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k-space_masking_for_MRI_denoising

DOI

Graduate k-space masking for MRI image denoising and blurring (on the example of Agilent FID data).

Figure1 Figure2

The repository contains:

  1. Python script grad_mask_kspace.py.
  2. Description.pdf presenting:
  • short introduction to the topic,
  • how the code works,
  • sample results.
  1. Sample FID data in the mems_20190406_02.fid folder.
  2. Sample results illustration in Fig1.png and Fig2.png.

Literature reference (for the sample data)

Beata Wereszczyńska, Alcohol-fixed specimens for high-contrast post-mortem MRI, Forensic Imaging, Volume 25, 2021, 200449, ISSN 2666-2256, https://doi.org/10.1016/j.fri.2021.200449. (https://www.sciencedirect.com/science/article/pii/S2666225621000208)

License

The software is licensed under the MIT license. The non-software content of this project is licensed under the Creative Commons Attribution 4.0 International license. See the LICENSE file for license rights and limitations.

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k-space_wght_msk_for_MRI_denoising - k-space weighting and masking for denoising of MRI image without blurring or losing contrast, as well as for brightening of the objects in the image with simultaneous noise reduction (on the example of Agilent FID data), https://doi.org/10.5281/zenodo.7367057 (https://github.com/BeataWereszczynska/k-space_wght_msk_for_MRI_denoising).

MRI_k-space-derived_details_edges - k-space based details/edges detection in MRI images with optional k-space based denoising and detail control (data import suitable for Agilent FID files, three binarization methods to choose from), https://doi.org/10.5281/zenodo.7388435 (https://github.com/BeataWereszczynska/MRI_k-space-derived_details_edges).