cgsense2023 0.0.6

Conjugate Gradient SENSE (CG-SENSE) MRI Reconstruction

CG-SENSE HOME

Step-by-step tutorial of Conjugate Gradient SENSE (CG-SENSE) reconstruction by Hung P. Do.

Notes

This tutorial was written by Hung Do based on the RRSG_Challenge_01 github repository.

• Original tutorial notebook

• Original python code:

  • helper_fun.py
  • linop.py
  • solver.py
  • recon.py

• Hung Do’s modifications:

  • Modified tutorial notebook
  • Development notebook for the cgsense2023 PyPI package

• cgsense2023 PyPI Package re-written: Details can be seen in the P4_HPD_cgsense section.

W.I.P Tasks

Contributions to the below tasks are highly appreciated!

• Document the standardization of the raw h5-file, otherwise read_data() has to be modified every time a deviation in h5-file is encountered.

• Refactor, simplify, modularize the gridding reconstructions. Currently, it is difficult to understand, especially for new learner.

• Add dstore flag in gradient descent or conjugate gradient optimization functions to store intermediate reconstruction in each iteration.

• Add theoretical descriptions on MRI, MRI recons, and gradient Descent, and gonjugate gradient algorithms.

• etc.

How to Cite

APA style:

Do, H. (2024). CG-SENSE Tutorial version 0.0.3 (0.0.3). Zenodo. https://doi.org/10.5281/zenodo.10547817

IEEE style:

H. Do, “CG-SENSE Tutorial version 0.0.3”. Zenodo, Jan. 21, 2024. doi: 10.5281/zenodo.10547817.

pip install

The cgsense2023 package was uploaded to PyPI and can be easily installed using the below command.

pip install cgsense2023

How to use

Plot Module

from cgsense2023.plot import *
import numpy as np

# Sequential - full - spoke radial trajectory
traj_full = gen_radial_traj(golden=False, full_spoke=True)
show_trajectory(traj_full, golden=True, figsize=(10,8))

# golden - full - spoke radial trajectory
traj_full_golden = gen_radial_traj(golden=True, full_spoke=True)
show_trajectory(traj_full_golden, golden=True, figsize=(10,8))

Nim, Nx, Ny = 12, 128, 256
x1 = np.random.randn(Nim, Nx, Ny)
x2 = np.random.randn(1, Nx, Ny)

show_image_grid(x1)

Warning: number of images (12) is larger than number of panels (2x5)!

show_image_grid(x2)

Metrics Module

from cgsense2023.metrics import *
import numpy as np

# same dimensions
Nim, Nx, Ny = 11, 128, 256
x1 = np.random.randn(1, Nx, Ny)
x2 = np.random.randn(1, Nx, Ny)
print_metrics(x1, x2)

+---------+-----------+
| Metrics |   Values  |
+---------+-----------+
|   MSE   | 1.999e+00 |
|   NMSE  | 2.000e+00 |
|   RMSE  | 1.414e+00 |
|  NRMSE  | 1.414e+00 |
|   PSNR  |   14.981  |
|   SSIM  | 0.0092604 |
+---------+-----------+

Gridding Reconstruction

from cgsense2023.math import *
from cgsense2023.io import *
import cgsense2023 as cgs2003
from cgsense2023.mri import *
from cgsense2023.optimizer import *
import h5py
import numpy as np
import matplotlib.pyplot as plt

Data Paths

# path to the data file
fpath = '../testdata/rawdata_brain.h5'
# path to the reference results
rpath = '../testdata/CG_reco_inscale_True_denscor_True_reduction_1.h5'

with h5py.File(rpath, 'r') as rf:
    print(list(rf.keys()))
    ref_cg = np.squeeze(rf['CG_reco'][()][-1])
    ref_grid = np.squeeze(rf['Coil_images'][()])

['CG_reco', 'Coil_images']

ref_cg.shape, ref_grid.shape

((300, 300), (12, 300, 300))

Setup Parameters

# one stop shop for all Parameters and Data
params = setup_params(fpath, R=1)

['Coils', 'InScale', 'rawdata', 'trajectory']

Setup MRI Operator

# Set up MRI operator
mrimodel = MriImagingModel(params)
mriop = MriOperator(data_par=params["Data"],optimizer_par=params["Optimizer"])
mriop.set_operator(mrimodel)
# Single Coil images after FFT
my_grid = mriop.operator.NuFFT.adjoint(params['Data']['rawdata_density_cor'])

my_grid.shape, ref_grid.shape

((12, 300, 300), (12, 300, 300))

# test gridding recon results
np.allclose(ref_grid, my_grid)

True

# test gridding recon results
np.array_equal(ref_grid, my_grid)

False

print_metrics(np.abs(ref_grid[0]), np.abs(my_grid[0]))

+---------+-----------+
| Metrics |   Values  |
+---------+-----------+
|   MSE   | 1.472e-24 |
|   NMSE  | 5.905e-15 |
|   RMSE  | 1.213e-12 |
|  NRMSE  | 7.684e-08 |
|   PSNR  |   154.87  |
|   SSIM  |    1.0    |
+---------+-----------+

show_image_grid(my_grid, figsize=(10,10), rows=3, cols=4)

show_image_grid(rss_rec(my_grid), figsize=(10,10))

Gradient Descent

guess = np.zeros((params['Data']['image_dim'],params['Data']['image_dim']))
SD_result, SD_residuals, SD_ref_res = steepest_descent(mriop, guess, 
                                            params['Data']['rawdata_density_cor'], 
                                            iters=50,
                                            ref=ref_cg)

Residuum at iter 50 : 6.553379e-06

show_compared_images(np.abs(ref_cg), np.abs(SD_result), diff_fac=10, 
                     labels=['Reference', 'Steepest Descent', 'diff'])

np.allclose(ref_cg, SD_result)

False

print_metrics(np.abs(ref_cg), np.abs(SD_result))

+---------+-----------+
| Metrics |   Values  |
+---------+-----------+
|   MSE   | 5.633e-14 |
|   NMSE  | 7.888e-05 |
|   RMSE  | 2.373e-07 |
|  NRMSE  | 8.882e-03 |
|   PSNR  |   55.242  |
|   SSIM  |   0.9988  |
+---------+-----------+

CG’s Semi-Convergence Behavior

CG_result, CG_residuals, CG_ref_res = conjugate_gradient(mriop, guess, 
                                            params['Data']['rawdata_density_cor'], 
                                            iters=50,
                                            ref=ref_cg)

Residuum at iter 50 : 2.993229e-06

plt.plot(np.log10(SD_ref_res),'*--', label='SD reference_norms');
plt.plot(np.log10(SD_residuals),'*--', label='SD residual_norms');
plt.plot(np.log10(CG_ref_res),'*--', label='CG reference_norms');
plt.plot(np.log10(CG_residuals),'*--', label='CG residual_norms');
plt.grid();
plt.xlabel("# iteration")
plt.ylabel("residuals (log10)")
plt.legend();

Conjugate Gradient vs. REF

Based on the “semi-convergence” plot above, the optimal number of iterations for CG and SD are around 10 and 28, respectively.

CG_result_vs_REF, _, _ = conjugate_gradient(mriop, guess, 
                                            params['Data']['rawdata_density_cor'], 
                                            iters=10,
                                            ref=ref_cg)

Residuum at iter 10 : 1.826174e-05

show_compared_images(np.abs(ref_cg), np.abs(CG_result_vs_REF), diff_fac=200000, 
                     labels=['Reference', 'Conjugate Gradient', 'diff'])

np.allclose(ref_cg, CG_result_vs_REF)

True

print_metrics(np.abs(ref_cg), np.abs(CG_result_vs_REF))

+---------+-----------+
| Metrics |   Values  |
+---------+-----------+
|   MSE   | 1.196e-22 |
|   NMSE  | 1.675e-13 |
|   RMSE  | 1.094e-11 |
|  NRMSE  | 4.093e-07 |
|   PSNR  |   141.97  |
|   SSIM  |    1.0    |
+---------+-----------+

Developer install

If you want to develop cgsense2023 yourself, please use an editable installation of cgsense2023.

git clone https://github.com/hdocmsu/cgsense2023.git

pip install -e "cgsense2023[dev]"

You also need to use an editable installation of nbdev, fastcore, and execnb.

Happy Coding!!!

References

This template was created based on the below references. The list is not exhaustive so if you notice any missing references, please report an issue. I will promptly correct it.

• RRSG_Challenge_01 github repository

• fastMRI github repository

• Original CG-SENSE Paper by Pruessmann et. al., 2001

• CG-SENSE revisited: Results from the first ISMRM reproducibility challenge

• Dwight Nishimura, (Published Jan 10, 2010), “Principles of Magnetic Resonance Imaging,” Stanford University, Palo Alto, CA

• Prof. James V. Burke’s Lecture on “The Conjugate Gradient Algorithm”

• Magnetic Resonance Image Reconstruction: Theory, Methods, and Applications, (2022)

• sigPy github repo

Invitation to Contributions

If you would like to contribute to improve this repository please feel free to propose here.

License

MIT License as seen from the Original Repo’s License

MIT License

Copyright (c) 2020 ISMRM Reproducible Research Study Group

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.