def consistencyCheck(ref_csv,
outputBshellFile=None,
outPutResolutionFile=None):
try:
ref_imgs, _ = read_imgs_masks(ref_csv)
except:
ref_imgs = read_imgs(ref_csv)
if isfile(outputBshellFile) and isfile(outPutResolutionFile):
ref_bvals = read_bvals(outputBshellFile)
ref_res = np.load(outPutResolutionFile)
else:
ref_bshell_img = ref_imgs[0]
print(f'Using {ref_bshell_img} to determine b-shells')
inPrefix = abspath(ref_bshell_img).split('.nii')[0]
ref_bvals = findBShells(inPrefix + '.bval', outputBshellFile)
ref_res = load(ref_bshell_img).header['pixdim'][1:4]
np.save(outPutResolutionFile, ref_res)
print('b-shells are', ref_bvals)
print('\nSite', ref_csv, '\n')
print('Checking consistency of b-shells among subjects')
check_bshells(ref_imgs, ref_bvals)
print('spatial resolution is', ref_res)
print('Checking consistency of spatial resolution among subjects')
check_resolution(ref_imgs, ref_res)
def separateAllBshells(ref_csv, ref_bvals_file, ncpu=4, outPrefix=None):
ref_bvals = read_bvals(ref_bvals_file)
try:
imgs, masks = read_imgs_masks(ref_csv)
except:
imgs = read_imgs(ref_csv)
masks = None
pool = Pool(int(ncpu))
for imgPath in imgs:
pool.apply_async(separateBshells,
kwds={
'imgPath': imgPath,
'ref_bvals': ref_bvals
},
error_callback=RAISE)
pool.close()
pool.join()
if outPrefix:
outPrefix = abspath(outPrefix)
else:
return
for bval in ref_bvals:
f = open(f'{outPrefix}_b{int(bval)}.csv', 'w')
if masks:
for imgPath, maskPath in zip(imgs, masks):
inPrefix = abspath(imgPath).split('.nii')[0]
bPrefix = inPrefix + f'_b{int(bval)}'
f.write(f'{bPrefix}.nii.gz,{maskPath}\n')
else:
for imgPath in imgs:
inPrefix = abspath(imgPath).split('.nii')[0]
bPrefix = inPrefix + f'_b{int(bval)}'
f.write(f'{bPrefix}.nii.gz\n')
f.close()
def joinAllBshells(tar_csv, ref_bvals_file, separatedPrefix=None, ncpu=4):
ref_bvals = read_bvals(ref_bvals_file)
if tar_csv:
try:
imgs, _ = read_imgs_masks(tar_csv)
except:
imgs = read_imgs(tar_csv)
pool = Pool(int(ncpu))
for imgPath in imgs:
pool.apply_async(joinBshells,
kwds=({
'imgPath': imgPath,
'ref_bvals': ref_bvals,
'sep_prefix': separatedPrefix
}),
error_callback=RAISE)
pool.close()
pool.join()
def main():
parser = argparse.ArgumentParser(description='''Warps diffusion measures (FA, MD, GFA) to template space
and then to MNI space. Finally, calculates mean FA over IITmean_FA_skeleton.nii.gz''')
parser.add_argument('-i', '--input', type=str, required=True,
help='a .txt/.csv file having one column for FA imgs, '
'or two columns for (img,mask) pair, the latter list is what you used in/obtained from harmonization.py. '
'See pnlbwh/dMRIharmonization documentation for more details')
parser.add_argument('-s', '--site', type= str, required=True,
help='site name for locating template FA and mask in tempalte directory')
parser.add_argument('-t', '--template', type=str, required=True,
help='template directory where Mean_{site}_FA.nii.gz and {site}_Mask.nii.gz is located')
parser.add_argument('--bshell_b', required=True, help='bvalue of the bshell')
parser.add_argument('--ncpu', help='number of cpus to use', default= '4')
args = parser.parse_args()
imgList=abspath(args.input)
siteName=args.site
templatePath=abspath(args.template)
bshell_b= int(args.bshell_b)
N_proc= int(args.ncpu)
# read FA image list
try:
imgs, _ = read_imgs_masks(imgList)
print('imgs,masks list is provided. FA images are assumed to be directoryOfImg/dti/ImgPrefix_FA.nii.gz, make sure they are there')
faImgs= []
for imgPath in imgs:
directory = dirname(imgPath)
prefix = basename(imgPath).split('.nii')[0]
faImg= pjoin(directory, 'dti', prefix+ '_FA.nii.gz')
if not isfile(faImg):
raise FileNotFoundError(f'{faImg} not found. Did you run \"--create --debug\" and \"--process --debug\" before?')
faImgs.append(faImg)
except:
faImgs= read_imgs(imgList)
print('FA image list is provided.')
# register and obtain *_InMNI_FA.nii.gz
mniFAimgs= sub2tmp2mni(templatePath, siteName, faImgs, bshell_b, N_proc)
# save statistics for future
statFile= os.path.join(self.templatePath, 'meanFAstat.txt')
f= open(statFile,'a')
stdout= sys.stdout
sys.stdout= f
print(datetime.now().strftime('%c'),'\n')
print('b-shell', bshell_b, '\n')
# pass *_InMNI_FA.nii.gz list to analyzeStat
site_means= analyzeStat(mniFAimgs)
print(f'{siteName} site: ')
printStat(site_means, mniFAimgs)
f.close()
sys.stdout= stdout
# print statistics on console
print('')
with open(statFile) as f:
print(f.read())
print('\nThe statistics are also saved in ', statFile)
def main():
parser = argparse.ArgumentParser(
description='''Warps diffusion measures (FA, MD, GFA) to template space
and then to MNI space. Finally, calculates mean FA over IITmean_FA_skeleton.nii.gz'''
)
parser.add_argument(
'-i',
'--input',
type=str,
required=True,
help=
'a .txt/.csv file that you used in/obtained from harmonization.py having two columns for (img,mask) pair. '
'See documentation for more details')
parser.add_argument(
'-s',
'--site',
type=str,
required=True,
help='site name for locating template FA and mask in template directory'
)
parser.add_argument(
'-t',
'--template',
type=str,
required=True,
help=
'template directory where Mean_{site}_FA.nii.gz and {site}_Mask.nii.gz is located'
)
parser.add_argument('--ncpu', help='number of cpus to use', default='4')
args = parser.parse_args()
imgList = abspath(args.input)
siteName = args.site
templatePath = abspath(args.template)
N_proc = int(args.ncpu)
# read FA image list
try:
imgs, _ = read_imgs_masks(imgList)
print(
'(Img,Mask) list is provided. FA images are assumed to be directoryOfImg/dti/ImgPrefix_FA.nii.gz, make sure they are there\n'
)
faImgs = []
for imgPath in imgs:
directory = dirname(imgPath)
prefix = basename(imgPath).split('.nii')[0]
faImg = pjoin(directory, 'dti', prefix + '_FA.nii.gz')
if not isfile(faImg):
raise FileNotFoundError(
f'{faImg} not found. Did you run \"--create --debug\" and \"--process --debug\" before?'
)
faImgs.append(faImg)
except:
faImgs = read_imgs(imgList)
print('FA image list is provided.')
# register and obtain *_InMNI_FA.nii.gz
mniFAimgs = sub2tmp2mni(templatePath, siteName, faImgs, N_proc)
# target harmonized
if imgList.endswith('.modified.harmonized'):
header = siteName + '_after'
# reference
elif imgList.endswith('.modified'):
header = siteName
# target unprocessed
else:
header = siteName + '_before'
# FIXME user FA image list will use the header {siteName+'_before'}, which is not correct all the time
# as shown in the above block:
# reference should use {siteName} while harmonized target should use {siteName+'_after'}
# impact of this discrepancy is minor since we deprecated use of FA image list
outPrefix = pjoin(templatePath, header)
print('\n\nComputing statistics\n\n')
print(f'{siteName} site: ')
site_means = analyzeStat(mniFAimgs)
generate_csv(faImgs, site_means, outPrefix)
# save statistics for future
statFile = pjoin(templatePath, 'meanFAstat.csv')
with open(statFile, 'a') as f:
f.write(datetime.now().strftime('%m/%d/%y %H:%M') +
',mean meanFA,std meanFA\n')
f.write(f'{header},{np.mean(site_means)},{np.std(site_means)}\n')
# print an empty line so future results, if appended, are visually separate
f.write('\n')
# print statistics on console
print('\n\nPrinting statistics\n\n')
with open(statFile) as f:
print(f.read())
# generate demonstrative plots
ebar = harm_plot([site_means], [header], outPrefix)
print(
f'\nDetailed statistics, summary results, and demonstrative plots are saved in:\n\n{outPrefix}_stat.csv'
f'\n{statFile}\n{ebar}\n')