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
