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.

Respiratory motion in living organisms is known to result in image blurring and loss of resolution, chiefly due to the lengthy acquisition times of the corresponding image acquisition methods. Optoacoustic tomography can effectively eliminate in vivo motion artifacts due to its inherent capacity for collecting image data from the entire imaged region following a single nanoseconds-duration laser pulse. However, multi-frame image analysis is often essential in applications relying on spectroscopic data acquisition or for scanning-based systems. Thereby, efficient methods to correct for image distortions due to motion are imperative. Herein, we demonstrate that efficient motion rejection in optoacoustic tomography can readily be accomplished by frame clustering during image acquisition, thus averting excessive data acquisition and post-processing. The algorithm’s efficiency for two- and three-dimensional imaging was validated with experimental whole-body mouse data acquired by spiral volumetric optoacoustic tomography (SVOT) and full-ring cross-sectional imaging scanners.

Precision-targeted ultrasonic neuromodulation offers immense potential for studying brain function and treating neurological diseases. Yet, its application has been limited by challenges in achieving precise spatio-temporal control and monitoring of ultrasound effects on brain circuits. Here we show that transcranial ultrasound elicits direct and highly focal responses, which can be dynamically steered at spatio-temporal scales relevant for neural function. Furthermore, holographic transcranial ultrasound stimulation allows direct control of the stimulated volume and actively modulates local and mid-range network projections, effectively lowering the activation threshold by an order of magnitude. To better understand this previously unexplored excitability regime not fully explained by the conventional pressure–frequency dyad, we developed a dual modelling framework, where both an empirical and a mechanistic model were constructed to capture the intricacies of holographic transcranial ultrasound stimulation. These models achieve qualitative agreement with our experimental results, suggesting that these findings are predominantly driven by putative network interactions. Our results bring insight on the complex interaction mechanisms of ultrasound with neural tissue and highlight its potential for the noninvasive interfacing of distributed brain networks.

Images rendered with common optoacoustic system implementations are often afflicted with distortions and poor visibility of structures, hindering reliable image interpretation and quantification of bio-chrome distribution. Among the practical limitations contributing to artifactual reconstructions are insufficient tomographic detection coverage and suboptimal illumination geometry, as well as inability to accurately account for acoustic reflections and speed of sound heterogeneities in the imaged tissues. Here we developed a convolutional neural network (CNN) approach for enhancement of optoacoustic image quality which combines training on both time-resolved signals and tomographic reconstructions. Reference human finger data for training the CNN were recorded using a full-ring array system that provides optimal tomographic coverage around the imaged object. The reconstructions were further refined with a dedicated algorithm that minimizes acoustic reflection artifacts induced by acoustically mismatch structures, such as bones. The combined methodology is shown to outperform other learning-based methods solely operating on image-domain data.

Precise noninvasive neuromodulation can potentially help better understanding the inner workings of the brain and tackle the rise of neurodegenerative diseases in an aging population. Deep brain electrical stimulation, which employs surgical implantation of electrodes, has become a treatment option for several neurological disorders including Parkinson’s disease and epilepsy.^1,2 Noninvasive brain stimulation techniques based on electric or magnetic fields are incapable of targeting deeper structures without affecting more superficially located tissues.³ In contrast, sound waves can be focused into tiny tissue volumes with minimal collateral effects.⁴ Focused ultrasound (FUS) thus has the potential to noninvasively target nearly any brain area, both in animal models and humans.^{5 – 8} Unlike the mechanisms of electric and magnetic field interaction with the neurons, the effects of ultrasonic fields at the cellular, network, and whole brain level have not been fully understood,^{9 – 12} chiefly due to the lack of efficient methods for noninvasive real-time observation of ultrasound neuromodulation (USNM) effects.