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.

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Functional ultrasound (fUS) using a 1-D-array transducer normally is insufficient to capture volumetric functional activity due to being restricted to imaging a single brain slice at a time. Typically, for volumetric fUS, functional recordings are repeated many times as the transducer is moved to a new location after each recording, resulting in a nonunique average mapping of the brain response and long scan times. Our objective was to perform volumetric 3-D fUS in an efficient and cost-effective manner. This was achieved by mounting a 1-D-array transducer to a high-precision motorized linear stage and continuously translating over the mouse brain in a sweeping manner. We show how the speed at which the 1-D-array is translated over the brain affects the sampling of the hemodynamic response (HR) during visual stimulation as well as the quality of the resulting power Doppler image (PDI). Functional activation maps were compared between stationary recordings, where only one functional slice is obtained for every recording, and our swept-3-D method, where volumetric fUS was achieved in a single functional recording. The results show that the activation maps obtained with our method closely resemble those obtained during a stationary recording for that same location, while our method is not restricted to functional imaging of a single slice. Lastly, a mouse brain subvolume of 6 mm is scanned at a volume rate of 1.5 s per volume, with a functional PDI reconstructed every 200\mu \text{m} , highlighting swept-3-D's potential for volumetric fUS. Our method provides an affordable alternative to volumetric fUS using 2-D-matrix transducers, with a high SNR due to using a fully sampled 1-D-array transducer, and without the need to repeat functional measurements for every 2-D slice, as is most often the case when using a 1-D-array. This places our swept-3-D method as a potentially valuable addition to conventional 2-D fUS, especially when investigating whole-brain functional connectivity, or when shorter recording durations are desired.

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Volumetric 3-D Doppler ultrasound imaging can be used to investigate large scale blood dynamics outside of the limited view that conventional 2-D power Doppler images (PDIs) provide. To create 3-D PDIs, 2-D-matrix array transducers can be used to insonify a large volume for every transmission; however, these matrices suffer from low sensitivity, high complexity, and high cost. More typically, a 1-D-Array transducer is used to scan a series of stationary 2-D PDIs, after which a 3-D volume is created by concatenating the 2-D PDIs in postprocessing, which results in long scan times due to repeated measurements. Our objective was to achieve volumetric 3-D Doppler ultrasound imaging with a high Doppler sensitivity, similar to that of a typical stationary recording using a 1-D-Array transducer, while being more affordable than using 2-D-matrix arrays. We achieved this by mounting a 1-D-Array transducer to a high-precision motorized linear stage and continuously translating over the mouse brain in a sweeping manner. For Part I of this article, we focused on creating the best vascular images by investigating how to best combine filtered beamformed ultrasound frames, which were not acquired at the same spatial locations, into PDIs. Part II focuses on the implications of sampling transient brain hemodynamics through functional ultrasound (fUS) while continuously translating over the mouse brain. In Part I, we show how the speed at which we sweep our 1-D-Array transducer affects the Doppler spectrum in a flow phantom. In vivo recordings were performed on the mouse brain while varying the sweeping speed, showing how higher sweeping speeds negatively affect the PDI quality. A weighting vector is found to combine frames while continuously moving over the mouse brain, allowing us to create swept PDIs of similar sensitivity when compared with those obtained using a stationary 1-D-Array while allowing a significantly higher 3-D Doppler volume rate and maintaining the benefits of having a low computational and monetary cost. We show that a vascular subvolume of 6 mm can be scanned in 2.5 s, with a PDI reconstructed every $200 \mu \text{m}$ , outperforming classical staged recording methods.

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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.

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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.

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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.

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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