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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.

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.

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., NeuroImage, 237, ISSN:1053-8119, ISSN:1095-9572

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., Neoplasia, 22 (12), ISSN:1522-8002, ISSN:1476-5586