Deblurring algorithms accounting for the finite detector size in photoacoustic tomography

Most reconstruction algorithms for photoacoustic tomography, like back projection or time reversal, work ideally for point-like detectors. For real detectors, which integrate the pressure over their finite size, images reconstructed by these algorithms show some blurring. Iterative reconstruction algorithms using an imaging matrix can take the finite size of real detectors directly into account, but the numerical effort is significantly higher compared to the use of direct algorithms. For spherical or cylindrical detection surfaces, the blurring caused by a finite detector size is proportional to the distance from the rotation center (spin blur) and is equal to the detector size at the detection surface. In this work, we apply deconvolution algorithms to reduce this type of blurring on simulated and on experimental data. Two particular deconvolution methods are compared, which both utilize the fact that a representation of the blurred image in polar coordinates decouples pixels at different radii from the rotation center. Experimental data have been obtained with a flat, rectangular piezoelectric detector measuring signals around a plastisol cylinder containing various small photoacoustic sources with variable distance from the center. Both simulated and experimental results demonstrate a nearly complete elimination of spin blur.