Marianna Inglese

Ricercatore

PHYS-06/A - Fisica per le scienze della vita, l'ambiente e i beni culturali

marianna.inglese@uniroma2.it

Biografia

Marianna Inglese, ricercatore di Fisica medica presso l’Università di Tor Vergata, ha conseguito la laurea magistrale in Ingegneria biomedica presso l’Università di Roma “La Sapienza” nel 2014. La sua tesi, incentrata sulla correzione delle immagini PET per piattaforme ibride PET/MRI, è stata portata a termine presso il Lawson Health Research Institute della University of Western Ontario.

Ha conseguito il dottorato di ricerca in Bioingegneria presso l’Università di Roma “La Sapienza” nel 2019, ricercando metodi avanzati di quantificazione della perfusione per dati dinamici PET e RM.

Marianna è assegnista di ricerca onoraria presso l’Imperial College di Londra, dove in precedenza ha lavorato sulla quantificazione dei dati PET dinamici e sull’applicazione dell’apprendimento automatico per gli studi radiomici.

Ha ricevuto diversi riconoscimenti, tra cui un “Magna cum laude” dall’ISMRM e un premio per il secondo posto al Perfusion Workshop e al PET/MRI Workshop dell’ISMRM. È membro dell’AIIC, del GNB, del Capitolo britannico e irlandese dell’ISMRM, dell’ISMRM, del BNOS e dell’AISUK.

Profili

Scopus

Orcid

Google Scholar

Pubmed

Insegnamenti

Gomp

Docenze

A. A. 2024 - 2025

Tecniche di fisiopatologia cardiocircolatoria e perfusione cardiovascolare

A. A. 2023 - 2024

Tecniche Di Fisiopatologia Cardiocircolatoria e Perfusione Cardiovascolare

A. A. 2022 - 2023

Tecniche Di Fisiopatologia Cardiocircolatoria e Perfusione Cardiovascolare

Ultime 5 pubblicazioni (Scopus)

Clustering Algorithm Reveals Dopamine-Motor Mismatch in Cognitively Preserved Parkinson's Disease; Annals of Clinical and Translational Neurology; 2026; DOI: 10.1002/acn3.70317

Glucose metabolism in hyper-connected regions predicts neurodegeneration and speed of conversion in Alzheimer’s disease; European Journal of Nuclear Medicine and Molecular Imaging; October 2025; DOI: 10.1007/s00259-025-07379-9

Multimodal Generative Modeling for DaT Scan Reconstruction in Parkinson's Disease; Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference; 1 July 2025; DOI: 10.1109/EMBC58623.2025.11252739

From Radiomics to Generative Models: Evaluating Early Radiation Effects in Metastatic Brain Lesions; Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference; 1 July 2025; DOI: 10.1109/EMBC58623.2025.11254503

Advancing Generalisable Neural Network-Based PET Quantification: A Multicenter [11C]PBR28 study; Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference; 1 July 2025; DOI: 10.1109/EMBC58623.2025.11251612

Ultime 5 pubblicazioni (PubMed)

Clustering Algorithm Reveals Dopamine-Motor Mismatch in Cognitively Preserved Parkinson's Disease
OBJECTIVE: To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts.
From Radiomics to Generative Models: Evaluating Early Radiation Effects in Metastatic Brain Lesions
Brain metastases (BM), along with primary central nervous system lymphomas and glioblastomas, represent the majority of malignant brain tumors encountered in clinical neuro-oncology, driving a need for advanced imaging techniques and post-processing methods to improve their characterization and treatment monitoring. In particular, stereotactic radiosurgery (SRS), a cornerstone treatment for BM, delivers high-dose, focused radiation (>20 Gy) to target lesions with minimal impact on surrounding...
Advancing Generalisable Neural Network-Based PET Quantification: A Multicenter [¹¹C]PBR28 study
Quantifying the volume of distribution (V(T)) in Positron Emission Tomography (PET) is widely considered the gold standard for assessing tracer binding. However, this process requires an accurate estimation of the tracer's input function (IF) obtained through arterial sampling and metabolite correction-procedures that are both invasive and technically demanding. To overcome these limitations, we introduce a neural network-based framework for estimating the IF directly from [^(11)C]PBR28 dynamic...
Multimodal Generative Modeling for DaT Scan Reconstruction in Parkinson's Disease
The creation of synthetic medical data that truly captures the statistical distribution of real-world patient information, while simultaneously protecting individual privacy, remains a formidable challenge for the clinical and scientific community. This challenge is especially pronounced in nuclear medicine research, where rigorous data sharing is hindered by tight regulations and ethical considerations. In this study, we introduce a multimodal deep learning model designed to reconstruct (and...
Generation of synthetic TSPO PET maps from structural MRI images
INTRODUCTION: Neuroinflammation, a pathophysiological process involved in numerous disorders, is typically imaged using [^(11)C]PBR28 (or TSPO) PET. However, this technique is limited by high costs and ionizing radiation, restricting its widespread clinical use. MRI, a more accessible alternative, is commonly used for structural or functional imaging, but when used using traditional approaches has limited sensitivity to specific molecular processes. This study aims to develop a deep learning...