Adaptive local boundary conditions to improve Deformable Image Registration
<|reference_start|>Adaptive local boundary conditions to improve Deformable Image Registration: 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.<|reference_end|>
@article{inacio2024adaptive,
    title={Adaptive local boundary conditions to improve Deformable Image
  Registration},
    author={Elo"ise Inacio, Luc Lafitte, Laurent Facq, Clair Poignard, Baudouin
  Denis de Senneville},
    journal={arXiv preprint arXiv:2405.12791},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12791},
    primaryClass={cs.CV physics.med-ph}
}