Double-stage delay-multiply-and-sum (DS-DMAS) is one of the algorithms proposed for photoacoustic image reconstruction where a linear-array transducer is used to detect signals. This algorithm provides a higher contrast image in comparison with the conventional delay-multiply-and-sum (DMAS) and delay-and-sum (DAS), but it imposes a high computational complexity. In this paper, open accelerators (OpenACC) GPU computation parallel approach is used to lessen the computational time and address the high computational time of the DSDMAS for photoacoustic image reconstruction process. Compared with sequential execution of the DS-DMAS on CPU, a speed-up of approximately 74× is achieved (for an image having 1024 × 1024 pixels). The proposed approach provides possibility to have an accurate reconstructed photoacoustic image with a reasonable frame rate. In addition, the higher the number of the image pixels, the higher speed-up is achieved. Using the suggested GPU implementation, it is feasible to reconstruct photoacoustic images having a size of 128 × 128, and 256 × 256 with a frame rate of 3 and 2, respectively.

Double-stage delay-multiply-and-sum (DS-DMAS) is an algorithm proposed for photoacoustic image reconstruction. The DS-DMAS algorithm offers a higher contrast than conventional delay-and-sum and delay-multiply and-sum but at the expense of higher computational complexity. Here, we utilized a compute unified device architecture (CUDA) graphics processing unit (GPU) parallel computation approach to address the high complexity of the DS-DMAS for photoacoustic image reconstruction generated from a commercial light-emitting diode (LED)–based photoacoustic scanner. In comparison with a single-threaded central processing unit (CPU), the GPU approach increased speeds by nearly 140-fold for 1024 × 1024 pixel image; there was no decrease in accuracy. The proposed implementation makes it possible to reconstruct photoacoustic images with frame rates of 250, 125, and 83.3 when the images are 64 × 64, 128 × 128, and 256 × 256, respectively. Thus, DS-DMAS can be efficiently used in clinical devices when coupled with CUDA GPU parallel computation.