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- W4296960537 abstract "Image registration of structural and microstructural data allows accurate alignment of anatomical and diffusion channels. However, existing techniques employ simple fusion-based approaches, which use a global weight for each modality, or empirically-driven approaches, which rely on pre-calculated local certainty maps. Here, we present a novel attention-based deep learning deformable image registration solution for aligning multi-channel neonatal MRI data. We learn optimal attention maps to weigh each modality-specific velocity field in a spatially varying fashion, thus allowing for local fusion of structural and microstructural images. We evaluate our proposed method on registrations of 30 multi-channel neonatal MRI to a standard structural and microstructural atlas, and compare it against models trained without the use of attention maps on either single or both modalities. We show that by combining the two channels through attention-driven image registration, we take full advantage of the two complementary modalities, and achieve the best overall alignment of both structural and microstructural data." @default.
- W4296960537 created "2022-09-25" @default.
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- W4296960537 date "2022-01-01" @default.
- W4296960537 modified "2023-09-27" @default.
- W4296960537 title "Attention-Driven Multi-channel Deformable Registration of Structural and Microstructural Neonatal Data" @default.
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- W4296960537 doi "https://doi.org/10.1007/978-3-031-17117-8_7" @default.
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