Carotid Strain Imaging with a Locally Optimized Adaptive Bayesian Regularized Motion Tracking Algorithm

Cardiovascular disease is the leading cause of death worldwide with atherosclerosis being a major contributor. Risk factors for atherosclerosis are largely modifiable, thus identification of novel biomarkers to identify early arterial injury and monitor treatment are desirable. We report on using a locally optimized Adaptive Bayesian Regularization (ABR) scheme for ultrasound carotid strain imaging to quantify mechanical properties of the arterial wall prior to and after atherosclerotic plaque deposition. ABR was integrated into a three-level block matching Lagrangian carotid strain imaging algorithm for a murine model of atherosclerosis. Two (1 male and 1 female) ApoE mice were fed a Western diet beginning at 6 weeks of age and the common carotid artery (CCA) was imaged at 6, 16 and 24 weeks using a high-frequency transducer. Axial temporal strain curves were derived for both left (LCCA) and right (RCCA) CCA and maximum accumulated strain index (MASI) was used to characterize the arterial wall. LCCA mean MASI at 6-, and 24-weeks were 16.18% and 10.84% and RCCA MASI were 26.48% and 17.39% respectively indicating a reduction in arterial strain or increased stiffening with atherosclerosis. Presence of plaque was confirmed with gross pathology at 24 weeks. Results show the potential of ABR for reliable estimation of arterial wall stiffness changes.