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.

Developing minimally invasive brain surgery by high-intensity focused ultrasound beams is of great interest in cancer therapy. However, the skull induces strong aberrations both in phase and amplitude, resulting in a severe degradation of the beam shape. Thus, an efficient brain tumor therapy would require an adaptive focusing, taking into account the effects of the skull. In this paper, we will show that the acoustic properties of the skull can be deduced from high resolution CT scans and used to achieve a noninvasive adaptive focusing. Simulations have been performed with a full 3-D finite differences code, taking into account all the heterogeneities inside the skull. The set of signals to be emitted in order to focus through the skull can thus be computed. The complete adaptive focusing procedure based on prior CT scans has been experimentally validated. This could have promising applications in brain tumor hyperthermia but also in transcranial ultrasonic imaging.

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