Frequency importance analysis for chemical exchange saturation transfer magnetic resonance imaging using permuted random forest

作者：Yibing Chen, Xujian Dang, Benqi Zhao, Zhensen Chen, Yingcheng Zhao, Fengjun Zhao, Zhuozhao Zheng, Xiaowei He, Jinye Peng, Xiaolei Song · 发表于：NMR in Biomedicine · 年份：2022 · DOI：10.1002/nbm.4744 · 被引用次数：11 · 研究领域：Lanthanide and Transition Metal Complexes、Advanced MRI Techniques and Applications、Electron Spin Resonance Studies

Chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) is a promising molecular imaging tool that allows sensitive detection of endogenous metabolic changes. However, because the CEST spectrum does not display a clear peak like MR spectroscopy, its signal interpretation is challenging, especially under 3‐T field strength or with a large saturation B 1 . Herein, as an alternative to conventional Z‐spectral fitting approaches, a permuted random forest (PRF) method is developed to determine featured saturation frequencies for lesion identification, so‐called CEST frequency importance analysis. Briefly, voxels in the CEST dataset were labeled as lesion and control according to multicontrast MR images. Then, by considering each voxel's saturation signal series as a sample, a permutation importance algorithm was employed to rank the contribution of saturation frequency offsets in the differentiation of lesion and normal tissue. Simulations demonstrated that PRF could correctly determine the frequency offsets (3.5 or −3.5 ppm) for classifying two groups of Z‐spectra, under a range of B 0 , B 1 conditions and sample sizes. For ischemic rat brains, PRF only displayed high feature importance around amide frequency at 2 h postischemia, reflecting that the pH changes occurred at an early stage. By contrast, the data acquired at 24 h postischemia exhibited high feature importance at multiple frequencies (amide, water, and lipids), which suggested the complex tissue ch...