Comparison of two accelerators for Monte Carlo radiation transport calculations, Nvidia Tesla M2090 GPU and Intel Xeon Phi 5110p coprocessor: A case study for X-ray CT imaging dose calculation

Liu, T.; Xu, X. George; Carothers, C.D.

doi:10.1016/j.anucene.2014.08.061

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…A GPU-accelerated Monte Carlo code, ARCHER, previously developed by members of this group was used in this study to calculate organ doses 20,21,43 . ARCHER is used in this study to simulate the transport of low-energy X-ray photons in heterogeneous media defined by the patient CT images where photoelectric effect, Compton scattering, and Rayleigh scattering can take place.…”

Section: B Organ Dose Calculationsmentioning

confidence: 99%

A method of rapid quantification of patient‐specific organ doses for CT using deep‐learning‐based multi‐organ segmentation and GPU‐accelerated Monte Carlo dose computing

Zhao¹,

Fang²,

Yan³

et al. 2020

Medical Physics

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Purpose: The ability to obtain patient-specific organ doses for CT will open the door to new applications such as personalized selection of scan factors and individualized risk assessment, leading to the ultimate goal of achieving lowdose and optimized CT imaging. One technical barrier to advancing CT dosimetry has been the lack of computational tools for automatic patient-specific multi-organ segmentation of CT images, coupled with rapid organ dose quantification.This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of radiosensitive organs from CT images and GPU-based Monte Carlo rapid organ dose calculation. Methods: A deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate radiosensitive organs from CT images. Two databases are used: the Lung CT Segmentation Challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients each with 5 segmented organs, and the Pancreas-CT (PCT) dataset that contains 43 abdominal CT scan patients each with 8 segmented organs. A five-fold cross-validation of the new method is performed on both sets of data. Dice Similarity Coefficient (DSC) is used to evaluate the segmentation performance against the ground truth. A GPU-based Monte Carlo dose code, ARCHER, is used to calculate patient-specific CT organ doses. The proposed method is tested in terms of Relative Dose Error (RDE). To demonstrate the potential improvement of the new methods, organ dose results are compared against those obtained for population-average phantoms used in an off-line dose reporting software, VirtualDose, at Massachusetts General Hospital. Results: For the group of 60 patients from LCTSC dataset, the median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.93 (heart), 0.88 (spinal cord) and 0.78 (esophagus). For the group of 43 patients from PCT dataset, the median DSCs are found to be 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.89 (stomach), 0.87 (gall bladder), 0.79(pancreas), 0.74 (esophagus), and 0.64 (duodenum). Comparing with the organ dose results from population-averaged phantoms, the new patient-specific method achieved the smaller RDE range on all organs: -4.3%~1.5% (vs -31.5%~33.9%) for the lung, -7.0%~2.3% (vs -15.2%~125.1%) for the heart, -18.8%~40.2% (vs -10.3%~124.1%) for the esophagus, -5.6%~1.6% (vs -20.3%~57.4%) for the spleen, -4.5%~4.6% (vs -19.5%~61.0%) for the pancreas, -2.3%~4.4% (vs -37.8%~75.8%) for the left kidney, -14.9%~5.4% (vs -39.9% ~14.6%) for the gallbladder, -0.9%~1.6%(vs -30.1%~72.5%) for the liver, and -23.0%~11.1% (vs -52.5%~-1.3%) for the stomach. The trained automatic segmentation tool takes less than 5 seconds in a patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel with the segmentation using the GPU-accelerated ARCHER code takes less than 4 seconds in a patient to achieve <0.5% statistical uncertainty in all organ doses for all 103 patients in the...

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Section: B Organ Dose Calculationsmentioning

confidence: 99%

A method of rapid quantification of patient‐specific organ doses for CT using deep‐learning‐based multi‐organ segmentation and GPU‐accelerated Monte Carlo dose computing

Zhao¹,

Fang²,

Yan³

et al. 2020

Medical Physics

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“…The user needs to implement Eqs. (15) and/or (16) on the accelerator(s), whereas the minimisation process is executed on the host. The main, and most time consuming, part, in the calculation is given by Eqs.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The main, and most time consuming, part, in the calculation is given by Eqs. (15) and (16). The only data transfer needed between the host and the accelerator is given by the small parameter set P, which should not lead to a bottleneck in the overall computation time.…”

Section: Problem Descriptionmentioning

confidence: 99%

The Dynamic Kernel Scheduler—Part 1

Adelmann

Locans

Suter

2016

Computer Physics Communications

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Emerging processor architectures such as GPUs and Intel MICs provide a huge performance potential for high performance computing. However developing software that uses these hardware accelerators introduces additional challenges for the developer. These challenges may include exposing increased parallelism, handling different hardware designs, and using multiple development frameworks in order to utilise devices from different vendors.The Dynamic Kernel Scheduler (DKS) is being developed in order to provide a software layer between the host application and different hardware accelerators. DKS handles the communication between the host and the device, schedules task execution, and provides a library of built-in algorithms. Algorithms available in the DKS library will be written in CUDA, OpenCL, and OpenMP. Depending on the available hardware, the DKS can select the appropriate implementation of the algorithm.The first DKS version was created using CUDA for the Nvidia GPUs and OpenMP for Intel MIC. DKS was further integrated into OPAL (Object-oriented Parallel Accelerator Library) in order to speed up a parallel FFT based Poisson solver and Monte Carlo simulations for particle matter interaction used for proton therapy degrader modelling. DKS was also used together with Minuit2 for parameter fitting, where χ 2 and max-log-likelihood functions were offloaded to the hardware accelerator. The concepts of the DKS, first results, and plans for the future will be shown in this paper.

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“…In order to obtain a good statistical result in calculations, millions of particles must be traced [17]. Therefore, it needs parallel-working super computers with high calculation power in some MC studies [18]. In this work, all calculations were applied by using MCNPX 2.7.0 code developed by Los Alamos National Laboratory (LANL) [19].…”

Section: Monte Carlo Methods and Photonuclear Reactionmentioning

confidence: 99%

Investigation of spherical and cylindrical catural Iridium targets by photonuclear reaction

2017

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Abstract. In this study, natural iridium consisting of isotopes has been irradiated with 21 MeV photons. The distribution of photons, electrons and neutrons fluxes in the spherical and cylindrical natural iridium target have been calculated using MCNPX 2.7.0 Monte Carlo code. The intensity of the photon fluxes on both targets has been compared to the 10 6 particle story by showing them as mesh and optimizing the two targets.

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Comparison of two accelerators for Monte Carlo radiation transport calculations, Nvidia Tesla M2090 GPU and Intel Xeon Phi 5110p coprocessor: A case study for X-ray CT imaging dose calculation

Cited by 16 publications

References 24 publications

A method of rapid quantification of patient‐specific organ doses for CT using deep‐learning‐based multi‐organ segmentation and GPU‐accelerated Monte Carlo dose computing

A method of rapid quantification of patient‐specific organ doses for CT using deep‐learning‐based multi‐organ segmentation and GPU‐accelerated Monte Carlo dose computing

The Dynamic Kernel Scheduler—Part 1

Investigation of spherical and cylindrical catural Iridium targets by photonuclear reaction

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