GPU-accelerated Double-stage Delay-multiply-and-sum Algorithm for Fast Photoacoustic Tomography Using LED Excitation and Linear Arrays. (September 2019)

• Record Type: Journal Article
• Authors: Miri Rostami, Seyyed Reza; Mozaffarzadeh, Moein; Ghaffari-Miab, Mohsen; Hariri, Ali; Jokerst, Jesse
• Is Part Of: Ultrasonic imaging. Volume 41:Number 5(2019)
• Page Start: 301
• Page End: 316
• Publication Date: 2019-09
• Subjects: graphics processing unit (GPU) -- central processing unit (CPU) -- compute unified device architecture (CUDA) -- photoacoustic imaging -- beamforming -- parallel computing -- double-stage delay-multiply-and-sum (DS-DMAS) -- linear-array imaging; Diagnostic ultrasonic imaging -- Methodology -- Periodicals; Ultrasonic testing -- Periodicals; Ultrasonic imaging -- Periodicals; Ultrasonography -- Periodicals; Échographie -- Méthodologie -- Périodiques; Essais par ultrasons -- Périodiques; Imagerie ultrasonore -- Périodiques; 616.07543
• DOI: 10.1177/0161734619862488
• Languages: English
• ISSNs: 0161-7346

Abstract

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.