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Although the use of multiple stacks can handle slice-to-volume motioncorrection and artifact removal problems, there are still several problems: 1)The slice-to-volume method usually uses slices as input, which cannot solve theproblem of uniform intensity distribution and complementarity in regions ofdifferent fetal MRI stacks; 2) The integrity of 3D space is not considered,which adversely affects the discrimination and generation of globallyconsistent information in fetal MRI; 3) Fetal MRI with severe motion artifactsin the real-world cannot achieve high-quality super-resolution reconstruction.To address these issues, we propose a novel fetal brain MRI high-quality volumereconstruction method, called the Radiation Diffusion Generation Model (RDGM).It is a self-supervised generation method, which incorporates the idea ofNeural Radiation Field (NeRF) based on the coordinate generation and diffusionmodel based on super-resolution generation. To solve regional intensityheterogeneity in different directions, we use a pre-trained transformer modelfor slice registration, and then, a new regionally Consistent Implicit NeuralRepresentation (CINR) network sub-module is proposed. CINR can generate theinitial volume by combining a coordinate association map of two differentcoordinate mapping spaces. To enhance volume global consistency anddiscrimination, we introduce the Volume Diffusion Super-resolution Generation(VDSG) mechanism. The global intensity discriminant generation fromvolume-to-volume is carried out using the idea of diffusion generation, andCINR becomes the deviation intensity generation network of the volume-to-volumediffusion model. Finally, the experimental results on real-world fetal brainMRI stacks demonstrate the state-of-the-art performance of our method.

Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model