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.

SummaryFunctional hyperemia, a regional increase of blood flow triggered by local neural activation, is used to map brain activity in health and disease. However, the spatial-temporal dynamics of functional hyperemia remain unclear. Two-photon imaging of the entire vascular arbor in NG2-creERT2;GCaMP6f mice shows that local synaptic activation, measured via oligodendrocyte precursor cell (OPC) Ca2+ signaling, generates a synchronous Ca2+ drop in pericytes and smooth muscle cells (SMCs) enwrapping all upstream vessels feeding the activated synapses. Surprisingly, the onset timing, direction, and amplitude of vessel diameter and blood velocity changes vary dramatically from juxta-synaptic capillaries back to the pial arteriole. These results establish a precise spatial-temporal sequence of vascular changes triggered by neural activity and essential for the interpretation of blood-flow-based imaging techniques such as BOLD-fMRI.

Hemodynamic changes in the brain are often used as surrogates of neuronal activity to infer the loci of brain activity. A major limitation of conventional Doppler ultrasound for the imaging of these changes is that it is not sensitive enough to detect the blood flow in small vessels where the major part of the hemodynamic response occurs. Here, we present a μDoppler ultrasound method able to detect and map the cerebral blood volume (CBV) over the entire brain with an important increase in sensitivity. This method is based on imaging the brain at an ultrafast frame rate (1 kHz) using compounded plane wave emissions. A theoretical model demonstrates that the gain in sensitivity of the μDoppler method is due to the combination of 1) the high signal-to-noise ratio of the gray scale images, resulting from the synthetic compounding of backscattered echoes; and 2) the extensive signal averaging enabled by the high temporal sampling of ultrafast frame rates. This μDoppler imaging is performed in vivo on trepanned rats without the use of contrast agents. The resulting images reveal detailed maps of the rat brain vascularization with an acquisition time as short as 320 ms per slice. This new method is the basis for a real-time functional ultrasound (fUS) imaging of the brain.