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Abstract #3982

VMamba UNet with Latent Diffusion Prior Guidance for MRI Reconstruction

Lingtong Zhang1 and Bensheng Qiu1
1School of Information Science and Technology, University of Science and Technology of China, Hefei, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: Latent space diffusion models1 can efficiently generate high-quality images. However, the inherent nonlinearity and nonconvexity of variational autoencoder decoders present challenges for their application to MRI reconstruction.

Goal(s): Effectively integrating both latent and pixel space information to guide MRI reconstruction indirectly.

Approach: We propose a VMamba2 UNet that employs a VMamba-based encoder to align latent and pixel space prior guidance, along with a convolutional decoder. Additionally, we introduce Latent Guided Attention (LGA) to effectively integrate information from both latent and pixel spaces.

Results: Experiments demonstrate that our method achieves superior performance compared to the baselines.

Impact: This work investigates the role of generative priors of latent diffusion models on MRI reconstruction tasks and facilitates the frontier studies of deep-learning-based rapid reconstruction methods.

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Keywords