Overcoming line broadening in real-time pure shift NMR spectroscopy презентация

Overcoming line broadening in real-time pure shift NMR spectroscopy Alexandra Shchukina, Krzysztof Kazimierczuk University of Warsaw, Centre of New Technologies, Poland Craig Butts, Ikenna Ndukwe Bristol University, UK

Overcoming line broadening in real-time pure shift NMR spectroscopy ...with what? Alexandra Shchukina, Krzysztof Kazimierczuk University of Warsaw, Centre of New Technologies, Poland Craig Butts, Ikenna Ndukwe Bristol University, UK

Overcoming line broadening in real-time pure shift NMR spectroscopy ...with CS reconstruction! Alexandra Shchukina, Krzysztof Kazimierczuk University of Warsaw, Centre of New Technologies, Poland Craig Butts, Ikenna Ndukwe Bristol University, UK

Pure shift NMR: what for and how Line broadening in real-time pure shift NMR CS reconstruction as a remedy Details of CS: the idea and its realization Applications

Pure shift NMR: what for and how Line broadening in real-time pure shift NMR CS reconstruction as a remedy Details of CS: the idea and its realization Applications

Pure shift NMR as a tool for homodecoupling

Selective pulses Spacially selective or Frequency-selective or BIRD-based pulse sequences ...

Pseudo-2D and real-time pure shift NMR

Line broadening with concatenation

Line broadening with concatenation

Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically

Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically for NMR: spectrum (Fourier transform of FID)

Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically for NMR: spectrum (Fourier transform of FID)

Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID

Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system)

Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system) CS reconstruction: subject to (out of all possible FIDs choose the one which gives the sparsest spectrum)

Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system) CS reconstruction: subject to (out of all possible FIDs choose the one which gives the sparsest spectrum) Taking noise into account:

Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system) CS reconstruction: subject to (out of all possible FIDs choose the one which gives the sparsest spectrum) Taking noise into account:

Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system) CS reconstruction: subject to (out of all possible FIDs choose the one which gives the sparsest spectrum) Taking noise into account: Iterative solution → family of algorithms