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This script fills lesions in the T1 image allowing different ways using the patch-based method Prados et al. MICCAI 2014.

Usage: niftkT1PDT2Lesions.py -t1 <filename> -lesion <filename> -out <filename> [options]

Mandatory Arguments:
 
  -t1 <filename>  : T1 image filename (NIFTI .nii .nii.gz)
  -lesion <filename>  : lesion mask filename in PD/T2 space (JIM FORMAT .roi or NIFTI .nii .nii.gz)
  -out <filename> : output image filename (NIFTI .nii .nii.gz)

Optional Arguments:

  -t2 <filename>  : T2 image filename (NIFTI .nii .nii.gz)
  -pd <filename>  : PD image filename (it is needed if you haven't Pseudo T1)
  -pdt2 <filename>  : give as input PDT2 in the same file (first time point PD, second time point T2), then you don't need to provide as input T2 and PD image as -t2 and -pd arguments
  -pt1 <filename> : specify pseudo T1 output filename
  -t1_space   : lesions are drawn in T1 space, then you don't need to provide as input T2 and PD image as -t2 and -pd arguments
  -debug    : debug mode doesn't delete temporary intermediate images
  -output_dir <path>  : specify the output dir name
  -bin      : nearest neighboor interpolation method (by default is linear) 
  -mask_mode <value>  : See Masker -h, by default any, possible values: half, all or any
  -dil <int>    : number of dilations for the lesion mask, by default is 0
  -atlas_t1 <filename>  : is the t1 atlas file (by default: /usr/share/fsl/data/standard/MNI152_T1_1mm.nii.gz)
  -atlas_t2 <filename>  : is the t2 atlas data file (by default: /local/SPM12b/canonical/avg152T2.nii)
  -atlas_mask <filename>: is the atlas mask file (by default: /usr/share/fsl/data/standard/MNI152_T1_1mm_brain_mask.nii.gz)
  -leap     : use LEAP method Chard et al. JMRI 2010, by default use the patch-based method Prados et al. MICCAI 2014