1 citations · 1 across the 5 of their papers we have counts for
6 papers
Female anatomies disguise ECG abnormalities following myocardial infarction: an AI-enabled modelling and simulation study
Hannah Smith, Abhirup Banerjee, Leto Luana Riebel +4
The electrocardiogram (ECG) is modulated by torso-heart anatomy, and this challenges patients' diagnosis and risk stratification. This study aims to quantify how torso-heart anatom…
AngioDG: Interpretable Channel-informed Feature-modulated Single-source Domain Generalization for Coronary Vessel Segmentation in X-ray Angiography
Mohammad Atwany, Mojtaba Lashgari, Robin P. Choudhury +2
Cardiovascular diseases are the leading cause of death globally, with X-ray Coronary Angiography (XCA) as the gold standard during real-time cardiac interventions. Segmentation of…
3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models
Jolanta Mozyrska, Marcel Beetz, Luke Melas-Kyriazi +3
Diffusion models have recently gained immense interest for their generative capabilities, specifically the high quality and diversity of the synthesized data. However, examples of…
An Automated Computational Pipeline for Generating Large-Scale Cohorts of Patient-Specific Ventricular Models in Electromechanical In Silico Trials
Ruben Doste, Julia Camps, Zhinuo Jenny Wang +8
In recent years, human in silico trials have gained significant traction as a powerful approach to evaluate the effects of drugs, clinical interventions, and medical devices. In si…
Personalized Topology-Informed Localization of Standard 12-Lead ECG Electrode Placement from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins
Lei Li, Hannah Smith, Yilin Lyu +5
Cardiac digital twins (CDTs) offer personalized in-silico cardiac representations for the inference of multi-scale properties tied to cardiac mechanisms. The creation of CDTs requi…
Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation
Wentian Xu, Matthew Moffat, Thalia Seale +8
Models for segmentation of brain lesions in multi-modal MRI are commonly trained for a specific pathology using a single database with a predefined set of MRI modalities, determine…