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Recover from a messy rebase.
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@arokem
arokem committed Feb 7, 2024
1 parent ca9f483 commit caf966c
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142 changes: 81 additions & 61 deletions mriqc/qc/diffusion.py
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Expand Up @@ -22,9 +22,6 @@
# https://www.nipreps.org/community/licensing/
#

import numpy as np


"""
Image quality metrics for diffusion MRI data
============================================
Expand All @@ -35,6 +32,13 @@
from dipy.core.gradients import GradientTable
from dipy.reconst.dti import TensorModel
from dipy.denoise.noise_estimate import piesno
from dipy.core.gradients import unique_bvals_magnitude
from dipy.core.gradients import round_bvals
from dipy.segment.mask import segment_from_cfa
from dipy.segment.mask import bounding_box

def noise_func(img, gtab):
pass

def noise_b0(data, gtab, mask=None):
"""
Expand Down Expand Up @@ -83,7 +87,7 @@ def cc_snr(data, gtab, bmag=None, mask=None):
gtab : GradientTable class instance or tuple
bmag : int
From dipy.core.gradients:
From dipy.core.gradients:
The order of magnitude that the bvalues have to differ to be
considered an unique b-value. B-values are also rounded up to
this order of magnitude. Default: derive this value from the
Expand Down Expand Up @@ -122,7 +126,7 @@ def cc_snr(data, gtab, bmag=None, mask=None):

b0_data = data[..., gtab.b0s_mask]
std_signal = np.std(b0_data[mask_cc_part], axis=-1)

# Per-shell calculation
rounded_bvals = round_bvals(gtab.bvals, bmag)
bvals = unique_bvals_magnitude(gtab.bvals, bmag)
Expand All @@ -139,7 +143,7 @@ def cc_snr(data, gtab, bmag=None, mask=None):

bval_data = data[..., rounded_bvals == bval]
bval_bvecs = gtab.bvecs[rounded_bvals == bval]

axis_X = np.argmin(np.sum((bval_bvecs-np.array([1, 0, 0]))**2, axis=-1))
axis_Y = np.argmin(np.sum((bval_bvecs-np.array([0, 1, 0]))**2, axis=-1))
axis_Z = np.argmin(np.sum((bval_bvecs-np.array([0, 0, 1]))**2, axis=-1))
Expand All @@ -162,83 +166,99 @@ def get_spike_mask(data, z_threshold=3, grouping_vals=None, bmag=None):
"""
Return binary mask of spike/no spike
def noise_func(img, gtab):
pass
def noise_b0(data, gtab, mask=None):
"""
Estimate noise in raw dMRI based on b0 variance.

Parameters
----------
"""
if mask is None:
mask = np.ones(data.shape[:3], dtype=bool)
b0 = data[..., ~gtab.b0s_mask]
return np.percentile(np.var(b0[mask], -1), (25, 50, 75))
data : numpy array
Data to be thresholded
z_threshold : :obj:`float`
Number of standard deviations above the mean to use as spike threshold
grouping_vals : numpy array
Values by which to group data for thresholding (bvals or full mask)
bmag : int
From dipy.core.gradients:
The order of magnitude that the bvalues have to differ to be
considered an unique b-value. B-values are also rounded up to
this order of magnitude. Default: derive this value from the
maximal b-value provided: $bmag=log_{10}(max(bvals)) - 1$.
def noise_piesno(data, n_channels=4):
Returns
---------
numpy array
"""
Estimate noise in raw dMRI data using the PIESNO [1]_ algorithm.

if grouping_vals is None:
threshold = (z_threshold*np.std(data)) + np.mean(data)
spike_mask = data > threshold
return spike_mask

Parameters
----------
threshold_mask = np.zeros(data.shape)

Returns
-------
rounded_grouping_vals = round_bvals(grouping_vals, bmag)
gvals = unique_bvals_magnitude(grouping_vals, bmag)

if grouping_vals.shape == data.shape:
for gval in gvals:
gval_data = data[rounded_grouping_vals == gval]
gval_threshold = (z_threshold*np.std(gval_data)) + np.mean(gval_data)
threshold_mask[rounded_grouping_vals == gval] = gval_threshold*np.ones(gval_data.shape)
else:
for gval in gvals:
gval_data = data[..., rounded_grouping_vals == gval]
gval_threshold = (z_threshold*np.std(gval_data)) + np.mean(gval_data)
threshold_mask[..., rounded_grouping_vals == gval] = gval_threshold*np.ones(gval_data.shape)

Notes
-----
spike_mask = data > threshold_mask

.. [1] Koay C.G., E. Ozarslan, C. Pierpaoli. Probabilistic Identification
and Estimation of Noise (PIESNO): A self-consistent approach and
its applications in MRI. JMR, 199(1):94-103, 2009.
"""
sigma, mask = piesno(data, N=n_channels, return_mask=True)
return sigma, mask
return spike_mask


def cc_snr(data, gtab):
def get_slice_spike_percentage(data, z_threshold=3, slice_threshold=.05):
"""
Calculate worse-/best-case signal-to-noise ratio in the corpus callosum
Return percentage of slices spiking along each dimension
Parameters
----------
data : ndarray

gtab : GradientTable class instance or tuple
data : numpy array
Data to be thresholded
z_threshold : :obj:`float`
Number of standard deviations above the mean to use as spike threshold
slice_threshold : :obj:`float`
Percentage of slice elements that need to be above spike threshold for slice to be considered spiking
Returns
---------
array
"""
if isinstance(gtab, GradientTable):
pass
spike_mask = get_spike_mask(data, z_threshold)

# XXX Per-shell calculation
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)
ndim = data.ndim
slice_spike_percentage = np.zeros(ndim)

from dipy.segment.mask import segment_from_cfa
from dipy.segment.mask import bounding_box
for ii in range(ndim):
slice_spike_percentage[ii] = np.mean(np.mean(spike_mask, ii) > slice_threshold)

threshold = (0.6, 1, 0, 0.1, 0, 0.1)
CC_box = np.zeros_like(data[..., 0])
return slice_spike_percentage

mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff

CC_box[bounds_min[0]:bounds_max[0],
bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
def get_global_spike_percentage(data, z_threshold=3):
"""
Return percentage of array elements spiking
mask_cc_part, cfa = segment_from_cfa(tensorfit, CC_box, threshold,
return_cfa=True)
Parameters
----------
data : numpy array
Data to be thresholded
z_threshold : :obj:`float`
Number of standard deviations above the mean to use as spike threshold
Returns
---------
float
"""
spike_mask = get_spike_mask(data, z_threshold)
global_spike_percentage = np.mean(np.ravel(spike_mask))

return global_spike_percentage

mean_signal = np.mean(data[mask_cc_part], axis=0)
def noise_func_for_shelled_data(shelled_data, gtab):
pass
38 changes: 1 addition & 37 deletions mriqc/qc/tests/test_diffusion.py
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Expand Up @@ -20,24 +20,14 @@
#
# https://www.nipreps.org/community/licensing/
#

import pytest
import os.path as op
import numpy as np
import nibabel as nib
from dipy.core.gradients import gradient_table
from dipy.data.fetcher import fetch_sherbrooke_3shell
from dipy.core.gradients import unique_bvals_magnitude, round_bvals
import os.path as op
from ..diffusion import noise_func, get_spike_mask, get_slice_spike_percentage, get_global_spike_percentage
from ..diffusion import noise_b0, noise_piesno

import numpy as np



import numpy as np
from mriqc.mriqc.qc import get_spike_mask, get_slice_spike_percentage, get_global_spike_percentage


class DiffusionData(object):
Expand Down Expand Up @@ -70,14 +60,6 @@ def shelled_data(self):
def ddata():
return DiffusionData()

def test_get_spike_mask(ddata):
img, gtab = ddata.get_fdata()
spike_mask = get_spike_mask(img, 2)

assert np.min(np.ravel(spike_mask)) == 0
assert np.max(np.ravel(spike_mask)) == 1
assert spike_mask.shape == img.shape


def test_noise_function(ddata):
img, gtab = ddata.get_fdata()
Expand Down Expand Up @@ -110,24 +92,6 @@ def test_get_global_spike_percentage(ddata):
assert global_spike_percentage <= 1


def test_get_global_spike_percentage(ddata):
img, gtab = ddata.get_fdata()
global_spike_percentage = get_global_spike_percentage(img, 2)

assert global_spike_percentage >= 0
assert global_spike_percentage <= 1


def test_with_shelled_data(ddata):
shelled_data, gtab = ddata.shelled_data()
noise_func_for_shelled_data(shelled_data, gtab)


def test_noise_b0(ddata):
data, gtab = ddata.get_data()
noise_b0(data, gtab)


def test_noise_piesno(ddata):
data, gtab = ddata.get_data()
noise_piesno(data)
noise_func_for_shelled_data(shelled_data, gtab)

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