DiffeoRaptor: Diffeomorphic Inter-modal Image Registration using RaPTOR

TL;DR

DiffeoRaptor is a novel diffeomorphic inter-modal image registration algorithm that uses a robust correlation metric and Fourier-based geodesic shooting, achieving accurate and smooth alignments in medical imaging applications.

Contribution

It introduces a new inter-modal registration method combining RaPTOR metric with FLASH algorithm for fast, accurate, and smooth diffeomorphic registration.

Findings

Achieved comparable or better accuracy than state-of-the-art methods.

Produced smoother deformations in inter-modal registration.

Validated on three public datasets for brain and abdominal images.

Abstract

Purpose: Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. Methods: We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio (RaPTOR) metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. Results: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image normalization (SyN). Conclusions: Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor.