Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion
  Generation Model
Junpeng Tan Xin Zhang Yao Lv Xiangmin Xu Gang Li
Although the use of multiple stacks can handle slice-to-volume motion
correction and artifact removal problems, there are still several problems: 1)
The slice-to-volume method usually uses slices as input, which cannot solve the
problem of uniform intensity distribution and complementarity in regions of
different fetal MRI stacks; 2) The integrity of 3D space is not considered,
which adversely affects the discrimination and generation of globally
consistent information in fetal MRI; 3) Fetal MRI with severe motion artifacts
in the real-world cannot achieve high-quality super-resolution reconstruction.
To address these issues, we propose a novel fetal brain MRI high-quality volume
reconstruction method, called the Radiation Diffusion Generation Model (RDGM).
It is a self-supervised generation method, which incorporates the idea of
Neural Radiation Field (NeRF) based on the coordinate generation and diffusion
model based on super-resolution generation. To solve regional intensity
heterogeneity in different directions, we use a pre-trained transformer model
for slice registration, and then, a new regionally Consistent Implicit Neural
Representation (CINR) network sub-module is proposed. CINR can generate the
initial volume by combining a coordinate association map of two different
coordinate mapping spaces. To enhance volume global consistency and
discrimination, we introduce the Volume Diffusion Super-resolution Generation
(VDSG) mechanism. The global intensity discriminant generation from
volume-to-volume is carried out using the idea of diffusion generation, and
CINR becomes the deviation intensity generation network of the volume-to-volume
diffusion model. Finally, the experimental results on real-world fetal brain
MRI stacks demonstrate the state-of-the-art performance of our method.