Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, deep image prior, unsupervised learning, deep image-pass filter, dynamic MRI
Motivation: For accelerating dynamic MRI, the gradient flow in the unsupervised deep image prior (DIP) methods lacks a direct pathway from the image domain to the network parameters, resulting in suboptimal performance.
Goal(s): To propose a novel approch to address DIP's drawback for robust dynamic MRI reconstruction.
Approach: Deep image-pass filter is proposed, replacing the random noise input of DIP with learnable image and constraint input consistent with output to establish an efficient gradient pathway from image domain.
Results: Experimental results in the reconstruction of both long-axis and short-axis dynamic cardiac cine MRI demonstrate that DIPF outperforms DIP and other state-of-the-art unsupervised methods.
Impact: We have introduced a new formulation for unsupervised MRI reconstruction, which will drive a series of research around this paradigm, including network architecture design, reconstuction models combining DIPF with other data priors, and more.
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