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Published on: September 17, 2017
Deterministic schedules for robust and reproducible non-uniform sampling in multidimensional NMR
Matthew T Eddy1, David Ruben, Robert G Griffin
1Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. meddy@mit.edu
Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|December 28, 2011
Summary
This study introduces a reproducible method for non-uniform sampling (NUS) schedules, enhancing NMR spectroscopy data quality. Deterministic sampling matches random sampling benefits while avoiding seed-related issues, improving spectral resolution.
Area of Science:
- Nuclear Magnetic Resonance (NMR) Spectroscopy
- Data Acquisition Techniques
- Computational Chemistry
Background:
- Non-uniform sampling (NUS) in NMR reduces data acquisition time but can be sensitive to random seed selection, potentially introducing artifacts.
- Optimizing sampling schedules is crucial for maximizing spectral information content and quality.
- Existing methods for generating NUS schedules may not consistently mitigate sampling artifacts or ensure reproducibility.
Purpose of the Study:
- To develop a general, reproducible, and simple method for generating non-uniform sampling (NUS) schedules for NMR spectroscopy.
- To demonstrate that this deterministic sampling approach preserves the advantages of random sampling while eliminating seed-dependent pitfalls.
- To compare the performance of deterministic versus random sampling schedules in 2D HSQC spectra processed with the SIFT method.
Main Methods:
- Generated sampling schedules using a discrete cumulative distribution function (CDF) that approximates a desired continuous probability density function.
- Employed a Gaussian probability density function for generating schedules in 2D HSQC experiments.
- Processed NMR data using the Spectroscopy by Integration of Frequency and Time domain data (SIFT) method.
Main Results:
- Deterministic NUS schedules performed comparably to random schedules at critical sampling densities.
- Deterministic NUS schedules significantly outperformed random schedules at lower sampling densities (half critical density).
- The developed method demonstrated robustness and applicability across different probability density functions and dimensions.
Conclusions:
- The proposed method for generating deterministic NUS schedules is effective and reproducible.
- This approach offers a reliable alternative to random sampling, reducing artifacts and improving spectral quality, especially under sparse sampling conditions.
- The method's generalizability allows for broad application in various multidimensional NMR experiments.
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