[ENH] Use different masking thresholds for denoising and classification

docs/approach.rst

@@ -108,12 +108,9 @@ this voxel), so the line is fit to all available data.
     ``tedana`` actually performs and uses two sets of :math:`T_{2}^*`/:math:`S_0` model fits.
     In one case, ``tedana`` estimates :math:`T_{2}^*` and :math:`S_0` for voxels with good signal in at
     least two echoes.
-    The resulting "limited" :math:`T_{2}^*` and :math:`S_0` maps are used throughout
-    most of the pipeline.
     In the other case, ``tedana`` estimates :math:`T_{2}^*` and :math:`S_0` for voxels
     with good data in only one echo as well, but uses the first two echoes for those voxels.
-    The resulting "full" :math:`T_{2}^*` and :math:`S_0` maps are used to generate the
-    optimally combined data.
+    The resulting "full" :math:`T_{2}^*` and :math:`S_0` maps are used throughout the rest of the pipeline.
 
 .. image:: /_static/a05_loglinear_regression.png
 

docs/outputs.rst

@@ -13,19 +13,19 @@ Outputs of the tedana workflow
 Filename                                            Content
 ================================================    =====================================================
 dataset_description.json                            Top-level metadata for the workflow.
-T2starmap.nii.gz                                    Limited estimated T2* 3D map.
+T2starmap.nii.gz                                    Full estimated T2* 3D map.
                                                     Values are in seconds.
                                                     The difference between the limited and full maps
                                                     is that, for voxels affected by dropout where
-                                                    only one echo contains good data, the full map
-                                                    uses the single echo's value while the limited
-                                                    map has a NaN.
-S0map.nii.gz                                        Limited S0 3D map.
+                                                    only one echo contains good data, the full map uses
+                                                    the T2* estimate from the first two echoes, while the
+                                                    limited map has a NaN.
+S0map.nii.gz                                        Full S0 3D map.
                                                     The difference between the limited and full maps
                                                     is that, for voxels affected by dropout where
-                                                    only one echo contains good data, the full map
-                                                    uses the single echo's value while the limited
-                                                    map has a NaN.
+                                                    only one echo contains good data, the full map uses
+                                                    the S0 estimate from the first two echoes, while the
+                                                    limited map has a NaN.
 desc-optcom_bold.nii.gz                             Optimally combined time series.
 desc-optcomDenoised_bold.nii.gz                     Denoised optimally combined time series. Recommended
                                                     dataset for analysis.
@@ -79,15 +79,19 @@ If ``verbose`` is set to True:
 ==============================================================  =====================================================
 Filename                                                        Content
 ==============================================================  =====================================================
-desc-full_T2starmap.nii.gz                                      Full T2* map/time series.
+desc-limited_T2starmap.nii.gz                                   Limited T2* map/time series.
                                                                 Values are in seconds.
-                                                                The difference between the limited and full maps is
-                                                                that, for voxels affected by dropout where only one
-                                                                echo contains good data, the full map uses the
-                                                                single echo's value while the limited map has a NaN.
-                                                                Only used for optimal combination.
-desc-full_S0map.nii.gz                                          Full S0 map/time series. Only used for optimal
-                                                                combination.
+                                                                The difference between the limited and full maps
+                                                                is that, for voxels affected by dropout where
+                                                                only one echo contains good data, the full map uses
+                                                                the S0 estimate from the first two echoes, while the
+                                                                limited map has a NaN.
+desc-limited_S0map.nii.gz                                       Limited S0 map/time series.
+                                                                The difference between the limited and full maps
+                                                                is that, for voxels affected by dropout where
+                                                                only one echo contains good data, the full map uses
+                                                                the S0 estimate from the first two echoes, while the
+                                                                limited map has a NaN.
 echo-[echo]_desc-[PCA|ICA]_components.nii.gz                    Echo-wise PCA/ICA component weight maps.
 echo-[echo]_desc-[PCA|ICA]R2ModelPredictions_components.nii.gz  Component- and voxel-wise R2-model predictions,
                                                                 separated by echo.

tedana/resources/config/outputs.json

@@ -4,11 +4,11 @@
         "bidsv1.5.0": "desc-adaptiveGoodSignal_mask"
     },
     "t2star img": {
-        "orig": "t2sv",
+        "orig": "t2svG",
         "bidsv1.5.0": "T2starmap"
     },
     "s0 img": {
-        "orig": "s0v",
+        "orig": "s0vG",
         "bidsv1.5.0": "S0map"
     },
     "combined img": {
@@ -47,13 +47,13 @@
         "orig": "lowk_ts_OC",
         "bidsv1.5.0": "desc-optcomRejected_bold"
     },
-    "full t2star img": {
-        "orig": "t2svG",
-        "bidsv1.5.0": "desc-full_T2starmap"
+    "limited t2star img": {
+        "orig": "t2sv",
+        "bidsv1.5.0": "desc-limited_T2starmap"
     },
-    "full s0 img": {
-        "orig": "s0vG",
-        "bidsv1.5.0": "desc-full_S0map"
+    "limited s0 img": {
+        "orig": "s0v",
+        "bidsv1.5.0": "desc-limited_S0map"
     },
     "whitened img": {
         "orig": "ts_OC_whitened",

tedana/tests/data/fiu_four_echo_outputs.txt

@@ -20,10 +20,10 @@ desc-PCA_mixing.tsv
 desc-PCA_stat-z_components.nii.gz
 desc-T1likeEffect_min.nii.gz
 desc-adaptiveGoodSignal_mask.nii.gz
-desc-full_S0map.nii.gz
-desc-full_T2starmap.nii.gz
 desc-globalSignal_map.nii.gz
 desc-globalSignal_timeseries.tsv
+desc-limited_S0map.nii.gz
+desc-limited_T2starmap.nii.gz
 desc-optcomAcceptedMIRDenoised_bold.nii.gz
 desc-optcomAccepted_bold.nii.gz
 desc-optcomDenoised_bold.nii.gz

tedana/tests/data/nih_five_echo_outputs_t2smap.txt

@@ -1,6 +1,6 @@
 dataset_description.json
-desc-full_S0map.nii.gz
-desc-full_T2starmap.nii.gz
+desc-limited_S0map.nii.gz
+desc-limited_T2starmap.nii.gz
 desc-optcom_bold.nii.gz
 S0map.nii.gz
 T2starmap.nii.gz

tedana/tests/data/nih_five_echo_outputs_verbose.txt

@@ -18,8 +18,8 @@ desc-PCA_metrics.tsv
 desc-PCA_mixing.tsv
 desc-PCA_stat-z_components.nii.gz
 desc-adaptiveGoodSignal_mask.nii.gz
-desc-full_S0map.nii.gz
-desc-full_T2starmap.nii.gz
+desc-limited_S0map.nii.gz
+desc-limited_T2starmap.nii.gz
 desc-optcomAccepted_bold.nii.gz
 desc-optcomDenoised_bold.nii.gz
 desc-optcomPCAReduced_bold.nii.gz

tedana/tests/test_t2smap.py

@@ -31,9 +31,9 @@ def test_basic_t2smap1(self):
         assert len(img.shape) == 3
         img = nib.load(op.join(out_dir, 'S0map.nii.gz'))
         assert len(img.shape) == 3
-        img = nib.load(op.join(out_dir, 'desc-full_T2starmap.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_T2starmap.nii.gz'))
         assert len(img.shape) == 3
-        img = nib.load(op.join(out_dir, 'desc-full_S0map.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_S0map.nii.gz'))
         assert len(img.shape) == 3
         img = nib.load(op.join(out_dir, 'desc-optcom_bold.nii.gz'))
         assert len(img.shape) == 4
@@ -57,9 +57,9 @@ def test_basic_t2smap2(self):
         assert len(img.shape) == 4
         img = nib.load(op.join(out_dir, 'S0map.nii.gz'))
         assert len(img.shape) == 4
-        img = nib.load(op.join(out_dir, 'desc-full_T2starmap.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_T2starmap.nii.gz'))
         assert len(img.shape) == 4
-        img = nib.load(op.join(out_dir, 'desc-full_S0map.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_S0map.nii.gz'))
         assert len(img.shape) == 4
         img = nib.load(op.join(out_dir, 'desc-optcom_bold.nii.gz'))
         assert len(img.shape) == 4
@@ -83,9 +83,9 @@ def test_basic_t2smap3(self):
         assert len(img.shape) == 3
         img = nib.load(op.join(out_dir, 'S0map.nii.gz'))
         assert len(img.shape) == 3
-        img = nib.load(op.join(out_dir, 'desc-full_T2starmap.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_T2starmap.nii.gz'))
         assert len(img.shape) == 3
-        img = nib.load(op.join(out_dir, 'desc-full_S0map.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_S0map.nii.gz'))
         assert len(img.shape) == 3
         img = nib.load(op.join(out_dir, 'desc-optcom_bold.nii.gz'))
         assert len(img.shape) == 4
@@ -109,9 +109,9 @@ def test_basic_t2smap4(self):
         assert len(img.shape) == 4
         img = nib.load(op.join(out_dir, 'S0map.nii.gz'))
         assert len(img.shape) == 4
-        img = nib.load(op.join(out_dir, 'desc-full_T2starmap.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_T2starmap.nii.gz'))
         assert len(img.shape) == 4
-        img = nib.load(op.join(out_dir, 'desc-full_S0map.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_S0map.nii.gz'))
         assert len(img.shape) == 4
         img = nib.load(op.join(out_dir, 'desc-optcom_bold.nii.gz'))
         assert len(img.shape) == 4
@@ -135,9 +135,9 @@ def test_t2smap_cli(self):
         assert len(img.shape) == 3
         img = nib.load(op.join(out_dir, 'S0map.nii.gz'))
         assert len(img.shape) == 3
-        img = nib.load(op.join(out_dir, 'desc-full_T2starmap.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_T2starmap.nii.gz'))
         assert len(img.shape) == 3
-        img = nib.load(op.join(out_dir, 'desc-full_S0map.nii.gz'))
+        img = nib.load(op.join(out_dir, 'desc-limited_S0map.nii.gz'))
         assert len(img.shape) == 3
         img = nib.load(op.join(out_dir, 'desc-optcom_bold.nii.gz'))
         assert len(img.shape) == 4

tedana/workflows/t2smap.py

@@ -171,21 +171,27 @@ def t2smap_workflow(data, tes, out_dir='.', mask=None,
     -----
     This workflow writes out several files, which are described below:
 
-    ==========================    =================================================
+    ============================= =================================================
     Filename                      Content
-    ==========================    =================================================
-    T2starmap.nii.gz              Limited estimated T2* 3D map or 4D timeseries.
+    ============================= =================================================
+    T2starmap.nii.gz              Estimated T2* 3D map or 4D timeseries.
                                   Will be a 3D map if ``fitmode`` is 'all' and a
                                   4D timeseries if it is 'ts'.
-    S0map.nii.gz                  Limited S0 3D map or 4D timeseries.
-    desc-full_T2starmap.nii.gz    Full T2* map/timeseries. The difference between
+    S0map.nii.gz                  S0 3D map or 4D timeseries.
+    desc-limited_T2starmap.nii.gz Limited T2* map/timeseries. The difference between
                                   the limited and full maps is that, for voxels
                                   affected by dropout where only one echo contains
-                                  good data, the full map uses the single echo's
-                                  value while the limited map has a NaN.
-    desc-full_S0map.nii.gz        Full S0 map/timeseries.
+                                  good data, the full map uses the T2* estimate
+                                  from the first two echos, while the limited map
+                                  will have a NaN.
+    desc-limited_S0map.nii.gz     Limited S0 map/timeseries. The difference between
+                                  the limited and full maps is that, for voxels
+                                  affected by dropout where only one echo contains
+                                  good data, the full map uses the S0 estimate
+                                  from the first two echos, while the limited map
+                                  will have a NaN.
     desc-optcom_bold.nii.gz       Optimally combined timeseries.
-    ==========================    =================================================
+    ============================= =================================================
     """
     out_dir = op.abspath(out_dir)
     if not op.isdir(out_dir):
@@ -234,36 +240,36 @@ def t2smap_workflow(data, tes, out_dir='.', mask=None,
 
     # set a hard cap for the T2* map/timeseries
     # anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
-    cap_t2s = stats.scoreatpercentile(t2s_limited.flatten(), 99.5,
+    cap_t2s = stats.scoreatpercentile(t2s_full.flatten(), 99.5,
                                       interpolation_method='lower')
     cap_t2s_sec = utils.millisec2sec(cap_t2s * 10.)
     LGR.debug('Setting cap on T2* map at {:.5f}s'.format(cap_t2s_sec))
-    t2s_limited[t2s_limited > cap_t2s * 10] = cap_t2s
+    t2s_full[t2s_full > cap_t2s * 10] = cap_t2s
 
     LGR.info('Computing optimal combination')
     # optimally combine data
     OCcatd = combine.make_optcom(catd, tes, masksum, t2s=t2s_full,
                                  combmode=combmode)
 
     # clean up numerical errors
-    for arr in (OCcatd, s0_limited, t2s_limited):
+    for arr in (OCcatd, s0_full, t2s_full):
         np.nan_to_num(arr, copy=False)
 
-    s0_limited[s0_limited < 0] = 0
-    t2s_limited[t2s_limited < 0] = 0
+    s0_full[s0_full < 0] = 0
+    t2s_full[t2s_full < 0] = 0
 
     io_generator.save_file(
-        utils.millisec2sec(t2s_limited),
+        utils.millisec2sec(t2s_full),
         't2star img',
     )
-    io_generator.save_file(s0_limited, 's0 img')
+    io_generator.save_file(s0_full, 's0 img')
     io_generator.save_file(
-        utils.millisec2sec(t2s_full),
-        'full t2star img',
+        utils.millisec2sec(t2s_limited),
+        'limited t2star img',
     )
     io_generator.save_file(
-        s0_full,
-        'full s0 img',
+        s0_limited,
+        'limited s0 img',
     )
     io_generator.save_file(OCcatd, 'combined img')