Here are some of the tools that we have developed to support our research. If you think they might be useful and would like to try them out, please contact us for more information.
VasEX : Vascular Extraction Tool
VasEX implements an automated vascular image segmentation pipeline, that specialises in high-resolution ex vivo datasets containing tens of thousands of vessels. To date, it has been successfully applied to cryomicrotome, CT/micro-CT, MR and confocal microscopy images. VasEX is fully automatic, able to handle large datasets (4000x4000x4000 voxels), is GPU accelerated and multi-PC parallelised, and reconstructs to sub-voxel accuracy.
S2S : Segmentation to Simulation Tool
S2S is a turn-key solution for creating simulation-quality biventricular and LV cavity finite element meshes from cardiac segmentation. This tool was primarily developed in the VP2HF project to generate personalised meshes and is based on VTK and CGAL libraries. S2S works from labelled surface segmentation of MR or CT data and also performs post-processing tasks such as boundary patch detection and AHA region labelling. S2S is fully automated.
CHeart : A multi-physics Parallel Computing Engine
CHeart is our in-house developed mpi-based finite element solver package. It implements continuum mechanics (fluid, solid, poro mechanics, advection-diffusion-reaction equation, 1D network flow, elastic wave equation), ODEs (lumped parameter haemodynamics, dynamic CellML integration), parameter estimation (via Kalman filters) and more. CHeart is written ground-up to enable solutions of arbitrary coupling between physics problems, providing a flexible, extensible and scalable solution to biomedical modelling applications. More details can be found here: www.cheart.co.uk
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Signals : Unified Cathlab Data Analysis Environment
Signals was born out of our collaboration between interventional cardiologists at King's College London who are collecting simultaneous LV (pressure-volume) and coronary (pressure-velocity) catheter measurements. These data have not been measured together in real-time before, and significantly expands our ability to investigate flow-contraction coupling and mechanisms underlying coronary arterial waves. Signals offers a full work flow from data import to signal pre-processing, temporal alignment, physiological index calculation and visualisation in an interactive manner. Comprehensive P-V loop metrics and coronary wave intensity parameters (over 30 different indices so far) can be obtained. For a brief overview, check out our video tutorial below.
autoWIA : Parameter-free Coronary Wave Intensity Analysis
autoWIA is a Matlab function for performing wave intensity analysis which also implements a novel automatic signal pre-processing (smoothing) for pressure and velocity traces. Since the outcomes of wave intensity analysis depends heavily on the manner in which it is processed, consistent processing of signals is essential for the standardisation of WIA-derived indices. Most pre-processing codes in use depend on the Savitzky-Golay filter, which has two parameters that are variable among publications and practitioners. autoWIA removes the dependence on these parameters by automatic identification of the optimal smoothing parameters.
A Collection of Image Denoising Filters
Here you will find a set of image denoising filter Matlab implementations that were used in our recent comparative study on microCT data. In particular, the Total Variation denoising was found to be highly effective at improving SNR and CNR while preserving fine vascular details. Learn more about the study here and obtain the code at the below link.