Macro-mechanics controls quantum mechanics: mechanically controllable quantum conductance switching of an electrochemically fabricated atomic-scale point contact.

Here, we present a silver atomic-scale device fabricated and operated by a combined technique of electrochemical control (EC) and mechanically controllable break junction (MCBJ). With this EC-MCBJ technique, we can perform mechanically controllable bistable quantum conductance switching of a silver quantum point contact (QPC) in an electrochemical environment at room temperature. Furthermore, the silver QPC of the device can be controlled both mechanically and electrochemically, and the operating mode can be changed from 'electrochemical' to 'mechanical', which expands the operating mode for controlling QPCs. These experimental results offer the perspective that a silver QPC may be used as a contact for a nanoelectromechanical relay.

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.

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.

Optoacoustic image segmentation based on signal domain analysis.

Efficient segmentation of optoacoustic images has importance in enhancing the diagnostic and quantification capacity of this modality. It may also aid in improving the tomographic reconstruction accuracy by accounting for heterogeneous optical and acoustic tissue properties. In particular, when imaging through complex biological tissues, the real acoustic properties often deviate considerably from the idealized assumptions of homogenous conditions, which may lead to significant image artifacts if not properly accounted for. Although several methods have been proposed aiming at estimating and accounting for the complex acoustic properties in the image domain, accurate delineation of structures is often hindered by low contrast of the images and other artifacts produced due to incomplete tomographic coverage and heuristic assignment of the tissue properties during the reconstruction process. In this letter, we propose instead a signal domain analysis approach that retrieves acoustic properties of the object to be reconstructed from characteristic features of the detected optoacoustic signals prior to image reconstruction. Performance of the proposed method is first tested in simulation and experiment using two-dimensional tissue-mimicking phantoms. Significant improvements in the segmentation abilities and overall reconstructed image quality are further showcased in experimental cross-sectional data acquired from a human finger.

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

PURPOSE: 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.

Real-time optoacoustic tomography of indocyanine green perfusion and oxygenation parameters in human finger vasculature.

We interrogated whether optoacoustic tomography could be employed to study blood functional parameters and biodistribution of injected fluorescent agents in humans. Using a multichannel scanner at a frame rate of 10 images per second, we obtained cross-sectional images of the human finger in real time, before and after the administration of indocyanine green. We demonstrated that multispectral optoacoustic tomography can sense fast flow kinetics and resolve spatiotemporal 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.

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.

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.