Evaluation of Different Approximations for Correlation Coefficients in Stochastic FDTD to Estimate SAR Variance in a Human Head Model

Masumnia-Bisheh, Khadijeh; Ghaffari‐Miab, Mohsen; Zakeri, Bijan

doi:10.1109/temc.2016.2614128

Cited by 34 publications

(12 citation statements)

References 19 publications

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“…For the 1D simulations performed in [18], it appears that the suggested line of work is successful, even when the correlation coefficients are extracted from only 100 MC simulations (which correspond to only 1% of the runs required to obtain reference data). These concepts are also extended to two-dimensional problems in [19], where the variance of the specific absorption rate in the human head due to plane-wave illumination is computed. Again, considerable reduction of the error of the Stochastic FDTD method is verified (although it should be mentioned that the actual problem setup is not two-dimensional in reality).…”

Section: A Possible Improvementmentioning

confidence: 99%

A Short Review of FDTD‐Based Methods for Uncertainty Quantification in Computational Electromagnetics

Zygiridis

2017

Mathematical Problems in Engineering

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We provide a review of selected computational methodologies that are based on the deterministic finite-difference time-domain algorithm and are suitable for the investigation of electromagnetic problems involving uncertainties. As it will become apparent, several alternatives capable of performing uncertainty quantification in a variety of cases exist, each one exhibiting different qualities and ranges of applicability, which we intend to point out here. Given the numerous available approaches, the purpose of this paper is to clarify the main strengths and weaknesses of the described methodologies and help the potential readers to safely select the most suitable approach for their problem under consideration.

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Section: A Possible Improvementmentioning

confidence: 99%

A Short Review of FDTD‐Based Methods for Uncertainty Quantification in Computational Electromagnetics

Zygiridis

2017

Mathematical Problems in Engineering

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“…Multiple-core graphics processing units (GPUs) along with the central processing unit (CPU) can address this issue. Indeed, improvements in hardware, parallelism directives, and parallel processing power [41][42][43][44] have led GPUs to be used extensively in PAI systems. [45][46][47][48][49][50][51][52][53][54] However, the advantages of double-stage delay-multiply-and-sum (DS-DMAS) have not yet been combined with the power of GPU processing.…”

Section: Introductionmentioning

confidence: 99%

GPU-accelerated Double-stage Delay-multiply-and-sum Algorithm for Fast Photoacoustic Tomography Using LED Excitation and Linear Arrays

et al. 2019

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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.

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“…Graphics Processing Unit (GPU) hardware along with the central processing unit (CPU) are being utilized to solve this problem. In comparison with the past, because of the hardware technology advantages, parallel processing power, and powerful parallelism directives, [29][30][31] GPU has been largely utilized in PAI systems. [32][33][34][35][36][37][38][39] Recently, we have proposed double-stage delay-multiply-and-sum (DS-DMAS) leading to a higher contrast in comparison with DMAS.…”

Section: Introductionmentioning

confidence: 99%

OpenACC GPU implementation of double-stage delay-multiply-and-sum algorithm: toward enhanced real-time linear-array photoacoustic tomography

Rostami

Mozaffarzadeh

Hariri

et al. 2019

Photons Plus Ultrasound: Imaging and Sensing 2019

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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 DS-DMAS 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.

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Evaluation of Different Approximations for Correlation Coefficients in Stochastic FDTD to Estimate SAR Variance in a Human Head Model

Cited by 34 publications

References 19 publications

A Short Review of FDTD‐Based Methods for Uncertainty Quantification in Computational Electromagnetics

A Short Review of FDTD‐Based Methods for Uncertainty Quantification in Computational Electromagnetics

GPU-accelerated Double-stage Delay-multiply-and-sum Algorithm for Fast Photoacoustic Tomography Using LED Excitation and Linear Arrays

OpenACC GPU implementation of double-stage delay-multiply-and-sum algorithm: toward enhanced real-time linear-array photoacoustic tomography

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