Thank you. I feel like Julia has matured enough finally to start migrating all of my MRI research over to it. So far I have found no barriers whatsoever. I recommend it enthusiastically to all of my colleagues. They are probably getting tired of hearing about it. ;-)
My biggest wish-list item (as a medical imager) would be native Julia plotting that is similar to Matplotlib. I'd rather not have to require that people have Python alongside Julia. Makes Julia sound less mature. I tried Gadfly, but when most of what you plot is images, Gadfly makes less sense. (Maybe something is missing in the grammar that includes images as something separate from rectbins.) I also got bit pretty hard by the pair borking bug in Color.jl, which was very annoying. On Tuesday, December 16, 2014 1:42:16 PM UTC-6, Isaiah wrote: > This is exciting! Congratulations on the release. > > On Tue, Dec 16, 2014 at 1:50 PM, David Smith <[email protected] > <javascript:>> 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 >> >>
