A comprehensive framework of GPU-accelerated image reconstruction for photoacoustic computed tomography

The repository provides code of the paper with the same name as this repository.

A 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, GAPAT), helping the research society 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 approximately 871 times when reconstructing extremely large-scale 3D PACT images on a dual-GPU platform compared to a 24-core workstation CPU. Besides this manuscript, we shared example codes in the 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: photoacoustic computed tomography, large-scale data size, GPU-accelerated method, Taichi Lang for python, multiple GPU platform.

Installation

The code requires python>=3.8, and pre-installing CUDA on your computer is strongly recommended (no specific version requirement). Firstly clone the repository locally.

git clone https://github.com/ddffwyb/GAPAT.git
cd GAPAT

We recommend using the conda environment to install the cuda toolkit. The following command installs the cuda toolkit.

conda create -n gapat python=3.9 -y
conda activate gapat
conda install -c conda-forge cudatoolkit=11.8

Then install the dependencies listed in the requirements.txt file.

pip install -r requirements.txt

Getting Started

We provide an example of the in vivo experimental by using a synthetic planar array, which is consistent with the experiments and parameters described in Section 3.2 of the paper. In the config folder, we place configuration files in .yaml format, and by setting the device parameter to gpu to activate GPU support. In the data folder, we provide a set of PACT data of synthetic planar scanning arms. In the src folder, we provide the source code of our framework. The example.py file provides a demonstration, and the usage method of command line is as follows.

python example.py --config_file config.yaml --result_file recon.mat --kernel das

Results

240x240x80 Grids

	CPU	K-wave CPU	K-wave GPU	3090 Ti Single	3090 Ti Dual
DAS	3034s	606.6s	18.7s	13.2s	10.8s
DAS with Angle Limit	/	/	/	13.3s	10.9s
FBP	/	/	/	13.8s	11.1s

600x600x200 Grids

	CPU	K-wave CPU	K-wave GPU	3090 Ti Single	3090 Ti Dual
DAS	43784s	6044.7s	768.4s	100.0s	55.0s
DAS with Angle Limit	/	/	/	100.5s	55.4s
FBP	/	/	/	103.7s	56.5s

1200x1200x400 Grids

	CPU	K-wave CPU	K-wave GPU	3090 Ti Single	3090 Ti Dual
DAS	335259s	44690.4s	out of GPU memory	749.7s	385.1s
DAS with Angle Limit	/	/	/	757.3s	392.5s
FBP	/	/	/	781.8s	402.5s