Diffusion MRI requires repeated k-space acquisitions over multiple diffusion-encoding directions, making acquisition time dependent on both spatial and angular sampling. Existing joint k-q methods either associate directional parameters with fixed voxels or separate spatial reconstruction from angular completion. Howev...
Zhi-Bo Chen, Yajuan Huang, Yu Guan et al.· 0 citations
The proposed method enables high-quality dynamic MRI reconstruction by exploiting intrinsic ACS information, offering a practical solution without fully-sampled training data, and achieves competitive performance comparable to supervised models without requiring any fully-sampled data or external training labels.
Yu Guan, Wen-Chao Deng, Qi Qi et al.· Physics in Medicine and Biol...· 0 citations
The proposed K-space Gaussian Representation (KGR), the first explicit continuous representation formulated directly in the native k-space domain, suggests that explicit continuous parameterization of native k-space provides a principled framework for integrating continuous signal modeling with structured low-rank reco...
It is suggested that high-dimensional representation provides a general and model-agnostic mechanism for improving diffusion-based MRI reconstruction in noisy settings, offering a new perspective on robust k-space generative modeling for practical inverse problems.
Yu Guan, Tianjian Huang, Qinrong Cai et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
