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
Parallel magnetic resonance imaging reconstruction is an ill-posed inverse problem under undersampling. Multi-coil acquisition and Hankel lifting expose complementary repeated information: observations of the same anatomy across coils and repeated local k-space neighborhoods in overlapping windows. These dependencies g...
Ming-Yu Hu, Si-Quan Zhu, Xi-Jun Zhong et al.· 0 citations
Low-field magnetic resonance imaging (MRI) systems are highly practical in resource-constrained scenarios due to their low cost and portability, which is regarded as an important alternative to conventional high-field MRI. However, they suffer from significant challenges caused by low signal intensity, resulting in the...
Tao Zhou, Qi Liu, Ziru Li et al.· IEEE Transactions on Instrum...· 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
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