Fetal MRI Reconstruction by Global Diffusion and Consistent Implicit Representation

J Tan, X Zhang, C Qing, C Yang, H Zhang, G Li… - … Conference on Medical …, 2024 - Springer
J Tan, X Zhang, C Qing, C Yang, H Zhang, G Li, X Xu
International Conference on Medical Image Computing and Computer-Assisted …, 2024Springer
Although the utilization of multi-stacks can solve fetal MRI motion correction and artifact
removal problems, there are still problems of regional intensity heterogeneity, and global
consistency discrimination in 3D space. To this end, we propose a novel coarse-to-fine self-
supervised fetal brain MRI Radiation Diffusion Generation Model (RDGM). Firstly, we
propose a novel self-supervised regionally Consistent Implicit Neural Representation
(CINR) network with a double-spatial voxel association consistency mechanism to solve …
Abstract
Although the utilization of multi-stacks can solve fetal MRI motion correction and artifact removal problems, there are still problems of regional intensity heterogeneity, and global consistency discrimination in 3D space. To this end, we propose a novel coarse-to-fine self-supervised fetal brain MRI Radiation Diffusion Generation Model (RDGM). Firstly, we propose a novel self-supervised regionally Consistent Implicit Neural Representation (CINR) network with a double-spatial voxel association consistency mechanism to solve regional intensity heterogeneity. CINR enhances regional 3D voxel association and complementarity by two-voxel mapping spaces to generate coarse MRI. We also fine-tune the weighted slice reconstruction loss to improve the network reconstruction performance. Moreover, we propose the Global Diffusion Discriminative Generation (GDDG) fine module to enhance volume global consistency and discrimination. The noise diffusion is used to transform the global intensity discriminant information in 3D volume. The experiments on two real-world fetal MRI datasets demonstrate that RDGM achieves state-of-the-art results.
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