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Pitfalls in compressed sensing reconstruction and how to avoid them.

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  • 1Centre of New Technologies, University of Warsaw, Banacha 2C, 02-097, Warsaw, Poland.

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|November 13, 2016
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Summary

Non-uniform sampling (NUS) accelerates multidimensional NMR experiments by reconstructing sparse data. This study details common compressed sensing (CS) reconstruction issues and offers solutions for optimal parameter setting in NMR data processing.

Keywords:
CLEANIterative soft thresholdingIteratively re-weighted least squaresLow-rankMatching pursuitNon-uniform sampling

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Area of Science:

  • * Biophysical Chemistry
  • * Analytical Chemistry
  • * Structural Biology

Background:

  • * Multidimensional NMR offers high spectral resolution for biological macromolecules but requires extensive experimental time.
  • * Non-uniform sampling (NUS) reduces acquisition time by undersampling data, necessitating advanced reconstruction algorithms.
  • * Compressed sensing (CS) is a prominent method for NUS data reconstruction, assuming spectral sparsity.

Purpose of the Study:

  • * To elucidate common challenges encountered during compressed sensing (CS) reconstructions in multidimensional NMR.
  • * To provide practical guidance for optimizing NUS acquisition and processing parameters.
  • * To aid both users and developers in understanding and improving CS-based NMR data reconstruction.

Main Methods:

  • * Discussion of problem sources in CS reconstructions, including low sampling, incorrect sparsity assumptions, and inappropriate stopping criteria.
  • * Analysis of issues related to signal extrapolation during reconstruction.
  • * Provision of MATLAB codes for several CS algorithms used in NMR as supplementary material.

Main Results:

  • * Identification of key factors leading to imperfect NUS reconstructions in multidimensional NMR.
  • * Explanation of the underlying mechanisms causing reconstruction failures.
  • * Practical insights into troubleshooting common CS reconstruction problems.

Conclusions:

  • * Understanding the limitations and potential pitfalls of CS in NUS is crucial for reliable NMR data processing.
  • * This work provides a framework for setting acquisition and processing parameters to improve reconstruction quality.
  • * The insights and provided codes aim to enhance the application and development of CS-based NUS methods in NMR spectroscopy.