Photoacoustic quantification of tissue oxygenation using conditional invertible neural networks.

Intelligent systems in interventional healthcare depend on the reliable perception of the environment. In this context, photoacoustic tomography (PAT) has emerged as a non-invasive, functional imaging modality with great clinical potential. Current research focuses on converting the high-dimensional, not human-interpretable spectral data into the underlying functional information, specifically the blood oxygenation. One of the largely unexplored issues stalling clinical advances is the fact that the quantification problem is ambiguous, i.e. that radically different tissue parameter configurations could lead to almost identical photoacoustic spectra. In the present work, we tackle this problem with conditional Invertible Neural Networks (cINNs). Going beyond traditional point estimates, our network is used to compute an approximation of the conditional posterior density of tissue parameters given the measurement. To this end, an automatic mode detection algorithm extracts the plausible solution from the sample-based posterior. According to a comprehensive validation study based on both synthetic and real images, our approach is well-suited for exploring ambiguity in quantitative PAT.

Depth-Specific Hypoxic Responses to Spreading Depolarizations in Gyrencephalic Swine Cortex Unveiled by Photoacoustic Imaging.

Spreading depolarizations (SDs) are a marker of brain injury and have a causative effect on ischemic lesion progression. The hemodynamic responses elicited by SDs are contingent upon the metabolic integrity of the affected tissue, with vasoconstrictive reactions leading to pronounced hypoxia often indicating poor outcomes. The stratification of hemodynamic responses within different cortical layers remains poorly characterized. This pilot study sought to elucidate the depth-specific hemodynamic changes in response to SDs within the gray matter of the gyrencephalic swine brain. Employing a potassium chloride-induced SD model, we utilized multispectral photoacoustic imaging (PAI) to estimate regional cerebral oxygen saturation (rcSO2%) changes consequent to potassium chloride-induced SDs. Regions of interest were demarcated at three cortical depths covering up to 4 mm. Electrocorticography (ECoG) strips were placed to validate the presence of SDs. Through PAI, we detected 12 distinct rcSO2% responses, which corresponded with SDs detected in ECoG. Notably, a higher frequency of hypoxic responses was observed in the deeper cortical layers compared to superficial layers, where hyperoxic and mixed responses predominated (p < 0.001). This data provides novel insights into the differential oxygenation patterns across cortical layers in response to SDs, underlining the complexity of cerebral hemodynamics post-injury.

Mild hypothermia reduces spreading depolarizations and infarct size in a swine model.

Spreading depolarizations (SDs) have been linked to infarct volume expansion following ischemic stroke. Therapeutic hypothermia provides a neuroprotective effect after ischemic stroke. This study aimed to evaluate the effect of hypothermia on the propagation of SDs and infarct volume in an ischemic swine model. Through left orbital exenteration, middle cerebral arteries were surgically occluded (MCAo) in 16 swine. Extensive craniotomy and durotomy were performed. Six hypothermic and five normothermic animals were included in the analysis. An intracranial temperature probe was placed right frontal subdural. One hour after ischemic onset, mild hypothermia was induced and eighteen hours of electrocorticographic (ECoG) and intrinsic optical signal (IOS) recordings were acquired. Postmortem, 4 mm-thick slices were stained with 2,3,5-triphenyltetrazolium chloride to estimate the infarct volume. Compared to normothermia (36.4 ± 0.4°C), hypothermia (32.3 ± 0.2°C) significantly reduced the frequency and expansion of SDs (ECoG: 3.5 ± 2.1, 73.2 ± 5.2% vs. 1.0 ± 0.7, 41.9 ± 21.8%; IOS 3.9 ± 0.4, 87.6 ± 12.0% vs. 1.4 ± 0.7, 67.7 ± 8.3%, respectively). Further, infarct volume among hypothermic animals (23.2 ± 1.8% vs. 32.4 ± 2.5%) was significantly reduced. Therapeutic hypothermia reduces infarct volume and the frequency and expansion of SDs following cerebral ischemia.

Spatial and temporal frequency band changes during infarct induction, infarct progression, and spreading depolarizations in the gyrencephalic brain.

Aim: To describe the spatial and temporal electrocorticographic (ECoG) changes after middle cerebral artery occlusion (MCAo), including those caused by spreading depolarization (SD) in the pig brain. Methods: The left middle cerebral arteries (MCAs) were clipped in six pigs. The clipping procedure lasted between 8 and 12 min, achieving a permanent occlusion (MCAo). Five-contact ECoG stripes were placed bilaterally over the frontoparietal cortices corresponding to the irrigation territory of the MCA and anterior cerebral artery (ACA). ECoG recordings were performed around 24 h: 1 h before and 23 h after the MCAo, and SDs were quantified. Five-minute ECoG signal segments were sampled before, 5 min, and 4, 8, and 12 h after cerebral artery occlusion and before, during, and after the negative direct current shift of the SDs. The power spectrum of the signals was decomposed into delta, theta, alpha, beta, and gamma bands. Descriptive statistics, Wilcoxon matched-pairs signed-rank tests, and Friedman tests were performed. Results: Electrodes close to the MCAo showed instant decay in all frequency bands and SD onset during the first 5 h. Electrodes far from the MCAo exhibited immediate loss of fast frequencies and progressive decline of slow frequencies with an increased SD incidence between 6 and 14 h. After 8 h, the ACA electrode reported a secondary reduction of all frequency bands except gamma and high SD incidence within 12-17 h. During the SD, all electrodes showed a decline in all frequency bands. After SD passage, frequency band recovery was impaired only in MCA electrodes. Conclusion: ECoG can identify infarct progression and secondary brain injury. Severe disturbances in all the frequency bands are generated in the cortices where the SDs are passing by.

Photoacoustic image synthesis with generative adversarial networks.

Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the optical inverse problem with supervised machine learning were hampered by the absence of labeled reference data. While this bottleneck has been tackled by simulating training data, the domain gap between real and simulated images remains an unsolved challenge. We propose a novel approach to PAT image synthesis that involves subdividing the challenge of generating plausible simulations into two disjoint problems: (1) Probabilistic generation of realistic tissue morphology, and (2) pixel-wise assignment of corresponding optical and acoustic properties. The former is achieved with Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data. According to a validation study on a downstream task our approach yields more realistic synthetic images than the traditional model-based approach and could therefore become a fundamental step for deep learning-based quantitative PAT (qPAT).

Semantic segmentation of multispectral photoacoustic images using deep learning.

Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic and ultrasound imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.

SIMPA: an open-source toolkit for simulation and image processing for photonics and acoustics.

SIGNIFICANCE: Optical and acoustic imaging techniques enable noninvasive visualisation of structural and functional properties of tissue. The quantification of measurements, however, remains challenging due to the inverse problems that must be solved. Emerging data-driven approaches are promising, but they rely heavily on the presence of high-quality simulations across a range of wavelengths due to the lack of ground truth knowledge of tissue acoustical and optical properties in realistic settings. AIM: To facilitate this process, we present the open-source simulation and image processing for photonics and acoustics (SIMPA) Python toolkit. SIMPA is being developed according to modern software design standards. APPROACH: SIMPA enables the use of computational forward models, data processing algorithms, and digital device twins to simulate realistic images within a single pipeline. SIMPA's module implementations can be seamlessly exchanged as SIMPA abstracts from the concrete implementation of each forward model and builds the simulation pipeline in a modular fashion. Furthermore, SIMPA provides comprehensive libraries of biological structures, such as vessels, as well as optical and acoustic properties and other functionalities for the generation of realistic tissue models. RESULTS: To showcase the capabilities of SIMPA, we show examples in the context of photoacoustic imaging: the diversity of creatable tissue models, the customisability of a simulation pipeline, and the degree of realism of the simulations. CONCLUSIONS: SIMPA is an open-source toolkit that can be used to simulate optical and acoustic imaging modalities. The code is available at: https://github.com/IMSY-DKFZ/simpa, and all of the examples and experiments in this paper can be reproduced using the code available at: https://github.com/IMSY-DKFZ/simpa_paper_experiments.

Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern.

PURPOSE: Photoacoustic tomography (PAT) is a novel imaging technique that can spatially resolve both morphological and functional tissue properties, such as vessel topology and tissue oxygenation. While this capacity makes PAT a promising modality for the diagnosis, treatment, and follow-up of various diseases, a current drawback is the limited field of view provided by the conventionally applied 2D probes. METHODS: In this paper, we present a novel approach to 3D reconstruction of PAT data (Tattoo tomography) that does not require an external tracking system and can smoothly be integrated into clinical workflows. It is based on an optical pattern placed on the region of interest prior to image acquisition. This pattern is designed in a way that a single tomographic image of it enables the recovery of the probe pose relative to the coordinate system of the pattern, which serves as a global coordinate system for image compounding. RESULTS: To investigate the feasibility of Tattoo tomography, we assessed the quality of 3D image reconstruction with experimental phantom data and in vivo forearm data. The results obtained with our prototype indicate that the Tattoo method enables the accurate and precise 3D reconstruction of PAT data and may be better suited for this task than the baseline method using optical tracking. CONCLUSIONS: In contrast to previous approaches to 3D ultrasound (US) or PAT reconstruction, the Tattoo approach neither requires complex external hardware nor training data acquired for a specific application. It could thus become a valuable tool for clinical freehand PAT.

Learned spectral decoloring enables photoacoustic oximetry.

The ability of photoacoustic imaging to measure functional tissue properties, such as blood oxygenation sO[Formula: see text], enables a wide variety of possible applications. sO[Formula: see text] can be computed from the ratio of oxyhemoglobin HbO[Formula: see text] and deoxyhemoglobin Hb, which can be distuinguished by multispectral photoacoustic imaging due to their distinct wavelength-dependent absorption. However, current methods for estimating sO[Formula: see text] yield inaccurate results in realistic settings, due to the unknown and wavelength-dependent influence of the light fluence on the signal. In this work, we propose learned spectral decoloring to enable blood oxygenation measurements to be inferred from multispectral photoacoustic imaging. The method computes sO[Formula: see text] pixel-wise, directly from initial pressure spectra [Formula: see text], which represent initial pressure values at a fixed spatial location [Formula: see text] over all recorded wavelengths [Formula: see text]. The method is compared to linear unmixing approaches, as well as pO[Formula: see text] and blood gas analysis reference measurements. Experimental results suggest that the proposed method is able to obtain sO[Formula: see text] estimates from multispectral photoacoustic measurements in silico, in vitro, and in vivo.