Package: cmR 1.1
cmR: Analysis of Cardiac Magnetic Resonance Images
Computes maximum response from Cardiac Magnetic Resonance Images using spatial and voxel wise spline based Bayesian model. This is an implementation of the methods described in Schmid (2011) <doi:10.1109/TMI.2011.2109733> "Voxel-Based Adaptive Spatio-Temporal Modelling of Perfusion Cardiovascular MRI". IEEE TMI 30(7) p. 1305 - 1313.
Authors:
cmR_1.1.tar.gz
cmR_1.1.zip(r-4.5)cmR_1.1.zip(r-4.4)cmR_1.1.zip(r-4.3)
cmR_1.1.tgz(r-4.4-any)cmR_1.1.tgz(r-4.3-any)
cmR_1.1.tar.gz(r-4.5-noble)cmR_1.1.tar.gz(r-4.4-noble)
cmR_1.1.tgz(r-4.4-emscripten)cmR_1.1.tgz(r-4.3-emscripten)
cmR.pdf |cmR.html✨
cmR/json (API)
NEWS
# Install 'cmR' in R: |
install.packages('cmR', repos = c('https://bioimaginggroup.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bioimaginggroup/cmr/issues
- cmrdata_sim - Simulated data for CMR package.
- input_sim - Simulated data for CMR package.
- maxresp_sim - Simulated data for CMR package.
Last updated 1 years agofrom:68b32e4cfe. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 10 2024 |
R-4.5-win | OK | Nov 10 2024 |
R-4.5-linux | OK | Nov 10 2024 |
R-4.4-win | OK | Nov 10 2024 |
R-4.4-mac | OK | Nov 10 2024 |
R-4.3-win | OK | Nov 10 2024 |
R-4.3-mac | OK | Nov 10 2024 |
Exports:bullseyecmrcmr.localcmr.spaceimageMBFpseudobullseyermvnormcanon
Dependencies:dotCall64fieldslatticemapsMatrixplotrixRcppspamviridisLite
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Bullseye plot | bullseye |
Bayesian analysis of cardiovascular magnetic resonance imaging | cmr |
Spline analysis of cardiovascular magnetic resonance imaging | cmr.local |
Spatial spline analysis of cardiovascular magnetic resonance imaging | cmr.space |
Simulated data for CMR package. | cmrdata_sim |
Plotting of (voxelwise) cardiac MBF | imageMBF |
Simulated data for CMR package. | input_sim |
Simulated data for CMR package. | maxresp_sim |
Pseudo bullseye plot | pseudobullseye |
Draw random vectors from multivariate Gaussian in canonical form | rmvnormcanon |