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

Medicina e Chirurgia (Italian)
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)

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

Last 5 articles (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.