Iterative image reconstruction algorithms have the potential to reduce the computational time required for photoacoustic tomography (PAT). An iterative deconvolution-based photoacoustic reconstruction with sparsity regularization (iDPARS) is presented which enables us to solve large-scale problems. The method deals with the limited angle of view and the directivity effects associated with clinically relevant photoacoustic tomography imaging with conventional ultrasound transducers. Our Graphics Processing Unit (GPU) implementation is able to reconstruct large 3-D volumes (100×100×100) in less than 10 minutes. The simulation and experimental results demonstrate iDPARS provides better images than DAS in terms of contrast-to-noise ratio and Root-Mean-Square errors.
We define a deconvolution based photoacoustic reconstruction with sparsity regularization (DPARS) algorithm for image restoration from projections. The proposed method is capable of visualizing tissue in the presence of constraints such as the specific directivity of sensors and limited-view Photoacoustic Tomography (PAT). The directivity effect means that our algorithm treats the optically-generated ultrasonic waves based on which direction they arrive at the transducer. Most PA image reconstruction methods assume that sensors have omni-directional response; however, in practice, the sensors show higher sensitivity to the ultrasonic waves coming from one specific direction. In DPARS, the sensitivity of the transducer to incoming waves from different directions are considered. Thus, the DPARS algorithm takes into account the relative location of the absorbers with respect to the transducers, and generates a linear system of equations to solve for the distribution of absorbers. The numerical conditioning and computing times are improved by the use of a sparse Discrete Fourier Transform (DCT) representation of the distribution of absorption coefficients. Our simulation results show that DPARS outperforms the conventional Delay-and-Sum reconstruction method in terms of CNR and RMS errors. Experimental results confirm that DPARS provides images with higher resolution than DAS.
We have integrated photo-acoustic imaging into an automated breast ultrasound scanner (ABUS) with the goal of simultaneously performing ultrasound (US) and multi-spectral photo-acoustic tomography (PAT). This was accomplished with minimal change to the existing automated scanner by coupling laser light into an optical fiber for flexible and robust light delivery. We present preliminary tomography data acquired with this setup, including a simple resolution-testing geometry and a tissue phantom. Integrating PAT into the ABUS such that breast imaging is possible will require illumination from below the transducer dome. To that end, we are moving towards a fiber-based, localized illumination geometry which is fixed relative to the transducer. By illuminating locally (only near the current acquisition slice), this approach reduces overall light exposure at the tissue surface, allowing higher light intensity per acquisition (which translates to higher absorber contrast), while remaining below safe exposure thresholds. We present time-domain simulations of photo-acoustic imaging under non-uniform illumination conditions, and test one potential weighting scheme which can be used to extract absorber locations.