Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit: MIAL Super-Resolution Toolkit v2.1.0

Medical Image Analysis Laboratory Super-Resolution ToolKit 2 (MIALSRTK2) consists of a set of C++ and Python processing and workflow tools necessary to perform motion-robust super-resolution fetal MRI reconstruction in the BIDS Apps framework.

This new version of mialsrtk

• Computation of high resolution segmentation maps from low-resolution segmentations - do_reconstruct_labels option.
• Added an optional super-resolution reconstruction assessment stage where the SR reconstruction is compared to a provided high resolution ground truth image - do_srr_assessment option.
• Added the possibility to re-orient the SR reconstruction into the anatomical planes defined by the spatio-temporal atlas of Gholipour et al. (2017) used in NiftyMIC - do_anat_orientation option.

• Improved flexibility of the pipeline. Added several options to be given in the config to facilitate various experiments. Details are available in the documentation
  • skip_preprocessing allows to directly run registration and TV reconstruction without pre-processing.
  • --run_type: sr (default) or preprocessing. Allows to run only preprocessing.
• Improved computational workflow.
  • Added an option to perform reconstruction using mulitple TV parameters efficiently, without needing to register the image several times.
  • Added a reduction of the field-of-view at the beginning of pre-processing to accelerate computations.
• Output organisation.
  • Creation of various subworkflows for a clearer view at the processing graph.
  • Improved the naming of outputs of pymialsrtk to be BIDS compliant.
• User-friendliness.
  • Given a subject, a run that fails will immediately crash and move to the next subject, instead of silently failing all modules. The message will be recorder and output at the end of the pipeline. This makes error tracking easier throughout the pipeline.
  • Reports were updated to display directly the image of the SR reconstruction rather than the processing graph.