Fbooja

The accompanying codes provide an implementation in PyTorch of the methods in "Compressed sensing with a jackknife and a bootstrap" by Aaron Defazio, Mark Tygert, Rachel Ward, and Jure Zbontar.

The codes depend on Matplotlib, NumPy, Pillow, PyTorch, Scikit-image, and SciPy.

If using Anaconda of Download Anaconda, installation is possible via issuing the commands conda install numpy; conda install matplotlib; conda install scipy; conda install Pillow; conda install scikit-image; conda install pytorch torchvision -c pytorch

If using pip of Install pip, installation is possible via issuing the commands pip3 install numpy; pip3 install matplotlib; pip3 install scipy; pip3 install Pillow; pip3 install scikit-image; pip3 install torch torchvision

For help, try python bootstrap.py --help; python bootstrap2.py --help; python jackknife.py --help; python jackknife2.py --help; python admm.py --help; python admm2.py --help;

Each includes a toy example for a unit test that runs when issuing the commands python bootstrap.py; python bootstrap2.py; python jackknife.py; python jackknife2.py; python admm.py; python admm2.py; python radialines.py;

bootstrap.py and bootstrap2.py implement the bootstrap for estimating errors in reconstructions from compressed sensing, the former for horizontal sampling, and the latter for radial sampling.

jackknife.py and jackknife2.py implement the jackknife for estimating errors in reconstructions from compressed sensing, the former for horizontal sampling, and the latter for radial sampling.

admm.py and admm2.py perform compressed sensing via the alternating direction method of multipliers for maximizing fidelity to Fourier-domain measurements minus a total-variation penalty.

ctorch.py extends PyTorch to complex numbers.

radialines.py generates pixels on a Cartesian grid that intersect radial lines emanating from the origin at specified angles.

Whereas bootstrap.py and jackknife.py should be useful for clinical practice, bootstrap2.py and jackknife2.py are hacks full of code duplication from bootstrap.py and jackknife.py, useful only for prototyping. The "2" versions include admm2.py and radialines.py, which are similarly useful only for experimentation. Radially retained sampling patterns used in clinical practice do not fall on Cartesian grids. The "2" versions give a proof of principle, nothing more.

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brains.sh is a bash script that generates all figures in the paper available at PDF

brains.sh processes the scans of brains in the subdirectory brains and saves the results to a new subdirectory, brainsout.

Running brains.sh is required to process the LaTeX source in the subdirectory tex; fbooja.tex depends on the results stored to the new subdirectory brainsout.

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License

This fbooja software is licensed under the LICENSE file (the MIT license) in the root directory of this source tree.