Spatio-temporal Fetal Brain MRI Atlas embedded (Gholipour et al., 2017)

v0.6

Spatio-temporal Fetal Brain MRI Atlas added (Gholipour et al., 2017)

Minor change in default parameters (run_reconstruction_pipeline)

run_reconstruction_pipeline uses mask-constrained bias field correction by default to speed-up preprocessing steps

NiftyMIC version export to JSON config file

Each script execution exports the respective parameter configuration into a JSON configuration file.
Since this release, the used NiftyMIC version gets exported too.

SDA/SRR for motion-corrected mask recon

• SDA/SRR mask reconstruction from motion corrected slices
• SDA reconstructions possible to speed up test computations
• nifti2dicom/multiply/run_reconstruction_pipeline executables added
• various bug fixes

Slice thickness of stack acquisitions can be specified

• Slice thickness of image stack acquisitions can be manually specified
• Choice between FLIRT/RegAladin for volume-to-volume registration

Outlier-robust SRR; Python 3 support

v0.3

Outlier-robust SRR; Python 3 compatibility

Version 0.2

• Volumetric reconstruction of isotropic, high-resolution volume from (motion corrupted) 2D ultra-fast MRI of low-resolution slices
  • Motion correction based on rigid slice-to-volume registration
  • Multiple reconstruction approaches:
    • TK0L2, TK1L2, TVL2 and HuberL2
    • Robust L2 term choices
• "Optimal" reconstruction parameters can be estimated via parameter study script (based on NSoL)
• Available preprocessing methods:
  • N4ITK bias field correction
  • Intensity correction
• Validation approaches (new in v0.2): Quantitative and qualitative comparison of original and simulated slices projected from estimated HR volume

Version 0.1

• Volumetric reconstruction of isotropic, high-resolution volume from (motion corrupted) 2D ultra-fast MRI of low-resolution slices
  • Motion correction based on rigid slice-to-volume registration
  • Multiple reconstruction approaches:
    • TK0L2, TK1L2, TVL2 and HuberL2
    • Robust L2 term choices
• "Optimal" reconstruction parameters can be estimated via parameter study script (based on NSoL)
• Available preprocessing methods:
  • N4ITK bias field correction
  • Intensity correction