There are 1 repository under cardiac topic.
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.
Segment Source Distribution
MICCAI 2023 code for the paper: Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis. EchoDiffusion is a collection of video diffusion models trained from scratch on the EchoNet-Dynamic dataset with the imagen-pytorch repo.
Code for the analysis of cardiac motion and cardiac pathology classification
[STACOM-MICCAI 2019] Deep Learning Registration for Cardiac Motion Tracking
Machine Learning project to predict heart diseases
This is an implementation of unsupervised multiple kernel learning (U-MKL) for dimensionality reduction, which builds upon a supervised MKL technique by Lin et al (10.1109/TPAMI.2010.183).
[Robust cardiac MR image segmentation foundation model] This code contains the most powerful cardiac segmentation model trained from UK biobank dataset with superior performance on out-of-domain datasets. This model can be used out-of-box, and serve as a foundation model for further finetuning
Tool for creating idealised cardiac geometries and microstructure
Next generation cardiac mechanics solver based on FEniCSx
Project to study sound stimulus synchronous, asynchronous and isochronous with the heartbeat during sleep.
GPU implementation of a Full Search Block Matching Motion Estimation Algorithm
Reprogram-Seq: Rational reprogramming of cellular states by combinatorial perturbation
This repository implements a robust deep learning method (LFBNet) for medical image segmentation using a two systems approach. Learning fast and slow strategy for robust medical image analysis.
Sussex Psychophysiology Research Protocol (SuPREP)
R package for Mass Univariate Analysis of 3D Phenotypes
Automatically generate cardiac segmentations, contours, and meshes from SAX MR images
This a reaction-diffusion PDE solver in 3D implemented with C/C++/CUDA and OpenGL interoperability. In addition, the media has rotational anisotropy to account for the tissue fiber effects.
Adapted version of the Laplace–Dirichlet Rule-Based (LDRB) algorithm for generating ventricular fibers
EZ-Spiral and Matlab code to reproduce computations in [Sandstede & Scheel (2020)].
cDWI, cDTI, cardiacDTI, design of DWI sequences (SE, STEAM, TRSE), gradient sampling shells, processing helix angle (HA), sheet angle (E2A, SA), transverse angle (TA)
Self-supervised method for cardiac phase detection in 4D CMR. Model consists of a deformable registration part, the derivation of the per-voxel deformation angle and a rule-set based on the physiological properties of a contracting ventricle. Implemented in TF2.X. Koehler et al. 2022, STACOM workshop @ MICCAI
Application to measure the user's heart rate
An end-to-end deep learning solution to perform motion correction (MC) and super-resolution (SR) concurrently in CMR SAX slices. Author: Zhennong Chen, PhD