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20222026
most citedSRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data

2 citations · 2 across the 8 of their papers we have counts for

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eess.IV2026

A physics-informed foundation model for quantitative diffusion MRI

Zihan Li, Jialan Zheng, Ziyu Li +18

Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only noninvasive window into whole-bra…

eess.IV2026

Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI

Mingxuan Liu, Yingqi Hao, Yi Liao +17

Prenatal germinal matrix-intraventricular hemorrhage (GMH-IVH) is a leading cause of infant mortality and neurodevelopmental impairment, yet its manual diagnosis and lesion segment…

eess.IV2022★ 2 cited

SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data

Jiaxin Xiao, Zihan Li, Berkin Bilgic +3

Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a bar…