Nonuniform sampling by quantiles

Craft, D. Levi; Sonstrom, Reilly E.; Rovnyak, Virginia; Rovnyak, David

doi:10.1016/j.jmr.2018.01.014

Cited by 15 publications

(35 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…QS has a potentially large parameter space ( shape , bias , etp ; see Section 2), and the first step was to determine a narrower range of quantile schedules to focus on in this work. Work to date on quantile schedules has supported choosing sinusoidal or decaying polynomial shapes with moderate values of the bias parameter, which was developed in more detail in a thorough grid search of quantile parameters . In consideration of prior work, the QS approaches herein used a sinusoidal shape and used bias and etp parameters on order of 1.5–2.5.…”

Section: Resultsmentioning

confidence: 99%

“…NUS usually selects samples from the grid defined by uniformly sampled points, and varying strategies are used to achieve randomness, minimize gaps, promote reproducibility, and adhere to various weightings of the samples . The use of uniform tracts in the sampling schedule, frequently termed bursts, can contribute to suppressing aliasing, enable powerful direct acquisition NUS experiments in pure shift spectroscopy, and exert control over sampling artifacts .…”

Section: Methodsmentioning

confidence: 99%

“…In this work, two current approaches for selecting weighted schedules from a uniform grid were considered. One‐dimensional NUS schedules were generated using the quantile‐directed and the Poisson gap methods, each of which offer online schedule generation: (QSCHED: http://qsched.bison.link/) and (HMS Poisson Gap: http://gwagner.med.harvard.edu/intranet/hmsIST/gensched_new.html).…”

Section: Methodsmentioning

confidence: 99%

“…The QS approach computes sampling schedules that best conform to the desired weighting by dividing weighting functions into regions of equal probability; QS can be viewed as a member of several related algorithms which reduce seed dependence in weighted random sampling . The quantile method implemented in the QSched program supports flexible design of the weighting of the sampling schedule.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Developing nonuniform sampling strategies to improve sensitivity and resolution in 1,1‐ADEQUATE experiments

Roginkin

Ndukwe

Craft

et al. 2020

Magnetic Reson in Chemistry

Self Cite

View full text Add to dashboard Cite

Nonuniform sampling (NUS) strategies are developed for acquiring highly resolved 1,1‐ADEQUATE spectra, in both conventional and homodecoupled (HD) variants with improved sensitivity. Specifically, the quantile‐directed and Poisson gap methods were critically compared for distributing the samples nonuniformly, and the quantile schedules were further optimized for weighting. Both maximum entropy and iterative soft thresholding spectral estimation algorithms were evaluated. All NUS approaches were robust when the degree of data reduction is moderate, on the order of a 50% reduction of sampling points. Further sampling reduction by NUS is facilitated by using weighted schedules designed by the quantile method, which also suppresses sampling noise well. Seed independence and the ability to specify the sample weighting in quantile scheduling are important in optimizing NUS for 1,1‐ADEQUATE data acquisition. Using NUS yields an improvement in sensitivity, while also making longer evolution times accessible that would be difficult or impractical to attain by uniform sampling. Theoretical predictions for the sensitivity enhancements in these experiments are in the range of 5–20%; NUS is shown to disambiguate weak signals, reveal some nJCC correlations obscured by noise, and improve signal strength relative to uniform sampling in the same experimental time. This work presents sample schedule development for applying NUS to challenging experiments. The schedules developed here are made available for general use and should facilitate the broader utilization of ADEQUATE experiments (including 1,1‐, 1,n‐, and HD‐ variants) for challenging structure elucidation problems.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Developing nonuniform sampling strategies to improve sensitivity and resolution in 1,1‐ADEQUATE experiments

Roginkin

Ndukwe

Craft

et al. 2020

Magnetic Reson in Chemistry

Self Cite

View full text Add to dashboard Cite

show abstract

“…This work revealed NUS spectral artifacts to be aliases, and the role of the greatest common divisor (GCD) for determining the effective bandwidth. These observations are a conceptual gold mine, hinting at connections with number theory (for a sampling series with a small GCD you cannot do better than a series of primes) and the “subharmonic tracts” noted by Rovnyak and colleagues, There could well be more gold in the mine, making Bretthort's manuscript well worth visiting (or re‐visiting).…”

Section: The View From Outside Nmrmentioning

confidence: 99%

An irregular sampler

Hoch

2017

Concepts Magnetic Resonance

View full text Add to dashboard Cite

The historical evolution of sparse sampling methods in multidimensional NMR is important for understanding them in the context of developments outside of NMR. This brief, anecdotal history provides context, but also points to potential sources of insights into sparse sampling that have yet to be utilized in NMR.Advances in sparse sampling for multidimensional NMR represent a confluence of many disparate threads. K E Y W O R D Scompressed sensing, non-Fourier methods, nonuniform sampling, signal processing, sparse recovery, spectrum analysis

show abstract

Revisiting aliasing noise to build more robust sparsity in nonuniform sampling 2D‐NMR

Cullen

Marchiori

Rovnyak

2023

Magnetic Reson in Chemistry

Self Cite

View full text Add to dashboard Cite

A continuing priority is to better understand and resolve the barriers to using nonuniform sampling (NUS) in challenging small molecule 2D NMR with subsampling of the Nyquist grid (a.k.a. coverage) below 50%. Possible causes for artifacts, often termed sampling noise, in 1D-NUS of 2D-NMR are revisited here, where weak aliasing artifacts are a growing concern as NUS becomes sparser. As NUS schedules become sparser, repeat sequences are shown to occur in the dense sampling regions early in the sampling schedule, causing aliasing artifacts in resulting spectra. An intuitive screening approach that detects patterns in sampling schedules based on a convolutional filter was implemented. Sampling schedules that have low proportions of repeat sequences show significantly reduced artifacts. Another route to remediate early repeat sequences is a short period of uniform sampling at the beginning of the schedule, which also leads to a significant suppression of unwanted sampling noise. Combining the repeat sequence filter with a survey of HSQC and LR-HSQMBC experiments, it is shown that very short initial uniform regions of about 2%-4% of the sampling space can ameliorate repeat sequences in sparser NUS and lead to robust spectral reconstructions by iterative soft thresholding (IST), even when the point spread function is unchanged. Using the principles developed here, a suite of 'one-click' schedules was developed for broader use.

show abstract

Nonuniform sampling by quantiles

Cited by 15 publications

References 34 publications

Developing nonuniform sampling strategies to improve sensitivity and resolution in 1,1‐ADEQUATE experiments

Developing nonuniform sampling strategies to improve sensitivity and resolution in 1,1‐ADEQUATE experiments

An irregular sampler

Revisiting aliasing noise to build more robust sparsity in nonuniform sampling 2D‐NMR

Contact Info

Product

Resources

About