Increasing Axial Resolution of Ultrasonic Imaging with a Joint Sparse Representation Model

The axial resolution of ultrasonic imaging is confined by the temporal width of acoustic pulse generated by the transducer, which has a limited bandwidth. Deconvolution can eliminate this effect and, therefore, improve the resolution. However, most ultrasonic imaging methods perform deconvolution scan line by scan line, and therefore the information embedded within the neighbor scan lines is unexplored, especially for those materials with layered structures such as blood vessels. In this paper, a joint sparse representation model is proposed to increase the axial resolution of ultrasonic imaging. The proposed model combines the sparse deconvolution along the axial direction with a sparsity-favoring constraint along the lateral direction. Since the constraint explores the information embedded within neighbor scan lines by connecting nearby pixels in the ultrasound image, the axial resolution of the image improves after deconvolution. The results on simulated data showed that the proposed method can increase resolution and discover layered structure. Moreover, the results on real data showed that the proposed method can measure carotid intima-media thickness automatically with good quality ( $0.56\pm 0.03$ versus $0.60\pm 0.06$ mm manually).