Timeline for Reconstruction of NMR spectrum by entropy maximization (MaxEnt)
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 23, 2022 at 22:25 | comment | added | Landak | As someone who is a professional MR(I) physicist, I should state that compressed sensing techniques are widely used clinically in the reconstruction of proton imaging data, in which we have the luxury of two extra bits of information, related to a) obtaining data from multiple independent RF coils, rather than one probe; and b) knowledge of what "real" images look like: sparse in the Wavelet domain, and well-reconstructed by penalised total-variation terms. We acquire noisy, aliased data, and used CS to sort out aliasing. Spectra are different, but much similar work is underway. | |
| Oct 23, 2022 at 14:14 | comment | added | Buck Thorn♦ | I first encountered MaxEnt as a routine available within a suite of advanced NMR processing tools written by Hoch and Stern (this would be the Cambridge Mass algorithm). I believe it still underlies the newer online versions (at UConn). Bruker has incorporated NUS as a standard tool into their software and there are non-standard add-ons that do not follow with Topspin written by 3rd parties, usually academics. | |
| Oct 23, 2022 at 14:03 | comment | added | ACR | Do you remember what software was introduced to you? I always see Cambridge algorithm everywhere. Skilling et al. were applied mathematicians. Hard to follow their paper. Basically, the problem is the non-linear nature of entropy function, the presence of natural log makes it difficult. | |
| Oct 23, 2022 at 13:59 | comment | added | Buck Thorn♦ | The nonlinear issue complicates quantitation. One workaround is to inject digital signals where no signal is expected, as a calibration. In general you need to perform calibrations, which is possible with nonlinear data, but full of potential pitfalls (quantitation is generally difficult with noisy and potentially overlapping spectra). | |
| Oct 23, 2022 at 13:56 | comment | added | Buck Thorn♦ | @AChem when it was first introduced the signal enhancement aspect of MaxEnt was emphasized, but the ability to apply it to nonlinearly sampled or truncated data is what sets it apart, typically when combined with the signal enhancement. For instance, you can sample nonlinearly where most of the signal in the time domain shows up (in the direct dimension early on, before relaxation dampens it), or simply until you feel sufficient s/n has been achieved (something that can be tricky otherwise in MD NMR). | |
| Oct 23, 2022 at 12:46 | comment | added | ACR | Also note the bottom figure, the relative intensities are not preserved that well after MaxEnt, esp. on the right hand side. | |
| Oct 23, 2022 at 12:44 | comment | added | ACR | Right (+1), but let us focus on uniformly sampled data. This is my main concern with non-linearity of MaxEnt. If MaxEnt always change intensities in a non-linear fashion, then the reconstructed NMR intensities cannot be used for integration. What is the use then? This is what Hoch says, who is indeed a world expert in this field...however many others contest that MaxEnt is still useful. I know of a far simpler way of doing this, by a so-called power transform. It is "silently" applied in charged aerosol detectors of HPLC. You just raise the data to an integer power. | |
| Oct 23, 2022 at 8:36 | comment | added | Buck Thorn♦ | I should add that NMR time is very expensive. Problems with experiment duration can also be circumvented by purchasing multiple instruments. Not so easy when each costs on the order of £millions. | |
| Oct 23, 2022 at 8:32 | history | answered | Buck Thorn♦ | CC BY-SA 4.0 |