3 papers
cs.CV2026
Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction
Qing Wu, Xuanyu Tian, Chenhe Du +4
Implicit neural representations (INRs) have emerged as a powerful paradigm for medical imaging via physics-informed unsupervised learning. Classical INRs optimize an entire network…
cs.CV2026
Zero-shot Low-Field MRI Enhancement via Diffusion-Based Adaptive Contrast Transport
Muyu Liu, Chenhe Du, Xuanyu Tian +5
Low-field (LF) magnetic resonance imaging (MRI) democratizes access to diagnostic imaging but is fundamentally limited by low signal-to-noise ratio and significant tissue contrast…
eess.IV2025
Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction
Haonan Zhang, Guoyan Lao, Yuyao Zhang +1
Quantitative magnetic resonance imaging (qMRI) provides tissue-specific parameters vital for clinical diagnosis. Although simultaneous multi-parametric qMRI (MP-qMRI) technologies…