def test_roi_in_volume():
data_shape = (11, 11, 11, 64)
roi_center = np.array([5, 5, 5])
roi_radii = np.array([5, 5, 5])
roi_radii_out = _roi_in_volume(data_shape, roi_center, roi_radii)
npt.assert_array_equal(roi_radii_out, np.array([5, 5, 5]))
roi_radii = np.array([6, 6, 6])
roi_radii_out = _roi_in_volume(data_shape, roi_center, roi_radii)
npt.assert_array_equal(roi_radii_out, np.array([5, 5, 5]))
roi_center = np.array([4, 4, 4])
roi_radii = np.array([5, 5, 5])
roi_radii_out = _roi_in_volume(data_shape, roi_center, roi_radii)
npt.assert_array_equal(roi_radii_out, np.array([4, 4, 4]))
data_shape = (11, 11, 1, 64)
roi_center = np.array([5, 5, 0])
roi_radii = np.array([5, 5, 0])
roi_radii_out = _roi_in_volume(data_shape, roi_center, roi_radii)
npt.assert_array_equal(roi_radii_out, np.array([5, 5, 0]))
roi_center = np.array([2, 5, 0])
roi_radii = np.array([5, 10, 2])
roi_radii_out = _roi_in_volume(data_shape, roi_center, roi_radii)
npt.assert_array_equal(roi_radii_out, np.array([2, 5, 0]))
def cube_crop_data(data):
shape = np.array(data.shape[:3])
roi_center = shape // 2
roi_radii = _roi_in_volume(shape, roi_center, shape // 3)
roi_mask = _mask_from_roi(shape, roi_center, roi_radii)
return data * roi_mask
def mask_for_response_msmt(gtab,
data,
roi_center=None,
roi_radii=10,
wm_fa_thr=0.7,
gm_fa_thr=0.2,
csf_fa_thr=0.1,
gm_md_thr=0.0007,
csf_md_thr=0.002):
""" Computation of masks for multi-shell multi-tissue (msmt) response
function using FA and MD.
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data (4D)
roi_center : array-like, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radii : int or array-like, (3,)
radii of cuboid ROI
wm_fa_thr : float
FA threshold for WM.
gm_fa_thr : float
FA threshold for GM.
csf_fa_thr : float
FA threshold for CSF.
gm_md_thr : float
MD threshold for GM.
csf_md_thr : float
MD threshold for CSF.
Returns
-------
mask_wm : ndarray
Mask of voxels within the ROI and with FA above the FA threshold
for WM.
mask_gm : ndarray
Mask of voxels within the ROI and with FA below the FA threshold
for GM and with MD below the MD threshold for GM.
mask_csf : ndarray
Mask of voxels within the ROI and with FA below the FA threshold
for CSF and with MD below the MD threshold for CSF.
Notes
-----
In msmt-CSD there is an important pre-processing step: the estimation of
every tissue's response function. In order to do this, we look for voxels
corresponding to WM, GM and CSF. This function aims to accomplish that by
returning a mask of voxels within a ROI and who respect some threshold
constraints, for each tissue. More precisely, the WM mask must have a FA
value above a given threshold. The GM mask and CSF mask must have a FA
below given thresholds and a MD below other thresholds. To get the FA and
MD, we need to fit a Tensor model to the datasets.
"""
if len(data.shape) < 4:
msg = """Data must be 4D (3D image + directions). To use a 2D image,
please reshape it into a (N, N, 1, ndirs) array."""
raise ValueError(msg)
if isinstance(roi_radii, numbers.Number):
roi_radii = (roi_radii, roi_radii, roi_radii)
if roi_center is None:
roi_center = np.array(data.shape[:3]) // 2
roi_radii = _roi_in_volume(data.shape, np.asarray(roi_center),
np.asarray(roi_radii))
roi_mask = _mask_from_roi(data.shape[:3], roi_center, roi_radii)
list_bvals = unique_bvals_tolerance(gtab.bvals)
if not np.all(list_bvals <= 1200):
msg_bvals = """Some b-values are higher than 1200.
The DTI fit might be affected."""
warnings.warn(msg_bvals, UserWarning)
ten = TensorModel(gtab)
tenfit = ten.fit(data, mask=roi_mask)
fa = fractional_anisotropy(tenfit.evals)
fa[np.isnan(fa)] = 0
md = mean_diffusivity(tenfit.evals)
md[np.isnan(md)] = 0
mask_wm = np.zeros(fa.shape, dtype=np.int64)
mask_wm[fa > wm_fa_thr] = 1
mask_wm *= roi_mask
md_mask_gm = np.zeros(md.shape, dtype=np.int64)
md_mask_gm[(md < gm_md_thr)] = 1
fa_mask_gm = np.zeros(fa.shape, dtype=np.int64)
fa_mask_gm[(fa < gm_fa_thr) & (fa > 0)] = 1
mask_gm = md_mask_gm * fa_mask_gm
mask_gm *= roi_mask
md_mask_csf = np.zeros(md.shape, dtype=np.int64)
md_mask_csf[(md < csf_md_thr) & (md > 0)] = 1
fa_mask_csf = np.zeros(fa.shape, dtype=np.int64)
fa_mask_csf[(fa < csf_fa_thr) & (fa > 0)] = 1
mask_csf = md_mask_csf * fa_mask_csf
mask_csf *= roi_mask
msg = """No voxel with a {0} than {1} were found.
Try a larger roi or a {2} threshold for {3}."""
if np.sum(mask_wm) == 0:
msg_fa = msg.format('FA higher', str(wm_fa_thr), 'lower FA', 'WM')
warnings.warn(msg_fa, UserWarning)
if np.sum(mask_gm) == 0:
msg_fa = msg.format('FA lower', str(gm_fa_thr), 'higher FA', 'GM')
msg_md = msg.format('MD lower', str(gm_md_thr), 'higher MD', 'GM')
warnings.warn(msg_fa, UserWarning)
warnings.warn(msg_md, UserWarning)
if np.sum(mask_csf) == 0:
msg_fa = msg.format('FA lower', str(csf_fa_thr), 'higher FA', 'CSF')
msg_md = msg.format('MD lower', str(csf_md_thr), 'higher MD', 'CSF')
warnings.warn(msg_fa, UserWarning)
warnings.warn(msg_md, UserWarning)
return mask_wm, mask_gm, mask_csf
def mask_for_response_ssst(gtab, data, roi_center=None, roi_radii=10,
fa_thr=0.7):
""" Computation of mask for single-shell single-tissue (ssst) response
function using FA.
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data (4D)
roi_center : array-like, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radii : int or array-like, (3,)
radii of cuboid ROI
fa_thr : float
FA threshold
Returns
-------
mask : ndarray
Mask of voxels within the ROI and with FA above the FA threshold.
Notes
-----
In CSD there is an important pre-processing step: the estimation of the
fiber response function. In order to do this, we look for voxels with very
anisotropic configurations. This function aims to accomplish that by
returning a mask of voxels within a ROI, that have a FA value above a
given threshold. For example we can use a ROI (20x20x20) at
the center of the volume and store the signal values for the voxels with
FA values higher than 0.7 (see [1]_).
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2004. Direct estimation of the
fiber orientation density function from diffusion-weighted MRI
data using spherical deconvolution
"""
if len(data.shape) < 4:
msg = """Data must be 4D (3D image + directions). To use a 2D image,
please reshape it into a (N, N, 1, ndirs) array."""
raise ValueError(msg)
if isinstance(roi_radii, numbers.Number):
roi_radii = (roi_radii, roi_radii, roi_radii)
if roi_center is None:
roi_center = np.array(data.shape[:3]) // 2
roi_radii = _roi_in_volume(data.shape, np.asarray(roi_center),
np.asarray(roi_radii))
roi_mask = _mask_from_roi(data.shape[:3], roi_center, roi_radii)
ten = TensorModel(gtab)
tenfit = ten.fit(data, mask=roi_mask)
fa = fractional_anisotropy(tenfit.evals)
fa[np.isnan(fa)] = 0
mask = np.zeros(fa.shape, dtype=np.int64)
mask[fa > fa_thr] = 1
if np.sum(mask) == 0:
msg = """No voxel with a FA higher than {} were found.
Try a larger roi or a lower threshold.""".format(str(fa_thr))
warnings.warn(msg, UserWarning)
return mask
