OBJECTIVE
3D quantitative magnetic resonance imaging (qMRI) enables noninvasive tissue characterization but often requires prolonged acquisition times for multi-contrast imaging, limiting its broader clinical adoption. Although deep learning has shown promise for accelerated MRI reconstruction, supervised methods rely...
Jing-Ran Xu, Seng Jia, Yuan-Biao Yang et al.· Physics in Medicine and Biol...· 0 citations
Three-dimensional (3D) multi-contrast magnetic resonance imaging (MCMRI) provides rich anatomical and quantitative information but requires long acquisition times, motivating k-space undersampling. However, reconstruction of large volumetric datasets imposes substantial computational and memory demands. To address this...
Jing-Ran Xu, Dong Liang, Hai-Rong Zheng et al.· 0 citations
TenF-INR is proposed, a novel unsupervised framework that integrates low-rank tensor modeling with INR, where each factor matrix in the tensor decomposition is modeled as a learnable factor function within a low-rank decomposition, reducing the parameter space and computational burden.
Yuan-Yuan Liu, Yuan-Biao Yang, Jing Cheng et al.· IEEE journal of biomedical a...· 1 citation
I2SB-Inversion is proposed, a multi-contrast guided reconstruction framework based on the Schrödinger Bridge that achieves a a high acceleration factor of R=11.38 and consistently outperforms existing methods in both quantitative and qualitative evaluations.
Yue Wang, Yuan-Biao Yang, Zhuo-xu Cui et al.· IEEE Transactions on Medical...· 0 citations
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