In this paper, we address the problem of tissue motion compensation in blood flow estimation from ultrafast Doppler sequences. The goal is to improve the estimation of tumor contours, offering neurosurgeons better visualization and thereby leading them to make better decisions while performing brain surgery. Estimating these contours can be done by solving the problem of separation of blood flow and tissue in ultrasound images. To solve this problem, we focus on a recently developed variant of the Robust Principal Component Analysis (RPCA)-based method by embedding a deconvolution step into the algorithm in order to improve the resolution of the reconstructed blood flow. However, this approach is prone to failure in the presence of tissue motion. In this work, we propose to overcome this limitation by incorporating a motion compensation step into the above RPCA-based method. We implement and quantitatively compare motion compensation algorithms based on the Lucas-Kanade and Demon registration methods on simulation data. In addition, preliminary results are obtained on in vivo data. We show that a motion compensation step allows to improve the perception of thin vascular vessels and to reduce the amount of noise on the estimated flow.