  The objective of this study is to develop a deep-learning based detection and
diagnosis technique for carotid atherosclerosis using a portable freehand 3D
ultrasound (US) imaging system. A total of 127 3D carotid artery scans were
acquired using a portable 3D US system which consisted of a handheld US scanner
and an electromagnetic tracking system. A U-Net segmentation network was
firstly applied to extract the carotid artery on 2D transverse frame, then a
novel 3D reconstruction algorithm using fast dot projection (FDP) method with
position regularization was proposed to reconstruct the carotid artery volume.
Furthermore, a convolutional neural network was used to classify healthy and
diseased cases qualitatively. 3D volume analysis methods including longitudinal
image acquisition and stenosis grade measurement were developed to obtain the
clinical metrics quantitatively. The proposed system achieved sensitivity of
0.714, specificity of 0.851 and accuracy of 0.803 respectively for diagnosis of
carotid atherosclerosis. The automatically measured stenosis grade illustrated
good correlation (r=0.762) with the experienced expert measurement. The
developed technique based on 3D US imaging can be applied to the automatic
diagnosis of carotid atherosclerosis. The proposed deep-learning based
technique was specially designed for a portable 3D freehand US system, which
can provide more convenient carotid atherosclerosis examination and decrease
the dependence on clinician's experience.
