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4     |     Environment setup
-
# Assumes data/rf-shimming-7t is found at $HOME
-path_data = os.path.join(os.getenv('HOME'),"data/rf-shimming-7t/ds004906")
+
@@ -488,9 +486,9 @@ 

5     |     Process anat/T2starw (GRE)file_suffix = 'crop' else: file_suffix = 'reg' - fname_result_sc = os.path.join(derivative_results, f"{subject}_acq-{shim_mode}_T2starw_label-SC.csv") + fname_result_sc = os.path.join(path_results, f"{subject}_acq-{shim_mode}_T2starw_label-SC.csv") !sct_extract_metric -i {subject}_acq-{shim_mode}_T2starw_{file_suffix}.nii.gz -f {subject}_acq-CoV_T2starw_crop_seg.nii.gz -method wa -vert 3:9 -vertfile {subject}_acq-CoV_T2starw_crop_seg_labeled.nii.gz -perslice 1 -o {fname_result_sc} - fname_result_csf = os.path.join(derivative_results, f"{subject}_acq-{shim_mode}_T2starw_label-CSF.csv") + fname_result_csf = os.path.join(path_results, f"{subject}_acq-{shim_mode}_T2starw_label-CSF.csv") !sct_extract_metric -i {subject}_acq-{shim_mode}_T2starw_{file_suffix}.nii.gz -f {subject}_acq-CoV_T2starw_crop_label-CSF_seg.nii.gz -method wa -vert 3:9 -vertfile {subject}_acq-CoV_T2starw_crop_seg_labeled.nii.gz -perslice 1 -o {fname_result_csf}

@@ -529,15 +527,6 @@

5     |     Process anat/T2starw (GRE)# Number of subjects determines the number of rows in the subplot n_rows = len(subjects) -# Create a figure with multiple subplots -fig, axes = plt.subplots(n_rows, 1, figsize=(10, 6 * n_rows)) -font_size = 18 -line_width = 3 - -# Check if axes is an array or a single object -if n_rows == 1: - axes = [axes] - # Data storage for statistics data_stats = [] @@ -546,20 +535,20 @@

5     |     Process anat/T2starw (GRE)# Iterate over each subject and create a subplot for i, subject in enumerate(subjects): - ax = axes[i] + t2_data_plotly[subject]={} for shim_mode in shim_modes: # Initialize list to collect data for this shim method method_data = [] # Get signal in SC - file_csv = os.path.join(derivative_results, f"{subject}_acq-{shim_mode}_T2starw_label-SC.csv") + file_csv = os.path.join(path_results, f"{subject}_acq-{shim_mode}_T2starw_label-SC.csv") df = pd.read_csv(file_csv) data_sc = df['WA()'] data_sc_smoothed = smooth_data(data_sc) # Get signal in CSF - file_csv = os.path.join(derivative_results, f"{subject}_acq-{shim_mode}_T2starw_label-CSF.csv") + file_csv = os.path.join(path_results, f"{subject}_acq-{shim_mode}_T2starw_label-CSF.csv") df = pd.read_csv(file_csv) data_csf = df['WA()'] data_csf_smoothed = smooth_data(data_csf) @@ -580,43 +569,12 @@

5     |     Process anat/T2starw (GRE)if method_data: t2_data_plotly[subject][shim_mode]=method_data - # Plotting each file's data separately - for resampled_data in method_data: - ax.plot(x_grid, resampled_data, label=f"{shim_mode}", linewidth=line_width) - # Compute stats on the non-resampled data (to avoid interpolation errors) mean_data = np.mean(data_sc_csf_ratio) sd_data = np.std(data_sc_csf_ratio) data_stats.append([subject, shim_mode, mean_data, sd_data]) else: t2_data_plotly[subject][shim_mode]=None - - # Set x-ticks and labels for the bottom subplot - if i == n_rows - 1: # Check if it's the last subplot - # Create a secondary axis for vertebral level labels - secax = ax.secondary_xaxis('bottom') - secax.set_xticks(label_positions) - secax.set_xticklabels(vertebral_levels, fontsize=font_size-4) - secax.tick_params(axis='x', which='major', length=0) # Hide tick marks - - ax.set_xticks(custom_xticks) - ax.set_xticklabels([''] * len(custom_xticks)) - ax.tick_params(axis='x', which='major', length=0) # Hide tick marks for the primary axis - - ax.set_title(f'{subject}', fontsize=font_size) - ax.set_ylabel('CSF/Cord T2starw signal ratio', fontsize=font_size) - ax.tick_params(axis='y', which='major', labelsize=font_size-4) - - # Add legend only to the first subplot - if i == 0: - ax.legend(fontsize=font_size) - - ax.grid(True) - -# Adjust the layout so labels and titles do not overlap -plt.tight_layout() -plt.savefig(os.path.join(path_results, 'fig_gre_csf-sc_ratio.png'), dpi=300, format='png') -plt.show()

@@ -688,6 +646,41 @@

5     |     Process anat/T2starw (GRE) +
                 Anova
+=======================================
+          F Value Num DF  Den DF Pr > F
+---------------------------------------
+Shim_Mode  9.0936 6.0000 24.0000 0.0000
+=======================================
+
+
+
Paired t-tests:
+                 Pair   P-Value  Adjusted P-Value
+0       (CP, patient)  0.005972          0.025083
+1        (CP, volume)  0.075373          0.131902
+2         (CP, phase)  0.011296          0.033889
+3           (CP, CoV)  0.000156          0.003279
+4        (CP, target)  0.030643          0.080437
+5        (CP, SAReff)  0.197466          0.276452
+6   (patient, volume)  0.205065          0.269148
+7    (patient, phase)  0.296440          0.366191
+8      (patient, CoV)  0.001437          0.010061
+9   (patient, target)  0.154750          0.232125
+10  (patient, SAReff)  0.031202          0.072804
+11    (volume, phase)  0.413730          0.457281
+12      (volume, CoV)  0.404085          0.471432
+13   (volume, target)  0.552388          0.552388
+14   (volume, SAReff)  0.075590          0.122108
+15       (phase, CoV)  0.009898          0.034641
+16    (phase, target)  0.496343          0.521160
+17    (phase, SAReff)  0.005612          0.029461
+18      (CoV, target)  0.043267          0.082600
+19      (CoV, SAReff)  0.001394          0.014638
+20   (target, SAReff)  0.036743          0.077159
+
+
+
@@ -804,15 +797,6 @@

6     |     Process fmap/TFL (flip angle maps)# Number of subjects determines the number of rows in the subplot n_rows = len(subjects) -# Create a figure with multiple subplots -fig, axes = plt.subplots(n_rows, 1, figsize=(10, 6 * n_rows)) -font_size = 18 -line_width = 3 - -# Check if axes is an array or a single object -if n_rows == 1: - axes = [axes] - # Data storage for statistics data_stats = [] @@ -821,9 +805,7 @@

6     |     Process fmap/TFL (flip angle maps)# Iterate over each subject and create a subplot for i, subject in enumerate(subjects): - - ax = axes[i] - + os.chdir(os.path.join(path_data, subject, "fmap")) b1_data_plotly[subject]={} @@ -831,7 +813,7 @@

6     |     Process fmap/TFL (flip angle maps)# Initialize list to collect data for this shim method method_data = [] - file_csv = os.path.join(derivative_results, f"{subject}_TB1map_{shim_mode}.csv") + file_csv = os.path.join(path_results, f"{subject}_TB1map_{shim_mode}.csv") df = pd.read_csv(file_csv) wa_data = df['WA()'] @@ -852,7 +834,6 @@

6     |     Process fmap/TFL (flip angle maps)# Plotting each file's data separately for resampled_data in method_data: b1_data_plotly[subject][shim_mode]=resampled_data - ax.plot(x_grid, resampled_data, label=f"{shim_mode}", linewidth=line_width) # Compute stats on the non-resampled data (to avoid interpolation errors) mean_data = np.mean(wa_data) @@ -860,34 +841,6 @@

6     |     Process fmap/TFL (flip angle maps)data_stats.append([subject, shim_mode, mean_data, sd_data]) else: b1_data_plotly[subject][shim_mode]=None - - # Set x-ticks and labels for the bottom subplot - if i == n_rows - 1: # Check if it's the last subplot - # Create a secondary axis for vertebral level labels - secax = ax.secondary_xaxis('bottom') - secax.set_xticks(label_positions) - secax.set_xticklabels(vertebral_levels, fontsize=font_size-4) - secax.tick_params(axis='x', which='major', length=0) # Hide tick marks - - ax.set_xticks(custom_xticks) - ax.set_xticklabels([''] * len(custom_xticks)) - ax.tick_params(axis='x', which='major', length=0) # Hide tick marks for the primary axis - - ax.set_title(f'{subject}', fontsize=font_size) - ax.set_ylabel('B1+ efficiency [nT/V]', fontsize=font_size) - ax.tick_params(axis='y', which='major', labelsize=font_size-4) - - # Add legend only to the first subplot - if i == 0: - ax.legend(fontsize=font_size) - - ax.grid(True) - -# Adjust the layout so labels and titles do not overlap -plt.tight_layout() -plt.savefig(os.path.join(path_results, 'fig_b1plus.png'), dpi=300, format='png') - -plt.show() @@ -957,6 +910,40 @@

6     |     Process fmap/TFL (flip angle maps) +
+
                 Anova
+=======================================
+          F Value Num DF  Den DF Pr > F
+---------------------------------------
+Shim_Mode 14.0743 6.0000 24.0000 0.0000
+=======================================
+
+Paired t-tests:
+                 Pair   P-Value  Adjusted P-Value
+0       (CP, patient)  0.007728          0.040570
+1        (CP, volume)  0.009006          0.037825
+2         (CP, phase)  0.079896          0.104863
+3           (CP, CoV)  0.318010          0.371012
+4        (CP, target)  0.010284          0.030853
+5        (CP, SAReff)  0.563249          0.622539
+6   (patient, volume)  0.821572          0.862651
+7    (patient, phase)  0.005550          0.116560
+8      (patient, CoV)  0.036644          0.059195
+9   (patient, target)  0.006302          0.066176
+10  (patient, SAReff)  0.010083          0.035289
+11    (volume, phase)  0.006565          0.045958
+12      (volume, CoV)  0.032112          0.056197
+13   (volume, target)  0.027717          0.052915
+14   (volume, SAReff)  0.012891          0.030078
+15       (phase, CoV)  0.989088          0.989088
+16    (phase, target)  0.011515          0.030226
+17    (phase, SAReff)  0.070778          0.099089
+18      (CoV, target)  0.236842          0.292570
+19      (CoV, SAReff)  0.061149          0.091724
+20   (target, SAReff)  0.018986          0.039871
+
+
+
+
+_images/index_51_0.png +

Figure 2. B1+ efficiency for one participant (sub-05) across all seven RF shimming conditions. The top left panel shows the tfl_b1map magnitude image with an overlay of the mask that was used to perform RF shimming. Text inserts show the mean (in nT/V) and CoV (in %) of B1+ efficiency along the spinal cord between C3 and T2.

+
+
+ + +
+
+ +

Figure 3. B1+ efficiency (A) and CSF/Cord signal ratio from the GRE scan (B) across subjects and across different RF shimming conditions. Data were measured in the spinal cord from C3 to T2 vertebral levels. To match the x-ticks across subjects, the C2-C3 and the T2-T3 intervertebral discs of each subject were aligned with that of the PAM50 template [De Leener et al., 2018], and the curves were linearly scaled.

diff --git a/reports/index.log b/reports/index.log deleted file mode 100644 index 8731d6f..0000000 --- a/reports/index.log +++ /dev/null @@ -1,66 +0,0 @@ -Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/jupyter_cache/executors/utils.py", line 51, in single_nb_execution - executenb( - File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/client.py", line 1204, in execute - return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() - File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/util.py", line 84, in wrapped - return just_run(coro(*args, **kwargs)) - File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/util.py", line 62, in just_run - return loop.run_until_complete(coro) - File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete - return future.result() - File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/client.py", line 663, in async_execute - await self.async_execute_cell( - File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/client.py", line 965, in async_execute_cell - await self._check_raise_for_error(cell, cell_index, exec_reply) - File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/client.py", line 862, in _check_raise_for_error - raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) -nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: ------------------- -# Download data and define path variables - -# Google Colab-Only -if notebook=='colab': - !datalad install https://github.com/OpenNeuroDatasets/ds004906.git - os.chdir("ds004906") - !datalad get . # uncomment for production - # !datalad get sub-01/ # debugging - # Get derivatives containing manual labels - !datalad get derivatives - -if notebook!='colab': - # Switch working directory to $HOME/data/rf-shimming-7t/ds004906 - os.chdir(os.path.join(os.getenv('HOME'),"data/rf-shimming-7t/ds004906")) - if notebook=='neurolibre-clean': - flist = open('cleanup.txt', 'r') - for f in flist: - fname = f.rstrip('\n') - fname = Path(os.getcwd()) / Path(fname) - - # or, if you get rid of os.chdir(path) above, - # fname = os.path.join(path, f.rstrip()) - if os.path.isfile(fname): # this makes the code more robust - print('Removing file: ' + str(fname)) - os.remove(fname) - elif os.path.isdir(fname): - print('Removing directory: ' + str(fname)) - rmtree(fname) - - # also, don't forget to close the text file: - flist.close() - ------------------- - ---------------------------------------------------------------------------- -FileNotFoundError Traceback (most recent call last) -Cell In[5], line 14 - 10 get_ipython().system('datalad get derivatives') - 12 if notebook!='colab': - 13 # Switch working directory to $HOME/data/rf-shimming-7t/ds004906 ----> 14 os.chdir(os.path.join(os.getenv('HOME'),"data/rf-shimming-7t/ds004906")) - 15 if notebook=='neurolibre-clean': - 16 flist = open('cleanup.txt', 'r') - -FileNotFoundError: [Errno 2] No such file or directory: '/home/runner/data/rf-shimming-7t/ds004906' -FileNotFoundError: [Errno 2] No such file or directory: '/home/runner/data/rf-shimming-7t/ds004906' -