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Title: Dual-Attention Recurrent Networks for Affine Registration of Neuroimaging Data
Neuroimaging data typically undergoes several preprocessing steps before further analysis and mining can be done. Affine image registration is one of the important tasks during preprocessing. Recently, several image registration methods which are based on Convolutional Neural Networks have been proposed. However, due to the high computational and memory requirements of CNNs, these methods cannot be used in real-time for large neuroimaging data like fMRI. In this paper, we propose a Dual-Attention Recurrent Network (DRN) which uses a hard attention mechanism to allow the model to focus on small, but task-relevant, parts of the input image – thus reducing computational and memory costs. Furthermore, DRN naturally supports inhomogeneity between the raw input image (e.g., functional MRI) and the image we want to align it to (e.g., anatomical MRI) so it can be applied to harder registration tasks such as fMRI coregistration and normalization. Extensive experiments on two different datasets demonstrate that DRN significantly reduces the computational and memory costs compared with other neural network-based methods without sacrificing the quality of image registration  more » « less
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Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
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Sponsoring Org:
National Science Foundation
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    The ability to register image data to a common coordinate system is a critical feature of virtually all imaging studies. However, in spite of the abundance of literature on the subject and the existence of several variants of registration algorithms, their practical utility remains problematic, as commonly acknowledged even by developers of these methods.


    A new registration method is presented that utilizes a Hamiltonian formalism and constructs registration as a sequence of symplectomorphic maps in conjunction with a novel phase space regularization. For validation of the framework a panel of deformations expressed in analytical form is developed that includes deformations based on known physical processes in MRI and reproduces various distortions and artifacts typically present in images collected using these different MRI modalities.


    The method is demonstrated on the three different magnetic resonance imaging (MRI) modalities by mapping between high resolution anatomical (HRA) volumes, medium resolution diffusion weighted MRI (DW‐MRI) and HRA volumes, and low resolution functional MRI (fMRI) and HRA volumes.


    The method has shown an excellent performance and the panel of deformations was instrumental to quantify its repeatability and reproducibility in comparison to several available alternative approaches.

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