To obtain the initial pressure from the collected data on a planar sensor arrangement in photoacoustic tomography, there exists an exact analytic frequency-domain reconstruction formula. An efficient realization of this formula needs to cope with the evaluation of the data’s Fourier transform on a nonequispaced mesh. We use the nonuniform fast Fourier transform to handle this issue and show its feasibility in three-dimensional experiments with real and synthetic data. This is done in comparison to the standard approach that uses linear, polynomial, or nearest neighbor interpolation. Moreover, we investigate the effect and the utility of flexible sensor location to make optimal use of a limited number of sensor points. The computational realization is accomplished by the use of a multidimensional nonuniform fast Fourier algorithm, where nonuniform data sampling is performed both in frequency and spatial domain. Examples with synthetic and real data show that both approaches improve image quality.
Elastography is implemented by applying a mechanical force to a specimen and visualizing the resulting displacement. As a basis of elastographic imaging typically ultrasound, optical coherence tomography or magnetic resonance imaging are used. Photoacoustics has not been viewed as a primary imaging modality for elastography, but only as a complementary method to enhance the contrast in ultrasound elastography. The reason is that photoacoustics is considered speckle free , which hinders application of speckle tracking algorithms. However, while conventional ultrasound only uses a single frequency, photoacoustics utilizes a broad frequency spectrum. We are therefore able to generate artificial texture by using a frequency band limited part of the recorded data. In this work we try to assess the applicability of this technique to photoacoustic tomography. We use Agar phantoms with predefined Young's moduli and laterally apply a 50μm static compression. Pre- and post compression data are recorded via a Fabry Pérot interferometer planar sensor setup and reconstructed via a non-uniform-FFT reconstruction algorithm. A displacement vector field, between pre- and post compressed data is then determined via optical flow algorithms. While the implementation of texture generation during post processing reduces image quality overall, it turns out that it improves the detection of moving patterns and is therefore better suited for elastography.