TL;DR
A novel cross-domain DL framework for MRI reconstruction that leverages graph-based convolutional neural networks to model the autoregressive nature of Fourier features and integrates spatial domain networks and FDNs through a cross-lattice structure, enhancing feature extraction by promoting dense representation through alternating layers.
A comprehensive comparative evaluation of six U-Net variants, including U-Net, Attention U-Net, Residual U-Net, Recurrent-Residual (R2) U-Net, Residual-Attention U-Net (RAU-Net), and U-Net++, for image domain reconstruction of low-field MRI data from the M4RAW dataset is presented.
Results indicate that DMFT provides a promising framework for accurate and efficient cardiac MRIreconstruction, and structure aware prior guidance offers additional interpretability support in Healthcare 4.0 scenarios.
Deep learning–based multi-contrast Magnetic Resonance (MCMR) super-resolution (SR) has achieved notable success in accelerating image acquisition and improving image quality. However, significant challenges remain when dealing with the volumetric data: 1) Most existing MCMR SR methods primarily rely on single-slice inf...
This work comprehensively investigate the integration of attention mechanisms, both self-attention and cross-attention, within the Learned Primal-Dual (LPD) framework, and highlights the promise of transformer-integrated LPD frameworks for enhancing PET reconstruction quality and robustness in low-count imaging setting...
DRCSA-Net achieves high accuracy on all four public datasets and demonstrates good effectiveness and stability in the single-/dual-path comparison, ablation experiments, and robustness analysis, providing a structurally clear and stable implementation scheme for brain tumor MRI classification.
COJEPA is presented, a self-supervised framework for volumetric brain MRI that combines a joint-embedding predictive architecture (JEPA) with a contrastive loss (CO), targeting two complementary properties: local predictivity and global discriminability.
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