## 2 Materials and Methods
### Estimation and Post-Processing of IMT, Longitudinal and Radial Motion Signals of CCA  
For this study, CAROLAB software was used in order to estimate IMT, longitudinal and radial motions of CCA [10; 22; 23]. This software is used for the analysis of ultrasound B-mode image sequences and assesses the longitudinal motion with a speckle-tracking approach that is based on the block-matching (BM) method [10]. The main point of the BM framework is to detect the motion \(d(n)\) between two consequent frames by comparing pixel blocks of consecutive images \(I(n\)-1) and \(I(n)\). The motion corresponds to the displacement between the center point \(p(n\)-1) of the reference block and the center point \(p(n)\) of the best-matched block. This results in the shift of the center point \(p(n)\) between images \(I(n\)-1) and \(I(n)\). Pixel blocks alignment in images \(I(n\)-1) and \(I(n)\) takes place only within the search window, i.e., in the defined maximum margin around the center point of the reference and best-match blocks. After summing up all the displacements \(d(n)\) received, the \(p(n)\) point motion trajectory is estimated. In order to cope with the issue of speckle decorrelation, a pixel-wise Kalman filter is used to update the reference block [10]. Once the estimation is done, a fully-automatic technique based on front propagation is used [22] in order to segment the IM complex and track temporal variations in the IMT in CCA B-mode ultrasound images.  
From right and left CCA image sequences, the higher-quality video sequence was chosen and finally one CCA image sequence was used for every subject. Subjects presenting low-quality video sequences or having less than two full heart cycles detected with CAROLAB were rejected from the study. Three subjects (9%) were rejected from the healthy volunteers' group while twenty subjects (29%) were rejected from the at-risk patients' group.  
A region of interest (ROI) containing a well-contrasted speckle pattern of the distal vessel wall for longitudinal and radial motion, and clearly visible IM complex for IMT variation were chosen in the first frame of each B-mode sequence. A kernel of the ROI was selected manually, with size 3 \(\times\) 0.5 mm as seen in Figure 1a. Estimated signals of longitudinal motion, radial motion and temporal variation in the IMT were saved for further post-processing in MATLAB.  
Post-processing algorithm was developed in MATLAB. The CAROLAB output signals were loaded in original form as shown in Figure 2a.  
Figure 1: (**a**) Common carotid artery (CCA) echoscopy image with preselected kernel (red rectangular) for the estimation of the longitudinal motion and the segmented intima-media (IM) complex of both proximal and distal arterial walls (yellow lines) for temporal variation of intima-media thickness (IMT) and radial motion estimation in CAROLAB. (**b**) CAROLAB output signals detrended and filtered for better observation in a single diagram. A post-processed diameter signal was used to select two consequent heart cycles (red shadowed area). Only this time interval was used for quantitative parametrization of the signals.  
Figure 2: _Cont._  
Motion signals were then filtered with a band-pass IIR filter (\(\text{f}_{\text{pass-lower}}\) = 0.9 Hz and \(\text{f}_{\text{pass-higher}}\) = 8 Hz) and then detrended, subtracting the mean of the resulting signal. After this, two consequent heart cycles were selected manually in time. Only this time segment (see red shadowed area in Figure 1b for an example) was used in all signals for the evaluation of time domain parameters: Peak-to-peak amplitude change of the arterial diameter and IMT between systole and diastole, AV and RMS values of temporal variation of IMT (IMT\({}_{0}\) and IMT\({}_{\text{RMS}}\)), and longitudinal and radial motions (X\({}_{0}\), D\({}_{0}\) X\({}_{\text{RMS}}\), D\({}_{\text{RMS}}\)) of two consequent heart cycles.