Visualisation of PET data in the Fly Algorithm

dc.contributor.authorAbbood, Zainab Alien_US
dc.contributor.authorRocchisani, Jean-Marieen_US
dc.contributor.authorVidal, Francken_US
dc.contributor.editorKatja Bühler and Lars Linsen and Nigel W. Johnen_US
dc.date.accessioned2015-09-14T04:49:10Z
dc.date.available2015-09-14T04:49:10Z
dc.date.issued2015en_US
dc.description.abstractWe 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.description.sectionheadersPostersen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicineen_US
dc.identifier.doi10.2312/vcbm.20151227en_US
dc.identifier.isbn978-3-905674-82-8en_US
dc.identifier.issn2070-5786en_US
dc.identifier.pages211-212en_US
dc.identifier.urihttps://doi.org/10.2312/vcbm.20151227en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.8 [Computer Graphics]en_US
dc.subjectApplicationsen_US
dc.subjectMedical G.1.6 [Mathematics of Computing]en_US
dc.subjectOptimizationen_US
dc.subjectGlobal optimizationen_US
dc.titleVisualisation of PET data in the Fly Algorithmen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
211-212.pdf
Size:
345.72 KB
Format:
Adobe Portable Document Format