Keywords: AI Diffusion Models, AI/ML Image Reconstruction
Motivation: ControlNet-based latent diffusion models (LDMs) are widely used in image restoration, but the effectiveness of the control information in MRI reconstruction task is unexplored.
Goal(s): This study investigates the impact of varying control information quality impacts on the generation fidelity of LDMs, aiming for facilitating the translation to clinical applications.
Approach: Three levels of conditional inputs quality generated from different data modalities and network structures were involved to guide the ControlNet-based LDM's reconstructing process.
Results: Visual and quantitative evaluations were conducted, revealing the crucial role of control quality in optimizing model performance and ensuring more reliable reconstructions.
Impact: A key prerequisite for translating LDM-based MRI reconstruction methods into clinical practice is resolving the trade-off between detail richness and fidelity. Our research contributes to advancing solutions for the reconstruction fidelity challenge.
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