Comprehensive framework of GPU-accelerated image reconstruction for photoacoustic computed tomography

Abstract

Significance: Photoacoustic computed tomography (PACT) is a promising non-invasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms.

Aim: In this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (GPU-accelerated photoacoustic computed tomography), to help the research community to grasp this advanced image reconstruction method.

Approach: We leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform.

Results: Notably, our framework can achieve an effective rate of $\sim 871$ times when reconstructing extremely large-scale three-dimensional PACT images on a dual-GPU platform compared to a 24-core workstation CPU. In this paper, we share example codes via GitHub.

Conclusions: Our approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine.

Keywords: GPU-accelerated method; Taichi Lang for Python; large-scale data size; multiple GPU platform; photoacoustic computed tomography.

Fig. 1

Flowchart of the pipeline of GAPAT.

Fig. 2

Detectors arranged in a Fibonacci array on a hemisphere.

Fig. 3

Schematic diagram of the scanning of linear array to form a synthetic planar array.

Fig. 4

Reconstructed maximum intensity projection (MIP) image of the arm using GAPAT by $1200\times 1200\times 400$ grids. Data that have been linearly normalized to fill the 0-1 interval.