Merge pull request #223 from alexdaniel654/feature/t1_neg_value_warning

T1 Magnitude Correct Data Detection

ukat/mapping/t1.py

@@ -52,10 +52,36 @@ def __init__(self, pixel_array, ti, parameters=2, mask=None, tss=0,
         self.tss = tss
         self.tss_axis = tss_axis
 
-        if np.nanmin(pixel_array) < 0:
+        # Assume the data has been magnitude corrected if the first
+        # percentile of the first inversion time is negative.
+        if np.percentile(pixel_array[..., 0], 1) < 0:
             self.mag_corr = True
+            neg_percent = (np.sum(pixel_array[..., 0] < 0)
+                           / pixel_array[..., 0].size)
+            if neg_percent < 0.05:
+                warnings.warn('Fitting data to a magnitude corrected '
+                              'inversion recovery curve however, less than 5% '
+                              'of the data from the first inversion is '
+                              'negative. If you have performed magnitude '
+                              'correction ignore this warning, otherwise the '
+                              'negative values could be due to noise or '
+                              'preprocessing steps  such as EPI distortion '
+                              'correction and  registration.\n'
+                              f'Percentage of first inversion data that is '
+                              f'negative = {neg_percent:.2%}')
         else:
             self.mag_corr = False
+            if np.nanmin(pixel_array) < 0:
+                warnings.warn('Negative values found in data from the first '
+                              'inversion but as the first percentile is not '
+                              'negative, it is assumed these are negative '
+                              'due to noise or preprocessing steps such as '
+                              'EPI distortion correction and registration. '
+                              'As such the data will be fit to the modulus of '
+                              'the recovery curve.\n'
+                              f'Min value = {np.nanmin(pixel_array[..., 0])}\n'
+                              '1st percentile = '
+                              f'{np.percentile(pixel_array[..., 0], 1)}')
 
         if self.parameters == 2:
             if self.mag_corr:

ukat/mapping/tests/test_t1.py

@@ -262,6 +262,33 @@ def test_tss_valid_axis(self):
                         inversion_list=np.linspace(0, 2000, 10),
                         affine=self.affine, tss=1, tss_axis=2)
 
+    def test_mag_corr_warning(self):
+        # Test warning for small number of negative values thus assuming no
+        # magnitude correction has been performed
+
+        # Make the absolute of the signal into a 4D array
+        signal_array = np.tile(np.abs(self.correct_signal_two_param),
+                               (10, 10, 3, 1))
+        # Add a single negative value to the signal
+        signal_array[0, 0, 0, 0] = -1
+
+        with pytest.warns(UserWarning):
+            mapper = T1(signal_array, self.t, self.affine, multithread=False)
+
+        # Test warning for enough negative values to assume magnitude
+        # correction has been performed but still not that many negative values
+
+        # Make the of the signal into a 4D array
+        signal_array = np.tile(np.abs(self.correct_signal_two_param),
+                               (10, 10, 3, 1))
+        # Add a row of signals with negative values to the image
+        # 3.3% of first inversion is negative but 1st percentile is negative.
+        signal_array[:, 0, 0, :] = self.correct_signal_two_param
+
+        with pytest.warns(UserWarning):
+            mapper = T1(signal_array, self.t, self.affine, multithread=False)
+
+
     def test_molli_2p_warning(self):
         signal_array = np.tile(self.correct_signal_three_param, (10, 10, 3, 1))
         with pytest.warns(UserWarning):