Using a 128-channel scanner at a frame rate of 10 images per second, we obtained cross-sectional images of the human finger, before and after the administration of Indocyanine Green (ICG). We demonstrate that multi-spectral optoacoustic tomography (MSOT) can sense fast flow kinetics and resolve spatio-temporal characteristics of a common fluorochrome in human vasculature at clinically relevant concentrations. We further register ICG images with oxygen saturation maps and anatomical views of the proximal interphalangeal joint of a healthy volunteer

Efficient non-negative constrained model-based inversion in optoacoustic tomography.

The inversion accuracy in optoacoustic tomography depends on a number of parameters, including the number of detectors employed, discrete sampling issues or imperfectness of the forward model. These parameters result in ambiguities on the reconstructed image. A common ambiguity is the appearance of negative values, which have no physical meaning since optical absorption can only be higher or equal than zero. We investigate herein algorithms that impose non-negative constraints in model-based optoacoustic inversion. Several state-of-the-art non-negative constrained algorithms are analyzed. Furthermore, an algorithm based on the conjugate gradient method is introduced in this work. We are particularly interested in investigating whether positive restrictions lead to accurate solutions or drive the appearance of errors and artifacts. It is shown that the computational performance of non-negative constrained inversion is higher for the introduced algorithm than for the other algorithms, while yielding equivalent results. The experimental performance of this inversion procedure is then tested in phantoms and small animals, showing an improvement in image quality and quantitativeness with respect to the unconstrained approach. The study performed validates the use of non-negative constraints for improving image accuracy compared to unconstrained methods, while maintaining computational efficiency