这篇论文用物理约束的编码方法搞定了CEST MRI的稀疏重建，39个采样点就能达到57.58 dB的PSNR，比传统方法准得多，适合做医学影像重建的同学看看。
本文提出Lorentz Encoding（LE），一种物理信息驱动的自监督框架，用于从稀疏采样数据重建CEST MRI高分辨率Z谱。LE通过将坐标投影到由洛伦兹剖面和学习基函数组合的物理约束空间，降低噪声并保证与物理模型的一致性。在人体脑部数据39点采样策略下，LE达到PSNR 57.58 dB和SSIM 0.9994，显著优于现有方法。其学习到的编码在潜在空间中形成连续几何轨迹，确保APT、NOE、MT等代谢物定量映射的准确性。
Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding
Multi-Pool Chemical Exchange Saturation Transfer (CEST) MRI provides valuable metabolic information but is clinically limited by long acquisition times. Although sparse sampling reduces scanning time, reconstructing high-resolution Z-spectra from limited data remains an ill-posed inverse problem. Conventional interpolation and generic Implicit Neural Rep-resentations (INRs) often lack physical constraints, leading to spectral artifacts and physically invalid signals. To address this, we propose Lorentz Encoding (LE), a physics-informed framework that formulates CEST reconstruction as a self-supervised reconstruction task via implicit continuous coordinate learning. Unlike generic positional encodings, LE regularizes the continuous spectral mapping by projecting sparse coordinates into a physically constrained space governed by a combination of parametric Lorentzian profiles with learnable basis functions. This mechanism effectively reduces noise and enforces consistency with physical models. Experiments on in vivo human brain data demonstrate that LE significantly outperforms state-of-the-art methods. Specifically, under a 39-point sampling strategy, LE achieves a PSNR of 57.58 dB and an SSIM of 0.9994. Furthermore, the learned physics-informed encodings form a continuous, geometrically ordered trajectory in the latent space, ensuring accurate quantitative metabo-lite mapping (APT, NOE, MT).