# mSSFP (Multi-SSFP Reconstruction Library)
mSSFP is library for image reconstuction for multi-acqusition SSFP. This library supports ssfp simulations, various phantom generators, and various ssfp recontructions using muliple phase-cycled ssfp images.
Steady-Stead Free Precession (SSFP) MRI is class of fast pulse sequence capable of generating high SNR images. However, SSFP is highly-sensitive to off-resonance effects, which cause banding artifacts. Multiple SSFP images with different phase cycle amounts can be combined to suppress banding artifacts and for the estimation of quantitative biomarker like T1/T2 relaxation parameter mappings. Multiple methods for band suppression have been developed over the years, and this library gives working code and notebook examples for a variety of these reconstrcution techniques.
## Notebooks
Jupyter notebooks for examples of how to use the mSSFP library.
- Basic SSFP Simulations ([notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/1_sspf_simulations.ipynb))
- Phantom Examples ([notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/2_phantoms.ipynb))
- SSFP Banding Artifact Removal ([notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/3_ssfp_band_removal.ipynb))
- PLANET for T2/T1 Mapping of SSFP ([notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/4_ssfp_brain_planet.ipynb))
- SuperFOV for accelerated SSFP ([simple notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/5_superFOV.ipynb), [detailed notebook](notebooks/5a_superFOV_detailed.ipynb))
## Features
### Simultations
- SSFP
### Phantoms
- Shepp-Logan phantom
- Simple block phantoms
- Brain phantom
### Banding Artifact Removal Recons
- Sum of squares
- Eliptical singal model
- Super field of view (superFOV)
### Quantitative MR Recons
- PLANET for T2/T1 mapping
## Development
This project requires python 3.8+ and has the dependancies in requirement.txt
To setup a python enviroment with conda:
> ```
> conda create -n mssfp python=3.8
> conda activate mssfp
> ```
> Then install packages with pip using requirements file
> ```
> pip install -r requirements.txt
> ```
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"description": "\n# mSSFP (Multi-SSFP Reconstruction Library)\n\nmSSFP is library for image reconstuction for multi-acqusition SSFP. This library supports ssfp simulations, various phantom generators, and various ssfp recontructions using muliple phase-cycled ssfp images. \n\nSteady-Stead Free Precession (SSFP) MRI is class of fast pulse sequence capable of generating high SNR images. However, SSFP is highly-sensitive to off-resonance effects, which cause banding artifacts. Multiple SSFP images with different phase cycle amounts can be combined to suppress banding artifacts and for the estimation of quantitative biomarker like T1/T2 relaxation parameter mappings. Multiple methods for band suppression have been developed over the years, and this library gives working code and notebook examples for a variety of these reconstrcution techniques.\n\n## Notebooks\n\nJupyter notebooks for examples of how to use the mSSFP library.\n\n- Basic SSFP Simulations ([notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/1_sspf_simulations.ipynb))\n- Phantom Examples ([notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/2_phantoms.ipynb))\n- SSFP Banding Artifact Removal ([notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/3_ssfp_band_removal.ipynb))\n- PLANET for T2/T1 Mapping of SSFP ([notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/4_ssfp_brain_planet.ipynb))\n- SuperFOV for accelerated SSFP ([simple notebook](https://github.com/michaelmendoza/mssfp/blob/master/notebooks/5_superFOV.ipynb), [detailed notebook](notebooks/5a_superFOV_detailed.ipynb))\n\n## Features\n\n### Simultations\n - SSFP\n\n### Phantoms\n - Shepp-Logan phantom\n - Simple block phantoms\n - Brain phantom\n### Banding Artifact Removal Recons\n - Sum of squares \n - Eliptical singal model \n - Super field of view (superFOV)\n### Quantitative MR Recons\n - PLANET for T2/T1 mapping\n\n## Development\n\nThis project requires python 3.8+ and has the dependancies in requirement.txt\n\nTo setup a python enviroment with conda:\n\n> ```\n> conda create -n mssfp python=3.8 \n> conda activate mssfp\n> ```\n> Then install packages with pip using requirements file \n> ```\n> pip install -r requirements.txt\n> ```\n",
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