Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3-D Ultrasound Imaging System

• Published in: IEEE transactions on ultrasonics, ferroelectrics, and frequency control Vol. 71; no. 2; pp. 266 - 279
• Main Authors: Li, Jiawen, Huang, Yunqian, Song, Sheng, Chen, Hongbo, Shi, Junni, Xu, Duo, Zhang, Haibin, Chen, Man, Zheng, Rui
• Format: Journal Article
• Language: English
• Published: United States The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.02.2024
• Subjects: Algorithms; Artificial neural networks; Atherosclerosis; Carotid arteries; Carotid Arteries - diagnostic imaging; Carotid Artery Diseases - diagnostic imaging; Constriction, Pathologic; Deep learning; Diagnosis; Humans; Image acquisition; Image Processing, Computer-Assisted - methods; Image reconstruction; Imaging, Three-Dimensional - methods; Machine learning; Portability; Quality; Regularization; Three dimensional analysis; Tracking systems; Ultrasonic imaging; Ultrasonography
• ISSN: 0885-3010; 1525-8955; 1525-8955
• DOI: 10.1109/TUFFC.2023.3345740

The objective of this study is to develop a deep-learning-based detection and diagnosis technique for carotid atherosclerosis (CA) using a portable freehand 3-D ultrasound (US) imaging system. A total of 127 3-D carotid artery scans were acquired using a portable 3-D US system, which consisted of a handheld US scanner and an electromagnetic (EM) tracking system. A U-Net segmentation network was first applied to extract the carotid artery on 2-D transverse frame, and then, a novel 3-D reconstruction algorithm using fast dot projection (FDP) method with position regularization was proposed to reconstruct the carotid artery volume. Furthermore, a convolutional neural network (CNN) was used to classify healthy and diseased cases qualitatively. Three-dimensional volume analysis methods, including longitudinal image acquisition and stenosis grade measurement, were developed to obtain the clinical metrics quantitatively. The proposed system achieved a sensitivity of 0.71, a specificity of 0.85, and an accuracy of 0.80 for diagnosis of CA. The automatically measured stenosis grade illustrated a good correlation ( r = 0.76) with the experienced expert measurement. The developed technique based on 3-D US imaging can be applied to the automatic diagnosis of CA. The proposed deep-learning-based technique was specially designed for a portable 3-D freehand US system, which can provide a more convenient CA examination and decrease the dependence on the clinician's experience.