Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding
Summary
This paper introduces Lorentz Encoding (LE), a physics-informed framework that uses implicit neural representations and physical constraints to reconstruct high-resolution CEST MRI from sparsely sampled data, achieving superior performance over existing methods.
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# Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding Source: [https://arxiv.org/abs/2607.06132](https://arxiv.org/abs/2607.06132) [View PDF](https://arxiv.org/pdf/2607.06132) > Abstract: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 \(APT, NOE, MT\)\. ## Submission history From: Dexuan Li \[[view email](https://arxiv.org/show-email/fe0ea8f9/2607.06132)\] **\[v1\]**Tue, 7 Jul 2026 10:49:07 UTC \(5,155 KB\)
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