This is exciting! Congratulations on the release.
On Tue, Dec 16, 2014 at 1:50 PM, David Smith <[email protected]> wrote: > > A few of us around here do medical imaging research, so I'm announcing the > release of DCEMRI.jl, a Julia module for processing dynamic contrast > enhanced magnetic resonance imaging (MRI) data. > > http://github.com/davidssmith/DCEMRI.jl > > To install, > > julia> Pkg.add("DCEMRI") > > To run a quick demo, > > julia> using DCEMRI > > julia> demo() > > To rerun the validations, > > julia> validate() > > (Validation can take a while, because the phantoms use a ridiculously > large number of time points, and the Levenberg-Marquardt fitting scales > poorly with number of measurements.) > > When you run these functions, PyPlot will show the resulting images after > the run is complete, and pdfs of the images will be saved in the module > directory by default, or another place if you specify. > > The models included currently are the standard and extended Tofts-Kety, > and both have been validated against the test phantoms provided by the > Quantitative Imaging Biomarkers Association. The execution speed is the > fastest of any code I've tried, by about an order of magnitude, on a > per-processor basis. You can fit a typical slice of in vivo data in about > 1-2 seconds on a decent machine. > > Several modes of operation are supported, including file-based processing > and passing data as function arguments and parameters as kwargs. See the > demo and the validation functions for examples of usage. Parallel > processing is supported, using either function parameters or by starting > julia with the '-p <n>' flag. I also have a command-line script and a > (simplistic) Matlab interface function. > > The code currently uses PyPlot for plotting, so you need Matplotlib > installed, and that is not handled automatically, but all of the Julia > dependencies are. > > A paper on the code is in press at PeerJ (https://peerj.com/preprints/670/ > ). > > Let me know what you think. > > Cheers, > Dave > >
