Ultrafast ultrasonic imaging is a rapidly developing field based on the unfocused transmission of plane or diverging ultrasound waves. This recent approach to ultrasound imaging leads to a large increase in raw ultrasound data available per acquisition. Bigger synchronous ultrasound imaging datasets can be exploited in order to strongly improve the discrimination between tissue and blood motion in the field of Doppler imaging. Here we propose a spatiotemporal singular value decomposition clutter rejection of ultrasonic data acquired at ultrafast frame rate. The singular value decomposition (SVD) takes benefits of the different features of tissue and blood motion in terms of spatiotemporal coherence and strongly outperforms conventional clutter rejection filters based on high pass temporal filtering. Whereas classical clutter filters operate on the temporal dimension only, SVD clutter filtering provides up to a four-dimensional approach (3D in space and 1D in time). We demonstrate the performance of SVD clutter filtering with a flow phantom study that showed an increased performance compared to other classical filters (better contrast to noise ratio with tissue motion between 1 and 10mm/s and axial blood flow as low as 2.6 mm/s). SVD clutter filtering revealed previously undetected blood flows such as microvascular networks or blood flows corrupted by significant tissue or probe motion artifacts. We report in vivo applications including small animal fUltrasound brain imaging (blood flow detection limit of 0.5 mm/s) and several clinical imaging cases, such as neonate brain imaging, liver or kidney Doppler imaging.

Changes in cerebral blood flow are associated with stroke, aneurysms, vascular cognitive impairment, neurodegenerative diseases and other pathologies. Brain angiograms, typically performed via computed tomography or magnetic resonance imaging, are limited to millimetre-scale resolution and are insensitive to blood-flow dynamics. Here, we show that ultrafast ultrasound localization microscopy of intravenously injected microbubbles enables transcranial imaging of deep vasculature in the adult human brain at microscopic resolution and the quantification of haemodynamic parameters. Adaptive speckle tracking to correct for micrometric brain-motion artefacts and for ultrasonic-wave aberrations induced during transcranial propagation allowed us to map the vascular network of tangled arteries, to functionally characterize blood-flow dynamics at a resolution of up to 25 μm, and to detect blood vortices in a small deep-seated aneurysm in a patient. Ultrafast ultrasound localization microscopy may facilitate the understanding of brain haemodynamics and of how vascular abnormalities in the brain are related to neurological pathologies.

Singular value decomposition of ultrafast imaging ultrasonic data sets has recently been shown to build a vector basis far more adapted to the discrimination of tissue and blood flow than the classical Fourier basis, improving by large factor clutter filtering and blood flow estimation. However, the question of optimally estimating the boundary between the tissue subspace and the blood flow subspace remained unanswered. Here, we introduce an efficient estimator for automatic thresholding of subspaces and compare it to an exhaustive list of thirteen estimators that could achieve this task based on the main characteristics of the singular components, namely the singular values, the temporal singular vectors, and the spatial singular vectors. The performance of those fourteen estimators was tested in vitro in a large set of controlled experimental conditions with different tissue motion and flow speeds on a phantom. The estimator based on the degree of resemblance of spatial singular vectors outperformed all others. Apart from solving the thresholding problem, the additional benefit with this estimator was its denoising capabilities, strongly increasing the contrast to noise ratio and lowering the noise floor by at least 5 dB. This confirms that, contrary to conventional clutter filtering techniques that are almost exclusively based on temporal characteristics, efficient clutter filtering of ultrafast Doppler imaging cannot overlook space. Finally, this estimator was applied in vivo on various organs (human brain, kidney, carotid, and thyroid) and showed efficient clutter filtering and noise suppression, improving largely the dynamic range of the obtained ultrafast power Doppler images.

Objective Shear wave elastography (SWE) enabled living tissue assessment of stiffness. This is routinely used for breast, thyroid and liver diseases, but there is currently no data for the brain. We aim to characterize elasticity of normal brain parenchyma and brain tumors using SWE. Patients with scheduled brain tumor removal were included in this study. In addition to standard ultrasonography, intraoperative SWE using an ultrafast ultrasonic device was used to measure the elasticity of each tumor and its surrounding normal brain. Data were collected by an investigator blinded to the diagnosis. Descriptive statistics, box plot analysis as well as intraoperator and interoperator reproducibility analysis were also performed. 63 patients were included and classified into four main types of tumor: meningiomas, low-grade gliomas, high-grade gliomas and metastasis. Young's Modulus measured by SWE has given new insight to differentiate brain tumors: 33.1 ± 5.9 kPa, 23.7 ± 4.9 kPa, 11.4 ± 3.6 kPa and 16.7 ± 2.5 kPa, respectively, for the four subgroups. Normal brain tissue has been characterized by a reproducible mean stiffness of 7.3 ± 2.1 kPa. Moreover, low-grade glioma stiffness is different from high-grade glioma stiffness (p = 0.01) and normal brain stiffness is very different from low-grade gliomas stiffness (p < 0.01). This study demonstrates that there are significant differences in elasticity among the most common types of brain tumors. With intraoperative SWE, neurosurgeons may have innovative information to predict diagnosis and guide their resection.

Functional neuroimaging modalities are crucial for understanding brain function, but their clinical use is challenging. Recently, the use of ultrasonic plane waves transmitted at ultrafast frame rates was shown to allow for the spatiotemporal identification of brain activation through neurovascular coupling in rodents. Using a customized flexible and noninvasive headmount, we demonstrate in human neonates that real-time functional ultrasound imaging (fUSI) is feasible by combining simultaneous continuous video-electroencephalography (EEG) recording and ultrafast Doppler (UfD) imaging of the brain microvasculature. fUSI detected very small cerebral blood volume variations in the brains of neonates that closely correlated with two different sleep states defined by EEG recordings. fUSI was also used to assess brain activity in two neonates with congenital abnormal cortical development enabling elucidation of the dynamics of neonatal seizures with high spatiotemporal resolution (200 μm for UfD and 1 ms for EEG). fUSI was then applied to track how waves of vascular changes were propagated during interictal periods and to determine the ictal foci of the seizures. Imaging the human brain with fUSI enables high-resolution identification of brain activation through neurovascular coupling and may provide new insights into seizure analysis and the monitoring of brain function.

In the last decade, ultrasound imaging has gained new capabilities and produced new insights in the field of neuroscience. The development of new concepts, such as ultrafast ultrasound, has enhanced Doppler sensitivity by orders of magnitude and has paved the way for ultrasonic functional neuroimaging. In this review, we position ultrasound in the field of neuroimaging and discuss how it complements current tools available to neurobiologists and clinicians.

No abstract

Rapid eye movement sleep (REMS) is a peculiar brain state combining the behavioral components of sleep and the electrophysiological profiles of wake. After decades of research our understanding of REMS still is precluded by the difficulty to observe its spontaneous dynamics and the lack of multimodal recording approaches to build comprehensive datasets. We used functional ultrasound (fUS) imaging concurrently with extracellular recordings of local field potentials (LFP) to reveal brain-wide spatiotemporal hemodynamics of single REMS episodes. We demonstrate for the first time the close association between global hyperemic events – largely outmatching wake levels in most brain regions – and local hippocampal theta (6–10 Hz) and fast gamma (80–110 Hz) events in the CA1 region. In particular, the power of fast gamma oscillations strongly correlated with the amplitude of subsequent vascular events. Our findings challenge our current understanding of neurovascular coupling and question the evolutionary benefit of such energy-demanding patterns in REMS function.