bart caldir

bart caldir#

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The bart caldir command in BART is used to estimate coil sensitivities from the k-space center of MRI data, which is generally a fully sampled area of the MRI data. This method is based on the approach by McKenzie et al. [1], which uses a direct estimation technique to determine coil sensitivity profiles. The calibration region’s size is automatically determined but limited by the {cal_size} parameter specified by the user.

[1] McKenzie CA, Yeh EN, Ohliger MA, Price MD, Sodickson DK. Self-calibrating parallel imaging with automatic coil sensitivity extraction. Magn Reson Med 2002; 47:529-538.

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

!bart caldir -h

Usage: caldir cal_size <input> <output> 

Estimates coil sensitivities from the k-space center using
a direct method (McKenzie et al.). The size of the fully-sampled
calibration region is automatically determined but limited by
{cal_size} (e.g. in the readout direction).

-h  help

Parameters#

  • cal_size: Specifies the maximum size of the fully-sampled calibration region, typically in the readout direction.

  • <input>: The input file, which is usually a k-space data file.

  • <output>: The output file where the estimated coil sensitivities will be stored.

  • -h: Displays help information.

Example 1: Estimates Coil Sensitivities Small cal_size (Using Python)#

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

import cfl
from bart import bart

Generate a multi-coil image in k-space using the phantom simulation tool#

multi_coil_kspace = bart(1, 'phantom -x 128 -k -s 8')

# Visualizing the multi-coil kspace data using Matplotlib 
plt.figure(figsize=(16,20))
for i in range(8):
    plt.subplot(1, 8, i+1)
    plt.imshow(abs(multi_coil_kspace[...,i])**.3, cmap='gray')
    plt.title('Kspace channel {}'.format(i))
_images/2e53dace62d1fc4470cf377f902531784fe84d24594aa9cbe1f707d16119fdbc.png

Estimated the coil sensitivity by using caldir with cal_size = 6#

coil_sen = bart(1, 'caldir 6', multi_coil_kspace)

Calibration region 6x6x1
Done.

# Visualizing the images using Matplotlib 
plt.figure(figsize=(16,20))
for i in range(8):
    plt.subplot(1, 8, i+1)
    plt.imshow(abs(coil_sen[...,i]), cmap='gray')
    plt.title('Coil sensitivity {}'.format(i))
_images/0d8fd620b6531391e6f9ce3d7704f6258934d8807c97ab60ca2a7ef0aab05e94.png

Example 2: Estimates Coil Sensitivities with Large cal_size (Using Python)#

Estimated the coil sensitivity by using caldir with “cal_size = 24”#

coil_sen_1 = bart(1, 'caldir 24', multi_coil_kspace)

Calibration region 24x24x1
Done.

# Visualizing the images using Matplotlib 
plt.figure(figsize=(16,20))
for i in range(8):
    plt.subplot(1, 8, i+1)
    plt.imshow(abs(coil_sen_1[...,i]), cmap='gray')
    plt.title('Coil sensitivity {}'.format(i))
_images/f404fca8086d0ec5c81a47ed971c136f06c9f546e4d8c132e41a293b6b6e8f58.png
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