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

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)

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

A hybrid [<sup>18</sup>F]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; 2025; DOI: 10.1007/s00259-025-07118-0

Genotype Characterization in Primary Brain Gliomas via Unsupervised Clustering of Dynamic PET Imaging of Short-Chain Fatty Acid Metabolism; IEEE Transactions on Radiation and Plasma Medical Sciences; 2025; DOI: 10.1109/TRPMS.2024.3514087

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging; Computer Methods and Programs in Biomedicine; November 2024; DOI: 10.1016/j.cmpb.2024.108375

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI–Dynamic Contrast-Enhanced challenge; Magnetic Resonance in Medicine; May 2024; DOI: 10.1002/mrm.29909

Ultime 5 pubblicazioni (PubMed)

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging
CONCLUSIONS: These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.
Bench to Bedside Development of [¹⁸F]Fluoromethyl-(1,2-²H₄)choline ([¹⁸F]D4-FCH)
Malignant transformation is characterised by aberrant phospholipid metabolism of cancers, associated with the upregulation of choline kinase alpha (CHKα). Due to the metabolic instability of choline radiotracers and the increasing use of late-imaging protocols, we developed a more stable choline radiotracer, [^(18)F]fluoromethyl-[1,2-²H(4)]choline ([^(18)F]D4-FCH). [^(18)F]D4-FCH has improved protection against choline oxidase, the key choline catabolic enzyme, via a ¹H/²D isotope effect,...
The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge
CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with...
Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis
CONCLUSIONS: SN-RPV may provide net-benefit in terms of earlier cancer diagnosis.
Feasibility of [¹⁸F]fluoropivalate hybrid PET/MRI for imaging lower and higher grade glioma: a prospective first-in-patient pilot study
CONCLUSION: Tumoural FPIA PET uptake is higher in HGG compared to LGG. This study supports further investigation of FPIA PET/MRI for brain tumour imaging in a larger patient population.