Decide whether to use absolute or complex values.

mri_specific_operations/denoise_mp2rage.py

-"""This module provides methods to denoise mp2rage MRI.
+"""
+Provide methods to denoise mp2rage MRI.
 
-adapted from RobustCombination function from Jose Marques, 
+adapted from RobustCombination function from Jose Marques,
 https://github.com/JosePMarques/MP2RAGE-related-scripts
 
-Applications: 
+Applications:
 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099676
 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210803
 """
@@ -18,45 +19,74 @@ from .unite_mp2rage import unite_mp2rage_robust
 
 
 def quadratic_formula_positive(a, b, c):
+    """Give positive solution of quadratic formula."""
     return (-b + np.sqrt(b**2 - 4 * a * c)) / (2 * a)
 
 
 def quadratic_formula_negative(a, b, c):
+    """Give negative solution of quadratic formula."""
     return (-b - np.sqrt(b**2 - 4 * a * c)) / (2 * a)
 
 
-def combine_mp2rage_robust(uniform_filepath: Union[str, Path],
-                           inv1_fielpath: Union[str, Path],
-                           inv2_filepath: Union[str, Path],
-                           denoised_output_filepath: Union[str, Path],
-                           regularization_factor: float = 6) -> None:
-    """Suppress background noise inside and outside the skull.
-
-    Args:
-        uniform_filepath (Union[str, Path]): [description]
-        inv1_fielpath (Union[str, Path]): [description]
-        inv2_filepath (Union[str, Path]): [description]
-        denoised_output_filepath (Union[str, Path]): [description]
-        regularization_factor (float, optional): [description]. Defaults to 6.
-
-    Note:
-        usually the regularization factor shouldn't be greater then 10, but that
-        is not the ase when the image is bias field corrected, in which case the
-        noise estimated at the edge of the image might not be such a good measure
+def combine_mp2rage_robust(
+        uniform_filepath: Union[str, Path],
+        inv1_fielpath: Union[str, Path],
+        inv2_filepath: Union[str, Path],
+        denoised_output_filepath: Union[str, Path],
+        regularization_factor: float = 6,
+        value_mode: str = "absolute",
+) -> None:
+    """
+    Suppress background noise inside and outside the skull.
+
+    Note
+    ----
+        usually the regularization factor shouldn't be greater then 10, but
+        that is not the case when the image is bias field corrected, in which
+        case the noise estimated at the edge of the image might not be such a
+        good measure.
+
+    Parameters
+    ----------
+    uniform_filepath : Union[str, Path]
+        DESCRIPTION.
+    inv1_fielpath : Union[str, Path]
+        DESCRIPTION.
+    inv2_filepath : Union[str, Path]
+        DESCRIPTION.
+    denoised_output_filepath : Union[str, Path]
+        DESCRIPTION.
+    regularization_factor : float, optional
+        DESCRIPTION. The default is 6.
+    value_mode : str, optional
+        DESCRIPTION. The default is 'absolute'.
+
+    Returns
+    -------
+    None
+        DESCRIPTION.
+
     """
+    if value_mode not in ["absolute", "complex"]:
+        raise ValueError("Value mode has to be absolute or complex.")
+
     mp2rage_nii = nib.load(uniform_filepath)
     inv1_nii = nib.load(inv1_fielpath)
     inv2_nii = nib.load(inv2_filepath)
 
-    mp2rage_img = mp2rage_nii.get_fdata()
-    inv1_img = inv1_nii.get_fdata()
-    inv2_img = inv2_nii.get_fdata()
+    mp2rage_img = mp2rage_nii.dataobj[:]
+    if value_mode == "complex":
+        inv1_img = inv1_nii.dataobj[:]
+        inv2_img = inv2_nii.dataobj[:]
+    elif value_mode == "absolute":
+        inv1_img = np.abs(inv1_nii.dataobj[:])
+        inv2_img = np.abs(inv2_nii.dataobj[:])
 
     # rescale the range of features to scale the range[-.5, .5]
     if mp2rage_img.min() >= 0 and mp2rage_img.max() >= 0.51:
         # assumes that it is getting only positive values
         mp2rage_img = (mp2rage_img - mp2rage_img.max() / 2) / mp2rage_img.max()
-        integerformat = 1
+        integerformat = False
     elif mp2rage_img.min() < 0:
         # rescale min max
         min_mp2rage = np.min(mp2rage_img)
@@ -65,9 +95,9 @@ def combine_mp2rage_robust(uniform_filepath: Union[str, Path],
                                                      min_mp2rage) - .5
         inv1_img = inv1_img / (max_mp2rage - min_mp2rage) * 2
         inv2_img = inv2_img / (max_mp2rage - min_mp2rage) * 2
-        integerformat = 1
+        integerformat = False
     else:
-        integerformat = 0
+        integerformat = True
 
     # computes correct polarity to inv1 image
     inv1_img = np.sign(mp2rage_img) * inv1_img
@@ -101,7 +131,7 @@ def combine_mp2rage_robust(uniform_filepath: Union[str, Path],
     mp2rage_robust = unite_mp2rage_robust(inv1_estimated, inv2_img,
                                           noiselevel**2)
 
-    if integerformat == 0:
+    if integerformat:
         denoised_mp2rage_img = mp2rage_robust
     else:
         denoised_mp2rage_img = np.round(4095 * (mp2rage_robust + 0.5))

mri_specific_operations/unite_mp2rage.py

@@ -3,11 +3,13 @@
 import numpy as np
 
 
-def unite_mp2rage_robust(TI1_img: np.ndarray,
-                         TI2_img: np.ndarray,
-                         beta: float = 1e-5) -> np.ndarray:
+def unite_mp2rage_robust(
+        TI1_img: np.ndarray,
+        TI2_img: np.ndarray,
+        beta: float = 1e-5,
+) -> np.ndarray:
     """Combine the two images in a robust way.
-    
+
     See also:
         https://archive.ismrm.org/2013/0269.html
         https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099676#pone.0099676.e004
@@ -25,4 +27,4 @@ def unite_mp2rage(TI1_img: np.ndarray,
     mp2rage = np.real(np.conj(TI1_img) * TI2_img) / np.clip(
         sum_imgs, a_min=beta, a_max=np.max(sum_imgs))
 
-    return mp2rage
\ No newline at end of file
+    return mp2rage