https://github.com/davidssmith/tinycs

minimal MATLAB-based compressed sensing MRI toolkit
https://github.com/davidssmith/tinycs

Last synced: 9 months ago
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minimal MATLAB-based compressed sensing MRI toolkit

• Host: GitHub
• URL: https://github.com/davidssmith/tinycs
• Owner: davidssmith
• License: gpl-2.0
• Created: 2013-07-08T20:03:02.000Z (over 12 years ago)
• Default Branch: master
• Last Pushed: 2014-12-05T22:00:44.000Z (about 11 years ago)
• Last Synced: 2025-03-30T08:41:41.245Z (10 months ago)
• Language: Matlab
• Size: 1.81 MB
• Stars: 24
• Watchers: 3
• Forks: 14
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          

# tinycs

*tinycs* is a minimal compressed sensing (CS) toolkit designed

to allow MR imaging scientists to design undersampled

acquisitions and reconstruct the resulting data with CS without

needing to be a CS expert.

Currently, TinyCS supports Cartesian geometries with a total

variation constrained reconstruction only.  If there is

sufficient interest, I can add additional acquisition designs

and sparsity constraints.

The Cartesian reconstruction is based on the split Bregman

code written by Tom Goldstein, originally available here:

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