Cerebral Autoregulation during Postural Change in Patients with Cervical Spinal Cord Injury—A Carotid Duplex Ultrasonography Study

Patients with a spinal cord injury (SCI) frequently experience sudden falls in blood pressure during postural change. Few studies have investigated whether the measurement of blood flow velocity within vessels can reflect brain perfusion during postural change. By performing carotid duplex ultrasonography (CDU), we investigated changes in cerebral blood flow (CBF) during postural changes in patients with a cervical SCI, determined the correlation of CBF change with presyncopal symptoms, and investigated factors affecting cerebral autoregulation. We reviewed the medical records of 100 patients with a cervical SCI who underwent CDU. The differences between the systolic blood pressure, diastolic blood pressure, and CBF volume in the supine posture and after 5 min at 50° tilt were evaluated. Presyncopal symptoms occurred when the blood flow volume of the internal carotid artery decreased by ≥21% after tilt. In the group that had orthostatic hypotension and severe CBF decrease during tilt, the body mass index and physical and functional scores were lower than in other groups, and the proportion of patients with a severe SCI was high. The higher the SCI severity and the lower the functional score, the higher the possibility of cerebral autoregulation failure. CBF should be assessed by conducting CDU in patients with a high-level SCI.

Abstract TP224: A Deep Learning Based-Algorithm to Automatically Measure Carotid Stenosis Using Cine-Images of Carotid Duplex Ultrasonography

Background and Objective: The present study was performed to develop an automated algorithm to measure the carotid stenosis by considering both deep learning and mathematical model using carotid duplex ultrasonography (CDU) images. Methods: We first obtained cine images of CDU from right and left carotid arteries of 18 ischemic stroke patients by continuous moving from supraclavicular to submandibular area. Then, we collected raw axial-CDU images from the cine-CDU images, and then, labelled segmentation of the stenosis caused by atherosclerotic plaque from the vessel wall in the individual axial image by two experts. To develop segmentation algorithm from the axial-CDU images, we first applied a deep learning algorithm to segment vessel lumen from vessel wall. Next, a mathematical algorithm using Gaussian mixture was used to segment atherosclerotic stenosis from the vessel lumen. Dice coefficient was obtained to evaluate whether the segmentation algorithms could accurately segment lumen of carotid artery and predict the stenosis severity measured by the experts using python packages including TensorFlow and scikit-learn on a workstation (Intel i9-7900X CPU, Nvidia Titan Xp GPU and 128G 2400GHz memory). Results: We finally collected total 13,586 raw axial-CDU images from the cine-CDU images of the 18 patients. After application of two steps of segmentation algorithms to the axial-CDU images, accuracy of the algorithm to segment lumen from the carotid vessel wall was mean 0.92 (±standard deviation 0.46) of dice coefficient. And, accuracy to estimate the stenotic area was mean 0.201 (±standard deviation 0.137) of dice coefficient. Conclusions: We proposed an algorithm to automatically quantify the carotid stenosis using two steps of approach. First, a deep learning based-algorithm to segment lumen of carotid artery; second, a mathematical model based-algorithm using Gaussian mixture to segment carotid stenosis from the lumen. Even though we need more studies to increase the accuracy to predict the stenosis, the present prediction algorithms provide a possible tool to automatically measure the severity and regional characteristics of carotid stenosis using cine-CDU images.