Background and Purpose: Oncological neurosurgery relies heavily on making continuous, intra-operative tumor-brain delineations based on image-guidance. Limitations of currently available imaging techniques call for the development of real-time image-guided resection tools, which allow for reliable functional and anatomical information in an intra-operative setting. Functional ultrasound (fUS), is a new mobile neuro-imaging tool with unprecedented spatiotemporal resolution, which allows for the detection of small changes in blood dynamics that reflect changes in metabolic activity of activated neurons through neurovascular coupling. We have applied fUS during conventional awake brain surgery to determine its clinical potential for both intra-operative functional and vascular brain mapping, with the ultimate aim of achieving maximum safe tumor resection. Methods: During awake brain surgery, fUS was used to image tumor vasculature and task-evoked brain activation with electrocortical stimulation mapping (ESM) as a gold standard. For functional imaging, patients were presented with motor, language or visual tasks, while the probe was placed over (ESM-defined) functional brain areas. For tumor vascular imaging, tumor tissue (pre-resection) and tumor resection cavity (post-resection) were imaged by moving the hand-held probe along a continuous trajectory over the regions of interest. Results: A total of 10 patients were included, with predominantly intra-parenchymal frontal and temporal lobe tumors of both low and higher histopathological grades. fUS was able to detect (ESM-defined) functional areas deep inside the brain for a range of functional tasks including language processing. Brain tissue could be imaged at a spatial and temporal resolution of 300 μm and 1.5–2.0 ms respectively, revealing real-time tumor-specific, and healthy vascular characteristics. Conclusion: The current study presents the potential of applying fUS during awake brain surgery. We illustrate the relevance of fUS for awake brain surgery based on its ability to capture both task-evoked functional cortical responses as well as differences in vascular characteristics between tumor and healthy tissue. As current neurosurgical practice is still pre-dominantly leaning on inherently limited pre-operative imaging techniques for tumor resection-guidance, fUS enters the scene as a promising alternative that is both anatomically and physiologically informative.@en

Functional ultrasound (fUS) indirectly measures brain activity by detecting changes in cerebral blood volume following neural activation. Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input signal based on the stimulus onsets, the so-called experimental paradigm (EP). However, the EP may not characterize the whole complexity of the activity-inducing signals that evoke the hemodynamic changes. Furthermore, the HRF is known to vary across brain areas and stimuli. To achieve an adaptable framework that can capture such dynamics of the brain function, we model the multivariate fUS time-series as convolutive mixtures and apply block-term decomposition on a set of lagged fUS autocorrelation matrices, revealing both the region-specific HRFs and the source signals that induce the hemodynamic responses. We test our approach on two mouse-based fUS experiments. In the first experiment, we present a single type of visual stimulus to the mouse, and deconvolve the fUS signal measured within the mouse brain’s lateral geniculate nucleus, superior colliculus and visual cortex. We show that the proposed method is able to recover back the time instants at which the stimulus was displayed, and we validate the estimated region-specific HRFs based on prior studies. In the second experiment, we alter the location of the visual stimulus displayed to the mouse, and aim at differentiating the various stimulus locations over time by identifying them as separate sources.@en

Current methods to measure blood flow using ultrafast Doppler imaging often make use of a Singular Value Decomposition (SVD). The SVD has been shown to be an effective way to remove clutter signals associated with slow moving tissue. Conventionally, the SVD is calculated from an ensemble of frames, after which the first dominant eigenvectors are removed. The Power Doppler Image (PDI) is then computed by averaging over the remaining components. The SVD method is computationally intensive and lacks flexibility due to the fixed ensemble length. We propose a method, based on the Projection Approximation Subspace Tracking (PAST) algorithm, which is computationally efficient and allows us to sequentially estimate and remove the principal components, while also offering flexibility for calculating the PDI, e.g. by using any convolutional filter. During a functional ultrasound (fUS) measurement, the intensity variations over time for every pixel were correlated to a known stimulus pattern. The results show that for a pixel chosen around the location of the stimulation electrode, the PAST algorithm achieves a higher Pearson correlation coefficient than the state-of-the-art SVD method, highlighting its potential to be used for fUS measurements.@en

Functional ultrasound (fUS) is a relatively new imaging modality to study the brain with a high spatiotemporal resolution and a wide field-of-view. In fUS detailed images of cerebral blood flow and volume are used to derive functional information, as changes in local flow and/or volume may reflect neuronal activation through neurovascular coupling. Most fUS studies so far have been performed in rats. Translating fUS to mice, which is a favorable animal model for neuroscience, pleads for a higher spatial resolution than what has been reported so far. As a consequence the temporal sampling of the blood flow should also be increased in order to adequately capture the wide range in blood velocities, as the Doppler shifts are inversely proportional to the spatial resolution. Here we present our first detailed images of the mouse brain vasculature at high spatiotemporal resolution. In addition we show some early experimental work on tracking brain activity upon local electrical stimulation.@en

Four-dimensional ultrasound imaging of complex biological systems such as the brain is technically challenging because of the spatiotemporal sampling requirements. We present computational ultrasound imaging (cUSi), an imaging method that uses complex ultrasound fields that can be generated with simple hardware and a physical wave prediction model to alleviate the sampling constraints. cUSi allows for high-resolution four-dimensional imaging of brain hemodynamics in awake and anesthetized mice.@en