Super-resolution photoacoustic imaging with a sparse array (Conference Presentation)

It has been shown that sub-diffraction structures can be resolved in acoustic resolution photoacoustic imaging thanks to norm-based iterative reconstruction algorithms exploiting prior knowledge of the point spread function (PSF) of the imaging system. Here, we demonstrate that super-resolution is still achievable when the receiving ultrasonic probe has much fewer elements than used conventionally (8 against 128). To this end, a proof-of-concept experiment was conducted. A microfluidic circuit containing five parallel microchannels (channel’s width 40μm, center-to center distance 180μm) filled with dye was exposed to 5ns laser pulses (=532nm, fluence=3.0mJ/cm2, PRF=100Hz). Photoacoustic signals generated by the sample were captured by a linear ultrasonic array (128 elements, pitch=0.1mm, fc=15MHz) connected to an acquisition device. The forward problem is modelled in a matrix form Y=AX, where Y are the measured photoacoustic signals and X is the object to reconstruct. The matrix A contained the PSFs at all points of the reconstruction grid, and was derived from a single PSF acquired experimentally for a 10-μm wide microchannel. For the reconstruction, we used a sparsity-based minimization algorithm. While the conventional image obtained by beamforming the signals measured with all the 128 elements of the probe cannot resolve the individual microchannels, our sparsity-based reconstruction leads to super-resolved images with only 8 elements of the probe (regularly spaced over the full probe aperture), with an image quality comparable to that obtained with all the 128 elements. These results pave the way towards super-resolution in 3D photoacoustic imaging with sparse transducers arrays.