MIND Demons: Symmetric Diffeomorphic Deformable Registration of MR and CT for Image-Guided Spine Surgery

Abstract

Intraoperative localization of target anatomy and critical structures defined in preoperative MR/CT images can be achieved through the use of multimodality deformable registration. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality-independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. The method, called MIND Demons, finds a deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the integrated velocity fields, a modality-insensitive similarity function suitable to multimodality images, and smoothness on the diffeomorphisms themselves. Direct optimization without relying on the exponential map and stationary velocity field approximation used in conventional diffeomorphic Demons is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, normalized MI (NMI) Demons, and MIND with a diffusion-based registration method (MIND-elastic). The method yielded sub-voxel invertibility (0.008 mm) and nonzero-positive Jacobian determinants. It also showed improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.7 mm compared to 11.3, 3.1, 5.6, and 2.4 mm for MI FFD, LMI FFD, NMI Demons, and MIND-elastic methods, respectively. Validation in clinical studies demonstrated realistic deformations with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.

To Align Multimodal Lumbar Spine Images via Bending Energy Constrained Normalized Mutual Information

“…The MIND is a feature-based method and it has been widely used in multimodal deformable registration [25,41]. It aims to represent the distinctive image structure in a local neighborhood and explore the similarity of small image patches by using Gaussian-weighted patch distances [25].…”

A Deformable 3D-3D Registration Framework Using Discrete Periodic Spline Wavelet and Edge Position Difference

“…Similarly, Figure 14 tively) than the diffeomorphic Demons (0.7215 and 0.8680, respectively) and SyN (0.7137 and 0.8940, respectively) methods. Moreover, the diffeomorphic Demons method has more outliers (53) than the proposed method (20) and SyN (32) for all the muscles combined.…”

“…Finally, the SM faces discontinuity in intensity problems along out of plane, since most medical imaging modalities keep spacing between the slices. Most studies on 3D-3D ISR have focused on optimization [13], [21], [29], local regularization [30], [31], multi resolution FFD [11], [12] and the application of the diffeomorphic log-demons algorithm [32], [33] to 3D-3D ISR. The most recently developed competitive registration algorithms are the multi resolution FFD and diffeomorphic log-demons approaches.…”

An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis

“…Several manually crafted metrics are frequently used, such as the sum of squared differences (SSD), cross-correlation (CC) [ 24 ], mutual information (MI) [ 25 ], normalized cross-correlation (NCC), and normalized mutual information (NMI). The optimization algorithms are mostly intensity-based [ 26 , 27 ] and feature-based [ 28 – 30 ]. Actually, image registration generally includes linear (rigid) registration and deformable (nonrigid) registration, where linear registration intends to globally align the two images, and deformable registration is used to correct local deformations.…”