Model-Based Deformable Registration of Preoperative 3D to Intraoperative Low-Resolution 3D and 2D Sequences of MR Images. In: Fichtinger G, Martel A, Peters T, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. Berlin: Springer Berlin Heidelberg; 2011. pp. 460-7. [DOI: 10.1007/978-3-642-23623-5_58]

Physical Constraint Finite Element Model for Medical Image Registration

Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student’s t-test demonstrated that our model statistically outperformed the other methods in comparison (p-values <0.05).

Multimodal image registration with joint structure tensor and local entropy

PURPOSE

Nonrigid registration of multimodal medical images remains a challenge in image-guided interventions. A common approach is to use mutual information (MI), which is robust to the intensity variations across modalities. However, primarily based on intensity distribution, MI does not take into account of underlying spatial and structural information of the images, which might lead to local optimization. To address such a challenge, this paper proposes a two-stage multimodal nonrigid registration scheme with joint structural information and local entropy.

METHODS

In our two-stage multimodal nonrigid registration scheme, both the reference image and floating image are firstly converted to a common space. A unified representation in the common space for the images is constructed by fusing the structure tensor (ST) trace with the local entropy (LE). Through the representation that reflects its geometry uniformly across modalities, the complicated deformation field is estimated using L(1) or L(2) distance.

RESULTS

We compared our approach to four other methods: (1) the method using LE, (2) the method using ST, (3) the method using spatially weighted LE and (4) the conventional MI-based method. Quantitative evaluations on 80 multimodal image pairs of different organs including 50 pairs of MR images with artificial deformations, 20 pairs of medical brain MR images and 10 pairs of breast images showed that our proposed method outperformed the comparison methods. Student's t test demonstrated that our method achieved statistically significant improvement on registration accuracy.

CONCLUSION

The two-stage registration with joint ST and LE outperformed the conventional MI-based method for multimodal images. Both the ST and the LE contributed to the improved registration accuracy.

GPU-based acceleration of computations in nonlinear finite element deformation analysis

The physics of deformation for biological soft-tissue is best described by nonlinear continuum mechanics-based models, which then can be discretized by the FEM for a numerical solution. However, computational complexity of such models have limited their use in applications requiring real-time or fast response. In this work, we propose a graphic processing unit-based implementation of the FEM using implicit time integration for dynamic nonlinear deformation analysis. This is the most general formulation of the deformation analysis. It is valid for large deformations and strains and can account for material nonlinearities. The data-parallel nature and the intense arithmetic computations of nonlinear FEM equations make it particularly suitable for implementation on a parallel computing platform such as graphic processing unit. In this work, we present and compare two different designs based on the matrix-free and conventional preconditioned conjugate gradients algorithms for solving the FEM equations arising in deformation analysis. The speedup achieved with the proposed parallel implementations of the algorithms will be instrumental in the development of advanced surgical simulators and medical image registration methods involving soft-tissue deformation.

GPU implementation of a deformable 3D image registration algorithm

We present a parallel implementation of a new deformable image registration algorithm using the Computer Unified Device Architecture (CUDA). The algorithm co-registers preoperative and intraoperative 3-dimensional magnetic resonance (MR) images of a deforming organ. It employs a linear elastic dynamic finite-element model of the deformation and distance measures such as mutual information and sum of squared differences to align volumetric image data sets. Computationally intensive elements of the method such as interpolation, displacement and force calculation are significantly accelerated using a Graphics Processing Unit (GPU). The result of experiments carried out with a realistic breast phantom tissue shows a 37 fold speedup for the GPU-based implementation compared with an optimized CPU-based implementation in high resolution MR image registration. The GPU implementation is capable of registering 512 × 512 × 136 image sets in just over 2 seconds, making it suitable for clinical applications requiring fast and accurate processing of medical images.