cardiac

NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing

Segment Source Distribution

Koma is a 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.

Machine Learning project to predict heart diseases

[STACOM-MICCAI 2019] Deep Learning Registration for Cardiac Motion Tracking

Code for the analysis of cardiac motion and cardiac pathology classification

A library to calculate parametric maps in MRI. For details see https://doi.org/10.1016/j.softx.2019.100369

Learned Half-Quadratic Splitting Network for Magnetic Resonance Image Reconstruction

Exercise Physiology Equations

Cardiac Action Potential Prediction (ApPredict) under drug-induced block of ion channels. This is a Chaste extension/bolt-on project.

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).

Reprogram-Seq: Rational reprogramming of cellular states by combinatorial perturbation

GPU implementation of a Full Search Block Matching Motion Estimation Algorithm

Next generation cardiac mechanics solver based on FEniCSx

R package for Mass Univariate Analysis of 3D Phenotypes

Hearth Rate Variability research using STM32F103 and MAX30100

Tool for creating idealised cardiac geometries and microstructure

Project to study sound stimulus synchronous, asynchronous and isochronous with the heartbeat during sleep.

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.

cDWI, cDTI, cardiacDTI, design of DWI sequences (SE, STEAM, TRSE), gradient sampling shells, processing helix angle (HA), sheet angle (E2A, SA), transverse angle (TA)