Investigating the Impact of Control Information on Fidelity of Detail Recovery in Latent Diffusion Models for Undersampled MRI 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.