Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis

TL;DR

This paper introduces FIRE, an unsupervised deep learning model that accurately registers multi-modality and intra-modality MRI images, enhancing clinical analysis and interpretability through inverse-consistency and topology preservation.

Contribution

The work presents a novel unsupervised registration framework that models both affine and non-rigid transformations simultaneously with inverse-consistency, improving registration accuracy across MRI modalities.

Findings

FIRE outperforms baseline methods on multi-modality brain MRI datasets.

FIRE achieves higher accuracy in intra-modality cardiac MRI registration.

The model maintains topology and anatomical integrity during registration.

Abstract

Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, affine and non-rigid image registration is not extensively investigated using a single unsupervised model architecture. In our work, we present an un-supervised deep learning registration methodology which can accurately model affine and non-rigid trans-formations, simultaneously. Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms. To address inverse-consistency, our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent rep-resentations, while involves two factorised transformation networks and an inverse-consistency loss to learn topology-preserving anatomical transformations. Overall, our model (named "FIRE") shows improved performances against the reference standard baseline method on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI data experiments.