Compressive Sensing Photoacoustic Imaging Receiver With Matrix-Vector-Multiplication SAR ADC
Abstract
Wearable photoacoustic imaging devices hold great promise for continuous health monitoring and point-of-care diagnostics. However, the large data volume generated by high-density transducer arrays presents a major challenge for realizing compact and power-efficient wearable systems. This article presents a photoacoustic imaging receiver (RX) that embeds compressive sensing directly into the hardware to address this bottleneck. The RX integrates 16 AFEs and four matrix-vector-multiplication (MVM) SAR ADCs that perform energy- and area-efficient analog-domain compression. The architecture achieves a 4× – 8× reduction in output data rate while preserving low-loss full-array information. The MVM SAR ADC executes passive and accurate MVM using user-defined programmable ternary weights. Two signal reconstruction methods are implemented: 1) an optimization approach using the fast iterative shrinkage-thresholding algorithm and 2) a learning-based approach employing implicit neural representation. Fabricated in 65-nm CMOS, the chip achieves an ADC’s signal-to-noise-distortion ratio (SNDR) of 57.5 dB at 20.41 MS/s, with an analog front-end (AFE) input-referred noise of 3.5 nV/ (Hz)1/2 . MVM linearity measurements show R2>0.999 across a wide range of weights and input amplitudes. The system is validated through phantom imaging experiments, demonstrating high-fidelity image reconstruction under up to 8× compression. The RX consumes 5.83 mW/channel and supports a general ternary-weighted measurement matrix, offering a compelling solution for next-generation miniaturized, wearable PA imaging systems.
Huan-Cheng Liao
Ph.D. Student (started in 2021)
Yumin Su
Ph.D. Student (started in 2023)
Yiwei Zou
Ph.D. Student (started in 2022)
Wei Wang
PhD 2025, now at Apple
