Adaptive local boundary conditions to improve Deformable Image Registration
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arXiv
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| Format: | Preprint |
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2024
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| author | Inacio, Eloïse Lafitte, Luc Facq, Laurent Poignard, Clair de Senneville, Baudouin Denis |
| author_facet | Inacio, Eloïse Lafitte, Luc Facq, Laurent Poignard, Clair de Senneville, Baudouin Denis |
| contents | Objective: In medical imaging, it is often crucial to accurately assess and correct movement during image-guided therapy. Deformable image registration (DIR) consists in estimating the required spatial transformation to align a moving image with a fixed one. However, it is acknowledged that, boundary conditions applied to the solution are critical in preventing mis-registration. Despite the extensive research on registration techniques, relatively few have addressed the issue of boundary conditions in the context of medical DIR. Our aim is a step towards customizing boundary conditions to suit the diverse registration tasks at hand.
Approach: We propose a generic, locally adaptive, Robin-type condition enabling to balance between Dirichlet and Neumann boundary conditions, depending on incoming/outgoing flow fields on the image boundaries. The proposed framework is entirely automatized through the determination of a reduced set of hyperparameters optimized via energy minimization.
Main results: The proposed approach was tested on a mono-modal CT thorax registration task and an abdominal CT to MRI registration task. For the first task, we observed a relative improvement in terms of target registration error of up to 12% (mean 4%), compared to homogeneous Dirichlet and homogeneous Neumann. For the second task, the automatic framework provides results closed to the best achievable.
Significance: This study underscores the importance of tailoring the registration problem at the image boundaries. In this research, we introduce a novel method to adapt the boundary conditions on a voxel-by-voxel basis, yielding optimized results in two distinct tasks: mono-modal CT thorax registration and abdominal CT to MRI registration. The proposed framework enables optimized boundary conditions in image registration without any a priori assumptions regarding the images or the motion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12791 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive local boundary conditions to improve Deformable Image Registration Inacio, Eloïse Lafitte, Luc Facq, Laurent Poignard, Clair de Senneville, Baudouin Denis Computer Vision and Pattern Recognition Medical Physics Objective: In medical imaging, it is often crucial to accurately assess and correct movement during image-guided therapy. Deformable image registration (DIR) consists in estimating the required spatial transformation to align a moving image with a fixed one. However, it is acknowledged that, boundary conditions applied to the solution are critical in preventing mis-registration. Despite the extensive research on registration techniques, relatively few have addressed the issue of boundary conditions in the context of medical DIR. Our aim is a step towards customizing boundary conditions to suit the diverse registration tasks at hand. Approach: We propose a generic, locally adaptive, Robin-type condition enabling to balance between Dirichlet and Neumann boundary conditions, depending on incoming/outgoing flow fields on the image boundaries. The proposed framework is entirely automatized through the determination of a reduced set of hyperparameters optimized via energy minimization. Main results: The proposed approach was tested on a mono-modal CT thorax registration task and an abdominal CT to MRI registration task. For the first task, we observed a relative improvement in terms of target registration error of up to 12% (mean 4%), compared to homogeneous Dirichlet and homogeneous Neumann. For the second task, the automatic framework provides results closed to the best achievable. Significance: This study underscores the importance of tailoring the registration problem at the image boundaries. In this research, we introduce a novel method to adapt the boundary conditions on a voxel-by-voxel basis, yielding optimized results in two distinct tasks: mono-modal CT thorax registration and abdominal CT to MRI registration. The proposed framework enables optimized boundary conditions in image registration without any a priori assumptions regarding the images or the motion. |
| title | Adaptive local boundary conditions to improve Deformable Image Registration |
| topic | Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2405.12791 |