Gaussian Representation for Deformable Image Registration
- URL: http://arxiv.org/abs/2406.03394v1
- Date: Wed, 5 Jun 2024 15:44:54 GMT
- Title: Gaussian Representation for Deformable Image Registration
- Authors: Jihe Li, Fabian Zhang, Xia Li, Tianhao Zhang, Ye Zhang, Joachim Buhmann,
- Abstract summary: We introduce a novel DIR approach employing parametric 3D Gaussian control points.
It provides an explicit and flexible representation for spatial fields between 3D medical images.
We validated our approach on the 4D-CT lung DIR-Lab and cardiac ACDC datasets, achieving an average target registration error (TRE) of 1.06 mm within a much-improved processing time of 2.43 seconds.
- Score: 12.226244219255197
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deformable image registration (DIR) is a fundamental task in radiotherapy, with existing methods often struggling to balance computational efficiency, registration accuracy, and speed effectively. We introduce a novel DIR approach employing parametric 3D Gaussian control points achieving a better tradeoff. It provides an explicit and flexible representation for spatial deformation fields between 3D volumetric medical images, producing a displacement vector field (DVF) across all volumetric positions. The movement of individual voxels is derived using linear blend skinning (LBS) through localized interpolation of transformations associated with neighboring Gaussians. This interpolation strategy not only simplifies the determination of voxel motions but also acts as an effective regularization technique. Our approach incorporates a unified optimization process through backpropagation, enabling iterative learning of both the parameters of the 3D Gaussians and their transformations. Additionally, the density of Gaussians is adjusted adaptively during the learning phase to accommodate varying degrees of motion complexity. We validated our approach on the 4D-CT lung DIR-Lab and cardiac ACDC datasets, achieving an average target registration error (TRE) of 1.06 mm within a much-improved processing time of 2.43 seconds for the DIR-Lab dataset over existing methods, demonstrating significant advancements in both accuracy and efficiency.
Related papers
- DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering [30.30749508345767]
Digitally reconstructed radiographs (DRRs) are simulated 2D X-ray images generated from 3D CT volumes.
We present a novel approach that marries realistic physics-inspired X-ray simulation with efficient, differentiable DRR generation.
arXiv Detail & Related papers (2024-06-04T17:39:31Z) - R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction [53.19869886963333]
3D Gaussian splatting (3DGS) has shown promising results in rendering image and surface reconstruction.
This paper introduces R2-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction.
arXiv Detail & Related papers (2024-05-31T08:39:02Z) - End-to-End Rate-Distortion Optimized 3D Gaussian Representation [33.20840558425759]
We formulate the compact 3D Gaussian learning as an end-to-end Rate-Distortion Optimization problem.
We introduce dynamic pruning and entropy-constrained vector quantization (ECVQ) that optimize the rate and distortion at the same time.
We verify our method on both real and synthetic scenes, showcasing that RDO-Gaussian greatly reduces the size of 3D Gaussian over 40x.
arXiv Detail & Related papers (2024-04-09T14:37:54Z) - An Accurate and Real-time Relative Pose Estimation from Triple Point-line Images by Decoupling Rotation and Translation [10.05584976985694]
3D-2D constraints provided by line features have been widely used in Visual Odometry (VO) and Structure-from-Motion (SfM) systems.
We propose a novel three-view pose solver based on rotation-translation decoupled estimation.
arXiv Detail & Related papers (2024-03-18T10:21:05Z) - Neural Graphics Primitives-based Deformable Image Registration for
On-the-fly Motion Extraction [9.599774878892665]
Intra-fraction motion in radiotherapy is commonly modeled using deformable image registration (DIR)
Existing methods often struggle to balance speed and accuracy, limiting their applicability in clinical scenarios.
This study introduces a novel approach that harnesses Neural Graphics Primitives (NGP) to optimize the displacement vector field (DVF)
We validate this approach on the 4D-CT lung dataset DIR-lab, achieving a target registration error (TRE) of 1.15pm1.15 mm within a remarkable time of 1.77 seconds.
arXiv Detail & Related papers (2024-02-08T11:09:27Z) - Plug-and-Play Regularization on Magnitude with Deep Priors for 3D Near-Field MIMO Imaging [0.0]
Near-field radar imaging systems are used in a wide range of applications such as concealed weapon detection and medical diagnosis.
We consider the problem of the three-dimensional (3D) complex-valued reflectivity by enforcing regularization on its magnitude.
arXiv Detail & Related papers (2023-12-26T12:25:09Z) - GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting [51.96353586773191]
We introduce textbfGS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping system.
Our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering.
Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets.
arXiv Detail & Related papers (2023-11-20T12:08:23Z) - SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D
Object Pose Estimation [66.16525145765604]
We introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios.
Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud.
Experiments demonstrate that our diffusion registration framework presents outstanding pose estimation performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets.
arXiv Detail & Related papers (2023-10-26T12:47:26Z) - Detecting Rotated Objects as Gaussian Distributions and Its 3-D
Generalization [81.29406957201458]
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects.
We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection.
We propose to model the rotated objects as Gaussian distributions.
We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation.
arXiv Detail & Related papers (2022-09-22T07:50:48Z) - Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration [63.15453821022452]
Recent developments in approaches based on deep learning have achieved sub-second runtimes for DiffIR.
We propose a simple iterative scheme that functionally composes intermediate non-stationary velocity fields.
We then propose a convex optimisation model that uses a regularisation term of arbitrary order to impose smoothness on these velocity fields.
arXiv Detail & Related papers (2021-09-26T19:56:45Z) - DeepGMR: Learning Latent Gaussian Mixture Models for Registration [113.74060941036664]
Point cloud registration is a fundamental problem in 3D computer vision, graphics and robotics.
In this paper, we introduce Deep Gaussian Mixture Registration (DeepGMR), the first learning-based registration method.
Our proposed method shows favorable performance when compared with state-of-the-art geometry-based and learning-based registration methods.
arXiv Detail & Related papers (2020-08-20T17:25:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.