def process_run(rundir, subid, logger):
"""
1. Find raw
2. Split func into xfm dir
3. Align func, create mean and 4d
4. Bias correct anat
5. Invxfm anat2func
6. Generate movement files
7. Clean up
"""
_, run = os.path.split(rundir)
rawdir = os.path.join(rundir, 'raw')
exists, mvdir, xfmdir = check_make_dirs(rundir, logger)
if exists:
return
anat, aparc, func = find_raw_files(rawdir, subid, logger)
if anat is None:
return
funcs = split_file(func, xfmdir, subid, run, logger)
if funcs is None:
return
movement, movedfuncs = align_funcs(funcs, logger)
exists, refunc = join_files(movedfuncs, rundir, subid, run)
if not exists:
return
#Write movement and displacement
logger.info('Write movement')
save_movement(movement, mvdir)
#Make mean from aligned
meanfunc = reg.make_mean(movedfuncs)
#Bias correct anat
b4anat = reg.n4_biascorrect(anat)
if b4anat is None:
logger.error('{0}: Bias correction failed'.format(anat))
return
#Bias correct mean
b4meanfunc = reg.n4_biascorrect(meanfunc)
if b4meanfunc is None:
logger.error('{0}: Bias correction failed'.format(meanfunc))
return
#Reslice mean to anat space
rb4meanfunc = reslice_mean(b4anat, b4meanfunc)
#Make mask by growing func (5x5x5 kernel) and mask with anat
mask = reg.grow_mask(rb4meanfunc, size= 3)
#Find mapping from func to anat
func2anat_xfm = reg.affine_register_mi(b4anat, b4meanfunc, mask= mask)
#Apply inverse mapping to anat
anat2func = reg.apply_transform(b4anat, '-i %s'%(func2anat_xfm),
target=meanfunc, use_nn = False)
#Apply transform to aparc nearest neighbor
aparc2func = reg.apply_transform(aparc, '-i %s'%(func2anat_xfm),
target=meanfunc, use_nn = True)
#Move
for item in (anat2func, aparc2func):
cmd = 'mv %s %s'%(item, rundir)
os.system(cmd)
def process_runs(rundir, subid, run3meanfunc, logger):
_, run = os.path.split(rundir)
rawdir = os.path.join(rundir, 'raw')
exists, mvdir, xfmdir = check_make_dirs(rundir, logger)
if exists:
return
_, run3 = os.path.split(run3meanfunc)
cmd = 'ln -s %s %s'%(run3meanfunc, xfmdir)
os.system(cmd)
run3meanfunc = os.path.join(xfmdir, run3)
func = os.path.join(rawdir, '{0}_4d_func.nii.gz'.format(subid))
funcs = split_file(func, xfmdir, subid, run, logger)
if funcs is None:
return
movement, affines, moved = make_affines(funcs, logger)
logger.info('Write movement')
save_movement(movement, mvdir)
meanfunc = reg.make_mean(moved)
for f in moved[1:]:
os.remove(f)
threshold_mean(meanfunc, logger, thresh=275)
b4meanfunc = reg.n4_biascorrect(meanfunc)
if b4meanfunc is None:
logger.error('{0}: Bias correction failed'.format(meanfunc))
return
movedfuncs = align_means(funcs, b4meanfunc, run3meanfunc, affines, logger)
exists, refunc = join_files(movedfuncs, rundir, subid, run)
if not exists:
return
def bias_correct(data, outdir): b4data = reg.n4_biascorrect(data) _, b4nme = os.path.split(b4data) cmd = 'mv %s %s'%(b4data, outdir) os.system(cmd) b4data = os.path.join(outdir, b4nme) return b4data
def bias_correct(data, logger, outdir):
b4data = reg.n4_biascorrect(data)
if b4data is None:
logger.error('{0}: Bias correction failed'.format(data))
return
_, b4nme = os.path.split(b4data)
cmd = 'mv %s %s'%(b4data, outdir)
os.system(cmd)
b4data = os.path.join(outdir, b4nme)
return b4data
def process_run3(rundir, subid, logger):
"""
1. Find raw
2. Split func into xfm dir
3. Align func, create mean and 4d
4. Bias correct anat
5. Invxfm anat2func
6. Generate movement files
7. Clean up
"""
_, run = os.path.split(rundir)
rawdir = os.path.join(rundir, 'raw')
exists, mvdir, xfmdir = check_make_dirs(rundir, logger)
if exists:
return
anat, aparc, func = find_raw_files(rawdir, subid, logger)
if anat is None:
return
funcs = split_file(func, xfmdir, subid, run, logger)
if funcs is None:
return
movement, movedfuncs = align_funcs(funcs, logger)
exists, refunc = join_files(movedfuncs, rundir, subid, run)
if not exists:
return
#Write movement and displacement
logger.info('Write movement')
save_movement(movement, mvdir)
#Make mean from aligned
meanfunc = reg.make_mean(movedfuncs)
#Bias correct anat
b4anat = reg.n4_biascorrect(anat)
if b4anat is None:
logger.error('{0}: Bias correction failed'.format(anat))
return
_, b4anatname = os.path.split(b4anat)
cmd = 'mv %s %s'%(b4anat, xfmdir)
os.system(cmd)
b4anat = os.path.join(xfmdir, b4anatname)
# threshold mean to remove "cruft" current value == 300
threshold_mean(meanfunc, logger, thresh=300)
#Bias correct mean
#b4meanfunc = reg.n4_biascorrect(meanfunc)
#if b4meanfunc is None:
# logger.error('{0}: Bias correction failed'.format(meanfunc))
# return
#Reslice mean to anat space
b4meanfunc = meanfunc
rb4meanfunc = reslice_mean(b4anat, b4meanfunc)
#Make mask by growing func (5x5x5 kernel) and mask with anat
# mask = reg.grow_mask(rb4meanfunc, size= 3)
#Find mapping from func to anat
func2anat_xfm = reg.affine_register_mi(b4anat, b4meanfunc, mask= None)
#Apply inverse mapping to anat
anat2func = reg.apply_transform(b4anat, '-i %s'%(func2anat_xfm),
target=meanfunc, use_nn = False)
#Apply transform to aparc nearest neighbor
aparc2func = reg.apply_transform(aparc, '-i %s'%(func2anat_xfm),
target=meanfunc, use_nn = True)
#Skull strip shit
pth, aparcnme = os.path.split(aparc2func)
aparc_mask = os.path.join(datadir, 'mask_{0}'.format(aparcnme))
mask_from_aparc(aparc2func, aparc_mask)
#masked_b4meanfunc = b4meanfunc.replace('n4_', 'masked_n4_')
#mask_img(b4meanfunc, aparc_mask, masked_b4meanfunc)
#Move
for item in (anat2func, aparc2func):
cmd = 'mv %s %s'%(item, rundir)
os.system(cmd)
return b4meanfunc
def process_runs(rundir, subid, run3meanfunc, logger):
_, run = os.path.split(rundir)
rawdir = os.path.join(rundir, 'raw')
exists, mvdir, xfmdir = check_make_dirs(rundir, logger)
if exists:
return
_, run3 = os.path.split(run3meanfunc)
cmd = 'cp %s %s'%(run3meanfunc, xfmdir)
os.system(cmd)
run3meanfunc = os.path.join(xfmdir, run3)
anat, aparc, func = find_raw_files(rawdir, subid, logger)
if anat is None:
return
funcs = split_file(func, xfmdir, subid, run, logger)
if funcs is None:
return
movement, affines, moved = make_affines(funcs, logger)
logger.info('Write movement')
save_movement(movement, mvdir)
meanfunc = reg.make_mean(moved)
#Remove old movedfuncs
newdir = os.path.join(xfmdir, 'first_realign')
os.mkdir(newdir)
for f in moved[1:]:
cmd = 'mv %s %s'%(f, newdir)
os.system(cmd)
#Bias correct anat
b4anat = reg.n4_biascorrect(anat)
if b4anat is None:
logger.error('{0}: Bias correction failed'.format(anat))
return
_, b4anatname = os.path.split(b4anat)
cmd = 'mv %s %s'%(b4anat, xfmdir)
os.system(cmd)
b4anat = os.path.join(xfmdir, b4anatname)
# threshold mean to remove "cruft" current value == 300
threshold_mean(meanfunc, logger, thresh=300)
#Bias correct mean
#b4meanfunc = reg.n4_biascorrect(meanfunc)
#if b4meanfunc is None:
# logger.error('{0}: Bias correction failed'.format(meanfunc))
# return
b4meanfunc = meanfunc
#Reslice mean to anat space
rb4meanfunc = reslice_mean(b4anat, b4meanfunc)
#Make mask by growing func (5x5x5 kernel) and mask with anat
# mask = reg.grow_mask(rb4meanfunc, size= 3)
#Find mapping from func to anat
func2anat_xfm = reg.affine_register_mi(b4anat, b4meanfunc, mask= None)
#Apply inverse mapping to anat
anat2func = reg.apply_transform(b4anat, '-i %s'%(func2anat_xfm),
target=meanfunc, use_nn = False)
#Apply transform to aparc nearest neighbor
aparc2func = reg.apply_transform(aparc, '-i %s'%(func2anat_xfm),
target=meanfunc, use_nn = True)
#Skull strip shit
pth, aparcnme = os.path.split(aparc2func)
aparc_mask = os.path.join(rundir, 'mask_{0}'.format(aparcnme))
mask_from_aparc(aparc2func, aparc_mask)
#masked_b4meanfunc = b4meanfunc.replace('n4_', 'masked_n4_')
#mask_img(b4meanfunc, aparc_mask, masked_b4meanfunc)
#Make new moved funcs based on 2 affines
movedfuncs = align_means(funcs, b4meanfunc, run3meanfunc, affines, logger)
exists, refunc = join_files(movedfuncs, rundir, subid, run)
if not exists:
return
#Move
for item in (anat2func, aparc2func):
cmd = 'mv %s %s'%(item, rundir)
os.system(cmd)
