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Found 22 papers, 6 papers with code
Transesophageal Echocardiography Generation using Anatomical Models
Data augmentation is commonly used to tackle this issue.
NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections by Neural Implicit Representation
Cardiovascular diseases (CVDs) are the most common health threats worldwide.
Personalized Topology-Informed 12-Lead ECG Electrode Localization from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins
However, current studies commonly rely on additional acquisition of torso imaging and manual/semi-automatic methods for ECG electrode localization.
Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation
We introduce a novel graph-based Retrieval-Augmented Generation (RAG) framework specifically designed for the medical domain, called \textbf{MedGraphRAG}, aimed at enhancing Large Language Model (LLM) capabilities for generating evidence-based medical responses, thereby improving safety and reliability when handling private medical data.
Deep Learning-based 3D Coronary Tree Reconstruction from Two 2D Non-simultaneous X-ray Angiography Projections
To the best of our knowledge, this is the first study that leverages deep learning to achieve 3D coronary tree reconstruction from two real non-simultaneous x-ray angiography projections.
Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey
Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms.
Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
We present a novel, lightweight dual-attention ECG network designed to capture complex ECG features essential for early HF risk prediction, despite the notable imbalance between low and high-risk groups.
Anatomical basis of human sex differences in ECG identified by automated torso-cardiac three-dimensional reconstruction
Methods: This work presents quantification of sex differences in ECG versus anatomical biomarkers in healthy and post-MI subjects, enabled by a novel, end-to-end automated pipeline for torso-ventricular anatomical reconstruction from clinically standard cardiac magnetic resonance imaging.
Multi-objective point cloud autoencoders for explainable myocardial infarction prediction
In this work, we present the multi-objective point cloud autoencoder as a novel geometric deep learning approach for explainable infarction prediction, based on multi-class 3D point cloud representations of cardiac anatomy and function.
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks
Global single-valued biomarkers of cardiac function typically used in clinical practice, such as ejection fraction, provide limited insight on the true 3D cardiac deformation process and hence, limit the understanding of both healthy and pathological cardiac mechanics.
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function.
3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers.
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference
In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform.
Segment Anything Model for Medical Images?
To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks.
Influence of Myocardial Infarction on QRS Properties: A Simulation Study
However, the influence of various MI properties on the QRS is not intuitively predictable. In this work, we have systematically investigated the effects of 17 post-MI scenarios, varying the location, size, transmural extent, and conductive level of scarring and border zone area, on the forward-calculated QRS.
Multi-Modality Cardiac Image Computing: A Survey
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases.
Deep Computational Model for the Inference of Ventricular Activation Properties
Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification.
Inference of ventricular activation properties from non-invasive electrocardiography
We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites).
A Deep learning Approach to Generate Contrast-Enhanced Computerised Tomography Angiography without the Use of Intravenous Contrast Agents
This pipeline is able to differentiate between visually incoherent soft tissue regions in non-contrast CT images.
A Deep Learning Approach to Automate High-Resolution Blood Vessel Reconstruction on Computerized Tomography Images With or Without the Use of Contrast Agent
Subsequent implementation of this network architecture within the aortic segmentation pipeline from both contrast-enhanced CTA and non-contrast CT images has allowed for accurate and efficient extraction of the entire aortic volume.
Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images
We refer to the network as Pulmonary Lobe Segmentation Network (PLS-Net), which is designed to efficiently exploit 3D spatial and contextual information from high-resolution volumetric CT images for effective volume-to-volume learning and inference.
Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks
Vasculature is known to be of key biological significance, especially in the study of cancer.
