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Deep Diffusion MRI Registration (DDMReg): code release via SlicerDMRI

Key Investigators

Project Description

In this project, we will work on releasing the code of a diffusion MRI registration method (Zhang et al 2022, IEEE TMI). We will provide the trained CNN model and testing cases for demonstration.


  1. Code cleaning.
  2. Release code.
  3. Documentation and example testing data.


Approach and Plan

  1. Code released at: DDMReg
  2. Pre-trained models are provided
  3. Exampe data and documentation are provided

Progress and Next Steps

  1. Code released as a standard python package.
  2. Intergate into SlicerDMRI so that users can use via Slicer interface.



Background and References

Zhang, Fan, William M. Wells, and Lauren J. O’Donnell. “Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration.” IEEE TMI (2022).

In this paper, we present a deep learning method, DDMReg, for accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. DDMReg is a novel method that uses joint whole-brain and tract-specific information for dMRI registration. Based on the successful VoxelMorph framework for image registration, we propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. DDMReg is an unsupervised method for deformable registration between pairs of dMRI datasets: it does not require nonlinearly pre-registered training data or the corresponding deformation fields as ground truth. We perform comparisons with four state-of-the-art registration methods on multiple independently acquired datasets from different populations (including teenagers, young and elderly adults) and different imaging protocols and scanners. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance compared to the state-of-the-art methods. Importantly, we demonstrate successful generalization of DDMReg to dMRI data from different populations with varying ages and acquired using different acquisition protocols and different scanners.