Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging
Copyright policy )Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging
This work is supported and funded by: NEXTGENERATIONEU (NGEU); the Ministry of University and Research (MUR); the National Recovery and Resilience Plan (NRRP); project MNESYS (PE0000006, to NT) - A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022); the MUR-PNRR M4C2I1.3 PE6 project PE00000019 Heal Italia (to NT); the NATIONAL CENTRE FOR HPC, BIG DATA AND QUANTUM COMPUTING, within the spoke “Multiscale Modeling and Engineering Applications” (to NT); the European Innovation Council (Project CROSSBRAIN - Grant Agreement 101070908, Project BRAINSTORM - Grant Agreement 101099355); the Horizon 2020 research and innovation Programme (Project EXPERIENCE - Grant Agreement 101017727). Matteo Ferrante is a Ph.D. student enrolled in the National PhD in Artificial Intelligence, XXXVII cycle, course on Health and Life Sciences, organized by Università Campus Bio-Medico di Roma.
Ferrante, M., Inglese, M., Brusaferri, L., Whitehead, A. C., Maccioni, L., Turkheimer, F. E., ... & Toschi, N. (2024). Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging. Computer methods and programs in biomedicine, 256, 108375. https://doi.org/10.1016/j.cmpb.2024.108375
- London South Bank University United Kingdom
- University College London United Kingdom
- King's College London United Kingdom
- Imperial College Healthcare NHS Trust United Kingdom
- Harvard University United States
Male, Adult, Pyridines, Metabolic imaging, 610, AIF; IDIF; Metabolic imaging; PET; Physics informed neural networks; TSPO, Settore PHYS-06/A - Fisica per le scienze della vita, AIF, Receptors, GABA, Image Processing, Computer-Assisted, Humans, Physics informed neural networks, metabolic imaging, 500, Brain, Reproducibility of Results, Middle Aged, TSPO; physics informed neural networks; PET; metabolic imaging; IDIF; AIF, PET, IDIF, Positron-Emission Tomography, Female, l'ambiente e i beni culturali, Neural Networks, Computer, TSPO, Algorithms, physics informed neural network
Male, Adult, Pyridines, Metabolic imaging, 610, AIF; IDIF; Metabolic imaging; PET; Physics informed neural networks; TSPO, Settore PHYS-06/A - Fisica per le scienze della vita, AIF, Receptors, GABA, Image Processing, Computer-Assisted, Humans, Physics informed neural networks, metabolic imaging, 500, Brain, Reproducibility of Results, Middle Aged, TSPO; physics informed neural networks; PET; metabolic imaging; IDIF; AIF, PET, IDIF, Positron-Emission Tomography, Female, l'ambiente e i beni culturali, Neural Networks, Computer, TSPO, Algorithms, physics informed neural network
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).12 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!