T
Tommaso Mansi
Researcher at Princeton University
Publications - 191
Citations - 5064
Tommaso Mansi is an academic researcher from Princeton University. The author has contributed to research in topics: Cardiac electrophysiology & Image registration. The author has an hindex of 34, co-authored 181 publications receiving 4341 citations. Previous affiliations of Tommaso Mansi include Siemens & University of Erlangen-Nuremberg.
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BookDOI
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal ArticleDOI
Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: A preliminary clinical validation
Maxime Sermesant,Radomir Chabiniok,Phani Chinchapatnam,Tommaso Mansi,Florence Billet,Philippe Moireau,Jean-Marc Peyrat,Kitty Wong,Jatin Relan,Kawal Rhode,Matthew Ginks,Pier D. Lambiase,Hervé Delingette,Michel Sorine,Christopher A. Rinaldi,Dominique Chapelle,Reza Razavi,Nicholas Ayache +17 more
TL;DR: How the personalisation of an electromechanical model of the myocardium can predict the acute haemodynamic changes associated with CRT is presented, demonstrating the potential of physiological models personalised from images and electrophysiology signals to improve patient selection and plan CRT.
Book ChapterDOI
Robust Non-rigid Registration Through Agent-Based Action Learning
Julian Krebs,Julian Krebs,Tommaso Mansi,Hervé Delingette,Li Zhang,Florin C. Ghesu,Shun Miao,Andreas Maier,Nicholas Ayache,Rui Liao,Ali Kamen +10 more
TL;DR: This paper investigates in this paper how DL could help organ-specific (ROI-specific) deformable registration, to solve motion compensation or atlas-based segmentation problems for instance in prostate diagnosis and presents a training scheme with a large number of synthetically deformed image pairs requiring only a small number of real inter-subject pairs.
Journal ArticleDOI
iLogDemons: A Demons-Based Registration Algorithm for Tracking Incompressible Elastic Biological Tissues
TL;DR: This work improves the logDemons by integrating elasticity and incompressibility for soft-tissue tracking, and replaces the Gaussian smoothing by an efficient elastic-like regulariser based on isotropic differential quadratic forms of vector fields.
Journal ArticleDOI
Learning a Probabilistic Model for Diffeomorphic Registration
TL;DR: In this article, a conditional variational autoencoder network is proposed to learn a low-dimensional probabilistic deformation model from data which can be used for the registration and the analysis of deformations.