AI RESEARCH
Energy-based Tissue Manifolds for Longitudinal Multiparametric MRI Analysis
arXiv CS.CV
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ArXi:2604.07180v1 Announce Type: new We propose a geometric framework for longitudinal multi-parametric MRI analysis based on patient-specific energy modelling in sequence space. Rather than operating on images with spatial networks, each voxel is represented by its multi-sequence intensity vector ($T1$, $T1c$, $T2$, FLAIR, ADC), and a compact implicit neural representation is trained via denoising score matching to learn an energy function $E_{\theta}(\mathbf{u})$ over $\mathbb{R}^d$ from a single baseline scan.