bibtext: @InProceedings{ Tan_Fetal_MICCAI2024,
                   author = { Tan, Junpeng and Zhang, Xin and Qing, Chunmei and Yang, Chaoxiang and Zhang, He and Li, Gang and Xu, Xiangmin },
                   title = { { Fetal MRI Reconstruction by Global Diffusion and Consistent Implicit Representation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Fetal MRI Reconstruction by Global Diffusion and Consistent Implicit Representation
authors: Tan, Junpeng Zhang, Xin Qing, Chunmei Yang, Chaoxiang Zhang, He Li, Gang Xu, Xiangmin