Accelerating Image Reconstruction in Three-Dimensional Optoacoustic Tomography on Graphics Processing Units

TL;DR

This paper introduces GPU-based parallelization strategies to significantly accelerate 3D optoacoustic tomography image reconstruction, enabling faster processing while maintaining accuracy, thus overcoming computational challenges inherent in 3D inverse problems.

Contribution

The paper presents the first parallelization strategies for 3D OAT image reconstruction algorithms using GPUs, achieving substantial speedups and enabling practical 3D imaging.

Findings

GPU implementations improve efficiency by up to 1250 times

Accurate 3D images reconstructed from simulated and experimental data

Parallelization significantly reduces computational time for 3D OAT reconstruction

Abstract

Purpose: Optoacoustic tomography (OAT) is inherently a three-dimensional (3D) inverse problem. However, most studies of OAT image reconstruction still employ two-dimensional (2D) imaging models. One important reason is because 3D image reconstruction is computationally burdensome. The aim of this work is to accelerate existing image reconstruction algorithms for 3D OAT by use of parallel programming techniques. Methods: Parallelization strategies are proposed to accelerate a filtered backprojection (FBP) algorithm and two different pairs of projection/backprojection operations that correspond to two different numerical imaging models. The algorithms are designed to fully exploit the parallel computing power of graphic processing units (GPUs). In order to evaluate the parallelization strategies for the projection/backprojection pairs, an iterative image reconstruction algorithm is implemented. Computer-simulation and experimental studies are conducted to investigate the computational efficiency and numerical accuracy of the developed algorithms. Results: The GPU implementations improve the computational efficiency by factors of 1, 000, 125, and 250 for the FBP algorithm and the two pairs of projection/backprojection operators, respectively. Accurate images are reconstructed by use of the FBP and iterative image reconstruction algorithms from both computer-simulated and experimental data. Conclusions: Parallelization strategies for 3D OAT image reconstruction are proposed for the first time. These GPU-based implementations significantly reduce the computational time for 3D image reconstruction, complementing our earlier work on 3D OAT iterative image reconstruction.