bart pattern

bart pattern#

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The bart pattern command in the BART calculates the sampling pattern of k-space data by identifying where the data is non-zero. This command is useful for visualizing and confirming undersampling in k-space

Where we can view the full usage string and optional arguments with the -h flag.

!bart pattern -h

Usage: pattern [-s d] <kspace> <pattern> 

Compute sampling pattern from kspace

-s bitmask    Squash dimensions selected by bitmask
-h            help

Where:

<kspace>: This is the input k-space data from which the sampling pattern is derived.

<pattern>: This is the output file where the computed sampling pattern will be stored.

Key aspects of the pattern command:#

Non-zero data: The command determines the sampling pattern by checking for non-zero values in the input data.

Output: The output of the command is often complex-valued, which may need to be cast to a real value using .real for visualization or further processing.

Example Workflow (python)#

# Importing the required libraries
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import cfl
from bart import bart

1. Generate a Fully Sampled k-Space Phantom Directly#

fully_sampled_kspace = bart(1, 'phantom -x 128 -k')

2. Create a Sampling Mask Using poisson#

-Y 128: Specifies the size of the k-space (128).

-y 2: Specifies the acceleration factor in the direction (5x acceleration)

sampling_mask = bart(1, 'poisson -Y 128 -y 2').squeeze()

points: 8210, grid size: 128x128 = 16384 (R = 1.995615)

3. Apply the Sampling Mask to Create Undersampled k-space by famc#

undersampled_kspace = bart(1, 'fmac', fully_sampled_kspace, sampling_mask)

# Visualizing the images using Matplotlib
fig, axes = plt.subplots(1, 2, figsize=(8, 4))

# Plot undersampled k-space
axes[0].imshow((abs(undersampled_kspace)**0.3), cmap='gray')
axes[0].set_title('Undersampled K-space')
axes[0].axis('off')  # Hide axes

# Plot fully sampled k-space
axes[1].imshow((abs(fully_sampled_kspace)**0.3), cmap='gray')
axes[1].set_title('Fully Sampled K-space')
axes[1].axis('off')  # Hide axes

plt.show()
_images/d290e53823054beb12356758a3ad72ca5344a3186450e97093cbe90deb2d7427.png

4. Computes the inverse Fourier transform of the k-space data to obtain the image by fft#

-i: Computes the inverse Fourier transform

-u: Unitary inverse Fourier transform

3: Corresponds to the first two dimensions

fully_sampled_image = bart(1, 'fft -i -u 3', fully_sampled_kspace)

undersampled_image = bart(1, 'fft -i -u 3', undersampled_kspace)

# Visualizing the images using Matplotlib
fig, axes = plt.subplots(1, 2, figsize=(8, 4))

# Plot undersampled image
axes[0].imshow((abs(undersampled_image)), cmap='gray')
axes[0].set_title('Undersampled Image')
axes[0].axis('off')  # Hide axes

# Plot fully sampled image
axes[1].imshow((abs(fully_sampled_image)), cmap='gray')
axes[1].set_title('Fully Sampled Image')
axes[1].axis('off')  # Hide axes

plt.show()
_images/567c73a438e5f2babeaa2b16273a278727296ae3933e7e19fdfc26b124cf1d38.png

Example#

pattern = bart(1, 'pattern', undersampled_kspace).real

# Visualizing the images using Matplotlib 
plt.figure(figsize=(4,6))
plt.imshow((abs(pattern)**.3), cmap='gray')
plt.title('Pattern')
plt.show()
_images/a86a593a418ee29db2963057f92786bfc7a10c488b661a64b30ef1586414372b.png
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