Photoacoustic (PA) imaging is an imaging concept with a high potential for real-time monitoring of functional tissue parameters such as blood oxygenation deep inside tissue. It measures the acoustic waves arising from the stress-confined thermal response of optical absorption in tissue.¹ More specifically, a PA signal $S(v)$ in a location $v$ is a pressure response to the locally absorbed energy $H(v)$, which, in turn, is a product of the absorption coefficient ${\mu }_a(v)$, the Grueneisen coefficient $\mathrm{\Gamma }(v)$ and the light fluence $\varphi (v)$

Reconstruction of photoacoustic (PA) images acquired with clinical ultrasound transducers is usually performed using the Delay and Sum (DAS) beamforming algorithm. Recently, a variant of DAS, referred to as Delay Multiply and Sum (DMAS) beamforming has been shown to provide increased contrast, signal-to-noise ratio (SNR) and resolution in PA imaging. The main reasons for the use of DAS beamforming in photoacoustics are its simple implementation, real-time capability, and the linearity of the beamformed image to the PA signal. This is crucial for the identification of different chromophores in multispectral PA applications. In contrast, current DMAS implementations are not responsive to the full spectrum of sound frequencies from a photoacoustic source and have not been shown to provide a reconstruction linear to the PA signal. Furthermore, due to its increased computational complexity, DMAS has not been shown yet to work in real-time. Here, we present an open-source real-time variant of the DMAS algorithm, signed DMAS (sDMAS), that ensures linearity in the original PA signal response while providing the increased image quality of DMAS. We show the applicability of sDMAS for multispectral PA applications, in vitro and in vivo. The sDMAS and reference DAS algorithms were integrated in the open-source Medical Imaging Interaction Toolkit (MITK) and are available as real-time capable implementations.

In medical applications, the accuracy and robustness of imaging methods are of crucial importance to ensure optimal patient care. While photoacoustic imaging (PAI) is an emerging modality with promising clinical applicability, state-of-the-art approaches to quantitative photoacoustic imaging (qPAI), which aim to solve the ill-posed inverse problem of recovering optical absorption from the measurements obtained, currently cannot comply with these high standards. This can be attributed to the fact that existing methods often rely on several simplifying a priori assumptions of the underlying physical tissue properties or cannot deal with realistic noise levels. In this manuscript, we address this issue with a new method for estimating an indicator of the uncertainty of an estimated optical property. Specifically, our method uses a deep learning model to compute error estimates for optical parameter estimations of a qPAI algorithm. Functional tissue parameters, such as blood oxygen saturation, are usually derived by averaging over entire signal intensity-based regions of interest (ROIs). Therefore, we propose to reduce the systematic error of the ROI samples by additionally discarding those pixels for which our method estimates a high error and thus a low confidence. In silico experiments show an improvement in the accuracy of optical absorption quantification when applying our method to refine the ROI, and it might thus become a valuable tool for increasing the robustness of qPAI methods.

Spreading depolarization (SD) is a self-propagating wave of near-complete neuronal depolarization that is abundant in a wide range of neurological conditions, including stroke. SD was only recently documented in humans and is now considered a therapeutic target for brain injury, but the mechanisms related to SD in complex brains are not well understood. While there are numerous approaches to interventional imaging of SD on the exposed brain surface, measuring SD deep in brain is so far only possible with low spatiotemporal resolution and poor contrast. Here, we show that photoacoustic imaging enables the study of SD and its hemodynamics deep in the gyrencephalic brain with high spatiotemporal resolution. As rapid neuronal depolarization causes tissue hypoxia, we achieve this by continuously estimating blood oxygenation with an intraoperative hybrid photoacoustic and ultrasonic imaging system. Due to its high resolution, promising imaging depth and high contrast, this novel approach to SD imaging can yield new insights into SD and thereby lead to advances in stroke, and brain injury research.

A robust and accurate quantitative measurement of blood oxygen saturation (${\mathrm{sO}}_2$) with optoacoustic (OA) imaging, also called photoacoustic imaging, is one of the most sought after goals of quantitative OA imaging research due to its wide range of immediate applications. Usually, quantitative OA imaging research aims to achieve an absolute quantification of optical properties, such as the absorption coefficient ${\mu }_{\mathrm{a}}$, from measured OA signals $S(\mathbf{d},t)$ recorded at times $t$ at detector position $\mathbf{d}$.^1,2 In brief, such a quantification of ${\mu }_{\mathrm{a}}$ encompasses a solution of two ill-posed inverse problems. (1) The acoustic inverse problem from $S(\mathbf{d},t)$ to an initial pressure spatial distribution $p_0(\mathbf{x})$. And (2), the optical inverse problem from $H({\mathbf{x}}_0)=p_0({\mathbf{x}}_0)/\mathrm{\Gamma }({\mathbf{x}}_0)=\varphi ({\mathbf{x}}_0,{\mu }_{\mathrm{a}}(\mathbf{x}),{\mu }_{\mathrm{s}}^{\prime }(\mathbf{x}))·{\mu }_{\mathrm{a}}({\mathbf{x}}_0)$ to ${\mu }_{\mathrm{a}}({\mathbf{x}}_0)$, at a location ${\mathbf{x}}_0$, with the Grüneisen parameter $\mathrm{\Gamma }$ and the reduced scattering coefficient ${\mu }_{\mathrm{s}}^{\prime }$. The fluence $\varphi$ is dependent on unknowns such as the absorption and scattering in the tissue surrounding ${\mathbf{x}}_0$. Quantitative OA imaging methods either depend on model-based inversion^{2 – 7} or data-driven approaches.^{8 – 13} These approaches perform well in silico but often struggle with the translation to real measurements in phantoms or in vivo.

Photoacoustic imaging through skull bone causes strong attenuation and distortion of the acoustic wavefront, which diminishes image contrast and resolution. As a result, transcranial photoacoustic measurements in humans have been challenging to demonstrate. In this study, we investigated the acoustic transmission through the human skull to design an ultrasound sensor suitable for transcranial PA imaging and sensing. We measured the frequency dependent losses of human cranial bones ex vivo, compared the performance of a range of piezoelectric and optical ultrasound sensors, and imaged skull phantoms using a PA tomograph based on a planar Fabry–Perot sensor. All transcranial photoacoustic measurements show the typical effects of frequency and thickness dependent attenuation and aberration associated with acoustic propagation through bone. The performance of plano-concave optical resonator ultrasound sensors was found to be highly suitable for transcranial photoacoustic measurements.