diff --git a/mne/gui/_ieeg_locate_gui.py b/mne/gui/_ieeg_locate_gui.py
index 001b7ff9c82..d6789f586d8 100644
--- a/mne/gui/_ieeg_locate_gui.py
+++ b/mne/gui/_ieeg_locate_gui.py
@@ -63,24 +63,27 @@ def _load_image(img, name, verbose=True):
             logger.info(f'Loading {img}')
         _check_fname(img, overwrite='read', must_exist=True, name=name)
         img = nib.load(img)
-    # get data reoriented to RAS and transform
-    orig_data = np.array(img.dataobj)
+    # get data
+    orig_data = np.array(img.dataobj).astype(np.float32)
+    # reorient data to RAS
+    ornt = nib.orientations.axcodes2ornt(
+        nib.orientations.aff2axcodes(img.affine)).astype(int)
+    ras_ornt = nib.orientations.axcodes2ornt('RAS')
+    ornt_trans = nib.orientations.ornt_transform(ornt, ras_ornt)
+    img_data = nib.orientations.apply_orientation(orig_data, ornt_trans)
     orig_mgh = nib.MGHImage(orig_data, img.affine)
-    vox_ras_t = orig_mgh.header.get_vox2ras_tkr()
-    canonical_img = nib.as_closest_canonical(orig_mgh)
-    # canonical voxels->real world->original voxels
-    canonical_ras_t = canonical_img.affine
-    canonical_orig_t = np.dot(canonical_ras_t, np.linalg.inv(orig_mgh.affine))
-    canonical_fs_t = np.dot(canonical_orig_t, vox_ras_t)
-    img_data = np.array(canonical_img.dataobj)
-    return img_data, canonical_fs_t
+    aff_trans = nib.orientations.inv_ornt_aff(ornt_trans, img.shape)
+    vox_ras_t = np.dot(orig_mgh.header.get_vox2ras_tkr(), aff_trans)
+    return img_data, vox_ras_t
 
 
 class ComboBox(QComboBox):
     """Dropdown menu that emits a click when popped up."""
+
     clicked = QtCore.pyqtSignal()
 
     def showPopup(self):
+        """Override show popup method to emit click."""
         self.clicked.emit()
         super(ComboBox, self).showPopup()
 
@@ -163,7 +166,7 @@ def _load_image_data(self, ct):
         """Get MRI and CT data to display and transforms to/from vox/RAS."""
         self._mri_data, self._vox_ras_t = _load_image(
             op.join(self._subject_dir, 'mri', 'brain.mgz'),
-            'MRI Image', reorient=True, verbose=self._verbose)
+            'MRI Image', verbose=self._verbose)
         self._ras_vox_t = np.linalg.inv(self._vox_ras_t)
         self._brain_on = True
 
@@ -173,7 +176,7 @@ def _load_image_data(self, ct):
                             [0, self._voxel_sizes[0], 0, self._voxel_sizes[1]]]
 
         # ready ct
-        self._ct_data, vox_ras_t = _load_image(ct, 'CT', reorient=True)
+        self._ct_data, vox_ras_t = _load_image(ct, 'CT', verbose=self._verbose)
         if self._mri_data.shape != self._ct_data.shape or \
                 not np.allclose(self._vox_ras_t, vox_ras_t, rtol=1e-6):
             raise ValueError('CT is not aligned to MRI, got '
@@ -227,6 +230,7 @@ def color_ch_radius(ch_image, xf, yf, group, radius):
     def _load_ch_coords(self, info):
         """Load previously determined electrode contact locations."""
         ch_coords = np.array([ch['loc'][:3] for ch in info['chs']])
+        head_mri_t = invert_transform(self._mri_head_t)
         chs = dict()
         for idx, ch_coord in enumerate(ch_coords):
             ch_name = info['chs'][idx]['ch_name']
@@ -249,8 +253,7 @@ def _load_ch_coords(self, info):
                     'this is to use saved locations from this GUI or you may '
                     ' want to remove previous channel locations with '
                     '`inst.set_montage(None)` **will erase previous data**.')
-            chs[ch_name] = apply_trans(
-                invert_transform(self._mri_head_t), ch_coord)
+            chs[ch_name] = apply_trans(head_mri_t, ch_coord) * 1000  # mm -> m
         return chs
 
     def _save_ch_coords(self):
@@ -279,7 +282,7 @@ def _plot_images(self):
         ct_min, ct_max = np.nanmin(self._ct_data), np.nanmax(self._ct_data)
         self._ct_cutoff = int(round(np.quantile(
             self._ct_data[~np.isnan(self._ct_data)], _CT_MIN_DEFAULT))) \
-            if self._brain_on else ct_min  # cut off by default if brain is on
+            if self._brain_on else int(round(ct_min))  # cut off if brain is on
         ct_cmap = 'autumn_r' if self._brain_on else 'gray'
         for axis in range(3):
             self._plt._axes[axis].clear()