Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation

Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality. Classical methods for numerically solving the photoacoustic wave equation relies on a fine discretization of space and can become computationally expensive for large computational grids. In this work, we apply Fourier Neural Operator (FNO) networks as a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium. Comparisons between the FNO network and pseudo-spectral time domain approach demonstrated that the FNO network generated comparable simulations with small errors and was several orders of magnitude faster. Moreover, the FNO network was generalizable and can generate simulations not observed in the training data

Image Reconstruction in Photoacoustic Computed Tomography with Acoustically Heterogeneous Media

Photoacoustic computed tomography (PACT), also known as optoacoustic or thermoacoustic tomography, is a rapidly emerging hybrid imaging modality that combines optical image contrast with ultrasound detection. The majority of currently available PACT image reconstruction algorithms are based on idealized imaging models that assume a lossless and acoustically homogeneous medium. However, in many applications of PACT these assumptions are violated and the induced photoacoustic (PA) wavefields are scattered and absorbed as they propagate to the receiving transducers. In those applications of PACT, the reconstructed images can contain significant distortions and artifacts if the inhomogeneous acoustic properties of the object are not accounted for in the reconstruction algorithm. In this dissertation, we develop and investigate a full-wave approach to iterative image reconstruction in PACT with acoustically heterogeneous lossy media. A key contribution of this work is the establishment of a discrete imaging model that is based on the exact PA wave equation and a procedure to implement an associated matched discrete forward and backprojection operator pair, which permits application of a variety of modern iterative image reconstruction algorithms that can mitigate the effects of noise, data incompleteness and model errors. Another key contribution is the development of an optimization approach to joint reconstruction (JR) of absorbed optical energy density and speed of sound in PACT, which is utilized to investigate the numerical properties of the JR problem and its feasibility in practice. We also develop a TR-based methodology to compensate for heterogeneous acoustic attenuation that obeys a frequency power law. In addition, we propose a image reconstruction methodology for transcranial PACT that employs detailed subject-specific descriptions of the acoustic properties of the skull to mitigate skull-induced distortions in the reconstructed image

Spatial Resolution and Contrast Enhancement in Photoacoustic Imaging with Filter Delay Multiply and Sum Beamforming Technique

Photoacoustic imaging is used to differentiate between tissue types based on light absorption. Different structures, such as vascular density of capillaries in human tissue, can be analysed and provide diagnostic information to detect early stage breast cancer. Delay and sum (DAS) beamforming is the traditional method to reconstruct photoacoustic images. However, for structures located deep in the tissue (>10 mm), signal to noise (SNR) of the photoacoustic signal drops significantly. This study proposes using filter delay multiply and sum (FDMAS) beamforming technique to increase the SNR and enhance the image quality. Experimental results showed that FDMAS beamformer improved the SNR by 6.9 dB and the lateral resolution by 48% compared to the DAS beamformer. Moreover, the effect of aperture size on the proposed method is presented as the sub-group FDMAS, which further increased the improvement in image quality