Quantification of [11C](r)-rolipram PET data with data driven approaches

Development of novel models and estimation methods for quantitative PET imaging

The principal aims of the project are:

1) Development of compartmental models to quantify PET tracers kinetics. Specifically, we developed novel models for [18F]FDG in skeletal muscle and in lungs (tracer measuring glucose metabolism), [11C]SCH442416 (highly selective adenosine A2A antagonist), [11C]PBR28 (ligand for the translocator protein TSPO) and [11C]Ro15-4513 (GABA-benzodiazepine ligand).

2) Development of estimation methods for the quantification of PET images at both ROI and pixel level. The research group has an extensive expertise in PET quantification by using Bayesian, population and spectral-based estimation approaches.

PET study at baseline and after drug administration

Quantification of PET data to evaluate system impairments in clinical conditions

The research group works on a wide range of PET ligands to assess possible disease conditions, such as [11C]SCH442416 and [11C]PBR28 in schizophrenia, [18F]FLT in breast cancer, [18F]FDG in acute lung injury and lung cancer.

Multimodal integration of mRNA and imaging data

Correlation of brain mRNA and imaging data

This work aims to test gene expression levels towards in vivo imaging data across unrelated normal subjects, comparing transcriptome maps derived from the Allen Human Brain Atlas (made freely available by the Allen Institute for Brain Science) and multimodal imaging techniques.

Blood measures

Arterial input function modeling studies and alternative non-invasive input function approaches

Quantification of PET data commonly requires parent tracer measurements and the correction for the presence of metabolites. Aim of this side of the research is the analysis of the arterial input functions and the study of possible alternative non-invasive input function approaches.

FAIR people: Alessandra Bertoldo, Gaia Rizzo, Matteo Tonietto