Identify the last good echo in adaptive mask instead of sum of good echoes

tedana/tests/test_utils.py

@@ -81,72 +81,77 @@ def test_make_adaptive_mask():
     data = io.load_data(fnames, n_echos=len(tes))[0]
 
     # Just dropout method
-    mask, masksum = utils.make_adaptive_mask(
+    mask, adaptive_mask = utils.make_adaptive_mask(
         data,
         mask=mask_file,
         threshold=1,
         methods=["dropout"],
     )
 
-    assert mask.shape == masksum.shape == (64350,)
-    assert np.allclose(mask, (masksum >= 1).astype(bool))
+    assert mask.shape == adaptive_mask.shape == (64350,)
+    assert np.allclose(mask, (adaptive_mask >= 1).astype(bool))
     assert mask.sum() == 49376
-    vals, counts = np.unique(masksum, return_counts=True)
+    vals, counts = np.unique(adaptive_mask, return_counts=True)
     assert np.allclose(vals, np.array([0, 1, 2, 3]))
-    assert np.allclose(counts, np.array([14974, 3682, 5128, 40566]))
+    assert np.allclose(counts, np.array([14974, 1816, 4428, 43132]))
 
     # Just decay method
-    mask, masksum = utils.make_adaptive_mask(
+    mask, adaptive_mask = utils.make_adaptive_mask(
         data,
         mask=mask_file,
         threshold=1,
         methods=["decay"],
     )
 
-    assert mask.shape == masksum.shape == (64350,)
-    assert np.allclose(mask, (masksum >= 1).astype(bool))
+    assert mask.shape == adaptive_mask.shape == (64350,)
+    assert np.allclose(mask, (adaptive_mask >= 1).astype(bool))
     assert mask.sum() == 60985  # This method can't flag first echo as bad
-    vals, counts = np.unique(masksum, return_counts=True)
+    vals, counts = np.unique(adaptive_mask, return_counts=True)
     assert np.allclose(vals, np.array([0, 1, 2, 3]))
     assert np.allclose(counts, np.array([3365, 4365, 5971, 50649]))
 
     # Dropout and decay methods combined
-    mask, masksum = utils.make_adaptive_mask(
+    mask, adaptive_mask = utils.make_adaptive_mask(
         data,
         mask=mask_file,
         threshold=1,
         methods=["dropout", "decay"],
     )
 
-    assert mask.shape == masksum.shape == (64350,)
-    assert np.allclose(mask, (masksum >= 1).astype(bool))
+    assert mask.shape == adaptive_mask.shape == (64350,)
+    assert np.allclose(mask, (adaptive_mask >= 1).astype(bool))
     assert mask.sum() == 49376
-    vals, counts = np.unique(masksum, return_counts=True)
+    vals, counts = np.unique(adaptive_mask, return_counts=True)
     assert np.allclose(vals, np.array([0, 1, 2, 3]))
-    assert np.allclose(counts, np.array([14974, 4386, 5604, 39386]))
+    assert np.allclose(counts, np.array([14974, 3109, 6248, 40019]))
 
     # Adding "none" should have no effect
-    mask, masksum = utils.make_adaptive_mask(
+    mask, adaptive_mask = utils.make_adaptive_mask(
         data,
         mask=mask_file,
         threshold=1,
         methods=["dropout", "decay", "none"],
     )
 
-    assert mask.shape == masksum.shape == (64350,)
-    assert np.allclose(mask, (masksum >= 1).astype(bool))
+    assert mask.shape == adaptive_mask.shape == (64350,)
+    assert np.allclose(mask, (adaptive_mask >= 1).astype(bool))
     assert mask.sum() == 49376
-    vals, counts = np.unique(masksum, return_counts=True)
+    vals, counts = np.unique(adaptive_mask, return_counts=True)
     assert np.allclose(vals, np.array([0, 1, 2, 3]))
-    assert np.allclose(counts, np.array([14974, 4386, 5604, 39386]))
+    assert np.allclose(counts, np.array([14974, 3109, 6248, 40019]))
 
     # Just "none"
-    mask, masksum = utils.make_adaptive_mask(data, mask=mask_file, threshold=1, methods=["none"])
+    mask, adaptive_mask = utils.make_adaptive_mask(
+        data,
+        mask=mask_file,
+        threshold=1,
+        methods=["none"],
+    )
 
-    assert mask.shape == masksum.shape == (64350,)
-    assert np.allclose(mask, (masksum >= 1).astype(bool))
+    assert mask.shape == adaptive_mask.shape == (64350,)
+    assert np.allclose(mask, (adaptive_mask >= 1).astype(bool))
     assert mask.sum() == 60985
-    vals, counts = np.unique(masksum, return_counts=True)
+    vals, counts = np.unique(adaptive_mask, return_counts=True)
     assert np.allclose(vals, np.array([0, 1, 2, 3]))
     assert np.allclose(counts, np.array([3365, 1412, 1195, 58378]))
 

tedana/utils.py

@@ -62,8 +62,8 @@ def make_adaptive_mask(data, mask, threshold=1, methods=["dropout"]):
         Without a mask limiting the voxels to consider,
         the adaptive mask will generally select voxels outside the brain as exemplars.
     threshold : :obj:`int`, optional
-        Minimum echo count to retain in the mask. Default is 1, which is
-        equivalent not thresholding.
+        Minimum echo count to retain in the mask.
+        Default is 1, which is equivalent to not thresholding.
     methods : :obj:`list`, optional
         List of methods to use for adaptive mask generation. Default is ["dropout"].
         Valid methods are "decay", "dropout", and "none".
@@ -103,9 +103,12 @@ def make_adaptive_mask(data, mask, threshold=1, methods=["dropout"]):
         -   This is the threshold for "good" data.
         -   The 1/3 value is arbitrary.
         -   If there was more than one exemplar voxel, retain the the highest value for each echo.
-    d.  For each voxel, count the number of echoes that have a mean value greater than the
+    d.  For each voxel, identify the last echo with a mean value greater than the
         corresponding echo's threshold.
 
+        -   Preceding echoes (including ones with mean values less than the threshold)
+            are considered "good" data.
+
     Decay
 
     Determine the echo at which the signal stops decreasing for each voxel.
@@ -187,9 +190,17 @@ def make_adaptive_mask(data, mask, threshold=1, methods=["dropout"]):
 
         LGR.info("Echo-wise intensity thresholds for adaptive mask: %s", lthrs)
 
-        # determine samples where absolute value is greater than echo-specific thresholds
-        # and count # of echos that pass criterion
-        dropout_adaptive_mask = (np.abs(echo_means) > lthrs).sum(axis=-1)
+        # Find the last good echo for each voxel
+        dropout_adaptive_mask = np.zeros(n_samples, dtype=np.int16)
+        for i_voxel in range(n_samples):
+            echo_means_voxel = np.abs(echo_means[i_voxel, :])
+            echos_over_threshold = echo_means_voxel > lthrs
+            # Find the index of the last True element in a 1D boolean array
+            last_true_index = (
+                (np.where(echos_over_threshold)[0][-1] + 1) if np.any(echos_over_threshold) else 0
+            )
+            dropout_adaptive_mask[i_voxel] = last_true_index
+
         adaptive_masks.append(dropout_adaptive_mask)
 
     if "decay" in methods: