Volver a Buscador de AI
[1] N.Orta, S. Rubí, I. Barceló, A.Espino, M. Oporto, H. Navalón, B. Luna, C.Sampol, C. Peña. PET-FDG cerebral en la valoración de la encefalitis autoinmune y síndrome paraneoplásico neurológico. Revista Española de Medicina Nuclear e Imagen Molecular. (2018).
[2] P. Aguiar, I. Domínguez-Prado, P. Fierro, Á. Rubial, J. Cortés. Estudio comparativo entre diferentes softwares de cuantificación de PET-FDG cerebral. Revista Española de Medicina Nuclear e Imagen Molecular. (2018).
[3] P. Santos, M.P. Garrrastachu Zumaran, I.Sánchez, M.C. Albornoz, X.L.E Boulevard, F.Cañete, A. Cabrera, R. Delgado, R. Ramírez. PET-18FDG como herramienta en el diagnóstico de Parkinsonianos, nuestra experiencia en 2015-1016. Revista Española de Medicina Nuclear e Imagen Molecular. (2018).
Detection of hypo and hypermetabolic zones
Algorithm designed for assisting in the diagnosis of abnormal brain areas with a PET-FDG
QUBIOTECH has developed a tool to serve as a nuclear medicine assistance by automatically processing and providing visual and quantitative information identifying and detecting hypometabolic or hypermetabolic zones in FDG-PET. The algorithm analysis is based on ROIs by using one of the most extensive databases of PET-FDG (>120 subjects) of normal individuals on the market for z-score extraction and % deviation from normality, followed by a parametric statistical analysis (SPM), voxel to voxel.
Applications and diseases
This tool is useful for giving neurological support to clinicians, facilitating the interpretation of the nuclear medicine report and identifying the patterns of different neurodegenerative conditions, thus reducing the inter-observer variability. It provides diagnostic assistance, identifying neurodegeneration in early stages of the disease and discovering the epileptogenic focus.
Detectable pathologies
Advantages
Analysis based on regions of interest comparing with a database of normals to extract z-scores and % deviation from normality.
Parametric statistical analysis voxel to voxel. Clear detection and identification of hypometabolic or hypermetabolic areas.
References
[1] N.Orta, S. Rubí, I. Barceló, A.Espino, M. Oporto, H. Navalón, B. Luna, C.Sampol, C. Peña. PET-FDG cerebral en la valoración de la encefalitis autoinmune y síndrome paraneoplásico neurológico. Revista Española de Medicina Nuclear e Imagen Molecular. (2018).
[2] P. Aguiar, I. Domínguez-Prado, P. Fierro, Á. Rubial, J. Cortés. Estudio comparativo entre diferentes softwares de cuantificación de PET-FDG cerebral. Revista Española de Medicina Nuclear e Imagen Molecular. (2018).
[3] P. Santos, M.P. Garrrastachu Zumaran, I.Sánchez, M.C. Albornoz, X.L.E Boulevard, F.Cañete, A. Cabrera, R. Delgado, R. Ramírez. PET-18FDG como herramienta en el diagnóstico de Parkinsonianos, nuestra experiencia en 2015-1016. Revista Española de Medicina Nuclear e Imagen Molecular. (2018).
