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Updated: May 20, 2026

A High-performance Compact Photoacoustic Tomography System for In Vivo Small-animal Brain Imaging
Published on: June 21, 2017
FPGA-based reconfigurable processor for ultrafast interlaced ultrasound and photoacoustic imaging
Umar Alqasemi1, Hai Li, Andrés Aguirre
1Biomedical Engineering Program, University of Connecticut, Storrs, CT, USA.
Abstract:
In this paper, we report, to the best of our knowledge, a unique field-programmable gate array (FPGA)-based reconfigurable processor for real-time interlaced co-registered ultrasound and photoacoustic imaging and its application in imaging tumor dynamic response. The FPGA is used to control, acquire, store, delay-and-sum, and transfer the data for real-time co-registered imaging. The FPGA controls the ultrasound transmission and ultrasound and photoacoustic data acquisition process of a customized 16-channel module that contains all of the necessary analog and digital circuits. The 16-channel module is one of multiple modules plugged into a motherboard; their beamformed outputs are made available for a digital signal processor (DSP) to access using an external memory interface (EMIF). The FPGA performs a key role through ultrafast reconfiguration and adaptation of its structure to allow real-time switching between the two imaging modes, including transmission control, laser synchronization, internal memory structure, beamforming, and EMIF structure and memory size. It performs another role by parallel accessing of internal memories and multi-thread processing to reduce the transfer of data and the processing load on the DSP. Furthermore, because the laser will be pulsing even during ultrasound pulse-echo acquisition, the FPGA ensures that the laser pulses are far enough from the pulse-echo acquisitions by appropriate time-division multiplexing (TDM). A co-registered ultrasound and photoacoustic imaging system consisting of four FPGA modules (64-channels) is constructed, and its performance is demonstrated using phantom targets and in vivo mouse tumor models.
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