Keywords: Image Reconstruction, AI/ML Image Reconstruction
Motivation: To develop a new deep-learning framework for highly accelerated cardiac cine reconstruction with sharp details and high SNR.
Goal(s): To reconstruct high-quality cine MRI with high undersampling rates using denoising diffusion probabilistic frameworks.
Approach: A diffusion model conditioned on slice information is trained to generate images of different phases. Data consistency enforced by k-space alignment controls the phase generation.
Results: The diffusion model reconstructs high-quality cine MRI from highly undersampled data, validated by radiologists’ evaluation.
Impact: A new framework with diffusion models is proposed for cardiac cine reconstruction. It utilizes high-quality reconstruction of generative models and provides reliable results by data consistency control. The method is applicable to other dynamic MRI reconstructions in highly undersampled scenarios.
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