Technical University of Cluj-Napoca, 28 Memorandumului str. 400114, Cluj-Napoca, Romania; Babeş-Bolyai University, Faculty of Physics, Doctoral School, 1 Kogălniceanu str., 400084 Cluj-Napoca, Romania.
Babeş-Bolyai University, Faculty of Physics, Doctoral School, 1 Kogălniceanu str., 400084 Cluj-Napoca, Romania; IMOGEN, County Emergency Hospital, Cluj-Napoca, Romania.
J Magn Reson. 2021 Mar;324:106915. doi: 10.1016/j.jmr.2021.106915.
Low field Nuclear Magnetic Resonance (LF-NMR) is a rich source of information for a wide range of samples types. These can be hard or soft solids, such as plastics or elastomers; bulk liquids or liquids absorbed in porous materials, and can come from biomaterials, biological tissues, archaeological artifacts, cultural heritage objects. LF-NMR instruments present a significant advance especially for in situ, ex situ and in vivo measurement of relaxation and diffusion. Moreover, high resolution 1D and 2D spectroscopy, as well as magnetic resonance (MR) imaging are available in these fields. In this work we discuss the advanced analysis of the data measured in LF-NMR from the perspectives of tertiary level that implies the analysis on principal components (PCA), and on the quaternary analysis that uses an artificial neural network (ANN). The principles of PCA and ANN are largely discussed. For the PCA analysis, a series of 52 spectra were analyzed, having been recorded in vivo by LF-NMR. Of these spectra, 38 were generated from normal uterus, 7 by uterus tissue with endometrial cancer, and another 7 were obtained from tissues of women with uterine cervical cancer. The PC1 vs PC2 plot was further analyzed using an artificial neural network, and the results are presented as 2D maps of probability. Furthermore, the perspectives of applying an ANN to solve the problem of Laplace-like inversion are discussed. An example of such ANN was presented and the performance was discussed. Finally, a model of complex ANN, capable to sequentially solve this kind of problems specific to LF-NMR is proposed and discussed.
低场核磁共振（LF-NMR）是广泛的样品类型的丰富信息来源。这些样品可以是硬或软的固体，如塑料或弹性体；块状液体或多孔材料中吸收的液体，并且可以来自生物材料、生物组织、考古文物、文化遗产。LF-NMR 仪器代表了一个重大进展，特别是在原位、非原位和体内测量弛豫和扩散方面。此外，这些领域还提供高分辨率的 1D 和 2D 光谱学以及磁共振（MR）成像。在这项工作中，我们从第三级的角度讨论了 LF-NMR 测量数据的高级分析，这意味着对主成分（PCA）的分析，以及对使用人工神经网络（ANN）的四级分析。广泛讨论了 PCA 和 ANN 的原理。对于 PCA 分析，分析了通过 LF-NMR 体内记录的一系列 52 个光谱。这些光谱中，38 个来自正常子宫，7 个来自子宫内膜癌的子宫组织，另外 7 个来自宫颈癌患者的组织。进一步使用人工神经网络分析 PC1 与 PC2 图，结果以概率的 2D 图呈现。此外，还讨论了将 ANN 应用于解决拉普拉斯逆问题的方法。提出了一个这样的 ANN 示例，并讨论了其性能。最后，提出并讨论了一种能够顺序解决 LF-NMR 特有的这种复杂 ANN 模型的问题。
