L
Lucian Mihai Itu
Researcher at Siemens
Publications - 114
Citations - 1720
Lucian Mihai Itu is an academic researcher from Siemens. The author has contributed to research in topics: Fractional flow reserve & Deep learning. The author has an hindex of 19, co-authored 97 publications receiving 1383 citations. Previous affiliations of Lucian Mihai Itu include Transilvania University of Brașov & Princeton University.
Papers
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Journal ArticleDOI
Applying Deep Neural Networks over Homomorphic Encrypted Medical Data.
Anamaria Vizitiu,Anamaria Vizitiu,Cosmin Ioan Niƫă,Cosmin Ioan Niƫă,Andrei Puiu,Andrei Puiu,Constantin Suciu,Constantin Suciu,Lucian Mihai Itu,Lucian Mihai Itu +9 more
TL;DR: The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models.
Patent
Method and System for Non-Invasive Computation of Hemodynamic Indices for Coronary Artery Stenosis
TL;DR: In this article, a method and system for non-invasive hemodynamic assessment of coronary artery stenosis based on medical image data is disclosed, where patient-specific anatomical measurements of the coronary arteries are extracted from medical images of a patient.
Patent
Method and system for hemodynamic assessment of aortic coarctation from medical image data
Kristof Ralovich,Lucian Mihai Itu,Viorel Mihalef,Puneet Sharma,Razvan Ioan Ionasec,Dime Vitanovski,Waldemar Krawtschuk,Dorin Comaniciu +7 more
TL;DR: In this article, a method and system for non-invasive hemodynamic assessment of aortic coarctation from medical image data, such as magnetic resonance imaging (MRI) data is disclosed.
Patent
Method and System for Prediction of Post-Stenting Hemodynamic Metrics for Treatment Planning of Arterial Stenosis
TL;DR: In this paper, a pre-stenting patient-specific anatomical model of the coronary arteries is extracted from medical image data of a patient, and a modified pressure-drop model is used to simulate an effect of stenting on a target stenosis region used to compute a pressure drop over the target arterial region.
Proceedings ArticleDOI
GPU accelerated blood flow computation using the Lattice Boltzmann Method
TL;DR: A numerical implementation based on a Graphics Processing Unit (GPU) for the acceleration of the execution time of the Lattice Boltzmann Method (LBM) for patient-specific blood flow computations, and hence, to obtain higher accuracy, double precision computations are employed.