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SUVref: reducing reconstruction-dependent variation in PET SUV

BACKGROUND: We propose a new methodology, reference Standardised Uptake Value (SUV(ref)), for reducing the quantitative variation resulting from differences in reconstruction protocol. Such variation that is not directly addressed by the use of SUV or the recently proposed PERCIST can impede compara...

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Detalles Bibliográficos
Autores principales: Kelly, Matthew D, Declerck, Jerome M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3251007/
https://www.ncbi.nlm.nih.gov/pubmed/22214348
http://dx.doi.org/10.1186/2191-219X-1-16
Descripción
Sumario:BACKGROUND: We propose a new methodology, reference Standardised Uptake Value (SUV(ref)), for reducing the quantitative variation resulting from differences in reconstruction protocol. Such variation that is not directly addressed by the use of SUV or the recently proposed PERCIST can impede comparability between positron emission tomography (PET)/CT scans. METHODS: SUV(ref )applies a reconstruction-protocol-specific phantom-optimised filter to clinical PET scans for the purpose of improving comparability of quantification. The ability of this filter to reduce variability due to differences in reconstruction protocol was assessed using both phantom and clinical data. RESULTS: SUV(ref )reduced the variability between recovery coefficients measured with the NEMA image quality phantom across a range of reconstruction protocols to below that measured for a single reconstruction protocol. In addition, it enabled quantitative conformance to the recently proposed EANM guidelines. For the clinical data, a significant reduction in bias and variance in the distribution of differences in SUV, resulting from differences in reconstruction protocol, greatly reduced the number of hot spots that would be misclassified as undergoing a clinically significant change in SUV. CONCLUSIONS: SUV(ref )significantly reduces reconstruction-dependent variation in SUV measurements, enabling increased confidence in quantitative comparison of clinical images for monitoring treatment response or disease progression. This new methodology could be similarly applied to reduce variability from scanner hardware.