13 citations · 24 across the 6 of their papers we have counts for
10 papers
Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound
Camilla Ferrario, Maelys Venet, Olivier Villemain +1
Cardiac model personalisation requires inferring mechanical parameters that are not directly measurable in vivo. Ultrafast ultrasound shear wave elastography (SWE) enables non-inva…
Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers
Zihao Wang, Yingyu Yang, Maxime Sermesant +1
Image registration is an essential but challenging task in medical image computing, especially for echocardiography, where the anatomical structures are relatively noisy compared t…
In-silico analysis of the influence of pulmonary vein configuration on left atrial haemodynamics and thrombus formation in a large cohort
Jordi Mill, Josquin Harrison, Benoit Legghe +7
Atrial fibrillation (AF) is considered the most common human arrhythmia. Around 99\% of thrombi in non-valvular AF are formed in the left atrial appendage (LAA). Studies suggest th…
Cardiac Motion Modeling with Parallel Transport and Shape Splines
Nicolas Guigui, Pamela Moceri, Maxime Sermesant +1
In cases of pressure or volume overload, probing cardiac function may be difficult because of the interactions between shape and deformations.In this work, we use the LDDMM framewo…
Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets
Jaume Banus, Maxime Sermesant, Oscar Camara +1
The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, ne…
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
Xiahai Zhuang, Jiahang Xu, Xinzhe Luo +20
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suff…