Assistant Professor
Macro-area: Fisica Applicata, didattica e storia della Fisica
SSD: FIS/07
marianna.inglese@uniroma2.it
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.
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,...
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...
CONCLUSIONS: SN-RPV may provide net-benefit in terms of earlier cancer diagnosis.
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.
Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of...
