This thesis is focused on biomedical engineering for clinical applications. The main goal of this work is to provide to clinicians an ultrasound mode to simultaneously extract wall motion and flow at high frame rates in arteries. Cardiovascular pathologies are a major cause of death and disability worldwide. Although the formation of such diseases is still not fully understood, it appears that some pathological markers from both wall and flow could allow an earlier detection. Because tissues are subject to fast and complex phenomena in the arteries, a high frame rate imaging modality seems highly relevant to extract as much information as possible on the condition of the cardiovascular system. Unfortunately, no technique is currently clinically used or even approved for the extraction of both flow and wall pathological markers at high frame rates. Therefore, in this thesis, I propose to design an ultrasound sequence and algorithm permitting to extract both aspects, at high frame rates on arteries, for a potential clinical application. There are three main scientific contributions in this thesis: i) the design of the ultrasound sequence with a 2D motion estimator, ii) a new adaptive clutter filtering approach, and iii) a clinical trial. The ultrasound sequence is based on plane wave acquisition permitting to yield frame rates up to 10 000 Hz in the carotid. The pipeline used an approach introducing a virtual lateral oscillation in ultrasound images which, coupled with a 2D phase-based estimator based on previous works from the literature, allows to extract vectorial velocity fields. Validations for both flow and wall motion estimation were performed on a commercial Doppler flow phantom and an in-house realistic carotid phantom was designed for the experiments. An adaptive clutter filtering technique was also developed and validated on volunteers based on tissue estimates, which permit to precisely remove tissue clutter from flow signals. Finally, the clinical trial was performed at the hospital with a group of volunteers and a group of patients. The ultrasound sequence, motion estimation algorithm, and adaptive clutter filtering approaches were well validated in the thesis. The method can provide both wall motion and flow estimates at high frame rates, with low errors and standard deviations. The adaptive clutter filtering approach permits to better extract the flow compared to other standard approaches. This improvement is especially noticeable close to the wall, which would allow accurate flow and stress measurements along arterial walls where plaques can form and develop. To conclude, the clinical trial has demonstrated the feasibility in a clinical environment with the extraction of wall motion, flow, and arterial parameters that showed differences between and within groups. This thesis is then a step toward clinical use of high frame rate ultrasound imaging for quantification of both wall motion and flow for pathological detection of cardiovascular diseases.