0278-0062 (c) 2015 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TMI.2015.2428634, IEEE Transactions on Medical Imaging >Manuscript TMI-2015-0189.R1 < 1 Abstract— 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 2.6mm/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 0.5mm/s) and several clinical imaging cases, such as neonate brain imaging, liver or kidney Doppler imaging. Index Terms—Blood flow, Doppler imaging, ultrafast imaging, ultrasound, singular value decomposition. Manuscript received September 30, 2014; revised March 10, 2015; accepted April 20, 2015. Copyright © 2010 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement n° 339244-FUSIMAGINE. This work was also supported by LABEX WIFI (Laboratory of Excellence ANR-10- LABX-24) within the French Program ‘Investments for the Future’ under reference ANR-10-IDEX-0001-02PSL, by the Assistance Publique-Hôpitaux de Paris and by PremUP Foundation, Paris 75006 France. C. Demené, T. Deffieux, M. Pernot, B.-F. Osmanski, J.-L. Gennisson, S. Franqui, J.-M. Correas, and M. Tanter are with the Institut Langevin, CNRS UMR 7587, INSERM U979, ESPCI ParisTech, Paris 75005, France.(e-mail: charlie.demene@espci.fr; thomas.deffieux@espci.fr; mathieu.pernot@espci.fr; I. INTRODUCTION XTENSIVE work has been conducted over the past 30 years in order to suppress clutter signals originating from stationary and slowly moving tissue as they introduce major artifacts in ultrasonic blood flow imaging [1]. This operation remains a major challenge for the visualization of vascular paths and the measurement of blood flow velocities because tissue echoes and blood scatterers echoes tend to share common characteristics, especially in two widespread clinical cases e.g. when blood flow velocities become low (in particular in small vessels) or when tissue motion is important. These two configurations correspond both to major applications in general ultrasound imaging. On the one hand, imaging slow blood flows and therefore microvasculature is an issue in most organs as skin, muscles, placenta, as well as in tumors for cancer diagnosis. It is also of major importance in emerging fields such as fUltrasound imaging of brain activity where the neurovascular coupling occurs locally in very small vessels. On the other hand, imaging blood flow in fast moving tissue is a major issue in applications such as cardiac or abdominal (liver, kidney,...) imaging. The reason why clutter filters fail to solve both situations mentioned above is due to the underlying assumption on which they are built. In the early history of Color Flow Imaging (CFI), clutter filtering has always been based on the fair assumption that tissue signal and blood flow signal have completely differing spectral characteristics: tissue motion is supposed very slow or non-existent whereas red blood cells are fast moving bruno-felix.osmanski@espci.fr; jl.gennisson@espci.fr; mickael.tanter@espci.fr). V. Biran and O. Baud are with INSERM U1141 and Neonatal Intensive Care Unit, Paris Diderot University, Children's hospital Robert Debré, APHP, Paris 75019, France. (valerie.biran@rdb.aphp.fr; olivier.baud@rdb.aphp.fr). L.-A. Sieu, A. Bergel, and I. Cohen are with Neuroscience Paris Seine, CNRS UMR8246, INSERM U1130, UPMC UMCR18, Paris, France. (lim- anna_s@hotmail.fr; antoine.bergel@cri-paris.org; ivan.cohen@upmc.fr). L.-A. Sieu is also with Institute of Translational Neurosciences (IHU-A- ICM), Pitié-Salpêtrière Hospital, Paris, France S. Franqui is with service de radiopédiatrie-Hôpital Bicêtre- Hôpitaux Universitaires Paris-Sud, Assistance Publique hôpitaux de Paris, Le Kremlin- Bicêtre 94270, France. (stephanie.franchi@bct.aphp.fr). J.-M. Correas is also with department of Adult Radiology, Necker University Hospital, Paris 75015, France, and with Rene Descartes Medical University, Paris 75006, France. (jean-michel.correas@nck.aphp.fr). Spatiotemporal Clutter Filtering of Ultrafast Ultrasound Data Highly Increases Doppler and fUltrasound Sensitivity Charlie Demené, Thomas Deffieux, Mathieu Pernot, Bruno-Félix Osmanski, Valérie Biran, Jean-Luc Gennisson, Lim-Anna Sieu, Antoine Bergel, Stéphanie Franqui, Jean-Michel Correas, Ivan Cohen, Olivier Baud, Mickael Tanter E