Marianna Inglese

Assistant Professor

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

marianna.inglese@uniroma2.it

Biography

Marianna Inglese, an Assistant Professor of Medical Physics at UNITOV, earned her master’s in Biomedical Engineering from the University of Rome “La Sapienza” in 2014. Her thesis focused on PET image correction for hybrid PET/MRI platforms, and she completed it at the University of Western Ontario’s Lawson Health Research Institute.

She obtained her PhD in Bioengineering from the University of Rome “La Sapienza” in 2019, researching advanced perfusion quantification methods for dynamic PET and MRI data.

Marianna is an honorary research fellow at Imperial College London, where she previously worked on quantifying dynamic PET data and applying machine learning for radiomic studies.

She received several awards, including a “Magna cum laude” from ISMRM and second-place awards at the ISMRM Perfusion Workshop and PET/MRI Workshop. She is a member of AIIC, GNB, the British and Irish Chapter of ISMRM, ISMRM, BNOS, and AISUK.

Profiles

Scopus

Orcid

Google Scholar

Pubmed

Insegnamenti

Gomp

Teachings

A. A. 2024 - 2025

Tecniche di fisiopatologia cardiocircolatoria e perfusione cardiovascolare (Italian)

A. A. 2023 - 2024

Tecniche Di Fisiopatologia Cardiocircolatoria e Perfusione Cardiovascolare (Italian)

A. A. 2022 - 2023

Tecniche Di Fisiopatologia Cardiocircolatoria e Perfusione Cardiovascolare (Italian)

Last 5 articles (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

Radiomics across modalities: a comprehensive review of neurodegenerative diseases; Clinical Radiology; June 2025; DOI: 10.1016/j.crad.2025.106921

A hybrid [18F]fluoropivalate PET-multiparametric MRI to detect and characterise brain tumour metastases based on a permissive environment for monocarboxylate transport; European Journal of Nuclear Medicine and Molecular Imaging; June 2025; DOI: 10.1007/s00259-025-07118-0

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

Last 5 articles (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...