Contents

Relaxometry

pyKNEEr computes:

  • Exponential or linear fitting [1,2]. Images with shortest TE or TSL can be rigidly registered to the image with longest TE or TSL

  • \(T_2\) maps from DESS acquisitions using Extended Phase Graph (EPG) modeling [3]

Exponential or linear fitting

Input: Image folder list

For the demo images, the input file is image_list_relaxometry_fitting.txt, which contains:

 [1] ./preprocessed
 [2] ./segmented
 [3] 4
 [4] i1 01_cubeQuant_01_orig.mha
 [5] i2 01_cubeQuant_02_orig.mha
 [6] i3 01_cubeQuant_03_orig.mha
 [7] i4 01_cubeQuant_04_orig.mha
 [8] bm 01_cubeQuant_01_prep_f.mha
 [9] cm 01_cubeQuant_01_prep_fc.mha

where:

  • Line 1: Preprocessed folder, containing the *_orig.mha images

  • Line 2: Segmented folder, containing femur and cartilage masks

  • Line 3: Number of images per acquisition

  • Lines 4-7: Images of the acquisition

  • Line 8: Name of femur mask

  • Line 9: Name of femoral cartilage mask

Tip

When using your own data:

  • Customize image_list_relaxometry_fitting.txt with your own image information

Executing relaxometry_fitting.ipynb

To calculate relaxometry maps:

  • Launch Jupyter notebook

  • In File Browser, navigate to relaxometry_fitting.ipynb, open it, and:

    • Customize the input variables:

      • method_flag (1 for linear fitting, 0 for exponential fitting)

      • registration_flag (1 to execute rigid registration, 0 otherwise)

      • n_of_cores (How do I choose the number of cores?)

      • output_file_name

    • Follow the instructions in the notebook

  • Save your notebook at the end of the process

Output: relaxometry maps

The outputs are in the folder relaxometry. For each subject, the fitting maps can be:

  • *_orig_map_exp_aligned.mha (e.g. 01_cubeQuant_01_orig_map_exp_aligned.mha): when the acquisitions are rigidly aligned to the first and the fitting is exponential

  • *_orig_map_lin_aligned.mha: when the acquisitions are rigidly aligned to the first and the fitting is linear

  • *_orig_map_exp.mha: when the acquisitions are not aligned to the first and the fitting is exponential

  • *_orig_map_lin.mha: when the acquisitions are not aligned to the first and the fitting is linear

Maps are computed only in the masked volumes to save computational time

Visualization: 2D and 3D maps, graph, and table

Relaxometry maps are visualized as:

  • 2D maps: For each subject three slices of the first acquisition are overlapped by the relaxation time map

  • 3D maps: Interactive visualization where only one map at the time can be visualized

  • Graph: Dots represent the average relaxation time per image and bars represents the standard deviation

  • Table: Numerical values of average and standard deviation of relaxation times are displayed in a table, also saved as .csv file for subsequent analysis

EPG modeling

Input: Image folder list

For the demo images, the input file is image_list_relaxometry_EPG.txt, which contains:

 [1] ./preprocessed
 [2] ./segmented
 [3] i1 01_DESS_01_orig.mha
 [4] i2 01_DESS_02_orig.mha
 [5] cm 01_DESS_01_prep_fc.mha

where:

  • Line 1: Preprocessed folder, containing the *_orig.mha images

  • Line 2: Segmented folder, containing cartilage masks

  • Lines 3-4: Images of the acquisition

  • Line 5: Name of femoral cartilage mask

Note

When using your own data:

  • Customize image_list_relaxometry_EPG.txt with your own image information

Execution, Output, and Visualization

Execution:

Output and visualization:

  • Follow the instructions above to know the output and how to visualize the results

References

[1] Borthakur A., Wheaton A.J., Gougoutas A.J., Akella S.V., Regatte R.R., Charagundla S.R., Reddy R. In vivo measurement of T1rho dispersion in the human brain at 1.5 tesla. J Magn Reson Imaging. Apr;19(4):403-9. 2004.

[2] Li X., Benjamin Ma C., Link T.M., Castillo D.D., Blumenkrantz G., Lozano J., Carballido-Gamio J., Ries M., Majumdar S. In vivo T1ρ and T2 mapping of articular cartilage in osteoarthritis of the knee using 3 T MRI. Osteoarthritis Cartilage. Jul;15(7):789-97. 2007.

[3] Sveinsson B, Chaudhari AS, Gold GE, Hargreaves BA. A simple analytic method for estimating T2 in the knee from DESS. Magn Reson Imaging. May;38:63-70. 2017.

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