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

TL;DR

This paper introduces RDGM, a self-supervised fetal MRI reconstruction method combining NeRF-inspired coordinate generation and diffusion models to improve 3D volume quality amidst motion artifacts and intensity heterogeneity.

Contribution

The paper presents a novel self-supervised framework integrating coordinate-based neural representation and diffusion models for high-quality fetal MRI reconstruction.

Findings

Achieves state-of-the-art reconstruction quality on real fetal MRI data.

Effectively handles motion artifacts and intensity heterogeneity.

Enhances global consistency and discrimination in 3D volumes.

Abstract

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.