biasgen 0.1.3
Utility functions to produce and visualize simulated bias fields generated according to Kern et. al's sinusoidal sensitivity model.
License: MIT
Requires Python: >=3.10

# biasgen

**biasgen** is a Python package which simulates magnetic resonance (MR) non-uniformities. This package allows users to generate custom bias fields by combining radiofrequency (RF) coil spatial information with frequency sampling information.

Bias fields are constructed using a sum-of-squares approach with coil sensitivity maps which follow the sinusoidal model of [Kern et. al](https://ieeexplore.ieee.org/document/6062681). Direct computation of sinusoidal sensitivities are done in closed form using the segment source solution of [Vinas and Sudhyadhom](https://arxiv.org/abs/2208.03059).

# Installation

Package **biasgen** can be installed using pip:
```
pip install biasgen
```
A CUDA accelerated version of **biasgen** is also available:
```
pip install biasgen[gpu]
```

# Usage

To start **biasgen** requires the user to define a set of coil positions and sampling frequencies. Coil positioning can be initialized through a sequence of *CoilSegment* objects or by using the predefined coil constructor routine *cage_constructor*. An example of how to use *cage_constructor* is provided below:
```python
import biasgen

# Shepp-Logan phantom example
ph = biasgen.phantom3d(n=128)

# returns a sequence of CoilSegment objects
coils = biasgen.cage_constructor(n_coils=2, center=(64,64,64), coil_height=128,
                                 length_to_space_ratio=0.35, ellip_axes=(90,65))

biasgen.view_coil_positioning(ph, coils)
```
<p align="center">
  <img width="300" height="300" src="https://github.com/lucianoAvinas/biasgen/raw/main/images/2coil_example.png">
</p>

Function *view_coil_positioning* provides a top-down view of *CoilSegment* sequences which can be useful when arranging coil cages. Next step is to define a sampling grid for the sinusoidal sensitivity model:
```python
sens_settings = biasgen.SensitivitySettings(grid_lengths=(5,5,5), grid_spacings=(1,1,1))
```

Inputting the sampling information and coil sequence into *compute_sensitivity* will return the sensitivity maps of each coils:
```python
# biasgen.use_gpu(True) # Uncomment if GPU is available

# first axis will index each coil contribution 
sens = biasgen.compute_sensitivity(coils, sens_settings, ph.shape, batch_sz=1, scale_fctr=0.5)
```

Arguments "batch_sz" and "scale_fctr" can be helpful for memory-limited devices. "batch_sz" determines how many segments are loaded into memory while "scale_fctr" provides a temporary spatial downsampling for computation. The simulated sensitivity maps can be viewed using the function *view_center_axes*:
```python
# mask for view_center_axes
ph_mask = ph > 0

# visualization requires that a mask is provided
biasgen.view_center_axes(abs(sens), ph_mask, ['Z-slice','Y-slice','X-slice'])
```
<p align="center">
  <img width="516" height="388" src="https://github.com/lucianoAvinas/biasgen/raw/main/images/2coil_sens_maps.png">
</p>

Finally we construct the measured bias field through a sum-of-squares procedure:
```python
bias = biasgen.bias_sum_of_squares(sens)

# scalar product application of bias to Shepp-Logan phantom
biasgen.view_center_axes(bias*ph, ph_mask, ['Z-slice','Y-slice','X-slice'])
```
<p align="center">
  <img width="516" height="186" src="https://github.com/lucianoAvinas/biasgen/raw/main/images/2coil_biased_phantom.png">
</p>

More detailed examples of sensitivity simualtions and visualizaitons can be found in the examples/bias.ipynb notebook.

# References
1. Guerquin-Kern M, Lejeune L, Pruessmann KP, Unser M. Realistic
Analytical Phantoms for Parallel Magnetic Resonance Imaging. IEEE
Transactions on Medical Imaging. 2012;31(3):626-636. 

2. Vinas L and Sudhyadhom A. Sinusoidal Sensitivity Calculation for Line Segment Geometries. arXiv.org:2208.03059 [physics.med-ph], Aug. 2022.