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Purpose  The motion of the common carotid artery tissue layers along the vessel axis during the cardiac cycle, observed in ultrasound imaging, is associated with the presence of established cardiovascular risk factors. However, the vast majority of the (semi‐)automatic methods devised to measure this so‐called  “longitudinal kinetics”  phenomenon are based on the tracking of a single point, thus failing to capture the overall — and potentially inhomogeneous — motion of the entire arterial wall. The aim of this work is to introduce a motion tracking a framework to simultaneously extract the temporal trajectory of a large collection of points (several hundred) horizontally aligned and spanning the entire exploitable width of the image, thus providing a dense motion field.    Method  The only action required from the user is to indicate the left and right borders of the region to be processed. A previously validated contour segmentation method is used to position one point in the arterial wall in each column of the image. Between two consecutive frames, the radial motion of each point is predetermined by the position of the segmentation contours. The longitudinal motion, which is the main focus of the present work, is determined in two steps. First, a series of independent block matching operations are carried out for all the tracked points. Here, the displacement of each point is not determined yet, instead the similarity map is stored. Then, an original dynamic‐programming approach is exploited to regularize the collection of similarity maps and estimate the globally optimal motion over the entire vessel wall. Sixty‐two atherosclerotic participants at high cardiovascular risk were involved in this study. Method training and validation was performed with 20 and 42 participants, respectively. The amplitude‐independent index  σX  was introduced to quantitate the motion inhomogeneity across the length of the artery.    Results  A dense displacement field, describing the longitudinal motion of the carotid far wall over time, was extracted from all participants. For each cine‐loop, the method was evaluated against manual reference tracings performed on three local points, and showed a good accuracy, with an average absolute error (±  STD ) of 150 (±163) μm. It also demonstrated an overall greater robustness compared to a previously validated method based on single‐point motion tracking. For all the 62 participants, the analyzed region had, in average, a width of 24.2 mm, involving the simultaneous tracking of 357 points along 151 temporal frames, and requiring a total computational time of 68 s. Analyzing the inhomogeneity of the carotid artery motion showed a strong correlation between  σX  and the presence of coronary artery disease ( β ‐coefficient = 0.586,  P  =   0.003).    Conclusions  To the best of our knowledge, this is the first time that a method is specifically proposed to assess the dense motion field corresponding to the longitudinal kinetics of the carotid far wall. This approach has potential to evaluate the homogeneity (or lack thereof) of the wall dynamics. The proposed method has promising performances to improve the analysis of arterial longitudinal motion and the understanding of the underlying patho‐physiological parameters.

medical physics

Dynamic Block Matching to assess the longitudinal component of the dense motion field of the carotid artery wall in B‐mode ultrasound sequences — Association with coronary artery disease