| dc.contributor.author | Abbood, Zainab Ali | en_US |
| dc.contributor.author | Rocchisani, Jean-Marie | en_US |
| dc.contributor.author | Vidal, Franck | en_US |
| dc.contributor.editor | Katja Bühler and Lars Linsen and Nigel W. John | en_US |
| dc.date.accessioned | 2015-09-14T04:49:10Z | |
| dc.date.available | 2015-09-14T04:49:10Z | |
| dc.date.issued | 2015 | en_US |
| dc.identifier.isbn | 978-3-905674-82-8 | en_US |
| dc.identifier.issn | 2070-5786 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/vcbm.20151227 | en_US |
| dc.description.abstract | We use the Fly algorithm, an artificial evolution strategy, to reconstruct positron emission tomography (PET) images. The algorithm iteratively optimises the position of 3D points. It eventually produces a point cloud, which needs to be voxelised to produce volume data that can be used with conventional medical image software. However, resulting voxel data is noisy. In our test case with 6,400 points the normalised cross-correlation (NCC) between the reference and the reconstruction is 85.53%; with 25,600 points it is 93.60%. This paper introduces a more robust 3D voxelisation method based on implicit modelling using metaballs to overcome this limitation. With metaballs, the NCC with 6,400 points increases up to 92.21%; and up to 96.26% with 25,600 points. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | I.3.8 [Computer Graphics] | en_US |
| dc.subject | Applications | en_US |
| dc.subject | Medical G.1.6 [Mathematics of Computing] | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Global optimization | en_US |
| dc.title | Visualisation of PET data in the Fly Algorithm | en_US |
| dc.description.seriesinformation | Eurographics Workshop on Visual Computing for Biology and Medicine | en_US |
| dc.description.sectionheaders | Posters | en_US |
| dc.identifier.doi | 10.2312/vcbm.20151227 | en_US |
| dc.identifier.pages | 211-212 | en_US |
