scholarly journals Sparse sampling methods in multidimensional NMR

2012 ◽  
Vol 14 (31) ◽  
pp. 10835-10843 ◽  
Author(s):  
Mehdi Mobli ◽  
Mark W. Maciejewski ◽  
Adam D. Schuyler ◽  
Alan S. Stern ◽  
Jeffrey C. Hoch
Keyword(s):  
Sampling Methods ◽  
Sparse Sampling ◽  
Multidimensional Nmr

scholarly journals Correction: Sparse sampling methods in multidimensional NMR

2016 ◽  
Vol 18 (28) ◽  
pp. 19482-19482
Author(s):  
Mehdi Mobli ◽  
Mark W. Maciejewski ◽  
Adam D. Schuyler ◽  
Alan S. Stern ◽  
Jeffrey C. Hoch
Keyword(s):  
Sampling Methods ◽  
Chem Phys ◽  
Sparse Sampling ◽  
Multidimensional Nmr ◽  
Phys Chem Chem Phys

Correction for ‘Sparse sampling methods in multidimensional NMR’ by Mehdi Mobli et al., Phys. Chem. Chem. Phys., 2012, 14, 10835–10843.

Parallel receivers and sparse sampling in multidimensional NMR

2011 ◽  
Vol 213 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Ēriks Kupče ◽  
Ray Freeman
Keyword(s):  
Sparse Sampling ◽  
Multidimensional Nmr

scholarly journals Randomization improves sparse sampling in multidimensional NMR

2008 ◽  
Vol 193 (2) ◽  
pp. 317-320 ◽  
Author(s):  
Jeffrey C. Hoch ◽  
Mark W. Maciejewski ◽  
Blagoje Filipovic
Keyword(s):  
Sparse Sampling ◽  
Multidimensional Nmr

Sparse-sampling methods for hyperspectral infrared microscopy

2019 ◽  
Author(s):  
Andreas C. Geiger ◽  
James R. Ulcickas ◽  
Youlin Liu ◽  
Mark F. Witinski ◽  
Romain Blanchard ◽  
...  
Keyword(s):  
Sampling Methods ◽  
Sparse Sampling ◽  
Infrared Microscopy

Fast acquisition of multidimensional NMR spectra of solids and mesophases using alternative sampling methods

2015 ◽  
Vol 53 (11) ◽  
pp. 927-939 ◽  
Author(s):  
Philippe Lesot ◽  
Krzysztof Kazimierczuk ◽  
Julien Trébosc ◽  
Jean-Paul Amoureux ◽  
Olivier Lafon
Keyword(s):  
Nmr Spectra ◽  
Sampling Methods ◽  
Multidimensional Nmr ◽  
Fast Acquisition

Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

1996 ◽  
Vol 54 ◽  
pp. 282-283
Author(s):  
Badrinath Roysam ◽  
Hakan Ancin ◽  
Douglas E. Becker ◽  
Robert W. Mackin ◽  
Matthew M. Chestnut ◽  
...  
Keyword(s):  
Image Analysis ◽  
Structural Changes ◽  
Sampling Methods ◽  
Spatial Clustering ◽  
Three Dimensional ◽  
Field Of View ◽  
Cell Counting ◽  
Depth Of Field ◽  
Tissue Cell ◽  
Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

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