Name: Physimals group, University of Nottingham
Type: Organization
Bio: The Physimals group applies inference (estimation) techniques from information engineering to biomedical data, primarily with a view to clinical application.
Location: Nottingham, UK
Blog: https://physimals.github.io/physimals
Physimals group, University of Nottingham's Projects
Perform z-deblurring of ASL data
OXASL plugin which allows volume selection for quality improvement using the ENABLE algorithm
OXASL plugin for multiphase ASL data
OXASL plugin for multi-TE ASL data
Python library for optimizing PLDs of a multi-PLD pCASL acquisition. Ported from Joe Woods' MATLAB code
Surface-based partial volume correction for OXASL pipeline
OXASL plugin for vessel-encoded ASL data
A command line tool for quantification of perfusion from ASL data
Physimals Website
Python inferface to the Fabber Bayesian model fitting toolkit
Fast data visualization and GUI tools for scientific / engineering applications
Application for analysis and modelling of volumetric medical imaging data
Quantiphyse plugin for ASL-MRI data
Quantiphyse plugin for CEST-MRI
Quantiphyse widget for analysing cerebrovascular reactivity (CVR)
Quantiphyse plugin for general perfusion imaging simulation
Quantiphyse plugin for DCE-MRI modelling
Quantiphyse widget for the DEEDS fully deformable registration method
Scripts for building and running Quantiphyse in a Docker container
Documentation for Quantiphyse
Quantiphyse plugin for DSC-MRI data
Fabber plugin for Quantiphyse
Quantiphyse plugin providing interface to selected tools from the FMRIB Software Library (FSL)
Quantiphyse widget for the new quantitative BOLD Fabber model
Supervoxels plugin for Quantiphyse
Quantiphyse plugin for Bayesian T1 mapping from VFA data
A place to submit conda recipes before they become fully fledged conda-forge feedstocks
Documentation for SVB
ASL Models for Stochastic Variational Bayes inference framework
Analysis pipeline code for ASL data from The Irish Longitudinal stuDy on Ageing