Cleaning reconst module

docs/source/modules/scilpy.reconst.rst

@@ -1,10 +1,42 @@
 scilpy.reconst package
 ======================
 
-scilpy.reconst.afd\_along\_streamlines module
+scilpy.reconst.aodf module
 ---------------------------------------------
 
-.. automodule:: scilpy.reconst.afd_along_streamlines
+.. automodule:: scilpy.reconst.aodf
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+scilpy.reconst.bingham module
+---------------------------------------------
+
+.. automodule:: scilpy.reconst.bingham
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+scilpy.reconst.divide module
+---------------------------------------------
+
+.. automodule:: scilpy.reconst.divide
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+scilpy.reconst.dwi module
+---------------------------------
+
+.. automodule:: scilpy.reconst.dwi
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+scilpy.reconst.fiber\_coherence module
+---------------------------------------------
+
+.. automodule:: scilpy.reconst.fiber_coherence
    :members:
    :undoc-members:
    :show-inheritance:
@@ -25,10 +57,18 @@ scilpy.reconst.frf module
    :undoc-members:
    :show-inheritance:
 
-scilpy.reconst.raw\_signal module
----------------------------------
+scilpy.reconst.mti module
+---------------------------------------------
+
+.. automodule:: scilpy.reconst.mti
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+scilpy.reconst.sh module
+---------------------------------------------
 
-.. automodule:: scilpy.reconst.raw_signal
+.. automodule:: scilpy.reconst.sh
    :members:
    :undoc-members:
    :show-inheritance:

scilpy/dwi/operations.py

@@ -0,0 +1,133 @@
+import logging
+import numpy as np
+
+
+def apply_bias_field(dwi_data, bias_field_data, mask_data):
+    """
+    ToDo: Explain formula why applying field = dividing?
+     + why we need to rescale after?
+
+    Parameters
+    ----------
+    dwi_data: np.ndarray
+        The 4D dwi data.
+    bias_field_data: np.ndarray
+        The 3D bias field.
+    mask_data: np.ndarray
+        The mask where to apply the bias field.
+
+    Returns
+    -------
+    dwi_data: np.ndarray
+        The modified 4D dwi_data.
+    """
+    nuc_dwi_data = np.divide(
+        dwi_data[mask_data],
+        bias_field_data[mask_data].reshape((len(mask_data[0]), 1)))
+
+    rescaled_nuc_data = _rescale_dwi(dwi_data[mask_data], nuc_dwi_data)
+    dwi_data[mask_data] = rescaled_nuc_data
+
+    return dwi_data
+
+
+def _rescale_intensity(val, slope, in_max, bc_max):
+    """
+    Rescale an intensity value given a scaling factor.
+    This scaling factor ensures that the intensity
+    range before and after correction is the same.
+
+    Parameters
+    ----------
+    val: float
+         Value to be scaled
+    scale: float
+         Scaling factor to be applied
+    in_max: float
+         Max possible value
+    bc_max: float
+         Max value in the bias correction value range
+
+    Returns
+    -------
+    rescaled_value: float
+         Bias field corrected value scaled by the slope
+         of the data
+    """
+
+    return in_max - slope * (bc_max - val)
+
+
+# https://github.com/stnava/ANTs/blob/master/Examples/N4BiasFieldCorrection.cxx
+def _rescale_dwi(in_data, bc_data):
+    """
+    Apply N4 Bias Field Correction to a DWI volume.
+    bc stands for bias correction. The code comes
+    from the C++ ANTS implmentation.
+
+    Parameters
+    ----------
+    in_data: ndarray (x, y, z, ndwi)
+         Input DWI volume 4-dimensional data.
+    bc_data: ndarray (x, y, z, ndwi)
+         Bias field correction volume estimated from ANTS
+         Copied for every dimension of the DWI 4-th dimension
+
+    Returns
+    -------
+    bc_data: ndarray (x, y, z, ndwi)
+         Bias field corrected DWI volume
+    """
+
+    in_min = np.amin(in_data)
+    in_max = np.amax(in_data)
+    bc_min = np.amin(bc_data)
+    bc_max = np.amax(bc_data)
+
+    slope = (in_max - in_min) / (bc_max - bc_min)
+
+    chunk = np.arange(0, len(in_data), 100000)
+    chunk = np.append(chunk, len(in_data))
+    for i in range(len(chunk)-1):
+        nz_bc_data = bc_data[chunk[i]:chunk[i+1]]
+        rescale_func = np.vectorize(_rescale_intensity, otypes=[np.float32])
+
+        rescaled_data = rescale_func(nz_bc_data, slope, in_max, bc_max)
+        bc_data[chunk[i]:chunk[i+1]] = rescaled_data
+
+    return bc_data
+
+
+def compute_dwi_attenuation(dwi_weights: np.ndarray, b0: np.ndarray):
+    """ Compute signal attenuation by dividing the dwi signal with the b0.
+
+    Parameters:
+    -----------
+    dwi_weights : np.ndarray of shape (X, Y, Z, #gradients)
+        Diffusion weighted images.
+    b0 : np.ndarray of shape (X, Y, Z)
+        B0 image.
+
+    Returns
+    -------
+    dwi_attenuation : np.ndarray
+        Signal attenuation (Diffusion weights normalized by the B0).
+    """
+    b0 = b0[..., None]  # Easier to work if it is a 4D array.
+
+    # Make sure that, in every voxels, weights are lower in the b0. Should
+    # always be the case, but with the noise we never know!
+    erroneous_voxels = np.any(dwi_weights > b0, axis=3)
+    nb_erroneous_voxels = np.sum(erroneous_voxels)
+    if nb_erroneous_voxels != 0:
+        logging.info("# of voxels where `dwi_signal > b0` in any direction: "
+                     "{}".format(nb_erroneous_voxels))
+        dwi_weights = np.minimum(dwi_weights, b0)
+
+    # Compute attenuation
+    dwi_attenuation = dwi_weights / b0
+
+    # Make sure we didn't divide by 0.
+    dwi_attenuation[np.logical_not(np.isfinite(dwi_attenuation))] = 0.
+
+    return dwi_attenuation

scilpy/dwi/utils.py

@@ -7,102 +7,6 @@
 from scilpy.image.utils import volume_iterator
 
 
-def apply_bias_field(dwi_data, bias_field_data, mask_data):
-    """
-    ToDo: Explain formula why applying field = dividing?
-     + why we need to rescale after?
-
-    Parameters
-    ----------
-    dwi_data: np.ndarray
-        The 4D dwi data.
-    bias_field_data: np.ndarray
-        The 3D bias field.
-    mask_data: np.ndarray
-        The mask where to apply the bias field.
-
-    Returns
-    -------
-    dwi_data: np.ndarray
-        The modified 4D dwi_data.
-    """
-    nuc_dwi_data = np.divide(
-        dwi_data[mask_data],
-        bias_field_data[mask_data].reshape((len(mask_data[0]), 1)))
-
-    rescaled_nuc_data = _rescale_dwi(dwi_data[mask_data], nuc_dwi_data)
-    dwi_data[mask_data] = rescaled_nuc_data
-
-    return dwi_data
-
-
-def _rescale_intensity(val, slope, in_max, bc_max):
-    """
-    Rescale an intensity value given a scaling factor.
-    This scaling factor ensures that the intensity
-    range before and after correction is the same.
-
-    Parameters
-    ----------
-    val: float
-         Value to be scaled
-    scale: float
-         Scaling factor to be applied
-    in_max: float
-         Max possible value
-    bc_max: float
-         Max value in the bias correction value range
-
-    Returns
-    -------
-    rescaled_value: float
-         Bias field corrected value scaled by the slope
-         of the data
-    """
-
-    return in_max - slope * (bc_max - val)
-
-
-# https://github.com/stnava/ANTs/blob/master/Examples/N4BiasFieldCorrection.cxx
-def _rescale_dwi(in_data, bc_data):
-    """
-    Apply N4 Bias Field Correction to a DWI volume.
-    bc stands for bias correction. The code comes
-    from the C++ ANTS implmentation.
-
-    Parameters
-    ----------
-    in_data: ndarray (x, y, z, ndwi)
-         Input DWI volume 4-dimensional data.
-    bc_data: ndarray (x, y, z, ndwi)
-         Bias field correction volume estimated from ANTS
-         Copied for every dimension of the DWI 4-th dimension
-
-    Returns
-    -------
-    bc_data: ndarray (x, y, z, ndwi)
-         Bias field corrected DWI volume
-    """
-
-    in_min = np.amin(in_data)
-    in_max = np.amax(in_data)
-    bc_min = np.amin(bc_data)
-    bc_max = np.amax(bc_data)
-
-    slope = (in_max - in_min) / (bc_max - bc_min)
-
-    chunk = np.arange(0, len(in_data), 100000)
-    chunk = np.append(chunk, len(in_data))
-    for i in range(len(chunk)-1):
-        nz_bc_data = bc_data[chunk[i]:chunk[i+1]]
-        rescale_func = np.vectorize(_rescale_intensity, otypes=[np.float32])
-
-        rescaled_data = rescale_func(nz_bc_data, slope, in_max, bc_max)
-        bc_data[chunk[i]:chunk[i+1]] = rescaled_data
-
-    return bc_data
-
-
 def extract_dwi_shell(dwi, bvals, bvecs, bvals_to_extract, tol=20,
                       block_size=None):
     """Extracts the DWI volumes that are on specific b-value shells. Many