A robust t1 noise suppression method in NMR spectroscopy.

Artefacts in high-resolution multidimensional nuclear magnetic resonance (NMR) spectra, known as t1 noise, can significantly downgrade the spectral quality and remain a significant noise source, limiting the sensitivity of most two-dimensional NMR experiments. In addition to highly sensitive hardware and experimental designs, data post-processing is a relatively simple and cost-effective method for suppressing t1 noise. In this study, histograms and quantiles were used to obtain a robust estimation of noise level. We constructed a weighted matrix to suppress the t1 noise. The weighted matrix was calculated from the logistic functions, which were adaptively computed from the spectrum. The proposed method is robust and effective in both simulations and actual experiments. Further, it can maintain the quantitative relationship of the spectrogram and is suitable for various complex peak types.

SpinSPJ: a novel NMR scripting system to implement artificial intelligence and advanced applications.

BACKGROUND: Software for nuclear magnetic resonance (NMR) spectrometers offer general functionality of instrument control and data processing; these applications are often developed with non-scripting languages. NMR users need to flexibly integrate rapidly developing NMR applications with emerging technologies. Scripting systems offer open environments for NMR users to write custom programs. However, existing scripting systems have limited capabilities for both extending the functionality of NMR software's non-script main program and using advanced native script libraries to support specialized application domains (e.g., biomacromolecules and metabolomics). Therefore, it is essential to design a novel scripting system to address both of these needs. RESULT: Here, a novel NMR scripting system named SpinSPJ is proposed. It works as a plug-in in the Java based NMR spectrometer software SpinStudioJ. In the scripting system, both Java based NMR methods and original CPython based libraries are supported. A module has been developed as a bridge to integrate the runtime environments of Java and CPython. The module works as an extension in the CPython environment and interacts with Java via the Java Native Interface. Leveraging this bridge, Java based instrument control and data processing methods of SpinStudioJ can be called with the CPython style. Compared with traditional scripting systems, SpinSPJ better supports both extending the non-script main program and implementing advanced NMR applications with a rich variety of script libraries. NMR researchers can easily call functions of instrument control and data processing as well as developing complex functionality (such as multivariate statistical analysis, deep learning, etc.) with CPython native libraries. CONCLUSION: SpinSPJ offers a user-friendly environment to implement custom functionality leveraging its powerful basic NMR and rich CPython libraries. NMR applications with emerging technologies can be easily integrated. The scripting system is free of charge and can be downloaded by visiting http://www.spinstudioj.net/spinspj .

A new gradient shimming method based on undistorted field map of B0 inhomogeneity.

Most existing gradient shimming methods for NMR spectrometers estimate field maps that resolve B0 inhomogeneity spatially from dual gradient-echo (GRE) images acquired at different echo times. However, the distortions induced by B0 inhomogeneity that always exists in the GRE images can result in estimated field maps that are distorted in both geometry and intensity, leading to inaccurate shimming. This work proposes a new gradient shimming method based on undistorted field map of B0 inhomogeneity obtained by a more accurate field map estimation technique. Compared to the traditional field map estimation method, this new method exploits both the positive and negative polarities of the frequency encoded gradients to eliminate the distortions caused by B0 inhomogeneity in the field map. Next, the corresponding automatic post-data procedure is introduced to obtain undistorted B0 field map based on knowledge of the invariant characteristics of the B0 inhomogeneity and the variant polarity of the encoded gradient. The experimental results on both simulated and real gradient shimming tests demonstrate the high performance of this new method.

Gradient shimming based on regularized estimation for B(0)-field and shim functions.

Mapping B0-field and shim functions spatially is a crucial step in the gradient shimming. The conventional estimation method used in the phase difference imaging technique takes no account for noise and T2(∗) effects, and is prone to create noisy and distorted field maps. This paper describes a new gradient shimming based on the regularized estimation for B0-field and shim functions. Based on a statistical model, the B0-field and shim function maps are estimated by a Penalized Maximum Likelihood method that minimizes two regularized least-squares cost functions, respectively. The first cost function of B0-field exploits the two facts that the noise in the phase difference measurements is Gaussian and the B0-field maps tend to be smooth. And the other one adds an additional fact that each shim function corresponds to a given spherical harmonic of the magnetic field. Significant improvements in the quality of field mapping and in the final shimming results are demonstrated through computer simulations as well as experiments, especially when the magnetic field homogeneity is poor.