Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding
This work addresses the need for faster CEST MRI acquisition without sacrificing reconstruction quality, which is important for clinical metabolic imaging.
The paper tackles the problem of long acquisition times in multi-pool CEST MRI by proposing Lorentz Encoding (LE), a physics-informed self-supervised framework for reconstructing high-resolution Z-spectra from sparse data. On in vivo human brain data with 39-point sampling, LE achieves a PSNR of 57.58 dB and an SSIM of 0.9994, outperforming state-of-the-art methods.
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).