def brain(image):
'''
Brain Extraction with FSL
Params:
- image: nifti object, scan to brain extract
Output:
- brain_image: nifti object, extracted brain
'''
affine = image.affine
header = image.header
tmpfile = 'tmpfile.nii.gz'
image.to_filename(tmpfile)
# FSL calls
mask = fslmaths(image).thr('0.000000').uthr(
'100.000000').bin().fillh().run()
fslmaths(image).mas(mask).run(tmpfile)
bet(tmpfile, tmpfile, fracintensity=0.01)
mask = fslmaths(tmpfile).bin().fillh().run()
image = fslmaths(image).mas(mask).run()
image = nib.Nifti1Image(image.get_data(), affine, header)
os.remove(tmpfile)
return image
def bias_estimation_calib(calib_name):
"""
Estimate the bias field from a calibration image using FAST.
Parameters
----------
calib_name: pathlib.Path
Path to the calibration image from which to estimate the
bias field.
Returns
-------
bias_field: Nifti1Image
"""
# perform brain extraction using BET
betted_m0 = bet(str(calib_name), LOAD, g=0.2, f=0.2, m=True)
# run FAST on BET-ed calibration image
fast_results = fast(betted_m0["output"],
out=LOAD,
type=3,
b=True,
nopve=True)
# extract and return bias field
bias_field = fast_results["out_bias"]
return bias_field
def generate_fmaps(pa_ap_sefms, params, config, distcorr_dir):
"""
Generate fieldmaps via topup for use with asl_reg.
Args:
asl_vol0: path to image of stacked blipped images (ie, PEdir as vol0,
(oPEdir as vol1), in this case stacked as pa then ap)
params: path to text file for topup --datain, PE directions/times
config: path to text file for topup --config, other args
distcorr_dir: directory in which to put output
Returns:
n/a, files 'fmap, fmapmag, fmapmagbrain.nii.gz' will be created in output dir
"""
pwd = os.getcwd()
os.chdir(distcorr_dir)
# Run topup to get fmap in Hz
topup_fmap = op.join(distcorr_dir, 'topup_fmap_hz.nii.gz')
cmd = (("topup --imain={} --datain={}".format(pa_ap_sefms, params) +
" --config={} --out=topup".format(config)) +
" --fout={} --iout={}".format(
topup_fmap, op.join(distcorr_dir, 'corrected_sefms.nii.gz')))
sp.run(cmd, shell=True)
fmap, fmapmag, fmapmagbrain = [
op.join(distcorr_dir, '{}.nii.gz'.format(s))
for s in ['fmap', 'fmapmag', 'fmapmagbrain']
]
# Convert fmap from Hz to rad/s
fmap_spc = rt.ImageSpace(topup_fmap)
fmap_arr_hz = nb.load(topup_fmap).get_data()
fmap_arr = fmap_arr_hz * 2 * np.pi
fmap_spc.save_image(fmap_arr, fmap)
# Mean across volumes of corrected sefms to get fmapmag
fmapmag_arr = nb.load(op.join(distcorr_dir,
"corrected_sefms.nii.gz")).get_data()
fmapmag_arr = fmapmag_arr.mean(-1)
fmap_spc.save_image(fmapmag_arr, fmapmag)
# Run BET on fmapmag to get brain only version
bet(fmap_spc.make_nifti(fmapmag_arr), output=fmapmagbrain)
os.chdir(pwd)
def run(wsp):
"""
Do initialization on supplied structural data - copy relevant image and do brain extraction
FIXME copy across all supplied structural data
"""
wsp.log.write("\nInitialising structural data\n")
wsp.sub("structural")
if wsp.fslanat:
wsp.log.write(
" - Using FSL_ANAT output directory for structural data: %s\n" %
wsp.fslanat)
biascorr = os.path.join(wsp.fslanat, "T1_biascorr")
biascorr_brain = os.path.join(wsp.fslanat, "T1_biascorr_brain")
if glob.glob(biascorr + ".*") and glob.glob(biascorr_brain + ".*"):
wsp.log.write(" - Using bias-corrected structural images\n")
wsp.structural.struc = Image(biascorr)
wsp.structural.brain = Image(biascorr_brain)
else:
wsp.log.write(" - Using non bias-corrected structural images\n")
wsp.structural.struc = Image(os.path.join(wsp.fslanat, "T1"))
wsp.structural.brain = Image(os.path.join(wsp.fslanat, "T1_brain"))
elif wsp.struc:
wsp.log.write(" - Using structural image provided by user: %s\n" %
wsp.struc.name)
wsp.structural.struc = wsp.struc
wsp.structural.brain = wsp.struc_brain
#elif wsp.structural.struc_lores
# wsp.log.write("Low-resolution tructural image: %s\n" % wsp.structural.struc_lores.name)
else:
wsp.log.write(
" - No structural data supplied - output will be native space only\n"
)
if wsp.structural.struc is not None and wsp.structural.brain is None:
wsp.log.write(" - Brain-extracting structural image\n")
bet_result = fsl.bet(wsp.structural.struc,
output=fsl.LOAD,
seg=True,
mask=True,
log=wsp.fsllog)
wsp.structural.brain = bet_result["output"]
#wsp.structural.brain_mask = bet_result["output_mask"]
if wsp.structural.brain is not None and wsp.structural.brain_mask is None:
# FIXME - for now get the mask by binarising the brain image for compatibility with oxford_asl
# although this gives slightly different results compared to using the mask returned by BET
wsp.structural.brain_mask = Image(
(wsp.structural.brain.data != 0).astype(np.int),
header=wsp.structural.struc.header)
if wsp.structural.struc is not None:
segment(wsp)
def get_mask(wsp):
"""
Generate the mask to use - if it has not been provided by the user
"""
if wsp.mask is None:
wsp.datamean = Image(np.mean(wsp.dscdata.data, axis=-1),
header=wsp.dscdata.header)
bet_result = fsl.bet(wsp.datamean,
seg=False,
mask=True,
output=fsl.LOAD,
log=wsp.fsllog)
wsp.mask = bet_result["output_mask"]
wsp.log.write(
" - Mask generated by running BET on time-averaged DSC data\n")
else:
wsp.log.write(" - Mask supplied by user\n")
def generate_fmaps(pa_ap_sefms,
params,
config,
distcorr_dir,
gdc_warp,
interpolation=3):
"""
Generate fieldmaps via topup for use with asl_reg.
Args:
asl_vol0: path to image of stacked blipped images (ie, PEdir as vol0,
(oPEdir as vol1), in this case stacked as pa then ap)
params: path to text file for topup --datain, PE directions/times
config: path to text file for topup --config, other args
distcorr_dir: directory in which to put output
gdc_warp: path to gradient_unwarp's gradient distortion correction warp
interpolation: order of interpolation to be used when applying registrations,
default=3
Returns:
n/a, files 'fmap, fmapmag, fmapmagbrain.nii.gz' will be created in output dir
"""
# set up logger
logger = logging.getLogger("HCPASL.distortion_estimation")
logger.info("Running generate_fmaps()")
logger.info(f"Spin Echo Field Maps: {pa_ap_sefms}")
logger.info(f"Topup param file: {params}")
logger.info(f"Topup config file: {config}")
logger.info(f"Topup output directory: {distcorr_dir}")
logger.info(f"Gradient distortion correction warp: {gdc_warp}")
logger.info(f"Interpolation order: {interpolation}")
pwd = os.getcwd()
os.chdir(distcorr_dir)
# apply gradient distortion correction to stacked SEFMs
gdc = rt.NonLinearRegistration.from_fnirt(coefficients=str(gdc_warp),
src=str(pa_ap_sefms),
ref=str(pa_ap_sefms),
intensity_correct=True)
gdc_corr_pa_ap_sefms = gdc.apply_to_image(src=str(pa_ap_sefms),
ref=str(pa_ap_sefms),
order=interpolation,
cores=1)
gdc_corr_pa_ap_sefms_name = distcorr_dir / "merged_sefms_gdc.nii.gz"
nb.save(gdc_corr_pa_ap_sefms, gdc_corr_pa_ap_sefms_name)
# Run topup to get fmap in Hz
topup_fmap = op.join(distcorr_dir, 'topup_fmap_hz.nii.gz')
topup_cmd = [
"topup", f"--imain={gdc_corr_pa_ap_sefms_name}", f"--datain={params}",
f"--config={config}", f"--out=topup",
f"--iout={op.join(distcorr_dir, 'corrected_sefms.nii.gz')}",
f"--fout={topup_fmap}",
f"--dfout={op.join(distcorr_dir, 'WarpField')}",
f"--rbmout={op.join(distcorr_dir, 'MotionMatrix')}",
f"--jacout={op.join(distcorr_dir, 'Jacobian')}", "--verbose"
]
logger.info(f"Topup command: {' '.join(topup_cmd)}")
process = sp.Popen(topup_cmd, stdout=sp.PIPE)
while 1:
retcode = process.poll()
line = process.stdout.readline().decode("utf-8")
logger.info(line)
if line == "" and retcode is not None:
break
if retcode != 0:
logger.info(f"retcode={retcode}")
logger.exception("Process failed.")
fmap, fmapmag, fmapmagbrain = [
op.join(distcorr_dir, '{}.nii.gz'.format(s))
for s in ['fmap', 'fmapmag', 'fmapmagbrain']
]
# Convert fmap from Hz to rad/s
logger.info("Converting fieldmap from Hz to rad/s.")
fmap_spc = rt.ImageSpace(topup_fmap)
fmap_arr_hz = nb.load(topup_fmap).get_data()
fmap_arr = fmap_arr_hz * 2 * np.pi
fmap_spc.save_image(fmap_arr, fmap)
# Apply gdc warp from gradient_unwarp and topup's EPI-DC
# warp (just generated) in one interpolation step
logger.info(
"Applying gdc and epi-dc to fieldmap images in one interpolation step."
)
pa_ap_sefms_gdc_dc = apply_gdc_and_topup(pa_ap_sefms,
distcorr_dir,
gdc_warp,
interpolation=interpolation)
# Mean across volumes of corrected sefms to get fmapmag
logger.info(
"Taking mean of corrected fieldmap images to get fmapmag.nii.gz")
fmapmag_img = nb.nifti1.Nifti1Image(
pa_ap_sefms_gdc_dc.get_fdata().mean(-1),
affine=pa_ap_sefms_gdc_dc.affine)
nb.save(fmapmag_img, fmapmag)
# Run BET on fmapmag to get brain only version
logger.info("Running BET on fmapmag for brain-extracted version.")
bet(fmapmag, output=fmapmagbrain)
os.chdir(pwd)
def correct_M0(subject_dir, mt_factors):
"""
Correct the M0 images for a particular subject whose data
is stored in `subject_dir`. The corrections to be
performed include:
- Bias-field correction
- Magnetisation Transfer correction
Inputs
- `subject_dir` = pathlib.Path object specifying the
subject's base directory
- `mt_factors` = pathlib.Path object specifying the
location of empirically estimated MT correction
scaling factors
"""
# load json containing info on where files are stored
json_dict = load_json(subject_dir)
# do for both m0 images for the subject, calib0 and calib1
calib_names = [json_dict['calib0_img'], json_dict['calib1_img']]
for calib_name in calib_names:
# get calib_dir and other info
calib_path = Path(calib_name)
calib_dir = calib_path.parent
calib_name_stem = calib_path.stem.split('.')[0]
# run BET on m0 image
betted_m0 = bet(calib_name, LOAD)
# create directories to store results
fast_dir = calib_dir / 'FAST'
biascorr_dir = calib_dir / 'BiasCorr'
mtcorr_dir = calib_dir / 'MTCorr'
create_dirs([fast_dir, biascorr_dir, mtcorr_dir])
# estimate bias field on brain-extracted m0 image
# run FAST, storing results in directory
fast_base = fast_dir / calib_name_stem
fast(
betted_m0['output'], # output of bet
out=str(fast_base),
type=3, # image type, 3=PD image
b=True, # output estimated bias field
nopve=True # don't need pv estimates
)
bias_name = fast_dir / f'{calib_name_stem}_bias.nii.gz'
# apply bias field to original m0 image (i.e. not BETted)
biascorr_name = biascorr_dir / f'{calib_name_stem}_restore.nii.gz'
fslmaths(calib_name).div(str(bias_name)).run(str(biascorr_name))
# apply mt_factors to bias-corrected m0 image
mtcorr_name = mtcorr_dir / f'{calib_name_stem}_mtcorr.nii.gz'
fslmaths(str(biascorr_name)).mul(str(mt_factors)).run(str(mtcorr_name))
# add locations of above files to the json
important_names = {
f'{calib_name_stem}_bias': str(bias_name),
f'{calib_name_stem}_bc': str(biascorr_name),
f'{calib_name_stem}_mc': str(mtcorr_name)
}
update_json(important_names, json_dict)
def main():
# argument handling
parser = argparse.ArgumentParser()
parser.add_argument("study_dir", help="Path of the base study directory.")
parser.add_argument("sub_number", help="Subject number.")
parser.add_argument(
"-g",
"--grads",
help="Filename of the gradient coefficients for gradient" +
"distortion correction (optional).")
parser.add_argument(
"-t",
"--target",
help="Which space we want to register to. Can be either 'asl' for " +
"registration to the first volume of the ASL series or " +
"'structural' for registration to the T1w image. Default " +
" is 'asl'.",
default="asl")
args = parser.parse_args()
study_dir = args.study_dir
sub_id = args.sub_number
grad_coefficients = args.grads
target = args.target
# For debug, re-use existing intermediate files
force_refresh = True
# Input, output and intermediate directories
# Create if they do not already exist.
sub_base = op.abspath(op.join(study_dir, sub_id))
grad_coefficients = op.abspath(grad_coefficients)
pvs_dir = op.join(sub_base, "T1w", "ASL", "PVEs")
t1_asl_dir = op.join(sub_base, "T1w", "ASL")
distcorr_dir = op.join(sub_base, "ASL", "TIs", "SecondPass", "DistCorr")
reg_dir = op.join(sub_base, 'T1w', 'ASL', 'reg')
t1_dir = op.join(sub_base, "T1w")
asl_dir = op.join(sub_base, "ASL", "TIs", "SecondPass", "STCorr2")
asl_out_dir = op.join(t1_asl_dir, "TIs", "DistCorr")
calib_out_dir = op.join(t1_asl_dir, "Calib", "Calib0", "DistCorr")
[
os.makedirs(d, exist_ok=True) for d in [
pvs_dir, t1_asl_dir, distcorr_dir, reg_dir, asl_out_dir,
calib_out_dir
]
]
# Images required for processing
asl = op.join(asl_dir, "tis_stcorr.nii.gz")
struct = op.join(t1_dir, "T1w_acpc_dc_restore.nii.gz")
struct_brain = op.join(t1_dir, "T1w_acpc_dc_restore_brain.nii.gz")
struct_brain_mask = op.join(t1_dir,
"T1w_acpc_dc_restore_brain_mask.nii.gz")
asl_vol0 = op.join(asl_dir, "tis_stcorr_vol1.nii.gz")
if (not op.exists(asl_vol0) or force_refresh) and target == 'asl':
cmd = "fslroi {} {} 0 1".format(asl, asl_vol0)
sp.run(cmd.split(" "), check=True)
# Create ASL-gridded version of T1 image
t1_asl_grid = op.join(t1_dir, "ASL", "reg",
"ASL_grid_T1w_acpc_dc_restore.nii.gz")
if (not op.exists(t1_asl_grid) or force_refresh) and target == 'asl':
asl_spc = rt.ImageSpace(asl)
t1_spc = rt.ImageSpace(struct)
t1_asl_grid_spc = t1_spc.resize_voxels(asl_spc.vox_size /
t1_spc.vox_size)
nb.save(
rt.Registration.identity().apply_to_image(struct, t1_asl_grid_spc),
t1_asl_grid)
# Create ASL-gridded version of T1 image
t1_asl_grid_mask = op.join(
reg_dir, "ASL_grid_T1w_acpc_dc_restore_brain_mask.nii.gz")
if (not op.exists(t1_asl_grid_mask) or force_refresh) and target == 'asl':
asl_spc = rt.ImageSpace(asl)
t1_spc = rt.ImageSpace(struct_brain)
t1_asl_grid_spc = t1_spc.resize_voxels(asl_spc.vox_size /
t1_spc.vox_size)
t1_mask = binarise_image(struct_brain)
t1_mask_asl_grid = rt.Registration.identity().apply_to_array(
t1_mask, t1_spc, t1_asl_grid_spc)
# Re-binarise downsampled mask and save
t1_asl_grid_mask_array = binary_fill_holes(
t1_mask_asl_grid > 0.25).astype(np.float32)
t1_asl_grid_spc.save_image(t1_asl_grid_mask_array, t1_asl_grid_mask)
# MCFLIRT ASL using the calibration as reference
calib = op.join(sub_base, 'ASL', 'Calib', 'Calib0', 'MTCorr',
'calib0_mtcorr.nii.gz')
asl = op.join(sub_base, 'ASL', 'TIs', 'tis.nii.gz')
mcdir = op.join(sub_base, 'ASL', 'TIs', 'SecondPass', 'MoCo',
'asln2m0.mat')
asl2calib_mc = rt.MotionCorrection.from_mcflirt(mcdir, asl, calib)
# Rebase the motion correction to target volume 0 of ASL
# The first registration in the series gives us ASL-calibration transform
calib2asl0 = asl2calib_mc[0].inverse()
asl_mc = rt.chain(asl2calib_mc, calib2asl0)
# Generate the gradient distortion correction warp
gdc_path = op.join(distcorr_dir, 'fullWarp_abs.nii.gz')
if (not op.exists(gdc_path) or force_refresh) and target == 'asl':
generate_gdc_warp(asl_vol0, grad_coefficients, distcorr_dir)
gdc = rt.NonLinearRegistration.from_fnirt(gdc_path,
asl_vol0,
asl_vol0,
intensity_correct=True,
constrain_jac=(0.01, 100))
# Stack the cblipped images together for use with topup
pa_sefm, ap_sefm = find_field_maps(study_dir, sub_id)
pa_ap_sefms = op.join(distcorr_dir, 'merged_sefms.nii.gz')
if (not op.exists(pa_ap_sefms) or force_refresh) and target == 'asl':
rt.ImageSpace.save_like(
pa_sefm,
np.stack(
(nb.load(pa_sefm).get_data(), nb.load(ap_sefm).get_data()),
axis=-1), pa_ap_sefms)
topup_params = op.join(distcorr_dir, 'topup_params.txt')
generate_topup_params(topup_params)
topup_config = "b02b0.cnf" # Note this file doesn't exist in scope,
# but topup knows where to find it
# Generate fieldmaps for use with asl_reg (via topup)
fmap, fmapmag, fmapmagbrain = [
op.join(distcorr_dir, '{}.nii.gz'.format(s))
for s in ['fmap', 'fmapmag', 'fmapmagbrain']
]
if ((not all([op.exists(p) for p in [fmap, fmapmag, fmapmagbrain]]))
or force_refresh) and target == 'asl':
generate_fmaps(pa_ap_sefms, topup_params, topup_config, distcorr_dir)
# get linear registration from asl to structural
if target == 'asl':
unreg_img = asl_vol0
elif target == 'structural':
# register perfusion-weighted image to structural instead of asl 0
unreg_img = op.join(sub_base, "ASL", "TIs", "SecondPass", "OxfordASL",
"native_space", "perfusion.nii.gz")
# apply gdc to unreg_img before getting registration to structural
# only apply to asl_vol0 as perfusion image has already had gdc applied
distcorr_out_dir = asl_out_dir if target == 'structural' else distcorr_dir
gdc_tis_vol1_name = op.join(distcorr_out_dir, "gdc_tis_vol1.nii.gz")
if (not op.exists(gdc_tis_vol1_name) or force_refresh) and target == 'asl':
gdc_tis_vol1 = gdc.apply_to_image(src=unreg_img, ref=unreg_img)
unreg_img = gdc_tis_vol1_name
nb.save(gdc_tis_vol1, unreg_img)
# Initial (linear) asl to structural registration, via first round of asl_reg
asl2struct_initial_path = op.join(
reg_dir,
'asl2struct_init.mat' if target == 'asl' else 'asl2struct_final.mat')
if not op.exists(asl2struct_initial_path) or force_refresh:
generate_asl2struct_initial(unreg_img, reg_dir, struct, struct_brain)
asl2struct_initial_path_temp = op.join(reg_dir, 'asl2struct.mat')
os.replace(asl2struct_initial_path_temp, asl2struct_initial_path)
asl2struct_initial = rt.Registration.from_flirt(asl2struct_initial_path,
src=unreg_img,
ref=struct)
# Get brain mask in asl space
if target == 'asl':
mask_name = op.join(reg_dir, "asl_vol1_mask_init.nii.gz")
else:
mask_name = op.join(reg_dir, "asl_vol1_mask_final.nii.gz")
if not op.exists(mask_name) or force_refresh:
asl_mask = generate_asl_mask(struct_brain, unreg_img,
asl2struct_initial)
rt.ImageSpace.save_like(unreg_img, asl_mask, mask_name)
# Brain extract volume 0 of asl series
gdc_unreg_img_brain = op.join(sub_base, "ASL", "TIs", "SecondPass",
"DistCorr", "gdc_tis_vol1_brain.nii.gz")
if (not op.exists(gdc_unreg_img_brain)
or force_refresh) and target == 'asl':
bet(unreg_img, gdc_unreg_img_brain)
unreg_img = gdc_unreg_img_brain
# Generate a binary WM mask in the space of the T1 (using FS' aparc+aseg)
wmmask = op.join(sub_base, "T1w", "wmmask.nii.gz")
if (not op.exists(wmmask) or force_refresh) and target == 'asl':
aparc_seg = op.join(t1_dir, "aparc+aseg.nii.gz")
wmmask_img = generate_wmmask(aparc_seg)
rt.ImageSpace.save_like(struct, wmmask_img, wmmask)
# Generate the EPI distortion correction warp via asl_reg --final
epi_dc_path = op.join(
distcorr_dir, 'asl2struct_warp_init.nii.gz'
if target == 'asl' else 'asl2struct_warp_final.nii.gz')
if not op.exists(epi_dc_path) or force_refresh:
epi_dc_path_temp = op.join(distcorr_dir, 'asl2struct_warp.nii.gz')
generate_epidc_warp(unreg_img, struct, struct_brain, mask_name, wmmask,
asl2struct_initial, fmap, fmapmag, fmapmagbrain,
distcorr_dir)
# rename warp so it isn't overwritten
os.replace(epi_dc_path_temp, epi_dc_path)
epi_dc = rt.NonLinearRegistration.from_fnirt(epi_dc_path,
mask_name,
struct,
intensity_correct=True,
constrain_jac=(0.01, 100))
# if ending in asl space, chain struct2asl transformation
if target == 'asl':
struct2asl_aslreg = op.join(distcorr_out_dir, "struct2asl.mat")
struct2asl_aslreg = rt.Registration.from_flirt(struct2asl_aslreg,
src=struct,
ref=asl)
epi_dc = rt.chain(epi_dc, struct2asl_aslreg)
# Final ASL transforms: moco, grad dc,
# epi dc (incorporating asl->struct reg)
asl = op.join(asl_dir, "tis_stcorr.nii.gz")
reference = t1_asl_grid if target == 'structural' else asl
asl_outpath = op.join(distcorr_out_dir, "tis_distcorr.nii.gz")
if not op.exists(asl_outpath) or force_refresh:
asl2struct_mc_dc = rt.chain(asl_mc, gdc, epi_dc)
asl_corrected = asl2struct_mc_dc.apply_to_image(src=asl,
ref=reference,
cores=mp.cpu_count())
nb.save(asl_corrected, asl_outpath)
# Final calibration transforms: calib->asl, grad dc,
# epi dc (incorporating asl->struct reg)
calib_outpath = op.join(calib_out_dir, "calib0_dcorr.nii.gz")
if (not op.exists(calib_outpath)
or force_refresh) and target == 'structural':
calib2struct_dc = rt.chain(calib2asl0, gdc, epi_dc)
calib_corrected = calib2struct_dc.apply_to_image(src=calib,
ref=reference)
nb.save(calib_corrected, calib_outpath)
# Final scaling factors transforms: moco, grad dc,
# epi dc (incorporating asl->struct reg)
sfs_name = op.join(asl_dir, "combined_scaling_factors.nii.gz")
sfs_outpath = op.join(distcorr_out_dir, "combined_scaling_factors.nii.gz")
if not op.exists(sfs_outpath) or force_refresh:
# don't chain transformations together if we don't have to
try:
asl2struct_mc_dc
except NameError:
asl2struct_mc_dc = rt.chain(asl_mc, gdc, epi_dc)
sfs_corrected = asl2struct_mc_dc.apply_to_image(src=sfs_name,
ref=reference,
cores=mp.cpu_count())
nb.save(sfs_corrected, sfs_outpath)
# create ti image in asl space
slicedt = 0.059
tis = [1.7, 2.2, 2.7, 3.2, 3.7]
sliceband = 10
ti_asl = op.join(sub_base, "ASL", "TIs", "timing_img.nii.gz")
if (not op.exists(ti_asl) or force_refresh) and target == 'asl':
create_ti_image(asl, tis, sliceband, slicedt, ti_asl)
# transform ti image into t1 space
ti_t1 = op.join(t1_asl_dir, "timing_img.nii.gz")
if (not op.exists(ti_t1) or force_refresh) and target == 'structural':
asl2struct = op.join(distcorr_dir, "asl2struct.mat")
asl2struct = rt.Registration.from_flirt(asl2struct,
src=asl,
ref=struct)
ti_t1_img = asl2struct.apply_to_image(src=ti_asl, ref=reference)
nb.save(ti_t1_img, ti_t1)
def setup_mtestimation(subject_dir, rois=[
'wm',
]):
"""
Perform the initial processing needed for estimation of the
MT Effect. This includes:
- Creating sub-directories for storing the results
- Finding T1 and mbPCASL directories and scans
- Split mbPCASL sequence into its constituent components
- Run fsl_anat
- Create a json to keep track of important files and
directories
"""
# get subject name
subject_name = subject_dir.parts[-1]
print(subject_name)
# create results directories
asl_dir = subject_dir / 'ASL'
calib0_dir = asl_dir / 'Calib/Calib0'
calib1_dir = asl_dir / 'Calib/Calib1'
create_dirs([asl_dir, calib0_dir, calib1_dir])
# obtain calibration images from ASL sequence
mbpcasl_dir = list(
(subject_dir / f'{subject_name}_V1_B/scans').glob('**/*mbPCASLhr'))[0]
mbpcasl = mbpcasl_dir / f'resources/NIFTI/files/{subject_name}_V1_B_mbPCASLhr_PA.nii.gz'
calib0_name = calib0_dir / 'calib0.nii.gz'
calib1_name = calib1_dir / 'calib1.nii.gz'
fslroi(str(mbpcasl), calib0_name, 88, 1)
fslroi(str(mbpcasl), calib1_name, 89, 1)
# initialise dict
json_name = asl_dir / 'ASL.json'
important_dict = {
"calib_dir": str(calib0_dir.parent),
"calib0_dir": str(calib0_dir),
"calib1_dir": str(calib1_dir),
"calib0_img": str(calib0_name),
"calib1_img": str(calib1_dir),
"json_name": str(json_name)
}
# structural directory
t1_dir = list(
(subject_dir / f'{subject_name}_V1_A/scans').glob('**/*T1w'))[0]
struc_dir = t1_dir / 'resources/NIFTI/files'
struc_name = list(struc_dir.glob(f'**/{subject_name}_*.nii.gz'))[0]
fsl_anat_dir = struc_dir / 'ASL/struc'
calib0struct_dir = struc_dir / 'ASL/Calib/Calib0'
calib1struct_dir = struc_dir / 'ASL/Calib/Calib1'
create_dirs([calib0struct_dir, calib1struct_dir])
# run fsl_anat
fsl_anat(str(struc_name),
str(fsl_anat_dir),
clobber=True,
nosubcortseg=True)
fsl_anat_dir = fsl_anat_dir.parent / f'{fsl_anat_dir.stem}.anat'
t1_name = fsl_anat_dir / 'T1_biascorr.nii.gz'
t1_brain_name = fsl_anat_dir / 'T1_biascorr_brain.nii.gz'
# get ventricles mask if csf
if 'csf' in rois:
# initialise atlas list
atlases.rescanAtlases()
harv_ox_prob_2mm = atlases.loadAtlas('harvardoxford-subcortical',
resolution=2.0)
vent_img = Image(harv_ox_prob_2mm.data[:, :, :, 2] +
harv_ox_prob_2mm.data[:, :, :, 13],
header=harv_ox_prob_2mm.header)
vent_img = fslmaths(vent_img).thr(0.1).bin().ero().run(LOAD)
# we already have registration from T1 to MNI
struc2mni_warp = fsl_anat_dir / 'MNI_to_T1_nonlin_field.nii.gz'
# apply warp to ventricles image
vent_t1_name = fsl_anat_dir / 'ventricles_mask.nii.gz'
applywarp(vent_img,
str(t1_brain_name),
str(vent_t1_name),
warp=str(struc2mni_warp))
# re-threshold
vent_t1 = fslmaths(str(vent_t1_name)).thr(
PVE_THRESHOLDS['csf']).bin().run(LOAD)
# mask pve estimate by ventricles mask
fslmaths(str(fsl_anat_dir / PVE_NAMES['csf'])).mas(vent_t1).run(
str(vent_t1_name))
# bias-field correction
for calib_name in (calib0_name, calib1_name):
calib_name_stem = calib_name.stem.split('.')[0]
# run bet
betted_m0 = bet(str(calib_name), LOAD)
# create directories to save results
fast_dir = calib_name.parent / 'FAST'
biascorr_dir = calib_name.parent / 'BiasCorr'
create_dirs([fast_dir, biascorr_dir])
# run FAST on brain-extracted m0 image
fast_base = fast_dir / calib_name_stem
fast(betted_m0['output'],
out=str(fast_base),
type=3,
b=True,
nopve=True)
bias_name = fast_dir / f'{calib_name_stem}_bias.nii.gz'
# apply bias field to original m0 image (i.e. not BETted)
biascorr_name = biascorr_dir / f'{calib_name_stem}_restore.nii.gz'
fslmaths(str(calib_name)).div(str(bias_name)).run(str(biascorr_name))
# obtain registration from structural to calibration image
mask_dir = biascorr_name.parent / 'masks'
create_dirs([
mask_dir,
])
cmd = [
'asl_reg', f'-i {biascorr_name}', f'-s {t1_name}',
f'--sbet {t1_brain_name}', f'-o {mask_dir}'
]
subprocess.run(" ".join(cmd), shell=True)
# apply transformation to the pve map
for tissue in rois:
roi_dir = mask_dir / tissue
create_dirs([
roi_dir,
])
if tissue == 'combined':
# check that gm and wm pves already exist
gm_pve = mask_dir / 'gm/pve_gm.nii.gz'
wm_pve = mask_dir / 'wm/pve_wm.nii.gz'
if not (gm_pve.exists() and wm_pve.exists()):
raise Exception("WM and GM PVEs don't exist.")
gm_mask_name = roi_dir / 'gm_mask.nii.gz'
wm_mask_name = roi_dir / 'wm_mask.nii.gz'
fslmaths(str(gm_pve)).thr(PVE_THRESHOLDS[tissue]).bin().run(
str(gm_mask_name))
fslmaths(str(wm_pve)).thr(PVE_THRESHOLDS[tissue]).bin().run(
str(wm_mask_name))
gm_masked_name = roi_dir / f'{calib_name_stem}_gm_masked'
wm_masked_name = roi_dir / f'{calib_name_stem}_wm_masked'
fslmaths(str(biascorr_name)).mul(str(gm_mask_name)).run(
str(gm_masked_name))
fslmaths(str(biascorr_name)).mul(str(wm_mask_name)).run(
str(wm_masked_name))
# update dictionary
new_dict = {
f'{calib_name_stem}_{tissue}_masked':
[str(gm_masked_name),
str(wm_masked_name)]
}
else:
if tissue == 'csf':
pve_struct_name = vent_t1_name
else:
pve_struct_name = fsl_anat_dir / PVE_NAMES[tissue]
pve_asl_name = roi_dir / f'pve_{tissue}.nii.gz'
struct2asl_name = mask_dir / 'struct2asl.mat'
applyxfm(str(pve_struct_name), str(biascorr_name),
str(struct2asl_name), str(pve_asl_name))
# threshold and binarise the ASL-space pve map
mask_name = roi_dir / f'{tissue}_mask.nii.gz'
fslmaths(str(pve_asl_name)).thr(
PVE_THRESHOLDS[tissue]).bin().run(str(mask_name))
# apply the mask to the calibration image
masked_name = mask_dir / f'{tissue}_masked.nii.gz'
fslmaths(str(biascorr_name)).mul(str(mask_name)).run(
str(masked_name))
# update dictionary
new_dict = {
f'{calib_name_stem}_{tissue}_masked': str(masked_name)
}
important_dict.update(new_dict)
# save json
with open(json_name, 'w') as fp:
json.dump(important_dict, fp, sort_keys=True, indent=4)
return (subject_dir, 0)
def setup_mtestimation(subject_dir,
coeffs_path,
rois=[
'wm',
],
interpolation=3,
ignore_dropouts=False,
force_refresh=True):
try:
# find files and separate calibration images from mbPCASL sequence
names_dict = setup(subject_dir)
calib0_name, calib1_name = [
names_dict[f"calib{n}_name"] for n in (0, 1)
]
calib_stems = [
c.stem.split(".")[0] for c in (calib0_name, calib1_name)
]
# create results directory
suf = "_ignoredropouts" if ignore_dropouts else ""
results_dirs = [
names_dict[f'calib{n}_dir'] / f"SEbased_MT_t1mask{suf}"
for n in (0, 1)
]
create_dirs(results_dirs)
t1_name, t1_brain_name = [
names_dict[name] for name in ("t1_name", "t1_brain_name")
]
# generate white matter mask in T1 space for use in BBRegistration
wm_mask = names_dict["aslt1_dir"] / "wm_mask.nii.gz"
if not wm_mask.exists() or force_refresh:
nb.save(generate_tissue_mask(names_dict["aparc_aseg"], "wm"),
wm_mask)
# setup distortion correction results directories
distcorr_dir = names_dict["calib0_dir"].parent / "DistCorr"
gdc_dir = distcorr_dir / "gradient_unwarp"
topup_dir = distcorr_dir / "topup"
calib_distcorr_dirs = [r / "DistCorr" for r in results_dirs]
create_dirs((distcorr_dir, gdc_dir, topup_dir, *calib_distcorr_dirs))
# gradient distortion correction
gdc_warp = gdc_dir / "fullWarp_abs.nii.gz"
if not gdc_warp.exists() or force_refresh:
distortion_correction.generate_gdc_warp(names_dict["calib0_name"],
coeffs_path, gdc_dir,
interpolation)
# topup
fmap, fmapmag, fmapmagbrain = [
topup_dir / f"fmap{ext}.nii.gz" for ext in ("", "mag", "magbrain")
]
if not all([f.exists()
for f in (fmap, fmapmag, fmapmagbrain)]) or force_refresh:
pa_ap_sefms = topup_dir / "merged_sefms.nii.gz"
distortion_correction.stack_fmaps(names_dict["pa_sefm"],
names_dict["ap_sefm"],
pa_ap_sefms)
topup_params = topup_dir / "topup_params.txt"
distortion_correction.generate_topup_params(topup_params)
topup_config = "b02b0.cnf"
distortion_correction.generate_fmaps(pa_ap_sefms, topup_params,
topup_config, topup_dir,
gdc_warp)
# load gdc warp
gdc_warp_reg = rt.NonLinearRegistration.from_fnirt(
coefficients=str(gdc_warp),
src=str(calib0_name),
ref=str(calib0_name),
intensity_correct=True)
# apply gdc and epidc to both calibration images
for calib_name, results_dir in zip((calib0_name, calib1_name),
calib_distcorr_dirs):
# apply gdc to the calibration image
calib_name_stem = calib_name.stem.split(".")[0]
gdc_calib_name = results_dir / f"{calib_name_stem}_gdc.nii.gz"
if not gdc_calib_name.exists() or force_refresh:
gdc_calib = gdc_warp_reg.apply_to_image(str(calib_name),
str(calib_name),
order=interpolation)
nb.save(gdc_calib, gdc_calib_name)
# estimate initial registration via asl_reg
asl_lin_reg = results_dir / "asl_reg_linear"
asl_lin_reg.mkdir(exist_ok=True)
asl2struct_lin = asl_lin_reg / "asl2struct.mat"
if not asl2struct_lin.exists() or force_refresh:
linear_asl_reg(gdc_calib_name, asl_lin_reg, t1_name,
t1_brain_name, wm_mask)
init_linear = rt.Registration.from_flirt(str(asl2struct_lin),
src=str(calib_name),
ref=str(t1_name))
# run bet - should I instead get mask from T1 here?
bet_results = results_dir / "bet"
bet_mask = results_dir / "bet_mask.nii.gz"
if not bet_mask.exists() or force_refresh:
betted_m0 = bet(str(gdc_calib_name),
str(bet_results),
g=0.2,
f=0.2,
m=True)
# get epi distortion correction warps
asl_nonlin_reg = results_dir / "asl_reg_nonlinear"
asl_nonlin_reg.mkdir(exist_ok=True)
struct2asl, asl2struct_warp = [
asl_nonlin_reg / n
for n in ("struct2asl.mat", "asl2struct_warp.nii.gz")
]
if not all([f.exists() for f in (asl2struct_warp, struct2asl)
]) or force_refresh:
distortion_correction.generate_epidc_warp(
str(gdc_calib_name), str(t1_name), t1_brain_name, bet_mask,
wm_mask, init_linear, fmap, fmapmag, fmapmagbrain,
str(asl_nonlin_reg))
# chain gradient and epi distortion correction warps together
asl2struct_warp_reg = rt.NonLinearRegistration.from_fnirt(
coefficients=str(asl2struct_warp),
src=str(calib_name),
ref=str(t1_name),
intensity_correct=True)
struct2asl_reg = rt.Registration.from_flirt(str(struct2asl),
src=str(t1_name),
ref=str(calib_name))
dc_warp = rt.chain(gdc_warp_reg, asl2struct_warp_reg,
struct2asl_reg)
dc_calib_name = results_dir / f"{calib_name_stem}_dc.nii.gz"
if not dc_calib_name.exists() or force_refresh:
dc_calib = dc_warp.apply_to_image(str(calib_name),
str(calib_name),
order=interpolation)
nb.save(dc_calib, dc_calib_name)
# estimate the bias field
bias_name = results_dir / f"{calib_name_stem}_bias.nii.gz"
sebased_dir = results_dir / "sebased"
if not bias_name.exists() or force_refresh:
sebased_dir.mkdir(exist_ok=True)
bias_field = bias_estimation(
dc_calib_name,
"sebased",
results_dir=sebased_dir,
t1_name=t1_name,
t1_brain_name=t1_brain_name,
aparc_aseg=names_dict["aparc_aseg"],
fmapmag=fmapmag,
fmapmagbrain=fmapmagbrain,
interpolation=interpolation,
force_refresh=force_refresh,
wmseg_name=wm_mask,
struct2asl=struct2asl)
nb.save(bias_field, bias_name)
else:
bias_field = nb.load(bias_name)
# bias correct the distortion-corrected calibration image
bc_calib_name = results_dir / f"{calib_name_stem}_restore.nii.gz"
if not bc_calib_name.exists() or force_refresh:
fslmaths(str(dc_calib_name)).div(bias_field).run(
str(bc_calib_name))
# create mask directories
roi_dirs = [results_dir / "masks" / roi for roi in rois]
create_dirs(roi_dirs)
# load Dropouts
dropouts_inv = nb.load(sebased_dir / "Dropouts_inv.nii.gz")
for roi, roi_dir in zip(rois, roi_dirs):
# get tissue masks in calibration image space
base_mask_call = partial(generate_tissue_mask_in_ref_space,
aparc_aseg=names_dict["aparc_aseg"],
ref_img=bc_calib_name,
struct2ref=struct2asl,
order=0)
tissues = ("gm", "wm") if roi == "combined" else (roi, )
names = [roi_dir / f"{t}_mask.nii.gz" for t in tissues]
if not all(n.exists() for n in names) or force_refresh:
if roi == "csf":
masks = [
base_mask_call(tissue=t, erode=True)
for t in tissues
]
else:
masks = [base_mask_call(tissue=t) for t in tissues]
[nb.save(m, n) for m, n in zip(masks, names)]
else:
masks = [nb.load(n) for n in names]
if ignore_dropouts:
# ignore dropout voxels
masks = [
nb.nifti1.Nifti1Image(
m.get_fdata() * dropouts_inv.get_fdata(),
affine=dropouts_inv.affine) for m in masks
]
# save
[nb.save(m, n) for m, n in zip(masks, names)]
# apply tissue masks to bias- and distortion- corrected images
calib_masked_names = [
roi_dir / f"{calib_name_stem}_{t}_masked.nii.gz"
for t in tissues
]
if not all(c.exists()
for c in calib_masked_names) or force_refresh:
[
fslmaths(str(bc_calib_name)).mul(mask).run(str(name))
for mask, name in zip(masks, calib_masked_names)
]
return (subject_dir, 1)
except Exception as e:
print(e)
return (subject_dir, e)
def test_bet():
with asrt.disabled(), run.dryrun(), mockFSLDIR(bin=('bet', )) as fsldir:
bet = op.join(fsldir, 'bin', 'bet')
result = fw.bet('input', 'output', mask=True, c=(10, 20, 30))
expected = (bet + ' input output', ('-m', '-c 10 20 30'))
assert checkResult(result.stdout[0], *expected, stripdir=[2])
def brain(wsp, img, thresh=0.5):
"""
Extract brain from wholehead image
"""
bet_result = fsl.bet(img, seg=True, mask=False, fracintensity=thresh, output=fsl.LOAD, log=wsp.fsllog)
return bet_result["output"]