Stage-by-stage Wavelet Optimization Refinement Diffusion Model for
Sparse-View CT Reconstruction
- URL: http://arxiv.org/abs/2308.15942v2
- Date: Sun, 3 Sep 2023 10:17:17 GMT
- Title: Stage-by-stage Wavelet Optimization Refinement Diffusion Model for
Sparse-View CT Reconstruction
- Authors: Kai Xu, Shiyu Lu, Bin Huang, Weiwen Wu, Qiegen Liu
- Abstract summary: We present an innovative approach named the Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for sparse-view CT reconstruction.
Specifically, we establish a unified mathematical model integrating low-frequency and high-frequency generative models, achieving the solution with optimization procedure.
Our method rooted in established optimization theory, comprising three distinct stages, including low-frequency generation, high-frequency refinement and domain transform.
- Score: 14.037398189132468
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Diffusion models have emerged as potential tools to tackle the challenge of
sparse-view CT reconstruction, displaying superior performance compared to
conventional methods. Nevertheless, these prevailing diffusion models
predominantly focus on the sinogram or image domains, which can lead to
instability during model training, potentially culminating in convergence
towards local minimal solutions. The wavelet trans-form serves to disentangle
image contents and features into distinct frequency-component bands at varying
scales, adeptly capturing diverse directional structures. Employing the Wavelet
transform as a guiding sparsity prior significantly enhances the robustness of
diffusion models. In this study, we present an innovative approach named the
Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for
sparse-view CT reconstruction. Specifically, we establish a unified
mathematical model integrating low-frequency and high-frequency generative
models, achieving the solution with optimization procedure. Furthermore, we
perform the low-frequency and high-frequency generative models on wavelet's
decomposed components rather than sinogram or image domains, ensuring the
stability of model training. Our method rooted in established optimization
theory, comprising three distinct stages, including low-frequency generation,
high-frequency refinement and domain transform. Our experimental results
demonstrate that the proposed method outperforms existing state-of-the-art
methods both quantitatively and qualitatively.
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