Zhen Tian, Feng Shi, Michael Folkerts, Nan Qin, Steve B. Jiang, Xun Jia
Monte Carlo (MC) method has been recognized the most accurate dose calculation method for radiotherapy. However, its extremely long computation time impedes clinical applications. Recently, a lot of efforts have been made to realize fast MC dose calculation on GPUs. Nonetheless, most of the GPU-based MC dose engines were developed in NVidia CUDA environment. This […]
View View   Download Download (PDF)   
Yangping Wang, Lian Li, Jianwu Dang, Chong Deng, Xiaogang Du
Dose Volume Histogram(DVH) is necessary for evaluating radiotherapy planning. With the increase of patient CT slices and the development of intensity-modulated radiation therapy(IMRT) technology, statistical process of DVH requires a large number of cubic interpolation calculation, and the sequential single threaded DVH code on the CPU can not meet the real-time requirement. The paper presents […]
View View   Download Download (PDF)   
H. Heinrich, P. Ziegenhein, C. P. Kamerling, H. Froening, U. Oelfke
Dose calculation methods in radiotherapy treatment planning require the radiological depth information of the voxels that represent the patient volume to correct for tissue inhomogeneities. This information is acquired by time consuming ray-tracing-based calculations. For treatment planning scenarios with changing geometries and real-time constraints this is a severe bottleneck. We implemented an algorithm for the […]
View View   Download Download (PDF)   
F. Ammazzalorso, T. Bednarz, U. Jelen
We demonstrate acceleration on graphic processing units (GPU) of automatic identification of robust particle therapy beam setups, minimizing negative dosimetric effects of Bragg peak displacement caused by treatment-time patient positioning errors. Our particle therapy research toolkit, RobuR, was extended with OpenCL support and used to implement calculation on GPU of the Port Homogeneity Index, a […]
View View   Download Download (PDF)   
Xun Jia, Peter Ziegenhein, Steve B Jiang
Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. The graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment […]
View View   Download Download (PDF)   
N. Henderson, K. Murakami, K. Amako, M. Asai, T. Aso, A. Dotti, A. Kimura, M. Gerritsen, H. Kurashige, J. Perl, T. Sasaki
Geant4 is a large-scale particle physics package that facilitates every aspect of particle transport simulation. This includes, but is not limited to, geometry description, material definition, tracking of particles passing through and interacting with matter, storage of event data, and visualization. As more detailed and complex simulations are required in different application domains, there is […]
View View   Download Download (PDF)   
J. Steven Kirtzic, David Allen, Ovidiu Daescu
With current advances in high performance computing, particularly the applications of GPUs, it is easy to see the need for a model for GPU algorithm development. We developed a model which offers a multi-grained approach intended to accommodate nearly any GPU. Radiation therapy is one of the most effective forms of cancer treatment available. In […]
View View   Download Download (PDF)   
Zilong Pan
The Edinburgh Cancer Centre at the Western General Hospital in Edinburgh is doing research on image analysis for predicting lung fibrosis induced by radiation as part of a treatment plan. They are developing a MATLAB code to analyse three dimensional Computed tomography (CT) images of patients but, because a standard three dimensional CT image is […]
View View   Download Download (PDF)   
Dmitry Tsigelnitskiy
The goal of this dissertation was to parallelize a dose calculation code for radiotherapy cancer treatment and explore the suitability of the new Intel Xeon Phi technology for such task. The source code proved to have many bugs and as such it took a long time to be able to produce consistent results. Thus, the […]
View View   Download Download (PDF)   
Jonny Gunnarsson
This thesis reviews the fast marching method as a technique for computing the distance transform on GPU in the context of a radiotherapy planning software. The method has some interesting characteristics that, given the right circumstances, allow the distance transform to be computed for fewer voxels than commonly used alternatives. This can result in beneficial […]
View View   Download Download (PDF)   
Dante Gama Dessavre
Radiotherapy is one of the main cancer treatments used today. It is a complex process that relies on finding the cancer in the images of the patients with the most accuracy possible in order to minimize the radiation that the surrounding organs receive. Given that a typical radiotherapy treatment process lasts for 6 weeks, ideally, […]
View View   Download Download (PDF)   
Reid Townson, Xun Jia, Zhen Tian, Yan Jiang Graves, Sergei Zavgorodni, Steve B Jiang
A novel phase-space source implementation has been designed for GPU-based Monte Carlo dose calculation engines. Due to the parallelized nature of GPU hardware, it is essential to simultaneously transport particles of the same type and similar energies but separated spatially to yield a high efficiency. We present three methods for phase-space implementation that have been […]
View View   Download Download (PDF)   
Page 1 of 3123

* * *

* * *

Follow us on Twitter

HGPU group

1658 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

335 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: