Visualisation of PET data in the Fly Algorithm

F.P. Vidal; Z.A. Abbood; J.M. Rocchisani

doi:10.2312/vcbm.20151227

Visualisation of PET data in the Fly Algorithm

F.P. Vidal, Z.A. Abbood, J.M. Rocchisani

Allbwn ymchwil: Cyfraniad at gynhadledd › Papur

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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.

Iaith wreiddiol	Saesneg
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.2312/vcbm.20151227
Statws	Cyhoeddwyd - 14 Medi 2015
Digwyddiad	Eurographics Workshop on Visual Computing for Biology and Medicine (EG VCBM) 14–15 September 2015 Chester, United Kingdom - Hyd: 3 Ion 0001 → …

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Cynhadledd	Eurographics Workshop on Visual Computing for Biology and Medicine (EG VCBM) 14–15 September 2015 Chester, United Kingdom
Cyfnod	3/01/01 → …

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10.2312/vcbm.20151227

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@conference{9ee3c4ac809c4af49043ee2c5f0b1bb1,

title = "Visualisation of PET data in the Fly Algorithm",

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.",

author = "F.P. Vidal and Z.A. Abbood and J.M. Rocchisani",

year = "2015",

month = sep,

day = "14",

doi = "10.2312/vcbm.20151227",

language = "English",

note = "Eurographics Workshop on Visual Computing for Biology and Medicine (EG VCBM) 14–15 September 2015 Chester, United Kingdom ; Conference date: 03-01-0001",

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TY - CONF

T1 - Visualisation of PET data in the Fly Algorithm

AU - Vidal, F.P.

AU - Abbood, Z.A.

AU - Rocchisani, J.M.

PY - 2015/9/14

Y1 - 2015/9/14

N2 - 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.

AB - 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.

U2 - 10.2312/vcbm.20151227

DO - 10.2312/vcbm.20151227

M3 - Paper

T2 - Eurographics Workshop on Visual Computing for Biology and Medicine (EG VCBM) 14–15 September 2015 Chester, United Kingdom

Y2 - 3 January 0001

ER -

Visualisation of PET data in the Fly Algorithm

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