@article{Tonietto2016326,

title = {Plasma radiometabolite correction in dynamic PET studies: Insights on the available modeling approaches},

author = {Matteo Tonietto, Gaia Rizzo, Mattia Veronese, Masahiro Fujita, Sami S Zoghbi, Paolo Zanotti-Fregonara, Alessandra Bertoldo},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961388641&partnerID=40&md5=caddf72668b5cba353c0ec8429066a39},

doi = {10.1177/0271678X15610585},

issn = {0271678X},

year  = {2016},

date = {2016-01-01},

journal = {Journal of Cerebral Blood Flow and Metabolism},

volume = {36},

number = {2},

pages = {326-339},

publisher = {Nature Publishing Group},

abstract = {Full kinetic modeling of dynamic PET images requires the measurement of radioligand concentrations in the arterial plasma. The unchanged parent radioligand must, however, be separated from its radiometabolites by chromatographic methods. Thus, only few samples can usually be analyzed and the resulting measurements are often noisy. Therefore, the measurements must be fitted with a mathematical model. This work presents a comprehensive analysis of the different models proposed in the literature to describe the plasma parent fraction (PPf) and of the alternative approaches for radiometabolite correction. Finally, we used a dataset of [ 11 C]PBR28 brain PET data as a case study to guide the reader through the PPf model selection process. © The Author(s) 2015.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Veronese2016,

title = {Spectral Analysis of Dynamic PET Studies: A Review of 20 Years of Method Developments and Applications},

author = { Mattia Veronese and Gaia Rizzo and Alessandra Bertoldo and Federico E Turkheimer},

url = {https://www.hindawi.com/journals/cmmm/2016/7187541/},

doi = {10.1155/2016/7187541},

issn = {1748-670X},

year  = {2016},

date = {2016-01-01},

journal = {Computational and Mathematical Methods in Medicine},

volume = {2016},

pages = {1--15},

abstract = {In Positron Emission Tomography (PET), spectral analysis (SA) allows the quantification of dynamic data by relating the radioactivity measured by the scanner in time to the underlying physiological processes of the system under investigation. Among the different approaches for the quantification of PET data, SA is based on the linear solution of the Laplace transform inversion whereas the measured arterial and tissue time-activity curves of a radiotracer are used to calculate the input response function of the tissue. In the recent years SA has been used with a large number of PET tracers in brain and nonbrain applications, demonstrating that it is a very flexible and robust method for PET data analysis. Differently from the most common PET quantification approaches that adopt standard nonlinear estimation of compartmental models or some linear simplifications, SA can be applied without defining any specific model configuration and has demonstrated very good sensitivity to the underlying kinetics. This characteristic makes it useful as an investigative tool especially for the analysis of novel PET tracers. The purpose of this work is to offer an overview of SA, to discuss advantages and limitations of the methodology, and to inform about its applications in the PET field.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rizzo2015,

title = {Pixel-based approach to assess CEUS kinetics parameters for differential diagnosis of rheumatoid arthritis.},

author = {Gaia Rizzo and Bernd Raffeiner and Alessandro Coran and Luca Ciprian and Ugo Fiocco and Costantino Botsios and Roberto Stramare and Enrico Grisan},

doi = {dx.doi.org/10.1117/1.JMI.2.3.034503},

year  = {2015},

date = {2015-09-11},

journal = {Journal of Medical Imaging},

volume = {2},

number = {3},

pages = {034503-1 034503-13},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Turkheimer2015,

title = {The methodology of TSPO imaging with Positron Emission Tomography},

author = { Federico E. Turkheimer and Gaia Rizzo and Peter S. Bloomfield and Owen D. Howes and Paolo Zanotti-Fregonara and Alessandra Bertoldo and Mattia Veronese},

doi = { http://dx.doi.org/10.1042/BST20150058},

year  = {2015},

date = {2015-08-03},

journal = {Biochemical Soc Transactions},

volume = {43},

number = {4},

pages = {586-592},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tonietto2015a,

title = {Improved models for plasma radiometabolite correction and their impact on kinetic quantification in PET studies},

author = { Matteo Tonietto and Mattia Veronese and Gaia Rizzo and Paolo Zanotti-Fregonara and Talakad G. Lohith and Masahiro Fujita and Sami S. Zoghbi and Alessandra Bertoldo},

url = {http://www.nature.com/jcbfm/journal/v35/n9/full/jcbfm201561a.html},

doi = {10.1038/jcbfm.2015.61},

issn = {0271-678x},

year  = {2015},

date = {2015-04-15},

journal = {J Cereb Blood Flow Metab},

volume = {35},

number = {9},

pages = {1462-1469},

keywords = {},

pubstate = {published},

tppubtype = {article}

}