Gaia Rizzo
Postdoctoral fellow
gaia.rizzo@gmail.com
+39-049-827-7595
Room 310 – 3rd floor – DEI/A
Short Bio
Gaia Rizzo was born in Venice in April 3, 1983. She graduated in Information Engineering at the University of Padova in 2007. She received the Ph.D. degree in Information Engineering (Bioengineering) from the Department of Information Engineering, University of Padova in 2012. From March 2014 she is a Senior Postdoctoral Researcher at the Department of Information Engineering, University of Padova. She was visiting researcher at the Division of Experimental Medicine, Imperial College of London (2008, 2010, 2011), and at the Department of Neuroimaging, King’s College London (2014).
Her research activity covers the following topics: A) quantification of PET images at region and voxel level; B) arterial input function modelling studies; C) correlation of genomic data with PET images of protein density; D) development of methods for the kinetic analysis of ultrasound images.
From 2011 to 2013, she has been project manager in the development of SAKE, a software for the quantification of PET data via Spectral Analysis. SAKE is currently used by 17 research centres worldwide and it is being taught within the course “Experimental Design and Practical Data Analysis in Positron Emission Tomography”, held at King’s College, London.
In 2011, Gaia Rizzo won a Young Researcher Award at the “Veneto Giovani Ricerca Futuro” event. In 2011 and 2012 she was awarded with a ISCBFM Young Investigator travel bursary at the BrainPET2011, and a Student Travel Stipend at the WMIC2012.
She collaborates with the Department of Neuroimaging, IoPPN, King’s College London, UK (Federico Turkheimer, Mattia Veronese, Paul Expert), the Department of Medicine, Imperial College, London, UK, (Jim Myers), the Psychiatry Imaging, Imperial College, London, UK (Peter Bloomfield), the Comprehensive Cancer Imaging Center, Imperial College, London, UK (Eric Aboagye), the Molecular Imaging Branch of the National Institute of Mental Health, Bethesda, USA (Masahiro Fujita, Robert Innis) and the University of Bordeaux, CNRS, France (Paolo Zanotti Fregonara).
Selected Publications
Bayesian quantification of contrast-enhanced ultrasound images with adaptive inclusion of an irreversible component Journal Article
In: IEEE Transactions on Medical Imaging, vol. 36, no. 4, pp. 1027 - 1036, 2017, ISSN: 1558-254X.
MENGA: A New Comprehensive Tool for the Integration of Neuroimaging Data and the Allen Human Brain Transcriptome Atlas Journal Article
In: PLoS ONE, vol. 11, no. 2, pp. e0148744, 2016.
Microglial Activity in People at Ultra High Risk of Psychosis and in Schizophrenia: An [11C]PBR28 PET Brain Imaging Study Journal Article
In: American Journal of Psychiatry, vol. 173, no. 1, pp. 44-52, 2016, (PMID: 26472628).
Pixel-based approach to assess CEUS kinetics parameters for differential diagnosis of rheumatoid arthritis. Journal Article
In: Journal of Medical Imaging, vol. 2, no. 3, pp. 034503-1 034503-13, 2015.
The methodology of TSPO imaging with Positron Emission Tomography Journal Article
In: Biochemical Soc Transactions, vol. 43, no. 4, pp. 586-592, 2015.
Kinetic modeling without accounting for the vascular component impairs the quantification of [11C]PBR28 brain PET data Journal Article
In: J Cereb Blood Flow Metab, vol. 34, no. 6, pp. 1060–1069, 2014.
SAKE: A new quantification tool for positron emission tomography studies Journal Article
In: Comput Methods Programs Biomed, vol. 111, no. 1, pp. 199-213, 2013, ISSN: 1872-7565 (Electronic) 0169-2607 (Linking).
All Publications
2019
Generalization of endothelial modelling of TSPO PET imaging: Considerations on tracer affinities Journal Article
In: J. Cereb. Blood Flow Metab., 2019, ISSN: 15597016.
2017
In: Rheumatology, vol. in press, 2017, ISSN: 1462-0332.
From macro to nano: linking quantitative CEUS perfusion parameters to CD4+ T cells subtypes in spondyloarthtitis Proceedings Article
In: Proceedings of ISBI, IEEE International Symposium on Biomedical Imaging, 2017.
Bayesian quantification of contrast-enhanced ultrasound images with adaptive inclusion of an irreversible component Journal Article
In: IEEE Transactions on Medical Imaging, vol. 36, no. 4, pp. 1027 - 1036, 2017, ISSN: 1558-254X.
Improving the quantification of contrast enhanced ultrasound using a Bayesian approach Conference
SPIE Conference on Medical Imaging, 2017.
Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis Conference
SPIE Conference on Medical Imaging, 2017.
In: Clinical Rheumatology, vol. 36, no. 2, pp. 391–399, 2017, ISSN: 1434-9949.
A Variational Bayesian inference method for parametric imaging of PET data Journal Article
In: Neuroimage, vol. 150, pp. 136–149, 2017.
2016
Parametric imaging of brain PET data using a Variational Bayesian inference approach Conference
Eleventh International Symposium on Functional Neuroreceptor Mapping of the Living Brain, (NeuroReceptor Mapping 2016), 2016, (oral).
The genomic plot: a new method for measuring specific receptor binding of a PET radioligand in human brain without pharmacological blockade Conference
Eleventh International Symposium on Functional Neuroreceptor Mapping of the Living Brain, (NeuroReceptor Mapping 2016), Boston (MA, USA), 13-17 July 2016 2016, (oral).