Improving the Detection of Small Lesions Using a State-of-the-Art Time-of-Flight PET/CT System and Small-Voxel Reconstructions Article Swipe

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Daniëlle Koopman , J.A. van Dalen , Martine C. M. Lagerweij , Hester Arkies , Jaep de Boer , Ad H. Oostdijk , Cornelis H. Slump , P.L. Jager ·

YOU? · · 2015 · Open Access · · DOI: https://doi.org/10.2967/jnmt.114.147215

Supported by a phantom study, there was a visual preference toward (18)F-FDG PET images reconstructed with 2 × 2 × 2 mm voxels and a profound increase in standardized uptake value and SNR for small lesions. Hence, it is expected that small-lesion detection improves using small-voxel reconstructions.

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Artificial Intelligence

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Voxel Nuclear medicine Imaging phantom Image quality Medicine Physics Radiology Computer science Image (mathematics) Artificial intelligence

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Type: article
Language: en
Landing Page: https://doi.org/10.2967/jnmt.114.147215
PDF: http://tech.snmjournals.org/content/43/1/21.full.pdf
OA Status: bronze
Cited By: 47
References: 31
Related Works: 10
OpenAlex ID: https://openalex.org/W2021605236

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DOI: https://doi.org/10.2967/jnmt.114.147215

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Title: Improving the Detection of Small Lesions Using a State-of-the-Art Time-of-Flight PET/CT System and Small-Voxel Reconstructions

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Type: article

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Language: en

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Publication year: 2015

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Publication date: 2015-01-22

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Authors: Daniëlle Koopman, J.A. van Dalen, Martine C. M. Lagerweij, Hester Arkies, Jaep de Boer, Ad H. Oostdijk, Cornelis H. Slump, P.L. Jager

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Landing page: https://doi.org/10.2967/jnmt.114.147215

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Concepts: Voxel, Nuclear medicine, Imaging phantom, Image quality, Medicine, Physics, Radiology, Computer science, Image (mathematics), Artificial intelligence

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Cited by: 47

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