[FIX] Add non-zero floor to T2* values (#585)

* Add floor function.

* Add test for _apply_t2s_floor.

tedana/decay.py

@@ -11,6 +11,35 @@
 RefLGR = logging.getLogger('REFERENCES')
 
 
+def _apply_t2s_floor(t2s, echo_times):
+    """
+    Apply a floor to T2* values to prevent zero division errors during
+    optimal combination.
+
+    Parameters
+    ----------
+    t2s : (S,) array_like
+        T2* estimates.
+    echo_times : (E,) array_like
+        Echo times in milliseconds.
+
+    Returns
+    -------
+    t2s_corrected : (S,) array_like
+        T2* estimates with very small, positive values replaced with a floor value.
+    """
+    t2s_corrected = t2s.copy()
+    echo_times = np.asarray(echo_times)
+    if echo_times.ndim == 1:
+        echo_times = echo_times[:, None]
+
+    eps = np.finfo(dtype=t2s.dtype).eps  # smallest value for datatype
+    temp_arr = np.exp(-echo_times / t2s)  # (E x V) array
+    bad_voxel_idx = np.any(temp_arr == 0, axis=0) & (t2s != 0)
+    t2s_corrected[bad_voxel_idx] = np.min(-echo_times) / np.log(eps)
+    return t2s_corrected
+
+
 def monoexponential(tes, s0, t2star):
     """
     Specifies a monoexponential model for use with scipy curve fitting
@@ -258,9 +287,11 @@ def fit_decay(data, tes, mask, adaptive_mask, fittype):
     t2s_limited[np.isinf(t2s_limited)] = 500.  # why 500?
     # let's get rid of negative values, but keep zeros where limited != full
     t2s_limited[(adaptive_mask_masked > 1) & (t2s_limited <= 0)] = 1.
+    t2s_limited = _apply_t2s_floor(t2s_limited, tes)
     s0_limited[np.isnan(s0_limited)] = 0.  # why 0?
     t2s_full[np.isinf(t2s_full)] = 500.  # why 500?
     t2s_full[t2s_full <= 0] = 1.  # let's get rid of negative values!
+    t2s_full = _apply_t2s_floor(t2s_full, tes)
     s0_full[np.isnan(s0_full)] = 0.  # why 0?
 
     t2s_limited = utils.unmask(t2s_limited, mask)

tedana/tests/test_decay.py

@@ -7,7 +7,7 @@
 import numpy as np
 import pytest
 
-from tedana import io, utils, decay as me
+from tedana import io, utils, combine, decay as me
 from tedana.tests.utils import get_test_data_path
 
 
@@ -58,6 +58,30 @@ def test_fit_decay_ts(testdata1):
     assert s0vG.ndim == 2
 
 
+def test__apply_t2s_floor():
+    """
+    _apply_t2s_floor applies a floor to T2* values to prevent a ZeroDivisionError during
+    optimal combination.
+    """
+    n_voxels, n_echos, n_trs = 100, 5, 25
+    echo_times = np.array([2, 23, 54, 75, 96])
+    me_data = np.random.random((n_voxels, n_echos, n_trs))
+    t2s = np.random.random((n_voxels)) * 1000
+    t2s[t2s < 1] = 1  # Crop at 1 ms to be safe
+    t2s[0] = 0.001
+
+    # First establish a failure
+    with pytest.raises(ZeroDivisionError):
+        _ = combine._combine_t2s(me_data, echo_times[None, :], t2s[:, None])
+
+    # Now correct the T2* map and get a successful result.
+    t2s_corrected = me._apply_t2s_floor(t2s, echo_times)
+    assert t2s_corrected[0] != t2s[0]  # First value should be corrected
+    assert np.array_equal(t2s_corrected[1:], t2s[1:])  # No other values should be corrected
+    combined = combine._combine_t2s(me_data, echo_times[None, :], t2s_corrected[:, None])
+    assert np.all(combined != 0)
+
+
 # SMOKE TESTS
 
 def test_smoke_fit_decay():