Cloudmrhub

Installation

pip install git+https://github.com/cloudmrhub-com/cloudmrhub.git

Suggestions

#create an environment 
python3 -m venv CMT
source CMT/bin/activate
pip install git+https://github.com/cloudmrhub-com/cloudmrhub.git

Example

Examples can be found in the git test directoy

The codes reads a signal and a noise kspace from siemens scanner and computes reconstructions and snr for RSS and B1

from cloudmrhub.cm2D import cm2DKellmanRSS, cm2DReconRSS, cm2DKellmanB1,cm2DReconB1
import cloudmrhub.cm as cm

import twixtools
import numpy as np
import matplotlib.pyplot as plt


import sys
s= sys.argv[1]
n=sys.argv[2]

# read the files
twix=twixtools.map_twix(s)
im_array = twix[0]['image']
im_array.flags['remove_os'] = True  # activate automatic os removal
im_array.flags['average']['Rep'] = True  # average all repetitions
im_array.flags['average']['Ave'] = True  # average all repetitions
S=np.transpose(im_array[0,0,0,0,0,0,0,0,0,0,0,0,0,:,:,:],[2,0,1])


twix=twixtools.map_twix(n)
im_array = twix[0]['image']
im_array.flags['remove_os'] = False  # activate automatic os removal
im_array.flags['average']['Rep'] = False  # average all repetitions
im_array.flags['average']['Ave'] = False # average all repetitions
N=np.transpose(im_array[0,0,0,0,0,0,0,0,0,0,0,0,0,:,:,:],[2,0,1])

# instantiate th classes
L=cm2DReconRSS()
L.setSignalKSpace(S)
L.setNoiseKSpace(N)

NBW=L.getNoiseBandWidth()
print(NBW)
nc2=L.getNoiseCovariance()
plt.figure()
plt.imshow(np.abs(nc2))
plt.colorbar()
plt.title(f'Noise Covariance Matrix BW:{NBW:03f}')
p=L.getPrewhitenedSignal()
plt.figure()
plt.imshow(np.abs(p[:,:,0]))
plt.colorbar()
plt.title('Prewhitened Kspace First Coil')



im=L.getOutput()

import matplotlib.pyplot as plt

plt.imshow(np.abs(im))
plt.colorbar()
plt.title('RSS Recon')

L=cm2DKellmanRSS()
L.setSignalKSpace(S)
L.setNoiseKSpace(N)
snr=np.abs(L.getOutput())


plt.imshow(snr)
plt.colorbar()
plt.title('RSS SNR')

Example SNR

import numpy as np
import cloudmrhub.cm2D as cm2D

import matplotlib.pyplot as plt
import  twixtools

s='cloudmrhub/data/meas_MID00024_FID188178_Multislice.dat'
n='cloudmrhub/data/meas_MID00027_FID188181_Multislice_no_RF.dat'

twix=twixtools.map_twix(s)
im_array = twix[0]['image']
im_array.flags['remove_os'] = True  # activate automatic os removal
im_array.flags['average']['Rep'] = True  # average all repetitions
im_array.flags['average']['Ave'] = True  # average all repetitions
S=np.transpose(im_array[0,0,0,0,0,0,0,0,0,0,0,0,0,:,:,:],[2,0,1])


twix=twixtools.map_twix(n)
im_array = twix[0]['image']
im_array.flags['remove_os'] = False  # activate automatic os removal
im_array.flags['average']['Rep'] = False  # average all repetitions
im_array.flags['average']['Ave'] = False # average all repetitions
N=np.transpose(im_array[0,0,0,0,0,0,0,0,0,0,0,0,0,:,:,:],[2,0,1])

L2=cm2D.cm2DSignalToNoiseRatioPseudoMultipleReplicas()
L=cm2D.cm2DReconRSS()
L2.numberOfReplicas=100
L.setNoiseKSpace(N)
L.setSignalKSpace(S)
L2.reconstructor=L

plt.subplot(221)
plt.imshow(np.abs(L2.getOutput()))
plt.tick_params(axis='both', which='both', bottom=False, top=False, labelbottom=False, right=False, left=False, labelleft=False)
plt.title('PMR')


R2=cm2D.cm2DSignalToNoiseRatioPseudoMultipleReplicasWein()
R=cm2D.cm2DReconRSS()
R2.numberOfReplicas=3
R2.boxSize=10
R.setNoiseKSpace(N)
R.setSignalKSpace(S)
R2.reconstructor=R



plt.subplot(222)
plt.imshow(np.abs(R2.getOutput()))
plt.tick_params(axis='both', which='both', bottom=False, top=False, labelbottom=False, right=False, left=False, labelleft=False)
plt.title('CR')


R=cm2D.cm2DKellmanRSS()
R.setNoiseKSpace(N)
R.setSignalKSpace(S)
plt.subplot(223)
plt.imshow(np.abs(R.getOutput()))
plt.tick_params(axis='both', which='both', bottom=False, top=False, labelbottom=False, right=False, left=False, labelleft=False)
plt.title('Kellman')

plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
plt.colorbar(plt.gcf().axes[0].images[0], ax=plt.gcf().axes)
# add a title for the figure
plt.suptitle("SNR", fontsize=16)


plt.show()

Roadmap

[x] SNR
    [x] Kellman
        [x] RSS
        [x] B1
        [x] Sense

    [x] Pseudo Multiple Replicas 
        [x] RSS
        [x] B1
        [x] Sense
        [x] Grappa

    [x] Pseudo Multiple Replicas Wien
        [x] RSS
        [x] B1
        [x] Sense
        [x] Grappa

    [ ] Multiple Replicas

[x] Recon

    [x] RSS
    [x] B1
    [x] Sense
    [x] Grappa
    

[ ] Coilsensitivity Maps:

    [x] Inner
    [x] BodyCoil
    [ ] Espirit

Cite Us

• Montin E, Lattanzi R. Seeking a Widely Adoptable Practical Standard to Estimate Signal-to-Noise Ratio in Magnetic Resonance Imaging for Multiple-Coil Reconstructions. J Magn Reson Imaging. 2021 Dec;54(6):1952-1964. doi: 10.1002/jmri.27816. Epub 2021 Jul 4. PMID: 34219312; PMCID: PMC8633048.

Dr. Eros Montin, PhD

46&2 just ahead of me!