Loading...
Please wait, while we are loading the content...
Similar Documents
Parallélisation sur GPU d'un algorithme de reconstruction 3D bayésien en tomographie X.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gac, Nicolas Vabre, Alexandre Mohammad-Djafari, Ali Buyens, Fanny Legoupil, Samuel |
| Copyright Year | 2009 |
| Abstract | An important number of image reconstruction algorithms are implemented in the literature on X-ray CT (Computed Tomography) data. The main family of methods are analytical, mainly filtered backprojection which are implemented generally in medical imaging for their fast reconstruction time. The limits of these methods appear when the number of projections is small, and/or not equidistributed around the object. In this specific context, iterative algebraic methods are implemented. A great number of them are mainly based on least square criterion. We propose a regularized version of iterative algorithms to improve results. The main problem that appears when using iterative algebraic methods is the computation time and especially for projection and backprojection steps. We propose to implement some steps of the iterations on GPU hardware. We present an original method based on a Bayesian statistical method for 3D tomographic reconstructions. The main interest is to apply it in a context of non-consistent data sets, for example with a small number of projections. We show a good quality of results and a significant speed up of the calculation with GPU implementation. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://hal.archives-ouvertes.fr/hal-00504759/file/GPU_Workshop_Obernai_mai_2009.pdf |
| Alternate Webpage(s) | https://hal.archives-ouvertes.fr/hal-00504759/document |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
