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Monday, March 31, 2008

Forward Linear Prediction in the Indirect Dimension of 2D Data

Forward linear prediction in 1D data is the calculation of new data points after the end of the acquisition time based on the observed data points. It can be used to artificially increase the acquisition time and thereby the spectral resolution. Forward linear prediction can also be used in the indirect dimension (F1) of 2D data to artificially increase the number of slices collected in the experiment and thereby improve the spectral resolution in the F1 domain. This represents a huge time saving as fewer slices need be acquired. The figure below shows the HMQC spectrum of 3-heptanone. The data in the top panel was collected with 256 slices in F1 and took 12.8 minutes to acquire. The data in the center panel was collected with 40 slices in F1 and took only 2.0 minutes to acquire. It has much lower resolution in F1 than the data in the top panel. The data in the bottom panel was produced from the same raw data as the spectrum in the center panel except forward linear prediction was applied in the F1 domain. It has comparable F1 resolution to that in the top panel but took less than 16% of the time to collect!

2 comments:

Anonymous said...

So what would you recommend, record 40 conventional slices and linearly predict up to 256 or record 40 NUS increments out of 256 and do NUS processing afterwards?

Glenn Facey said...

Anonymous,
I don't have enough experience yet with NUS to answer your question although I suspect it would depend on the particular sample.
Glenn