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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Shu-Hui Cai

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Open access Oct 2026

A wavelet-guided diffusion method for brain magnetic resonance parameter mapping using MOLED-based datasets: proof-of-principle.

OBJECTIVE Quantitative magnetic resonance imaging (qMRI) can noninvasively acquire parametric maps, providing quantitative biomarkers for clinical diagnosis. Owing to their distinct physical origins, various parametric maps have inherent differences in numerical scale. The existing deep learning methods primarily focus...

Che Wang, Lin-Yu Fan, Qi-Zhi Yang et al. · 0 citations
Open access Aug 2026

Rapid whole-brain susceptibility source separation using sub-minute three-dimensional multiple overlapping-echo detachment acquisition

Objective. Magnetic susceptibility source separation provides profound insights into tissue pathophysiology by disentangling the underlying paramagnetic and diamagnetic sources within the human brain. However, the highly time-consuming nature of existing acquisition techniques creates a critical need for rapid and reli...

Long-Kun Chen, Qin-Qin Yang, Shi-Wei Huang et al. · 0 citations
Open access Aug 2026

Robust T1ρ, T2, and T2* mapping via spin-locked MOLED with synthetic data-driven deep learning reconstruction

SL-MOLED provides accurate, repeatable, and artifact-robust quantitative mapping within a substantially reduced acquisition time, offering a promising framework for reliable multi-parametric MRI with potential for clinical translation.

Wei-Kun Chen, Qing Lin, Tai-Shan Kang et al. · 0 citations
Open access Aug 2026

Robust T1ρ, T2, and T2* mapping via spin-locked MOLED with synthetic data-driven deep learning reconstruction

Objective. To address the challenges of rapid and robust quantitative MRI, particularly for T1ρ mapping, by developing and evaluating a novel technique—spin-locked (SL) multiple overlapping echo detachment (SL-MOLED)—for efficient mapping of T1ρ, T2, and T2* relaxation times with reduced sensitivity to B0/B1 inhomogene...

Weikun Chen, Qing Lin, Taishan Kang et al. · 0 citations

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