Enhancing signals

• US 8,618,798 B2
• Filed: 03/27/2009
• Issued: 12/31/2013
• Est. Priority Date: 03/28/2008
• Status: Active Grant

• Current Assignee: King's College London
• Original Assignee: King's College London
• Inventors: Jakobsson, Andreas, Rowe, Michael, Smith, John, Butt, Naveed Razzaq, Gudmundson, Erik, Althoefer, Kaspar, Somasundaram, Samuel
• Primary Examiner(s): SHRIVASTAV, BRIJ B
• Application Number:
  US12/935,202
• Publication Number:
  US 20110210728A1
• Time in Patent Office: 1,740 Days
• Field of Search: 324300-322, 600407-445
• US Class Current: 324/307
• CPC Class Codes: G01N 24/08 by using nuclear magnetic resonance G01N24/12 takes precedence
• CPC Class Codes: G01N 24/10 by using electron paramagnetic resonance G01N24/12 takes precedence
• CPC Class Codes: G01R 33/441 Nuclear Quadrupole Resonance [NQR] Spectroscopy and Imaging
• CPC Class Codes: G01R 33/4616 using specific RF pulses or specific modulation schemes, e.g. stochastic excitation, adiabatic RF pulses, composite pulses, binomial pulses, Shinnar-le-Roux pulses, spectrally selective pulses not being used for spatial selection
• CPC Class Codes: G01R 33/4625 Processing of acquired signals, e.g. elimination of phase errors, baseline fitting, chemometric analysis
• CPC Class Codes: G01R 33/5608 Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels image data processing or generation, in general G06T

Abstract

A method of testing a sample comprising the steps of: applying an excitation to the sample; detecting a response signal from the sample; processing a first part and a second part of the response signal; and determining from the second part of the response signal information with which to enhance the first part of the response signal.

Claims

1. A method of testing for the presence of a species within a sample comprising the steps of:

applying a resonance inducing excitation to the sample;

detecting a resonance response signal from the sample comprising a signal-of-interest characteristic of the species and at least one corrupting signal;

processing a first part and a second part of the response signal; and

determining from the second part of the response signal information regarding the at least one corrupting signal with which to determine from the first part of the response signal the signal-of-interest.

2. The method according to claim 1, wherein the corrupting signal comprises a signal emanating externally to and/or not resulting from the excitation to the sample.

3. The method according to claim 1, wherein the applied excitation is a radio-frequency excitation.

4. The method according to claim 3, wherein the excitation excites in the sample at least one of:

a nuclear quadrupole resonance;

a nuclear magnetic resonance;

or

an electron paramagnetic resonance.

5. The method according to claim 1, wherein the corrupting signal comprises radio-frequency interference.

6. The method according to claim 1, wherein the method further comprises processing the second part of the response signal in order to obtain a model of the corrupting signal.

7. The method according to claim 6, wherein the model of the corrupting signal is used to reduce the effects of the corrupting signal in the first part of the response signal.

8. The method according to claim 1, wherein the excitation is a conventional pulse-sequence excitation.

9. The method according to claim 1, wherein the excitation is a stochastic excitation, such as a random or pseudo-random excitation.

10. The method according to claim 9, wherein the response signal is sampled by taking multiple samples between consecutive excitation pulses.

11. The method according to claim 9, wherein a signal-of-interest is obtained by cross-correlating the excitation signal with the time-domain response signal to produce a correlation-domain response signal.

12. The method according to claim 11, wherein the resulting correlation-domain signal is modelled as a gapped free-induction decay.

13. The method according to claim 11, wherein at least one algorithm is used to estimate at least one spectral parameter directly from the resulting correlation-domain signal.

14. The method according to claim 8, wherein the corrupting signal is modelled as vectors belonging to a low-rank linear interference subspace, embedded in wideband noise.

15. The method according to claim 14, wherein the second part of the response signal is used to make an estimate of the low-rank linear interference subspace.

16. The method according to claim 15, wherein vectors representing the response signal and the model of the corrupting signal are projected onto the space orthogonal to the interference subspace and thereby to reduce the influence of the corrupting signal in the part of the response signal containing the signal-of-interest,

17. The method according to claim 9, wherein the corrupting signal is modelled as vectors belonging to a low-rank linear interference subspace, embedded in wideband noise.

18. The method according to claim 17, wherein the second part of the response signal is used to make an estimate of the tow-rank linear interference subspace.

19. The method according to claim 18, wherein vectors representing the response signal and the model of the corrupting signal are projected onto the space orthogonal to the interference subspace and thereby to reduce the influence of the corrupting signal in the part of the response signal containing the signal-of-interest.

20. The method according to claim 8, wherein the corrupting signal is modelled as pure zero mean Gaussian noise.

21. The method according to claim 20, wherein the second part of the response signal is used to estimate a corresponding noise covariance matrix and to construct a transform to pre-whiten. any unknown noise colouring due to the corruptive signal and thereby to reduce the influence of the corruptive signal in the first part of the response signal containing the signal-of-interest.

22. The method according to claim 9, wherein the corrupting signal is modelled as pure zero mean Gaussian noise.

23. The method according to claim 22, wherein the second part of the response signal is used to estimate a corresponding noise covariance matrix and to construct a transform to pre-whiten any unknown noise colouring due to the corruptive signal and thereby to reduce the influence of the corruptive signal in the first part of the response signal containing the signal-of-interest.

24. The method according to claim 1, wherein the method is used to distinguish between real and counterfeit medicines.

25. The method according to claim 1, using only a single sensor.

26. The method according to claim 25, wherein the sensor is gradiometric.

27. An apparatus for testing for the presence of a species within a sample comprising:

a transmitter, adapted to apply a resonance inducing excitation to the sample;

a receiver, adapted to detect a resonance response signal from the sample comprising a signal-of-interest characteristic of the species and at least one corrupting signal;

a processor, adapted to;

process a first part and a second part of the response signal; and

determine from the second part of the response signal information regarding the at least one corrupting signal with which to determine from the first part of the response signal the signal-of-interest.

28. The apparatus according to claim 27, wherein the corrupting signal comprises a signal emanating externally to and/or not resulting from the excitation to the sample.

29. The apparatus according to claim 27, wherein the transmitter is adapted to apply a radio-frequency excitation.

30. The apparatus according to claim 29, wherein the excitation excites in the sample at least one of:

a nuclear quadrupole resonance;

a nuclear magnetic resonance;

or an electron paramagnetic resonance.

31. The apparatus according to claim 27, wherein the corrupting signal comprises radio-frequency interference.

32. The apparatus according to claim 27, wherein the processor is adapted to process the second part of the response signal in order to obtain a model of the corrupting signal.

33. The apparatus according to claim 32, wherein the processor is adapted to use the model of the corrupting signal to reduce the effects of the corrupting signal in the first part of the response signal.

34. The apparatus according to claim 27, wherein the transmitter is adapted to apply a conventional pulse-sequence excitation.

35. The apparatus according to claim 27, wherein the transmitter is adapted to apply a stochastic excitation, such as random or pseudo-random excitation.

36. The apparatus according to claim 35, further comprising spectrometer hardware for sampling the response signal by taking multiple samples between consecutive excitation pulses.

37. The apparatus according to claim 35, further comprising spectrometer hardware for obtaining a signal-of-interest by cross-correlating the excitation signal with the time-domain response signal to produce a correlation-domain response signal.

38. The apparatus according to claim 37, wherein the processor is adapted to model the resulting correlation-domain signal as a gapped free-induction decay.

39. The apparatus according to claim 37, wherein the processor is adapted to apply algorithms to estimate spectral parameters directly from the resulting correlation-domain signal.

40. The apparatus according to claim 34, wherein the processor is adapted to model the corrupting signal as vectors belonging to a low-rank linear interference subspace, embedded in wideband noise.

41. The apparatus according to claim 40, wherein the processor is adapted to use the second part of the response signal to make an estimate of the low-rank linear interference subspace.

42. The apparatus according to claim 41, wherein the processor is adapted to project vectors representing the response signal and the model of the corrupting signal onto the space orthogonal to the interference subspace to reduce the influence of the corrupting signal in the part of the response signal containing the signal-of-interest.

43. The apparatus according to claim 35, wherein the processor is adapted to model the corrupting signal as vectors belonging to a low-rank linear interference subspace, embedded in wideband noise.

44. The apparatus according to claim 43, wherein the processor is adapted to use the second part of the response signal to make an estimate of the low-rank linear interference subspace.

45. The apparatus according to claim 44, wherein the processor is adapted to project vectors representing the response signal and the model of the corrupting signal onto the space orthogonal to the interference subspace to reduce the influence of the corrupting signal in the part of the response signal containing the signal-of-interest.

46. The apparatus according to claim 34, wherein the processor is further adapted to model the corrupting signal as pure zero mean Gaussian noise.

47. The apparatus according to claim 36, wherein the processor is further adapted to use the second part of the response signal is to estimate the corresponding noise covariance matrix and to construct a pre-whitening transform for use in reducing the influence of the corruptive signal in the part of the response signal containing the signal-of-interest.

48. The apparatus according to claim 35, wherein the processor is further adapted to model the corrupting signal as pure zero mean Gaussian noise.

49. The apparatus according to claim 48, wherein the processor is further adapted to use the second part of the response signal is to estimate the corresponding noise covariance matrix and to construct a pre-whitening transform for use in reducing the influence of the corruptive signal in the part of the response signal containing the signal-of-interest.

50. The apparatus according to claim 27, wherein the apparatus is used to distinguish between real and counterfeit medicines.

51. The apparatus according to claim 27, comprising only a single sensor.

52. The apparatus according to claim 51, wherein the sensor is non-gradiometric.