Deep Group-wise Variational Diffeomorphic Image Registration

TL;DR

This paper introduces a deep learning framework for simultaneous multi-image registration that extends existing pair-wise methods to handle multiple images, using a mathematical model based on variational and diffeomorphic principles.

Contribution

It presents a novel general framework for multi-image registration, incorporating a likelihood based on normalized mutual information and a prior for deformation regularization.

Findings

Competitive performance in breast MRI and thoracic 4DCT registration

Faster registration compared to Elastix and VoxelMorph

Effective control over deformation regularization

Abstract

Deep neural networks are increasingly used for pair-wise image registration. We propose to extend current learning-based image registration to allow simultaneous registration of multiple images. To achieve this, we build upon the pair-wise variational and diffeomorphic VoxelMorph approach and present a general mathematical framework that enables both registration of multiple images to their geodesic average and registration in which any of the available images can be used as a fixed image. In addition, we provide a likelihood based on normalized mutual information, a well-known image similarity metric in registration, between multiple images, and a prior that allows for explicit control over the viscous fluid energy to effectively regularize deformations. We trained and evaluated our approach using intra-patient registration of breast MRI and Thoracic 4DCT exams acquired over multiple time points. Comparison with Elastix and VoxelMorph demonstrates competitive quantitative performance of the proposed method in terms of image similarity and reference landmark distances at significantly faster registration.