Hybrid spherical array for combined volumetric optoacoustic and B-mode ultrasound imaging.

Optoacoustic (OA) imaging has achieved tremendous progress with state-of-the-art systems providing excellent functional and molecular contrast, centimeter scale penetration into living tissues, and ultrafast imaging performance, making it highly suitable for handheld imaging in the clinics. OA can greatly benefit from efficient integration with ultrasound (US) imaging, which remains the routine method in bedside clinical diagnostics. However, such integration has not been straightforward since the two modalities typically involve different image acquisition strategies. Here, we present a new, to our knowledge, hybrid optoacoustic ultrasound (OPUS) imaging approach employing a spherical array with dedicated segments for each modality to enable volumetric OA imaging merged with conventional B-mode US. The system performance is subsequently showcased in healthy human subjects. The new OPUS approach hence represents an important step toward establishing OA in point-of-care diagnostic settings.

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.

Multimodal assessment of non-alcoholic fatty liver disease with transmission-reflection optoacoustic ultrasound.

Non-alcoholic fatty liver disease (NAFLD) is an umbrella term referring to a group of conditions associated to fat deposition and damage of liver tissue. Early detection of fat accumulation is essential to avoid progression of NAFLD to serious pathological stages such as liver cirrhosis and hepatocellular carcinoma. Methods: We exploited the unique capabilities of transmission-reflection optoacoustic ultrasound (TROPUS), which combines the advantages of optical and acoustic contrasts, for an early-stage multi-parametric assessment of NAFLD in mice. Results: The multispectral optoacoustic imaging allowed for spectroscopic differentiation of lipid content, as well as the bio-distributions of oxygenated and deoxygenated hemoglobin in liver tissues in vivo. The pulse-echo (reflection) ultrasound (US) imaging further provided a valuable anatomical reference whilst transmission US facilitated the mapping of speed of sound changes in lipid-rich regions, which was consistent with the presence of macrovesicular hepatic steatosis in the NAFLD livers examined with ex vivo histological staining. Conclusion: The proposed multimodal approach facilitates quantification of liver abnormalities at early stages using a variety of optical and acoustic contrasts, laying the ground for translating the TROPUS approach toward diagnosis and monitoring NAFLD in patients.

Full-view LED-based optoacoustic tomography.

Optoacoustic tomography is commonly performed with bulky and expensive short-pulsed solid-state lasers providing high per-pulse energies in the millijoule range. Light emitting diodes (LEDs) represent a cost-effective and portable alternative for optoacoustic signal excitation that can additionally provide excellent pulse-to-pulse stability. Herein, we introduce a full-view LED-based optoacoustic tomography (FLOAT) system for deep tissue in vivo imaging. It is based on a custom-made electronic unit driving a stacked array of LEDs, which attains 100 ns pulse width and highly stable (0.62 % standard deviation) total per-pulse energy of 0.48 mJ. The illumination source is integrated into a circular array of cylindrically-focused ultrasound detection elements to result in a full-view tomographic configuration, which plays a critical role in circumventing limited-view effects, enhancing the effective field-of-view and image quality for cross-sectional (2D) imaging. We characterized the FLOAT performance in terms of pulse width, power stability, excitation light distribution, signal-to-noise and penetration depth. FLOAT of the human finger revealed a comparable imaging performance to that achieved with the standard pulsed Nd:YAG laser. It is anticipated that this compact, affordable and versatile illumination technology will facilitate optoacoustic imaging developments in resource-limited settings for biological and clinical applications.

Real-time assessment of high-intensity focused ultrasound heating and cavitation with hybrid optoacoustic ultrasound imaging.

High-intensity focused ultrasound (HIFU) enables localized ablation of biological tissues by capitalizing on the synergistic effects of heating and cavitation. Monitoring of those effects is essential for improving the efficacy and safety of HIFU interventions. Herein, we suggest a hybrid optoacoustic-ultrasound (OPUS) approach for real-time assessment of heating and cavitation processes while providing an essential anatomical reference for accurate localization of the HIFU-induced lesion. Both effects could clearly be observed by exploiting the temperature dependence of optoacoustic (OA) signals and the strong contrast of gas bubbles in pulse-echo ultrasound (US) images. The differences in temperature increase and its rate, as recorded with a thermal camera for different HIFU pressures, evinced the onset of cavitation at the expected pressure threshold. The estimated temperatures based on OA signal variations were also within 10-20 % agreement with the camera readings for temperatures below the coagulation threshold (∼50 °C). Experiments performed in excised tissues as well as in a post-mortem mouse demonstrate that both heating and cavitation effects can be effectively visualized and tracked using the OPUS approach. The good sensitivity of the suggested method for HIFU monitoring purposes was manifested by a significant increase in contrast-to-noise ratio within the ablated region by > 10 dB and > 5 dB for the OA and US images, respectively. The hybrid OPUS-based monitoring approach offers the ease of handheld operation thus can readily be implemented in a bedside setting to benefit several types of HIFU treatments used in the clinics.

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging.

Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy.

Expediting Image Acquisition in Reflection Ultrasound Computed Tomography.

Reflection ultrasound computed tomography (RUCT) attains optimal image quality from objects that can be fully accessed from multiple directions, such as the human breast or small animals. Owing to the full-view tomography approach based on the compounding of images taken from multiple angles, RUCT effectively mitigates several deficiencies afflicting conventional pulse-echo ultrasound (US) systems, such as speckle patterns and interuser variability. On the other hand, the small interelement pitch required to fulfill the spatial sampling criterion in the circular transducer configuration used in RUCT typically implies the use of an excessive number of independent array elements. This increases the system's complexity and costs, and limits the achievable imaging speed. Here, we explore acquisition schemes that enable RUCT imaging with the reduced number of transmit/receive elements. We investigated the influence of the element size in transmission and reception in a ring array geometry. The performance of a sparse acquisition approach based on partial acquisition from a subset of the elements has been further assessed. A larger element size is shown to preserve contrast and resolution at the center of the field of view (FOV), while a reduced number of elements is shown to cause uniform loss of contrast and resolution across the entire FOV. The tradeoffs of achievable FOV, contrast-to-noise ratio, and temporal and spatial resolutions are assessed in phantoms and in vivo mouse experiments. The experimental analysis is expected to aid the development of optimized hardware and image acquisition strategies for RUCT and, thus, result in more affordable imaging systems facilitating wider adoption.

Preoperative Mapping of Lymphatic Vessels by Multispectral Optoacoustic Tomography.

Background: In lymphatic reconstructive surgery, visualization of lymph vessels is of paramount importance. Indocyanine green (ICG) lymphography is the current gold standard in preoperative lymphatic imaging. However, visualization of lymph vessels is often limited by an overlying dermal backflow of ICG, becoming particularly prominent in advanced lymphedema stages. Multispectral optoacoustic tomography (MSOT) has recently been introduced as a promising noninvasive tool for lymphatic imaging. Methods and Results: A single-center proof-of-concept study with a prospective observational design was conducted at the Department of Plastic Surgery and Hand Surgery of the University Hospital Zurich. Between February 2021 and August 2021, seven patients with different grades of lymphedema were analyzed by the MSOT Acuity system before undergoing lymphovenous anastomosis (LVA). Conventional ICG lymphography served as comparison. MSOT succeeded to accurately depict blood and lymphatic vessels at different locations in six patients, including areas of dermal backflow. The MSOT signal of lymph vessels further correlated well with their macroscopic appearance. Conclusion: We could successfully visualize lymphatic vessels in patients with lymphedema by MSOT and establish the new method for preoperative mapping and selection of incision sites for LVA. Regardless of dermal backflow patterns, MSOT proved to be a valuable approach for identifying and clearly discerning between lymphatic and blood vessels.

Deep learning of image- and time-domain data enhances the visibility of structures in optoacoustic tomography.

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.

Hemodynamic response to sensory stimulation in mice: Comparison between functional ultrasound and optoacoustic imaging.

Intense efforts are underway to develop functional imaging modalities for capturing brain activity at the whole organ scale with high spatial and temporal resolution. Functional optoacoustic (fOA) imaging is emerging as a new tool to monitor multiple hemodynamic parameters across the mouse brain, but its sound validation against other neuroimaging modalities is often lacking. Here we investigate mouse brain responses to peripheral sensory stimulation using both fOA and functional ultrasound (fUS) imaging. The two modalities operate under similar spatio-temporal resolution regime, with a potential to provide synergistic and complementary hemodynamic readouts. Specific contralateral activation was observed with sub-millimeter spatial resolution with both methods. Sensitivity to hemodynamic activity was found to be on comparable levels, with the strongest responses obtained in the oxygenated hemoglobin channel of fOA. While the techniques attained highly correlated hemodynamic responses, the differential fOA readings of oxygenated and deoxygenated haemoglobin provided complementary information to the blood flow contrast of fUS. The multi-modal approach may thus emerge as a powerful tool providing new insights into brain function, complementing our current knowledge generated with well-established neuroimaging methods.

Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images.

The highly complementary information provided by multispectral optoacoustics and pulse-echo ultrasound have recently prompted development of hybrid imaging instruments bringing together the unique contrast advantages of both modalities. In the hybrid optoacoustic ultrasound (OPUS) combination, images retrieved by one modality may further be used to improve the reconstruction accuracy of the other. In this regard, image segmentation plays a major role as it can aid improving the image quality and quantification abilities by facilitating modeling of light and sound propagation through the imaged tissues and surrounding coupling medium. Here, we propose an automated approach for surface segmentation in whole-body mouse OPUS imaging using a deep convolutional neural network (CNN). The method has shown robust performance, attaining accurate segmentation of the animal boundary in both optoacoustic and pulse-echo ultrasound images, as evinced by quantitative performance evaluation using Dice coefficient metrics.

Noninvasive multiparametric charac-terization of mammary tumors with transmission-reflection optoacoustic ultrasound.

Development of imaging methods capable of furnishing tumor-specific morphological, functional, and molecular information is paramount for early diagnosis, staging, and treatment of breast cancer. Ultrasound (US) and optoacoustic (OA) imaging methods exhibit excellent traits for tumor imaging in terms of fast imaging speed, ease of use, excellent contrast, and lack of ionizing radiation. Here, we demonstrate simultaneous tomographic whole body imaging of optical absorption, US reflectivity, and speed of sound (SoS) in living mice. In vivo studies of 4T1 breast cancer xenografts models revealed synergistic and complementary value of the hybrid imaging approach for characterizing mammary tumors. While neovasculature surrounding the tumor areas were observed based on the vascular anatomy contrast provided by the OA data, the tumor boundaries could be discerned by segmenting hypoechoic structures in pulse-echo US images. Tumor delineation was further facilitated by enhancing the contrast and spatial resolution of the SoS maps with a full-wave inversion method. The malignant lesions could thus be distinguished from other hypoechoic regions based on the average SoS values. The reported findings corroborate the strong potential of the hybrid imaging approach for advancing cancer research in small animal models and fostering development of new clinical diagnostic approaches.