Real-Time Ultrasound Biomicroscopy with Optoacoustic Arrays

Ya Shu
University of Delaware
University of Delaware, 2011


   title={Real-Time Ultrasound Biomicroscopy with Optoacoustic Arrays},

   author={Shu, Ya},



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Optical techniques are a promising technology to realize high frequency ultrasound arrays. High sensitivity and broad bandwidth have been demonstrated with optoacoustic sensors based on thin film etalons. A thin film etalon consists of a transparent layer (e.g. photoresist or parylene) with gold coatings on a glass substrate. One-dimensional (1-D) data acquisition is realized by two different detection approaches. We first demonstrate a 256-element 1-D receive array employing parallel detection. A fiber-coupled 785 nm diode laser is brought to a line focus (3.6 mm length) on the etalon. The line focus is imaged with a one-to-one lens relay onto a 512 element line scan CCD camera with a 14×14 microm pixel size. Both the transducer and probe laser are modulated by a frequency sweep from 10 MHz to 40 MHz in a 14 ms time window. The frequency domain data are processed off-line in MatLab. The signal processing includes background subtraction, spectral apodization, zero padding, and an inverse Fourier transform. Both the measured pulse duration and main lobe width agree well with the expected value from a 25 MHz f/2 transducer. We are currently developing a 500-element one-dimensional receive array implementing serial detection. A galvanometer-mounted mirror scans the optical focus across the etalon surface. The entire scanning range is about 5.3 mm. The transducer is modulated by a linear chirp that goes from 10 MHz to 40 MHz in 4 microseconds. The time-reverse of the excitation signal is used as the compression filter. Pulse compression is done off-line in MatLab. A signal-to-noise ratio (SNR) comparison between the pulsed and chirped excitation demonstrates that chirped excitation improves system SNR by over 20 dB. Finally, we investigate the possibility of using a graphics processing unit (GPU) to do pulse compression and beamforming with simulated data. The performance time is evaluated to demonstrate the GPU’s ability to perform data processing for video-rate ultrasound imaging.
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