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Using Affordable LED Arrays for Photo-Stimulation of Neurons
Published on: November 15, 2011
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GPU-accelerated Double-stage Delay-multiply-and-sum Algorithm for Fast Photoacoustic Tomography Using LED Excitation
Seyyed Reza Miri Rostami1, Moein Mozaffarzadeh2, Mohsen Ghaffari-Miab1
11 Computational Electromagnetics Laboratory, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Ultrasonic Imaging
|July 20, 2019
Summary
Accelerating photoacoustic image reconstruction using the Double-stage Delay-Multiply-and-Sum (DS-DMAS) algorithm is now feasible. A Compute Unified Device Architecture (CUDA) graphics processing unit (GPU) approach significantly enhances speed without compromising accuracy.
Area of Science:
- Biomedical Imaging
- Computational Imaging
- Photoacoustics
Background:
- Photoacoustic imaging offers high contrast for visualizing biological tissues.
- The Double-stage Delay-Multiply-and-Sum (DS-DMAS) algorithm improves image contrast.
- DS-DMAS algorithm faces challenges due to high computational complexity.
Purpose of the Study:
- To optimize the computational complexity of the DS-DMAS algorithm for photoacoustic image reconstruction.
- To implement a parallel computation approach using Compute Unified Device Architecture (CUDA) graphics processing units (GPUs).
- To evaluate the efficiency and accuracy of the GPU-accelerated DS-DMAS algorithm.
Main Methods:
- Utilized a CUDA GPU for parallel computation to accelerate the DS-DMAS algorithm.
- Compared the performance of the GPU-accelerated method against a single-threaded central processing unit (CPU).
- Tested the algorithm on images generated from a light-emitting diode (LED)-based photoacoustic scanner.
Main Results:
- The GPU approach achieved a speed increase of nearly 140-fold for 1024 × 1024 pixel images compared to CPU.
- No decrease in image reconstruction accuracy was observed with the GPU implementation.
- Achieved high frame rates (250, 125, and 83.3 fps) for various image resolutions (64×64, 128×128, and 256×256).
Conclusions:
- The CUDA GPU parallel computation effectively addresses the high computational complexity of the DS-DMAS algorithm.
- DS-DMAS, when coupled with CUDA GPU parallel computation, is suitable for efficient real-time photoacoustic image reconstruction.
- This optimized approach enables the practical clinical application of advanced photoacoustic imaging techniques.
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