Optoacoustic tomography based on insufficient spatial sampling of ultrasound waves leads to loss of contrast and artifacts on the reconstructed images. Compared to reconstructions based on L2-norm regularization, sparsity-based reconstructions may improve contrast and reduce image artifacts but at a high computational cost, which has so far limited their use to 2D optoacoustic tomography. Here we propose a fast, sparsity-based reconstruction algorithm for 3D optoacoustic tomography, based on gradient descent with Barzilai-Borwein line search (L1-GDBB). Using simulations and experiments, we show that the L1-GDBB offers fourfold faster reconstruction than the previously reported L1-norm regularized reconstruction based on gradient descent with backtracking line search. Moreover, the new algorithm provides higher-quality images with fewer artifacts than the L2-norm regularized reconstruction and the back-projection reconstruction.
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.
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.
The electrochemical reduction of CO2 is a pivotal technology for the defossilization of the chemical industry. Although first pilot-scale electrolyzers exist, water management and salt precipitation remain a major hurdle to long-term operation. In this work, we present the first high resolution neutron imaging (6 µm) of a zero-gap CO2 electrolyzer to uncover water distribution and salt precipitation under application-relevant operating conditions (200 mA cm− 2 at 2.8 V with a Faraday efficiency for CO of 99%). Precipitated salts penetrating the cathode gas diffusion layer can be observed, which are believed to block the CO2 gas transport and are therefore the major cause for the commonly observed decay in Faraday efficiency. Neutron imaging further shows higher carbonate accumulation under the cathode channel of the flow field compared to the land. In fact, a higher local reaction rate under the land compared to the channel can be estimated from the gas bubble generation on the opposing anode side.
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