Normalization of carotid plaque based strain indices using blood pressure measurements

Abstract

Ultrasound based Lagrangian carotid strain imaging (LCSI) utilizes physiological deformation caused by arterial pressure variations to generate strain tensor images. We have previously demonstrated the ability of maximum accumulated strain indices (MASI) and peak-to-trough indices derived from these corresponding strain tensor images to quantify carotid plaque vulnerability thereby enhancing their utility as vascular biomarkers. However, a critique of LCSI has been the lack of normalization of MASI and peak-to-trough strain indices to the physiological stimuli, namely the blood pressure. We report in this paper on the impact of normalization of these strain indices to blood pressure measurements. The blood pressure measurements are acquired immediately after the acquisition of radiofrequency data loops for strain imaging and carotid ultrasound images. All imaging was performed on human patients scheduled for a carotid endarterectomy (CEA) procedure (n=44 patients). Patients were further identified as symptomatic or asymptomatic based on clinical symptoms. Cognition was also assessed on these patients using the 60-minute neuropsychological test protocol following guidelines of the National Institute of Neurological Disorders and Canadian Stroke Network. Blood pressure measurements were utilized to normalize strain indices estimated from the axial, lateral and shear strain images over two cardiac cycles using systolic, diastolic and maximum arterial pressure (MAP) respectively. No significant differences in the area under the curve (AUC) estimates were obtained between MASI and peak-to-trough strain indices that were normalized to the systolic, diastolic, pulse pressure and maximum arterial pressure when compared to the unnormalized results reported previously. Although small improvements in the correlation of the strain indices with cognition parameters and AUC values were obtained with normalization, unnormalized strain indices on their own provided a significant correlation with the reduction in executive function reported with cognitive testing. For axial strain, the correlation of peak values with cognition were −0.49, −0.47, −0.5 and −0.49 for unnormalized, systolic, diastolic and MAP normalization respectively. The corresponding AUC values for classifiers designed using the maximum likelihood estimation model were 0.75, 0.73, 0.75, and 0.73 respectively.