renbem/niftymic repository overview

NiftyMIC⁠ is a Python-based open-source toolkit for research developed within the GIFT-Surg⁠ project to reconstruct an isotropic, high-resolution volume from multiple, possibly motion-corrupted, stacks of low-resolution 2D slices. The framework relies on slice-to-volume registration algorithms for motion correction and reconstruction-based Super-Resolution (SR) techniques for the volumetric reconstruction.

The algorithm and software were developed by Michael Ebner⁠ at the Wellcome/EPSRC Centre for Interventional and Surgical Sciences⁠, University College London (UCL)⁠ (2015 -- 2019), and the Department of Surgical and Interventional Sciences⁠, King's College London (KCL)⁠ (since 2019).

A detailed description of the NiftyMIC algorithm as part of a fully automated reconstruction framework for fetal brain MRI is found in EbnerWang2019⁠ and on the associated GitHub⁠ page.

Docker image notes:

• This image builds on SimpleReg dependencies⁠ (which builds on ITK_NiftyMIC⁠)
• CPU-version of MONAIfbs⁠ (replaced fetal_brain_seg⁠ in v0.8) is installed to automatically segment fetal brains
• The currently available version does not allow for ITK-Snap visualizations within Docker (i.e. no on-the-fly visualizations of intermediate/final reconstructions with '--verbose 1' possible within the Docker container)