Ultrasound Scanner ASIC with 1-D CNN-Based Echo Pattern Recognition for Arterial Distension Monitoring

This paper presents an A-mode ultrasound scanner application-specific integrated circuit (ASIC) for arterial distension monitoring. The ASIC operates with a single-element ultrasound probe, identifying a target artery through echo pattern recognition and reconstructing an arterial diameter waveform. A 1-D convolutional neural network (CNN) is employed to ensure accurate probe positioning by recognizing characteristic arterial wall echo patterns. Additionally, gradient-weighted class activation mapping (Grad-CAM) is utilized to adaptively localize arterial wall regions, facilitating the measurement of arterial diameter in each A-mode frame. The ASIC includes a high-voltage pulser, a transmit/receive (T/R) switch, an analog front-end, and a synthesized digital circuit for post processing. The ASIC has been fabricated in a 180-nm BCD process, occupying an active area of 2.8 mm2 with a power consumption of 1.65 mW. The fabricated ASIC was evaluated for CNN inference performance and accuracy of arterial distension estimation, achieving a CNN inference accuracy of 95% and a Pearson correlation coefficient (r) of 0.895. Compared to prior ultrasound scanners, the proposed ASIC achieves a high inference accuracy in echo pattern recognition and an efficient implementation of mixed-signal architecture, demonstrating high feasibility of a small footprint ultrasound module for physiological instrumentation.