Software
R Packages
kinfitr
An R package for analysis and quantification of PET time activity curve (TAC) data including a wide array of kinetic models including invasive and non-invasive models, as well as blood processing tools.
bloodstream
A BIDS app for processing of blood data accompanying PET BIDS data, providing a simplified and automated pipeline for processing, and modelling different components of the measured data. Builds upon kinfitr functions, but provides a simplified interface with a dockerised app and graphical user interface for model configuration.
kinfitr BIDS app
A dockerised BIDS app for performing kinetic modeling of PET imaging data. Like bloodstream, the kinfitr BIDS app provides a graphical user interface for interacting with the tool and specifying the appropriate model configuration options, together with a dockerised implementation. This tool is currently in an alpha stage of development, and new features are being added.
Other Software Packages
rwithings - Extract health data from Withings API
- Github repository
relfeas - Extrapolation of test-retest metrics for study design
- Github repository
pwrcontour - Power contour plots for power analysis
- Github repository
nls.multstart - Non-linear regression with multiple starting points (contributor)
- Github repository
staplr - PDF manipulation toolkit for R (contributor)
- Github repository
ggcorrplot2 - Correlation plots implemented using ggplot2 in R (contributor)
- Github repository
Analysis Methods
SiMBA (Simultaneous Multifactor Bayesian Analysis)
Advanced Bayesian analysis method for PET Time Activity Curve data
SiMBA is a method for analyzing PET time activity curve data using hierarchical Bayesian modelling to exploit similarities in the underlying parameters between individuals and regions, with implementations for both arterial input (i.e. invasive) and reference tissue models (i.e. non-invasive).
- Invasive Implementation: Github repository
- Non-Invasive Tissue: Github repository
Note: The invasive implementation requires a parametric representation of the arterial input function, which, in our initial paper, used a linear rise and tri-exponential decay. We have now extended this to use the FengConv model, which is much better. We have not yet, however, published a paper using this approach (we plan to soon). If you would like this code, please get in touch.
Video Tutorials
- bloodstream tutorial: (this is a little bit out of date, but an updated version should be available soon.)
All software is open source and freely available. We encourage contributions, bug reports, and feature requests through GitHub.