Chemical Exchange Saturation Transfer (CEST) MRI demonstrates its capability in significantly enhancing the detection of proteins and metabolites with low concentrations through exchangeable protons. The clinical application of CEST, however, is constrained by its low contrast and low signal-to-noise ratio (SNR) in the acquired data. Denoising, as one of the post-processing stages for CEST data, can effectively improve the accuracy of CEST quantification. In this work, by modeling spatial variant z-spectrums into low-dimensional subspace, we introduce Implicit Regression in Subspace (IRIS), which is an unsupervised denoising algorithm utilizing the excellent property of implicit neural representation for continuous mapping. Experiments conducted on both synthetic and in-vivo data demonstrate that our proposed method surpasses other CEST denoising methods regarding both qualitative and quantitative performance.

翻译：化学交换饱和转移（CEST）磁共振成像通过可交换质子，显著增强了低浓度蛋白质和代谢物的检测能力。然而，CEST的临床应用受限于其采集数据中较低的对比度和较低的信噪比。去噪作为CEST数据的后处理环节之一，可以有效提高CEST定量的准确性。在本工作中，通过将空间变化的z谱建模到低维子空间中，我们提出了子空间隐式回归（IRIS），这是一种利用隐式神经表示进行连续映射的优异特性的无监督去噪算法。在合成数据和活体数据上进行的实验表明，我们提出的方法在定性和定量性能上均优于其他CEST去噪方法。