def bstsvreg(sub):
# perform brainsuite and svreg processing
subdir = join('/big_disk/ajoshi/coding_ground/pvcthickness/HCP_data/', sub)
t1file = join('/data_disk/HCP_All', sub, 'T1w',
'T1w_acpc_dc_restore_brain.nii.gz')
if not isfile(t1file):
return
outt1 = join(subdir, 't1.nii.gz')
if isfile(join(subdir, 't1.roiwise.stats.txt')):
return
makedirs(subdir)
# compute 1mm downsampled image
system('flirt -in ' + t1file + ' -ref ' + t1file + ' -out ' + outt1 +
' -applyisoxfm 1 >/dev/null 2>&1')
# generate mask
msk = compute_background_mask(outt1)
msk.to_filename(join(subdir, 't1.mask.nii.gz'))
# make a copy of the original image
copyfile(outt1, join(subdir, 't1.bse.nii.gz'))
system(
'/big_disk/ajoshi/coding_ground/pvcthickness/cortical_extraction_nobse.sh '
+ join(subdir, 't1') + ' >/dev/null 2>&1')
system('/home/ajoshi/BrainSuite18a/svreg/bin/svreg.sh ' +
join(subdir, 't1') + ' -S >/dev/null 2>&1')
def load_fmri(filename, maskname='', sigma=3):
"""
Reads 4D fmri data.
Smooths using 3D Gaussian filter
Applies mask to data:
- If mask not provided, calculates binary mask
returns fmri data matrix (Time x Voxels)
"""
img = nib.load(filename)
print(img.shape)
rep_time = img.header.get_zooms()[-1]
img = image.smooth_img(img, sigma)
if maskname != '':
img_mask = nib.load(maskname)
else:
print('Mask not provided. Calculating mask ...')
img_mask = masking.compute_background_mask(img)
img = masking.apply_mask(img, img_mask)
print('Mask applied!')
print('Detrending data!')
img = signal.clean(img,
detrend=True,
high_pass=0.01,
standardize=False,
t_r=rep_time)
return img
def mask_background(self, apply=False):
"""Method to generate a mask matrix which does not consider blank pixels outside the brain area.
:param apply: {bool} whether the mask should directly be applied to extract the wanted pixels.
:return: mask as boolean ``numpy.array`` in the attribute :py:attr:`mask`. If ``apply=True``, data in the
attribute :py:attr:`data` is reshaped as well.
"""
self.mask = compute_background_mask(self.img)
if apply:
self.data = apply_mask(self.img, self.mask)
def set_mask(self, mask):
if mask == None or mask == "background":
try:
self.mask = masking.compute_background_mask(
self.nifti).get_data()
except:
self.mask = mask
warnings.warn(
"Unable to generate mask using selected method. This is expected behavior if you initialze a SearchLight object without data. A mask using the selected method will be created when you call run(). If this message appears when calling run(), please check your data."
)
elif hasattr(mask, "get_data"):
self.mask = mask.get_data()
else:
self.mask = mask
def test_compute_background_mask():
for value in (0, np.nan):
mean_image = value * np.ones((9, 9, 9))
mean_image[3:-3, 3:-3, 3:-3] = 1
mask = mean_image == 1
mean_image = Nifti1Image(mean_image, np.eye(4))
mask1 = compute_background_mask(mean_image, opening=False)
np.testing.assert_array_equal(get_data(mask1), mask.astype(np.int8))
# Check that we get a ValueError for incorrect shape
mean_image = np.ones((9, 9))
mean_image[3:-3, 3:-3] = 10
mean_image[5, 5] = 100
mean_image = Nifti1Image(mean_image, np.eye(4))
assert_raises(ValueError, compute_background_mask, mean_image)
# Check that we get a useful warning for empty masks
mean_image = np.zeros((9, 9, 9))
mean_image = Nifti1Image(mean_image, np.eye(4))
with warnings.catch_warnings(record=True) as w:
compute_background_mask(mean_image)
assert_equal(len(w), 1)
assert_true(isinstance(w[0].message, masking.MaskWarning))
def main(argv=None):
args = get_parser().parse_args(argv)
output_dir = op.join(args.dset, 'derivatives', args.deriv, args.sub,
args.ses)
os.makedirs(output_dir, exist_ok=True)
nii_files = glob(
op.join(args.dset, 'derivatives',
'3dTproject_denoise_acompcor_csfwm+12mo+0.35mm', args.sub,
args.ses, '*.nii.gz'))
for tmp_nii_file in nii_files:
print(tmp_nii_file)
imgs = nib.load(tmp_nii_file)
fsaverage = fetch_surf_fsaverage(mesh='fsaverage5')
mask = compute_background_mask(imgs)
surf_lh = surface.vol_to_surf(imgs,
fsaverage.pial_left,
radius=24,
interpolation='nearest',
kind='ball',
n_samples=None,
mask_img=mask)
surf_rh = surface.vol_to_surf(imgs,
fsaverage.pial_right,
radius=24,
interpolation='nearest',
kind='ball',
n_samples=None,
mask_img=mask)
time_series = np.transpose(np.vstack((surf_lh, surf_rh)))
correlation = ConnectivityMeasure(kind='correlation')
time_series = correlation.fit_transform([time_series])[0]
plotting.plot_matrix(time_series, figure=(10, 8))
plt.savefig(
op.join(
output_dir, '{0}-correlation_matrix.png'.format(
op.basename(tmp_nii_file).split('.')[0])))
plt.close()
with open(
op.join(
output_dir, '{0}-correlation_matrix.pkl'.format(
op.basename(tmp_nii_file).split('.')[0])), 'wb') as fo:
pickle.dump(time_series, fo)
def test_compute_background_mask():
for value in (0, np.nan):
mean_image = value * np.ones((9, 9, 9))
mean_image[3:-3, 3:-3, 3:-3] = 1
mask = mean_image == 1
mean_image = Nifti1Image(mean_image, np.eye(4))
mask1 = compute_background_mask(mean_image, opening=False)
np.testing.assert_array_equal(mask1.get_data(),
mask.astype(np.int8))
# Check that we get a ValueError for incorrect shape
mean_image = np.ones((9, 9))
mean_image[3:-3, 3:-3] = 10
mean_image[5, 5] = 100
mean_image = Nifti1Image(mean_image, np.eye(4))
assert_raises(ValueError, compute_background_mask, mean_image)
# Check that we get a useful warning for empty masks
mean_image = np.zeros((9, 9, 9))
mean_image = Nifti1Image(mean_image, np.eye(4))
with warnings.catch_warnings(record=True) as w:
compute_background_mask(mean_image)
assert_equal(len(w), 1)
assert_true(isinstance(w[0].message, masking.MaskWarning))
def estimateMask(im, st="background", logger=None):
"""
mask the wholehead image (if we don"t have one).
wrapper for NiLearn implementation
:param im: image
:param st: type of automatic extraction. epi for epi images,
background for all other.
:param logger: logger file
:return: mask
"""
from nilearn import masking
write_to_logger("Estimating masks...", logger)
if st == "epi":
mask = masking.compute_epi_mask(im)
else:
mask = masking.compute_background_mask(im)
return mask
def _resample_image(self, image_file, output_file, target_nii):
# Compute the background and extrapolate outside of the mask
log.info("Extrapolating %s", image_file)
niimg = nb.load(image_file)
affine = niimg.get_affine()
data = niimg.get_data().squeeze()
niimg = nb.Nifti1Image(data, affine, header=niimg.get_header())
bg_mask = compute_background_mask(niimg).get_data()
# Test if the image has been masked:
out_of_mask = data[np.logical_not(bg_mask)]
if np.all(np.isnan(out_of_mask)) or len(np.unique(out_of_mask)) == 1:
# Need to extrapolate
data = _extrapolate_out_mask(data.astype(np.float),
bg_mask, iterations=3)[0]
niimg = nb.Nifti1Image(data, affine, header=niimg.get_header())
del out_of_mask, bg_mask
log.info("Resampling %s", image_file)
resampled_nii = resample_img(
niimg, target_nii.get_affine(), target_nii.shape)
resampled_nii = nb.Nifti1Image(resampled_nii.get_data().squeeze(),
resampled_nii.get_affine(),
header=niimg.get_header())
if len(resampled_nii.shape) == 3:
resampled_nii.to_filename(output_file)
else:
# We have a 4D file
raise Exception('4D File.')
# assert len(resampled_nii.shape) == 4
# resampled_data = resampled_nii.get_data()
# affine = resampled_nii.get_affine()
# for index in range(resampled_nii.shape[-1]):
# # First save the files separately
# this_nii = nb.Nifti1Image(resampled_data[..., index], affine)
# this_id = int("%i%i" % (-row[1]['image_id'], index))
# this_file = os.path.join(output_dir, "%06d%s" % (this_id, ext))
# this_nii.to_filename(this_file)
# # Second, fix the dataframe
# out_df = out_df[out_df.image_id != row[1]['image_id']]
# this_row = row[1].copy()
# this_row.image_id = this_id
# out_df = out_df.append(this_row)
return output_file
def background_mask(self, calculate=True, apply=False, plot=True):
"""Compute the average background mask for all given brains.
:param calculate: {bool} do calculate or just use other options.
:param apply: {bool} if True, the mask is directly applied to cut down the brains in :py:attr:`data` and
reshape them into vectors
:param plot: {bool} whether the generated mask should be visualized in a plot. The first image in 4th
dimension is shown.
:return: mask matrix in the attribute :py:attr:`mask` and if `apply=True` data vectors in :py:attr:`data`.
"""
print("Computing background mask...")
if calculate:
self.mask = compute_background_mask(self.img)
if apply:
self.apply_mask()
if plot:
plot_roi(self.mask, Nifti1Image(self.img.dataobj[..., 0], self.img.affine))
print("\tBackground mask computed!")
def mp2rage_masked(filename_uni, filename_inv2, filename_output=None, threshold="70%"):
""" This is an alternativ approach to get rid of the unusual noise
distribution in the background of mp2rage images. It was inspired by
a suggestion on GitHub.com and works as follows:
1. thresholding the INV2 image
2. creating a background mask using nilearn's 'compute_background_mask'
3. applying that mask to clear the background from noise
Parameters
----------
filename_uni : string
path to the uniform T1-image (UNI)
filename_inv2 : string
path to the second inversion image (INV2)
filename_output : string (optional)
path to output image
threshold : float, string (optional)
absolute value (float) or percentage (string, e. g. '50%')
"""
# load data
image_uni = nib.load(filename_uni)
image_inv2 = nib.load(filename_inv2)
image_uni_fdata = image_uni.get_fdata()
image_inv2_fdata = image_inv2.get_fdata()
# scale UNI image values
if (np.amin(image_uni_fdata) >=0) and (np.amax(image_uni_fdata >= 0.51)):
scale = lambda x: (x - np.amax(image_uni_fdata)/2) / np.amax(image_uni_fdata)
image_uni_fdata = scale(image_uni_fdata)
image_mask = masking.compute_background_mask(image.threshold_img(image_inv2,threshold))
image_uni_fdata[image_mask.get_data()==0] = -.5
image_output = nib.Nifti1Image(image_uni_fdata, image_uni.affine, nib.Nifti1Header())
if filename_output:
nib.save(image_output, filename_output)
else:
return image_output
def download_and_resample(self, dest_dir, target,collection_ids=None,image_ids=None):
"""Downloads all stat maps and resamples them to a common space"""
target_nii = nb.load(target)
orig_path = os.path.join(dest_dir, "original")
mkdir_p(orig_path)
resampled_path = os.path.join(dest_dir, "resampled")
mkdir_p(resampled_path)
combined_df = self.get_images_with_collections_df()
# If the user has specified specific images
if image_ids:
combined_df = combined_df.loc[combined_df.image_id.isin(image_ids)]
# If the user wants to subset to a set of collections
if collection_ids:
if isinstance(collection_ids,str): collection_ids = [collection_ids]
combined_df = combined_df[combined_df['collection_id'].isin(collection_ids)]
out_df = combined_df.copy()
for row in combined_df.iterrows():
# Downloading the file to the "original" subfolder
_, _, ext = split_filename(row[1]['file'])
orig_file = os.path.join(orig_path, "%04d%s" % (row[1]['image_id'], ext))
if not os.path.exists(orig_file):
try:
print "Downloading %s" % orig_file
urllib.urlretrieve(row[1]['file'], orig_file)
# Compute the background and extrapolate outside of the mask
print "Extrapolating %s" % orig_file
niimg = nb.load(orig_file)
affine = niimg.get_affine()
data = niimg.get_data().squeeze()
niimg = nb.Nifti1Image(data, affine,header=niimg.get_header())
bg_mask = compute_background_mask(niimg).get_data()
# Test if the image has been masked:
out_of_mask = data[np.logical_not(bg_mask)]
if np.all(np.isnan(out_of_mask)) or len(np.unique(out_of_mask)) == 1:
# Need to extrapolate
data = _extrapolate_out_mask(data.astype(np.float), bg_mask,iterations=3)[0]
niimg = nb.Nifti1Image(data, affine,header=niimg.get_header())
del out_of_mask, bg_mask
# Resampling the file to target and saving the output in the "resampled" folder
resampled_file = os.path.join(resampled_path,"%06d%s" % (row[1]['image_id'], ext))
print "Resampling %s" % orig_file
resampled_nii = resample_img(niimg, target_nii.get_affine(),target_nii.shape)
resampled_nii = nb.Nifti1Image(resampled_nii.get_data().squeeze(),
resampled_nii.get_affine(),
header=niimg.get_header())
if len(resampled_nii.shape) == 3:
resampled_nii.to_filename(resampled_file)
else:
# We have a 4D file
assert len(resampled_nii.shape) == 4
resampled_data = resampled_nii.get_data()
affine = resampled_nii.get_affine()
for index in range(resampled_nii.shape[-1]):
# First save the files separately
this_nii = nb.Nifti1Image(resampled_data[..., index],affine)
this_id = int("%i%i" % (-row[1]['image_id'], index))
this_file = os.path.join(resampled_path,"%06d%s" % (this_id, ext))
this_nii.to_filename(this_file)
# Second, fix the dataframe
out_df = out_df[out_df.image_id != row[1]['image_id']]
this_row = row[1].copy()
this_row.image_id = this_id
out_df = out_df.append(this_row)
except:
print "Error downloading image id %s, retry this image." %(row[1]["image_id"])
return out_df
def neuropowerinput(request, neurovault_id=None, end_session=False):
'''step 2: input'''
# Create the session id for the user
sid = get_session_id(request)
# Get the template/step status
template = "neuropower/neuropowerinput.html"
steps = get_neuropower_steps(template, sid)
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="")
neurovault_id = request.GET.get('neurovault', '')
context = {"steps": steps}
# If the user has ended their session, give message
if end_session == True:
context["message"] = "Session has been successfully reset."
if neurovault_id:
neurovault_image = get_url(
"http://neurovault.org/api/images/%s/?format=json" %
(neurovault_id))
collection_id = str(neurovault_image['collection_id'])
neurovault_collection = get_url(
"http://neurovault.org/api/collections/%s/?format=json" %
(collection_id))
parsform = ParameterForm(
request.POST or None,
request.FILES or None,
default_url="",
err="",
initial={
"url": neurovault_image["file"],
"ZorT":
"T" if neurovault_image["map_type"] == "T map" else "Z",
"Subj": neurovault_collection["number_of_subjects"]
})
context["parsform"] = parsform
# fields = ['url','spmfile','maskfile','ZorT','Exc','Subj','Samples',
# 'alpha','Smoothx','Smoothy','Smoothz','Voxx','Voxy','Voxz']
return render(request, template, context)
if not request.method == "POST" or not parsform.is_valid():
context["parsform"] = parsform
return render(request, template, context)
else:
form = parsform.save(commit=False)
form.SID = sid
mapID = "%s_%s" % (str(sid), str(uuid.uuid4()))
form.mapID = mapID
peaktable = os.path.join(settings.MEDIA_ROOT, "peaktables",
"peaks_%s.csv" % (mapID))
form.peaktable = peaktable
form.save()
# handle data: copy to temporary location
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
mapID = "%s_%s" % (str(sid), str(uuid.uuid4()))
form.mapID = mapID
if not parsdata.url == "":
url = parsform.cleaned_data['url']
location = utils.create_temporary_copy(url,
mapID,
mask=False,
url=True)
elif not parsdata.spmfile == "":
spmfile = os.path.join(settings.MEDIA_ROOT, str(parsdata.spmfile))
location = utils.create_temporary_copy(spmfile,
mapID,
mask=False,
url=False)
form.location = location
form.save()
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
SPM = nib.load(parsdata.location)
# check if the IQR is realistic (= check whether these are Z- or T-values)
IQR = np.subtract(*np.percentile(SPM.get_data(), [75, 25]))
if IQR > 20:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="median")
context["parsform"] = parsform
return render(request, template, context)
# save other parameters
form.DoF = parsdata.Subj - 1 if parsdata.Samples == 1 else parsdata.Subj - 2
form.ExcZ = float(parsdata.Exc) if float(
parsdata.Exc) > 1 else -norm.ppf(float(parsdata.Exc))
# handle mask
if parsdata.maskfile == "":
mask = masking.compute_background_mask(SPM,
border_size=2,
opening=True)
nvox = np.sum(mask.get_data())
form.nvox = nvox
else:
maskfile = os.path.join(settings.MEDIA_ROOT,
str(parsdata.maskfile))
masklocation = utils.create_temporary_copy(maskfile,
mapID,
mask=True,
url=False)
mask = nib.load(masklocation).get_data()
# return error when dimensions are different
if SPM.get_data().shape != mask.shape:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="dim")
context["parsform"] = parsform
return render(request, template, context)
else:
SPM_masked = np.multiply(SPM.get_data(), mask)
SPM_nib = nib.Nifti1Image(SPM_masked, np.eye(4))
nib.save(SPM_nib, parsdata.location)
form.nvox = np.sum(mask)
form.save()
if parsdata.spmfile == "":
return HttpResponseRedirect('/neuropowerviewer/')
else:
return HttpResponseRedirect('/neuropowertable/')
def neuropowerinput(request, neurovault_id=None, end_session=False):
'''step 2: input'''
# Create the session id for the user
sid = get_session_id(request)
# get DB entry for sid
try:
neuropowerdata = NeuropowerModel.objects.get(SID=sid)
except NeuropowerModel.DoesNotExist:
neuropowerdata = None
# Get the template/step status
template = "neuropower/neuropowerinput.html"
context = {}
steps = get_neuropower_steps(template, sid)
context["steps"] = steps
# Initiate parameter form
parsform = ParameterForm(request.POST or None,
request.FILES or None,
instance=neuropowerdata,
default_url="URL to nifti image",
err="")
# Check if a message is passed
message = request.GET.get('message', '')
context['message'] = message
# Check if redirect from neurovault
neurovault_id = request.GET.get('neurovault', '')
if neurovault_id:
neurovault_data = get_neurovault_form(request, neurovault_id)
context['parsform'] = neurovault_data["parsform"]
if not neurovault_data["message"] == None:
context['message'] = neurovault_data["message"]
return render(request, template, context)
# Check if new user or if parameterform is invalid
if not request.method == "POST" or not parsform.is_valid():
context["parsform"] = parsform
return render(request, template, context)
else:
form = parsform.save(commit=False)
form.SID = sid
form.save()
# handle data: copy to local drive
neuropowerdata = NeuropowerModel.objects.get(SID=sid)
# create folder to save map local
if not os.path.exists("/var/maps/"):
os.mkdir("/var/maps/")
# make local copies of map and mask
map_local = "/var/maps/" + sid + "_map"
mask_local = "/var/maps/" + sid + "_mask"
if not neuropowerdata.map_url == "":
map_url = neuropowerdata.map_url
else:
map_url = "https://" + settings.AWS_S3_CUSTOM_DOMAIN + str(
neuropowerdata.spmfile.name)
map_local = create_local_copy(map_url, map_local)
if not neuropowerdata.maskfile == "":
mask_url = "https://" + settings.AWS_S3_CUSTOM_DOMAIN + str(
neuropowerdata.maskfile.name)
mask_local = create_local_copy(mask_url, mask_local)
# save map locations to database
form = parsform.save(commit=False)
form.map_url = map_url
form.map_local = map_local
if not neuropowerdata.maskfile == "":
form.mask_url = mask_url
form.mask_local = mask_local
else:
form.mask_local = mask_local
form.save()
# perform some higher level cleaning
error = None
neuropowerdata = NeuropowerModel.objects.get(SID=sid)
SPM = nib.load(neuropowerdata.map_local)
if len(SPM.shape) > 3:
if not SPM.shape[3] == 1 or len(SPM.shape) > 4:
error = "shape"
# check if the IQR is realistic (= check whether these are Z- or T-values)
IQR = np.subtract(*np.percentile(SPM.get_data(), [75, 25]))
if IQR > 20:
error = "median"
# save other parameters
form.DoF = neuropowerdata.Subj - 1 if neuropowerdata.Samples == 1 else neuropowerdata.Subj - 2
form.ExcZ = float(neuropowerdata.Exc) if float(
neuropowerdata.Exc) > 1 else -norm.ppf(float(neuropowerdata.Exc))
# if mask does not exist: create
if not error == 'shape':
if neuropowerdata.maskfile == "":
mask = masking.compute_background_mask(SPM,
border_size=2,
opening=True)
nvox = np.sum(mask.get_data())
form.mask_local = neuropowerdata.mask_local + ".nii.gz"
nib.save(mask, form.mask_local)
form.nvox = nvox
# if mask is given: check dimensions
else:
mask = nib.load(neuropowerdata.mask_local).get_data()
if SPM.get_data().shape != mask.shape:
error = "dim"
else:
form.nvox = np.sum(mask)
# throw error if detected
if error:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err=error)
context["parsform"] = parsform
return render(request, template, context)
else:
form.step = 1
form.save()
return HttpResponseRedirect('../neuropowertable/')
def neuropowerinput(request,neurovault_id=None,end_session=False):
'''step 2: input'''
# Create the session id for the user
sid = get_session_id(request)
# Get the template/step status
template = "neuropower/neuropowerinput.html"
steps = get_neuropower_steps(template,sid)
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="")
neurovault_id = request.GET.get('neurovault','')
context = {"steps":steps}
# If the user has ended their session, give message
if end_session == True:
context["message"] = "Session has been successfully reset."
if neurovault_id:
neurovault_image = get_url("http://neurovault.org/api/images/%s/?format=json" %(neurovault_id))
collection_id = str(neurovault_image['collection_id'])
neurovault_collection = get_url("http://neurovault.org/api/collections/%s/?format=json" %(collection_id))
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url = "",
err = "",
initial = {"url":neurovault_image["file"],
"ZorT":"T" if neurovault_image["map_type"] =="T map" else "Z",
"Subj":neurovault_collection["number_of_subjects"]})
context["parsform"] = parsform
# fields = ['url','spmfile','maskfile','ZorT','Exc','Subj','Samples',
# 'alpha','Smoothx','Smoothy','Smoothz','Voxx','Voxy','Voxz']
return render(request,template,context)
if not request.method=="POST" or not parsform.is_valid():
context["parsform"] = parsform
return render(request,template,context)
else:
form = parsform.save(commit=False)
form.SID = sid
mapID = "%s_%s" %(str(sid),str(uuid.uuid4()))
form.mapID = mapID
peaktable = os.path.join(settings.MEDIA_ROOT,"peaktables","peaks_%s.csv" %(mapID))
form.peaktable = peaktable
form.save()
# handle data: copy to temporary location
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
mapID = "%s_%s" %(str(sid),str(uuid.uuid4()))
form.mapID = mapID
if not parsdata.url == "":
url = parsform.cleaned_data['url']
location = utils.create_temporary_copy(url,mapID,mask=False,url=True)
elif not parsdata.spmfile == "":
spmfile = os.path.join(settings.MEDIA_ROOT,str(parsdata.spmfile))
location = utils.create_temporary_copy(spmfile,mapID,mask=False, url=False)
form.location = location
form.save()
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
SPM = nib.load(parsdata.location)
# check if the IQR is realistic (= check whether these are Z- or T-values)
IQR = np.subtract(*np.percentile(SPM.get_data(),[75,25]))
if IQR > 20:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url = "URL to nifti image",
err = "median")
context["parsform"] = parsform
return render(request,template,context)
# save other parameters
form.DoF = parsdata.Subj-1 if parsdata.Samples==1 else parsdata.Subj-2
form.ExcZ = float(parsdata.Exc) if float(parsdata.Exc)>1 else -norm.ppf(float(parsdata.Exc))
# handle mask
if parsdata.maskfile == "":
mask = masking.compute_background_mask(SPM,border_size=2, opening=True)
nvox = np.sum(mask.get_data())
form.nvox = nvox
else:
maskfile = os.path.join(settings.MEDIA_ROOT,str(parsdata.maskfile))
masklocation = utils.create_temporary_copy(maskfile,mapID,mask=True,url=False)
mask = nib.load(masklocation).get_data()
# return error when dimensions are different
if SPM.get_data().shape != mask.shape:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="dim")
context["parsform"] = parsform
return render(request,template,context)
else:
SPM_masked = np.multiply(SPM.get_data(),mask)
SPM_nib = nib.Nifti1Image(SPM_masked,np.eye(4))
nib.save(SPM_nib,parsdata.location)
form.nvox = np.sum(mask)
form.save()
if parsdata.spmfile == "":
return HttpResponseRedirect('/neuropowerviewer/')
else:
return HttpResponseRedirect('/neuropowertable/')
def download_images(dest_dir, images_df=None, target=None, resample=True):
"""Downloads images dataframe and resamples them to a common space"""
orig_path = os.path.join(dest_dir, "original")
mkdir_p(orig_path)
if resample == True:
if not target:
print("To resample you must specify a target!")
return
resampled_path = os.path.join(dest_dir, "resampled")
mkdir_p(resampled_path)
target_nii = nb.load(target)
if not isinstance(images_df, pandas.DataFrame):
images_df = get_images()
out_df = images_df.copy()
for row in images_df.iterrows():
# Downloading the file to the "original" subfolder
_, _, ext = split_filename(row[1]['file'])
orig_file = os.path.join(orig_path,
"%04d%s" % (row[1]['image_id'], ext))
if not os.path.exists(orig_file):
try:
print("Downloading %s" % orig_file)
urlretrieve(row[1]['file'], orig_file)
if resample == True:
# Compute the background and extrapolate outside of the mask
print("Extrapolating %s" % orig_file)
niimg = nb.load(orig_file)
affine = niimg.get_affine()
data = niimg.get_data().squeeze()
niimg = nb.Nifti1Image(data,
affine,
header=niimg.get_header())
bg_mask = compute_background_mask(niimg).get_data()
# Test if the image has been masked:
out_of_mask = data[np.logical_not(bg_mask)]
if np.all(np.isnan(out_of_mask)) or len(
np.unique(out_of_mask)) == 1:
# Need to extrapolate
data = _extrapolate_out_mask(data.astype(np.float),
bg_mask,
iterations=3)[0]
niimg = nb.Nifti1Image(data,
affine,
header=niimg.get_header())
del out_of_mask, bg_mask
# Resampling the file to target and saving the output in the "resampled" folder
resampled_file = os.path.join(
resampled_path, "%06d%s" % (row[1]['image_id'], ext))
print("Resampling %s" % orig_file)
resampled_nii = resample_img(niimg,
target_nii.get_affine(),
target_nii.shape)
resampled_nii = nb.Nifti1Image(
resampled_nii.get_data().squeeze(),
resampled_nii.get_affine(),
header=niimg.get_header())
if len(resampled_nii.shape) == 3:
resampled_nii.to_filename(resampled_file)
else:
# We have a 4D file
assert len(resampled_nii.shape) == 4
resampled_data = resampled_nii.get_data()
affine = resampled_nii.get_affine()
for index in range(resampled_nii.shape[-1]):
# First save the files separately
this_nii = nb.Nifti1Image(
resampled_data[..., index], affine)
this_id = int("%i%i" %
(-row[1]['image_id'], index))
this_file = os.path.join(resampled_path,
"%06d%s" % (this_id, ext))
this_nii.to_filename(this_file)
# Second, fix the dataframe
out_df = out_df[
out_df.image_id != row[1]['image_id']]
this_row = row[1].copy()
this_row.image_id = this_id
out_df = out_df.append(this_row)
except:
print("Error downloading image id %s, retry this image." %
(row[1]["image_id"]))
return out_df
def t1_pipeline(do_normalise_before=False,
do_segment=True,
do_normalise_after=False,
do_plot=True,
keep_tmp=True,
sub_name='sub-11',
sess_num='ses-17',
root_path='/neurospin/ibc'):
"""
Preprocess qMRI t1 images and then run estimation to generate t1-maps,
more details in scripts/qmri_README.md,
only one of do_normalise_before and do_normalise_after should be True,
both can be False
"""
DATA_LOC = join(root_path, 'sourcedata', sub_name, sess_num)
SAVE_TO = join(root_path, 'derivatives', sub_name, sess_num)
if do_normalise_before or do_normalise_after:
space = "MNI152"
else:
space = "individual"
if not exists(SAVE_TO):
makedirs(SAVE_TO)
start_time = time.time()
# data files
data_dir = DATA_LOC
niftis = []
jsons = []
for fi in listdir(join(data_dir, 'anat')):
if fi.split('_')[-1] == 'T1map.nii.gz':
niftis.append(join(data_dir, 'anat', fi))
elif fi.split('_')[-1] == 'T1map.json':
jsons.append(join(data_dir, 'anat', fi))
elif fi.split('_')[-1] == 'B1map.nii.gz':
b1_map_nifti = join(data_dir, 'anat', fi)
elif fi.split('_')[-1] == 'B1map.json':
b1_map_json = join(data_dir, 'anat', fi)
else:
continue
niftis.sort()
jsons.sort()
# preprocessing directory setup
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] copying necessary files...'.format(time_elapsed))
cwd = SAVE_TO
preproc_dir = join(cwd, 'tmp_t1', 'preproc')
if not exists(preproc_dir):
makedirs(preproc_dir)
# copy data files to tmp_t1 directory
cnt = 0
for nii, json in zip(niftis, jsons):
system('cp {} {}'.format(nii, preproc_dir))
niftis[cnt] = join(preproc_dir, nii.split(sep)[-1])
system('gunzip -df {}'.format(niftis[cnt]))
niftis[cnt] = join(preproc_dir,
nii.split(sep)[-1].split('.')[0] + '.nii')
system('cp {} {}'.format(json, preproc_dir))
jsons[cnt] = join(preproc_dir, json.split(sep)[-1])
cnt += 1
system('cp {} {}'.format(b1_map_nifti, preproc_dir))
b1_map_nifti = join(preproc_dir, b1_map_nifti.split(sep)[-1])
system('gunzip -df {}'.format(b1_map_nifti))
b1_map_nifti = join(preproc_dir,
b1_map_nifti.split(sep)[-1].split('.')[0] + '.nii')
system('cp {} {}'.format(b1_map_json, preproc_dir))
b1_map_json = join(preproc_dir, b1_map_json.split(sep)[-1])
# preprocessing step: spatial normalization to MNI space
# of T1 maps
if do_normalise_before:
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] segmenting highest flip angle image'.format(
time_elapsed))
image = niftis[-1]
out_info = segment(image, True)
# save normalised segments
segments = [
join(preproc_dir, f"w{out_info[segment].split('/')[-1]}")
for segment in ['gm', 'wm']
]
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] transforming images to MNI space...'.format(
time_elapsed))
normed_niftis = []
cnt = 0
for nii in niftis:
image = nii
if cnt == len(niftis) - 1:
b1_map = b1_map_nifti
out_info = to_MNI(image, data=out_info, func=b1_map)
normed_b1_map = out_info['func'][0]
else:
out_info = to_MNI(image, data=out_info)
normed_niftis.append(out_info['anat'])
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] \t transformed {}'.format(
time_elapsed,
nii.split(sep)[-1]))
cnt = cnt + 1
# preprocessing step: segmenting largest flip angle image
if do_segment:
time_elapsed = time.time() - start_time
if do_normalise_before:
print('[INFO, t={:.2f}s] creating a mask'.format(time_elapsed))
image = normed_niftis[-1]
mni = compute_brain_mask(image)
closed_mni = closing(mni, 12)
union = intersect_masks([mni, closed_mni], threshold=0)
else:
print('[INFO, t={:.2f}s] segmenting highest flip angle image'.
format(time_elapsed))
image = niftis[-1]
out_info = segment(image, True)
segments = [out_info['gm'], out_info['wm']]
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] \t segmented {}'.format(
time_elapsed,
image.split(sep)[-1]))
# preprocessing step: creating a mask
print('[INFO, t={:.2f}s] creating a mask using segments'.format(
time_elapsed))
add = math_img("img1 + img2", img1=segments[0], img2=segments[1])
if sub_name == 'sub-08':
full = compute_epi_mask(add, exclude_zeros=True)
else:
full = compute_epi_mask(add)
insides = compute_background_mask(full, opening=12)
union = intersect_masks([full, insides], threshold=0)
mask_file = join(preproc_dir, 'mask.nii')
union.to_filename(mask_file)
# estimation directory setup
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] starting estimation...'.format(time_elapsed))
recon_dir = join(cwd, 'tmp_t1', 'recon')
if not exists(recon_dir):
makedirs(recon_dir)
jsons_str = ' '.join(jsons)
if do_normalise_before:
niftis_str = ' '.join(normed_niftis)
b1_map_str = normed_b1_map
else:
niftis_str = ' '.join(niftis)
b1_map_str = b1_map_nifti
if do_segment == False:
mask_file = None
# estimation: parameter extraction
system(f"python3 ../scripts/qmri_t1_map_b1_params.py\
-v 1\
-s {sub_name}\
-o {recon_dir}\
-g {jsons_str}\
-b {b1_map_json}")
# estimation: t1 estimation
system(f"python3 ../scripts/qmri_t1_map_b1.py\
-v 1\
-s {sub_name}\
-o {recon_dir}\
-g {niftis_str}\
-b {b1_map_str}\
-r {join(recon_dir,f'{sub_name}_t1_map_b1.json')}\
-d fit\
-m {mask_file}")
recon_map = join(recon_dir, f'{sub_name}_T1map.nii.gz')
# postprocessing: normalization of reconstructed t1 map
if do_normalise_after:
postproc_dir = join(cwd, 'tmp_t1', 'postproc')
if not exists(postproc_dir):
makedirs(postproc_dir)
recon_map = join(recon_dir, f'{sub_name}_T1map.nii.gz')
system('cp {} {}'.format(recon_map, postproc_dir))
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] normalizing reconstructed t1 map...'.format(
time_elapsed))
image = join(postproc_dir, f'{sub_name}_T1map.nii.gz')
system('gunzip -df {}'.format(image))
image = join(postproc_dir, f'{sub_name}_T1map.nii')
out_info = to_MNI(image, segmented=out_info.nipype_results['segment'])
norm_recon_map = out_info['anat']
# doing the plots
if do_plot:
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] plotting the map...'.format(time_elapsed))
plot_dir = join(cwd, 'tmp_t1', 'plot')
if not exists(plot_dir):
makedirs(plot_dir)
if do_normalise_after:
t1_img = norm_recon_map
else:
t1_img = join(recon_dir, f'{sub_name}_T1map.nii.gz')
plot_thresholded_qmap(img=t1_img,
thresh="99",
map=f"{sub_name}_T1map_fit",
interactive=True,
coords=(10, 56, 43),
output_folder=plot_dir)
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] DONE'.format(time_elapsed))
# move derived files out and delete tmp_t1 directory
final_recon_map = join(SAVE_TO, f'{sub_name}_space-{space}_T1map.nii.gz')
if do_normalise_after:
system('gzip {}'.format(norm_recon_map))
recon_map = norm_recon_map + '.gz'
shutil.move(recon_map, final_recon_map)
if do_plot:
for fi in listdir(plot_dir):
plot_name = join(plot_dir, fi)
final_plot_name = join(
SAVE_TO,
final_recon_map.split(sep)[-1].split('.')[0] + '_' +
fi.split('_')[-1])
shutil.move(plot_name, final_plot_name)
if not keep_tmp:
shutil.rmtree(join(SAVE_TO, 'tmp_t1'))
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] created {} \n\n'.format(
time_elapsed, final_recon_map))
# Load the fMRI imaging
from nilearn.image import load_img
fmri_data = load_img('D:/FaceData/func_img/wrarun1.nii')
# Gain the mask
from nilearn import masking
mask = masking.compute_background_mask(fmri_data)
# Download the atlas from the internet
from nilearn import datasets
dataset = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm')
atlas_filename = dataset.maps
labels = dataset.labels
# Apply atlas to my data
from nilearn.image import resample_to_img
atlas = resample_to_img(atlas_filename, mask, interpolation='nearest')
# Gain the timeseries
from nilearn.input_data import NiftiLabelsMasker
def neuropowerinput(request, neurovault_id=None, end_session=False):
'''step 2: input'''
# Create the session id for the user
sid = get_session_id(request)
# Get the template/step status
template = "neuropower/neuropowerinput.html"
context = {}
steps = get_neuropower_steps(template, sid)
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="")
neurovault_id = request.GET.get('neurovault', '')
context["steps"] = steps
message = request.GET.get('message', '')
context['message'] = message
# If the user has ended their session, give message
if end_session == True:
context["message"] = "Session has been successfully reset."
if neurovault_id:
neurovault_image = get_url(
"http://neurovault.org/api/images/%s/?format=json" %
(neurovault_id))
collection_id = str(neurovault_image['collection_id'])
if not (neurovault_image['map_type'] == 'Z map'
or neurovault_image['map_type'] == 'T map'
or neurovault_image['analysis_level'] == None):
context[
"message"] = "Power analyses can only be performed on Z or T maps."
if not (neurovault_image['analysis_level'] == 'group'
or neurovault_image['analysis_level'] == None):
context[
"message"] = "Power analyses can only be performed on group statistical maps."
parsform = ParameterForm(
request.POST or None,
request.FILES or None,
default_url="",
err='',
initial={
"url": neurovault_image["file"],
"ZorT":
"T" if neurovault_image["map_type"] == "T map" else "Z",
"Subj": neurovault_image["number_of_subjects"]
})
context["parsform"] = parsform
return render(request, template, context)
if not request.method == "POST" or not parsform.is_valid():
context["parsform"] = parsform
return render(request, template, context)
else:
form = parsform.save(commit=False)
form.SID = sid
mapID = "%s_%s" % (str(sid), str(uuid.uuid4()))
form.mapID = mapID
form.save()
# handle data: copy to temporary location
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
mapID = "%s_%s" % (str(sid), str(uuid.uuid4()))
form.mapID = mapID
if not parsdata.url == "":
url = parsform.cleaned_data['url']
location = create_temporary_copy(url, mapID, mask=False, url=True)
elif not parsdata.spmfile == "":
spmfile = os.path.join(settings.MEDIA_ROOT, str(parsdata.spmfile))
location = create_temporary_copy(spmfile,
mapID,
mask=False,
url=False)
form.location = location
form.save()
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
SPM = nib.load(parsdata.location)
if len(SPM.shape) > 3:
if not SPM.shape[4] == 1 or len(SPM.shape) > 4:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="shape")
context["parsform"] = parsform
return render(request, template, context)
# check if the IQR is realistic (= check whether these are Z- or T-values)
IQR = np.subtract(*np.percentile(SPM.get_data(), [75, 25]))
if IQR > 20:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="median")
context["parsform"] = parsform
return render(request, template, context)
# save other parameters
form.DoF = parsdata.Subj - 1 if parsdata.Samples == 1 else parsdata.Subj - 2
form.ExcZ = float(parsdata.Exc) if float(
parsdata.Exc) > 1 else -norm.ppf(float(parsdata.Exc))
# handle mask
if parsdata.maskfile == "":
mask = masking.compute_background_mask(SPM,
border_size=2,
opening=True)
nvox = np.sum(mask.get_data())
masklocation = os.path.join(settings.MEDIA_ROOT,
"maps/mask_" + mapID + ".nii.gz")
nib.save(mask, masklocation)
form.nvox = nvox
else:
maskfile = os.path.join(settings.MEDIA_ROOT,
str(parsdata.maskfile))
masklocation = create_temporary_copy(maskfile,
mapID,
mask=True,
url=False)
mask = nib.load(masklocation).get_data()
# return error when dimensions are different
if SPM.get_data().shape != mask.shape:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="dim")
context["parsform"] = parsform
return render(request, template, context)
else:
form.nvox = np.sum(mask)
form.masklocation = masklocation
form.save()
if parsdata.spmfile == "":
return HttpResponseRedirect('../neuropowerviewer/')
else:
return HttpResponseRedirect('../neuropowertable/')
# Load fmri image
# Note: functions in learn can accept parameters as: image object or fmri filepath
from nilearn.image import load_img
fMRIData = load_img(r'C:\Users\wq\nilearn_data\haxby2001\subj2\bold.nii')
# Gain mask
from nilearn import masking
mask = masking.compute_background_mask(fMRIData)
# Download atlas from internet
from nilearn import datasets
dataset = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm')
atlas_filename = dataset.maps
labels = dataset.labels
# Apply atlas to my data
from nilearn.image import resample_to_img
Atlas = resample_to_img(atlas_filename, mask, interpolation='nearest')
# Gain the TimeSeries
from nilearn.input_data import NiftiLabelsMasker
masker = NiftiLabelsMasker(labels_img=Atlas,
standardize=True,
memory='nilearn_cache',
verbose=5)
time_series = masker.fit_transform(fMRIData)
# Extracting times series to build a functional connectome
from nilearn.connectome import ConnectivityMeasure
correlation_measure = ConnectivityMeasure(kind='correlation')
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
def download_images(dest_dir,images_df=None,target=None,resample=True):
"""Downloads images dataframe and resamples them to a common space"""
orig_path = os.path.join(dest_dir, "original")
mkdir_p(orig_path)
if resample == True:
if not target:
print "To resample you must specify a target!"
return
resampled_path = os.path.join(dest_dir, "resampled")
mkdir_p(resampled_path)
target_nii = nb.load(target)
if not isinstance(images_df,pandas.DataFrame):
images_df = get_images()
out_df = images_df.copy()
for row in images_df.iterrows():
# Downloading the file to the "original" subfolder
_, _, ext = split_filename(row[1]['file'])
orig_file = os.path.join(orig_path, "%04d%s" % (row[1]['image_id'], ext))
if not os.path.exists(orig_file):
try:
print "Downloading %s" % orig_file
urllib.urlretrieve(row[1]['file'], orig_file)
if resample == True:
# Compute the background and extrapolate outside of the mask
print "Extrapolating %s" % orig_file
niimg = nb.load(orig_file)
affine = niimg.get_affine()
data = niimg.get_data().squeeze()
niimg = nb.Nifti1Image(data, affine,header=niimg.get_header())
bg_mask = compute_background_mask(niimg).get_data()
# Test if the image has been masked:
out_of_mask = data[np.logical_not(bg_mask)]
if np.all(np.isnan(out_of_mask)) or len(np.unique(out_of_mask)) == 1:
# Need to extrapolate
data = _extrapolate_out_mask(data.astype(np.float), bg_mask,iterations=3)[0]
niimg = nb.Nifti1Image(data, affine,header=niimg.get_header())
del out_of_mask, bg_mask
# Resampling the file to target and saving the output in the "resampled" folder
resampled_file = os.path.join(resampled_path,"%06d%s" % (row[1]['image_id'], ext))
print "Resampling %s" % orig_file
resampled_nii = resample_img(niimg, target_nii.get_affine(),target_nii.shape)
resampled_nii = nb.Nifti1Image(resampled_nii.get_data().squeeze(),
resampled_nii.get_affine(),
header=niimg.get_header())
if len(resampled_nii.shape) == 3:
resampled_nii.to_filename(resampled_file)
else:
# We have a 4D file
assert len(resampled_nii.shape) == 4
resampled_data = resampled_nii.get_data()
affine = resampled_nii.get_affine()
for index in range(resampled_nii.shape[-1]):
# First save the files separately
this_nii = nb.Nifti1Image(resampled_data[..., index],affine)
this_id = int("%i%i" % (-row[1]['image_id'], index))
this_file = os.path.join(resampled_path,"%06d%s" % (this_id, ext))
this_nii.to_filename(this_file)
# Second, fix the dataframe
out_df = out_df[out_df.image_id != row[1]['image_id']]
this_row = row[1].copy()
this_row.image_id = this_id
out_df = out_df.append(this_row)
except:
print "Error downloading image id %s, retry this image." %(row[1]["image_id"])
return out_df
from nilearn import masking
mask = masking.compute_background_mask(
r'E:\home\bct_test\NC_01_0001\rs6_f8dGR_w3_rabrat_4D.nii')
print(mask.get_data().shape)
from nilearn.masking import apply_mask
masked_data = apply_mask(
r'E:\home\bct_test\NC_01_0001\rs6_f8dGR_w3_rabrat_4D.nii', mask)
print(masked_data.shape)
# masked_data shape is (timepoints, voxels). We can plot the first 150
# timepoints from two voxels
# And now plot a few of these
import matplotlib.pyplot as plt
plt.figure(figsize=(7, 5))
plt.plot(masked_data[:230, 98:100])
plt.xlabel('Time [TRs]', fontsize=16)
plt.ylabel('Intensity', fontsize=16)
plt.xlim(0, 150)
plt.subplots_adjust(bottom=.12, top=.95, right=.95, left=.12)
plt.show()
def neuropowerinput(request,neurovault_id=None,end_session=False):
'''step 2: input'''
# Create the session id for the user
sid = get_session_id(request)
# Get the template/step status
template = "neuropower/neuropowerinput.html"
context = {}
steps = get_neuropower_steps(template,sid)
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="")
neurovault_id = request.GET.get('neurovault','')
context["steps"] = steps
message = request.GET.get('message','')
context['message'] = message
# If the user has ended their session, give message
if end_session == True:
context["message"] = "Session has been successfully reset."
if neurovault_id:
neurovault_image = get_url("http://neurovault.org/api/images/%s/?format=json" %(neurovault_id))
collection_id = str(neurovault_image['collection_id'])
if not (neurovault_image['map_type'] == 'Z map' or neurovault_image['map_type'] == 'T map' or neurovault_image['analysis_level']==None):
context["message"] = "Power analyses can only be performed on Z or T maps."
if not (neurovault_image['analysis_level'] == 'group' or neurovault_image['analysis_level']==None):
context["message"] = "Power analyses can only be performed on group statistical maps."
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url = "",
err = '',
initial = {"url":neurovault_image["file"],
"ZorT":"T" if neurovault_image["map_type"] =="T map" else "Z",
"Subj":neurovault_image["number_of_subjects"]})
context["parsform"] = parsform
return render(request,template,context)
if not request.method=="POST" or not parsform.is_valid():
context["parsform"] = parsform
return render(request,template,context)
else:
form = parsform.save(commit=False)
form.SID = sid
mapID = "%s_%s" %(str(sid),str(uuid.uuid4()))
form.mapID = mapID
form.save()
# handle data: copy to temporary location
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
mapID = "%s_%s" %(str(sid),str(uuid.uuid4()))
form.mapID = mapID
if not parsdata.url == "":
url = parsform.cleaned_data['url']
location = create_temporary_copy(url,mapID,mask=False,url=True)
elif not parsdata.spmfile == "":
spmfile = os.path.join(settings.MEDIA_ROOT,str(parsdata.spmfile))
location = create_temporary_copy(spmfile,mapID,mask=False, url=False)
form.location = location
form.save()
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
SPM = nib.load(parsdata.location)
if len(SPM.shape)>3:
if not SPM.shape[4]==1 or len(SPM.shape)>4:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url = "URL to nifti image",
err = "shape")
context["parsform"] = parsform
return render(request,template,context)
# check if the IQR is realistic (= check whether these are Z- or T-values)
IQR = np.subtract(*np.percentile(SPM.get_data(),[75,25]))
if IQR > 20:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url = "URL to nifti image",
err = "median")
context["parsform"] = parsform
return render(request,template,context)
# save other parameters
form.DoF = parsdata.Subj-1 if parsdata.Samples==1 else parsdata.Subj-2
form.ExcZ = float(parsdata.Exc) if float(parsdata.Exc)>1 else -norm.ppf(float(parsdata.Exc))
# handle mask
if parsdata.maskfile == "":
mask = masking.compute_background_mask(SPM,border_size=2, opening=True)
nvox = np.sum(mask.get_data())
masklocation = os.path.join(settings.MEDIA_ROOT,"maps/mask_"+mapID+".nii.gz")
nib.save(mask,masklocation)
form.nvox = nvox
else:
maskfile = os.path.join(settings.MEDIA_ROOT,str(parsdata.maskfile))
masklocation = create_temporary_copy(maskfile,mapID,mask=True,url=False)
mask = nib.load(masklocation).get_data()
# return error when dimensions are different
if SPM.get_data().shape != mask.shape:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="dim")
context["parsform"] = parsform
return render(request,template,context)
else:
form.nvox = np.sum(mask)
form.masklocation = masklocation
form.save()
if parsdata.spmfile == "":
return HttpResponseRedirect('../neuropowerviewer/')
else:
return HttpResponseRedirect('../neuropowertable/')
def _run(self):
# NOTE TO FUTURE READERS:
# This code is extremely memory inefficient. I am currently working
# with a small dataset which allows for this inefficiency.
# Refactoring will be necessary at larger scales.
print("========== PREPROCESSING ==========")
# Set up structures to hold brain data, brain imgs, associated tags, and 1-hot mapping
brain_imgs = []
brain_data = []
brain_data_tags = []
tag_encoding_count = 0
self.brain_data_tag_encoding_map = {}
self.brain_data_tag_decoding_map = {}
# Retrieve names of all files in data_dirs
collection_filenames = self.all_data_paths()
# Loop over data files:
total_num_files = len(collection_filenames)
num_processed = 0
for filename in collection_filenames:
num_processed += 1
print("PERCENT COMPLETE: {0:.2f}%\r".format(100.0 * float(num_processed) / float(total_num_files)), end='')
# Ignore files that are not images.
if filename[-2:] != 'gz':
continue
# Load brain image.
brain_img = load_img(filename)
brain_imgs.append(brain_img)
# Load brain image metadata.
metadata_tags = self.labels_for_brain_image(filename)
brain_data_tags.append(metadata_tags)
# Downsample brain image.
downsampled_brain_img = resample_brain_img(brain_img, scale=self.scale)
downsampled_brain_img_data = downsampled_brain_img.get_data()
# Normalize brain image data.
normalized_downsampled_brain_img_data = normalize_brain_img_data(downsampled_brain_img_data)
brain_data.append(normalized_downsampled_brain_img_data)
# Build one hot encoding map.
if self.multi_tag_label_encoding:
for tag in metadata_tags:
if tag not in self.brain_data_tag_encoding_map:
self.brain_data_tag_encoding_map[tag] = tag_encoding_count
self.brain_data_tag_decoding_map[tag_encoding_count] = tag
tag_encoding_count += 1
else:
# Merge all metadata tags into a single string:
metadata_tags.sort()
metadata_tags = ",".join(metadata_tags)
if metadata_tags not in self.brain_data_tag_encoding_map:
self.brain_data_tag_encoding_map[metadata_tags] = tag_encoding_count
self.brain_data_tag_decoding_map[tag_encoding_count] = metadata_tags
tag_encoding_count += 1
# Compute dataset mask.
print("Computing dataset mask...\r", end='')
brain_data_mask = masking.compute_background_mask(brain_imgs)
# 1-hot encode all brain data tags.
print("1-hot encoding brain data tags...\r", end='')
for i in range(len(brain_data_tags)):
brain_data_tags[i] = self.encode_label(brain_data_tags[i])
# Write preprocessed brain data out as binary files.
print("Writing preprocessed brain data out to files...\r", end='')
brain_data_f = open(self.brain_data_path, 'wb')
brain_data_mask_f = open(self.brain_data_mask_path, 'wb')
brain_data_tags_f = open(self.brain_data_tags_path, 'wb')
brain_data_tags_encoding_f = open(self.brain_data_tags_encoding_path, 'wb')
brain_data_tags_decoding_f = open(self.brain_data_tags_decoding_path, 'wb')
print(" \r", end='')
pickle.dump(brain_data, brain_data_f)
pickle.dump(brain_data_mask, brain_data_mask_f)
pickle.dump(brain_data_tags, brain_data_tags_f)
pickle.dump(self.brain_data_tag_encoding_map, brain_data_tags_encoding_f)
pickle.dump(self.brain_data_tag_decoding_map, brain_data_tags_decoding_f)
def t2_pipeline(do_coreg=True,
do_normalise_before=False,
do_segment=True,
do_normalise_after=False,
do_plot=True,
keep_tmp=True,
sub_name='sub-11',
sess_num='ses-17',
root_path='/neurospin/ibc'):
"""
Preprocess qMRI t2 images and then run estimation to generate t2-maps,
more details in scripts/qmri_README.md,
only one of do_normalise_before and do_normalise_after should be True,
both can be False
"""
DATA_LOC = join(root_path, 'sourcedata', sub_name, sess_num)
SAVE_TO = join(root_path, 'derivatives', sub_name, sess_num)
if do_normalise_before or do_normalise_after:
space = "MNI152"
else:
space = "individual"
if not exists(SAVE_TO):
makedirs(SAVE_TO)
start_time = time.time()
# data files
data_dir = DATA_LOC
niftis = []
jsons = []
for fi in listdir(join(data_dir, 'anat')):
if fi.split('_')[-1] == 'T2map.nii.gz':
niftis.append(join(data_dir, 'anat', fi))
elif fi.split('_')[-1] == 'T2map.json':
jsons.append(join(data_dir, 'anat', fi))
else:
continue
niftis.sort()
jsons.sort()
run_count = 0
for nifti in niftis:
# preprocessing directory setup
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] copying necessary files...'.format(
time_elapsed))
cwd = SAVE_TO
preproc_dir = join(cwd, 'tmp_t2', 'preproc')
if not exists(preproc_dir):
makedirs(preproc_dir)
system('cp {} {}'.format(nifti, preproc_dir))
nifti = join(preproc_dir, nifti.split(sep)[-1])
system('gunzip -df {}'.format(nifti))
nifti = join(preproc_dir, nifti.split(sep)[-1].split('.')[0] + '.nii')
if do_coreg:
# t1 image as an anatomical image
t1_niftis = []
for fi in listdir(join(data_dir, 'anat')):
if fi.split('_')[-1] == 'T1map.nii.gz':
t1_niftis.append(join(data_dir, 'anat', fi))
t1_niftis.sort()
t1_nifti = t1_niftis[-1]
system('cp {} {}'.format(t1_nifti, preproc_dir))
t1_nifti = join(preproc_dir, t1_nifti.split(sep)[-1])
system('gunzip -df {}'.format(t1_nifti))
t1_nifti = join(preproc_dir,
t1_nifti.split(sep)[-1].split('.')[0] + '.nii')
# preprocessing step: spatial normalization to MNI space
# of T1 maps
if do_normalise_before:
image = nifti
if do_coreg:
t1_img = t1_nifti
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] segmenting the t1 image'.format(
time_elapsed))
out_info = segment(t1_img, False)
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] transforming images to MNI space...'.
format(time_elapsed))
out_info = to_MNI(image=t1_img, data=out_info, func=image)
normed_t1_img = out_info['anat']
normed_nifti = out_info['func'][0]
else:
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] segmenting the t2 image'.format(
time_elapsed))
out_info = segment(image, False)
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] transforming images to MNI space...'.
format(time_elapsed))
out_info = to_MNI(image, data=out_info)
normed_nifti = out_info['anat']
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] \t transformed {}'.format(
time_elapsed,
nifti.split(sep)[-1]))
# preprocessing step: transform t1 image to t2 space
if do_coreg:
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] transforming t1 image to t2 space...'.
format(time_elapsed))
if do_normalise_before:
t2_img = normed_nifti
t1_img = normed_t1_img
else:
t2_img = nifti
t1_img = t1_nifti
mean_t2 = mean_img(t2_img)
mean_t2_img = join(preproc_dir,
'mean_{}'.format(t2_img.split(sep)[-1]))
mean_t2.to_filename(mean_t2_img)
out_info = to_T2space(t2_img=mean_t2_img,
t1_img=t1_img,
output_dir=preproc_dir)
print('[INFO, t={:.2f}s] \t transformed {} to {} space'.format(
time_elapsed,
t1_img.split(sep)[-1],
mean_t2_img.split(sep)[-1]))
# preprocessing step: segmenting largest flip angle image
if do_segment:
if do_normalise_before:
time_elapsed = time.time() - start_time
print(
'[INFO, t={:.2f}s] creating a mask'.format(time_elapsed))
if do_coreg:
image = normed_t1_img
else:
image = normed_nifti
mni = compute_brain_mask(image)
closed_mni = closing(mni, 12)
union = intersect_masks([mni, closed_mni], threshold=0)
else:
if do_coreg:
image = t1_img
else:
image = nifti
time_elapsed = time.time() - start_time
print(
'[INFO, t={:.2f}s] segmenting the image for creating a mask'
.format(time_elapsed))
out_info = segment(image, False)
segments = [out_info['gm'], out_info['wm']]
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] \t segmented {}'.format(
time_elapsed,
image.split(sep)[-1]))
# preprocessing step: creating a mask
time_elapsed = time.time() - start_time
print(
'[INFO, t={:.2f}s] creating a mask using segments'.format(
time_elapsed))
add = math_img("img1 + img2",
img1=segments[0],
img2=segments[1])
if sub_name == 'sub-08':
full = compute_epi_mask(add, exclude_zeros=True)
else:
full = compute_epi_mask(add)
insides = compute_background_mask(full, opening=12)
union = intersect_masks([full, insides], threshold=0)
mask_file = join(preproc_dir, 'mask.nii')
union.to_filename(mask_file)
if do_coreg:
resampled_mask = resample_to_img(mask_file,
mean_t2_img,
clip=True)
rounded_resampled_mask = math_img('np.around(img1)',
img1=resampled_mask)
resampled_mask_img = join(preproc_dir, 'resampled_mask.nii')
rounded_resampled_mask.to_filename(resampled_mask_img)
else:
resampled_mask_img = mask_file
# estimation directory setup
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] starting estimation...'.format(time_elapsed))
recon_dir = join(cwd, 'tmp_t2', 'recon')
if not exists(recon_dir):
makedirs(recon_dir)
if do_normalise_before:
niftis_str = normed_nifti
else:
niftis_str = nifti
if do_segment == False:
mask_file = None
# estimation: t2 estimation
system(f"python3 ../scripts/qmri_t2_map.py\
-v 1\
-s {sub_name}\
-o {recon_dir}\
-n {niftis_str}\
-m {resampled_mask_img}")
recon_map = join(recon_dir, f'{sub_name}_T2map.nii.gz')
# postprocessing: normalization of reconstructed t1 map
if do_normalise_after:
postproc_dir = join(cwd, 'tmp_t2', 'postproc')
if not exists(postproc_dir):
makedirs(postproc_dir)
system('cp {} {}'.format(recon_map, postproc_dir))
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] normalizing reconstructed map...'.format(
time_elapsed))
image = join(postproc_dir, f'{sub_name}_T2map.nii.gz')
system('gunzip -df {}'.format(image))
image = join(postproc_dir, f'{sub_name}_T2map.nii')
out_info = to_MNI(image,
segmented=out_info.nipype_results['segment'])
norm_recon_map = out_info['anat']
# doing the plots
if do_plot:
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] plotting the map...'.format(
time_elapsed))
plot_dir = join(cwd, 'tmp_t2', 'plot')
if not exists(plot_dir):
makedirs(plot_dir)
if do_normalise_after:
t2_img = norm_recon_map
else:
t2_img = recon_map
plot_thresholded_qmap(img=t2_img,
thresh="95",
map=f"{sub_name}_T2map",
interactive=True,
coords=(10, 56, 43),
output_folder=plot_dir)
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] DONE'.format(time_elapsed))
# move derived files out and delete tmp_t2 directory
final_recon_map = join(
SAVE_TO,
f'{sub_name}_run-0{run_count+1}_space-{space}_T2map.nii.gz')
if do_normalise_after:
system('gzip {}'.format(norm_recon_map))
recon_map = norm_recon_map + '.gz'
shutil.move(recon_map, final_recon_map)
if do_plot:
for fi in listdir(plot_dir):
plot_name = join(plot_dir, fi)
final_plot_name = join(
SAVE_TO,
final_recon_map.split(sep)[-1].split('.')[0] + '_' +
fi.split('_')[-1])
shutil.move(plot_name, final_plot_name)
if not keep_tmp:
shutil.rmtree(join(SAVE_TO, 'tmp_t2'))
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] created {} \n\n'.format(
time_elapsed, final_recon_map))
run_count = run_count + 1