Imperial College London

Dr. Marianna Inglese

Faculty of MedicineDepartment of Surgery & Cancer

Honorary Research Associate
 
 
 
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Contact

 

marianna.inglese17

 
 
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Location

 

G1Hammersmith HospitalHammersmith Campus

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Summary

 

Summary

Marianna Inglese is a research associate working in the post-processing of MRI and PET data. Her key interest is in developing combined approaches for the improvement of perfusion PET and MRI quantification. She is also interested in developing new and advanced tools for the early diagnosis of Alzheimer's disease.

Marianna joined Imperial College London in 2017, while she was a PhD student. In 2018 she received her PhD from University of Rome La Sapienza, titled "Advanced perfusion quantification methods for dynamic PET and MRI data modelling" and supervised by Prof. Eric Aboagye and Prof. Febo Cincotti. Previously, she obtained her MSc in Biomedical Engineering with a project focused on the carrection of attenuation of breast RF coils in a hybrid PET/MRI scanner.She was supervised by Prof. Frank Prato, University of Western Ontario (Canada).

Publications

Journals

Shalom ES, Kim H, van der Heijden RA, et al., 2024, The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge., Magn Reson Med, Vol:91, Pages:1803-1821

Hunter B, Argyros C, Inglese M, et al., 2023, Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis, British Journal of Cancer, ISSN:0007-0920

Aboagye E, Islam S, Inglese M, et al., 2023, Feasibility of [18F]fluoropivalate hybrid PET/MRI for imaging lower and higher grade glioma: a prospective first-in-patient pilot study, European Journal of Nuclear Medicine and Molecular Imaging, Vol:50, ISSN:0340-6997, Pages:3982-3995

Inglese M, Ferrante M, Duggento A, et al., 2023, Spatiotemporal Learning of Dynamic Positron Emission Tomography Data Improves Diagnostic Accuracy in Breast Cancer, Ieee Transactions on Radiation and Plasma Medical Sciences, Vol:7, ISSN:2469-7311, Pages:630-637

Inglese M, Ferrante M, Boccato T, et al., 2023, Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction, Journal of Personalized Medicine, Vol:13

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