Dynamic multi-spectral CT imaging

• US 6,950,493 B2
• Filed: 06/25/2004
• Issued: 09/27/2005
• Est. Priority Date: 06/25/2003
• Status: Expired due to Fees

• Current Assignee: Forevision Imaging Technologies LLC
• Original Assignee: Guy M. Besson
• Inventors: Besson, Guy M.
• Primary Examiner(s): BRUCE, DAVID VERNON
• Application Number:
  US10/877,019
• Publication Number:
  US 20040264628A1
• Time in Patent Office: 459 Days
• Field of Search: 378/4, 378/5, 378/8, 378/15, 378/16, 378/19, 378/22, 378/901
• US Class Current: 378/16
• CPC Class Codes: A61B 6/027 characterised by the use of a particular data acquisition trajectory, e.g. helical or spiral
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/0487 Motor-assisted positioning
• CPC Class Codes: A61B 6/06 Diaphragms
• CPC Class Codes: A61B 6/4028 resulting in acquisition of views from substantially different positions, e.g. EBCT
• CPC Class Codes: A61B 6/4035 the source being combined with a filter or grating
• CPC Class Codes: A61B 6/4042 K-edge filters
• CPC Class Codes: A61B 6/405 Source units specially adapted to modify characteristics of the beam during the data acquisition process A61B6/4021, A61B6/4035 take precedence
• CPC Class Codes: A61B 6/4085 Cone-beams
• CPC Class Codes: A61B 6/4241 using energy resolving detectors, e.g. photon counting
• CPC Class Codes: A61B 6/4275 using a detector unit almost surrounding the patient, e.g. more than 180°
• CPC Class Codes: A61B 6/4441 the rigid structure being a C-arm or U-arm
• CPC Class Codes: A61B 6/4488 Means for cooling A61B6/045 takes precedence
• CPC Class Codes: A61B 6/482 involving multiple energy imaging
• CPC Class Codes: A61B 6/484 involving phase contrast X-ray imaging
• CPC Class Codes: A61B 6/488 involving pre-scan acquisition
• CPC Class Codes: A61B 6/508 for non-human patients
• CPC Class Codes: A61B 6/5288 involving retrospective matching to a physiological signal
• CPC Class Codes: A61B 6/541 involving acquisition triggered by a physiological signal
• CPC Class Codes: A61B 6/542 involving control of exposure
• CPC Class Codes: A61B 6/563 : involving image data transmission via a network
• CPC Class Codes: A61B 6/583 : using calibration phantoms
• CPC Class Codes: G21K 1/10 : Scattering devices; Absorbing devices; Ionising radiation filters
• CPC Class Codes: Y10S 378/901 : Computer tomography program or processor

Abstract

A multispectral X-ray imaging system uses a wideband source and filtration assembly to select for M sets of spectral data. Spectral characteristics may be dynamically adjusted in synchrony with scan excursions where an X-ray source, detector array, or body may be moved relative to one another in acquiring T sets of measurement data. The system may be used in projection imaging and/or CT imaging. Processed image data, such as a CT reconstructed image, may be decomposed onto basis functions for analytical processing of multispectral image data to facilitate computer assisted diagnostics. The system may perform this diagnostic function in medical applications and/or security applications.

Claims

1. A method of imaging and determining constituents of a body, the method comprising steps of:

(a) projecting X-ray radiation along paths through a body by scanning a beam of X-ray radiation with respect to the body in synchronicity with system configuration for selective emission of the X-ray radiation having M sets of spectral characteristics to provide T sets of measurements in M spectra, the paths extending from an X-ray source to a detector commensurate with an x-ray source excursion in such a manner as to acquire sufficient data for at least one of a tomographic reconstruction and a tomosynthesis reconstruction, the paths extending from an X-ray source to a detector;

(b) detecting the X-ray radiation with a plurality of detectors that each define an X-ray path terminus;

(c) generating a plurality of signals representative of attenuation of the X-ray radiation on the X-ray paths, the plurality of signals allocated to one of the T sets of measurements;

(d) iterating through the above steps (a) through (c) to acquire the T sets of measurements in M sets of spectra in consequence of the projecting step (a) to provide multispectral projection data, the iterations being performed in such a manner that there is spatial commonality between respective ones of the T sets of measurements where each one of the T sets of measurements is substantially spatially aligned with at least a subset of another of the T sets of measurements; and

(e) processing the multispectral projection data to provide first information for a at least one of a multispectral tomographic and a multispectral tomosynthesis reconstruction, to obtain image data decomposed onto basis functions representative of body constituents, and to obtain analytical results indicative of a diagnostic condition.

2. The method of claim 1, where the iterating step includes acquiring the T sets of measurements sequentially by adjusting the nature of the spectral characteristics between each iteration.

3. The method of claim 1, where the iterating step includes acquiring the T sets of measurements simultaneously by using photon-counting and energy-discriminating detectors to discriminate the nature of the spectral characteristics in each iteration.

4. The method of claim 1, where the processing step includes optimally reducing the T sets of data into M sets of data by signal processing methods, decomposing the multispectral projection data onto sets of known projection basis-functions, followed by reconstructing tomographic images for each of the basis-function sets, to obtain a stack of decomposed tomographic image basis-functions representative of body constituents for a given slice through the body.

5. The method of claim 1, where the processing step includes reconstructing the multi-spectral projection data to obtain a stack of multi-spectral tomographic images for a given slice through the body, followed by decomposing the stack of multi-spectral tomographic images onto a set of image basis-functions representative of body constituents for said slice through the body.

6. The method of claim 1, where the step of processing includes using one of a filtered-backprojection technique or iterative approach for correction of scatter.

7. The method of claim 1, wherein the step of processing includes decomposing a line integral function estimated from the measurement data onto a plurality of known basis functions, and performing an analysis of the multi-spectral image data and decomposed image data to produce an analytical result indicative of the diagnostic condition.

8. The method of claim 7, wherein the step of performing an analysis includes using data pre-processing, signal extraction, and feature analysis, in combination with a multivariate model to process multispectral and decomposed images of the body by mapping of analytical detection, characterization, and classification results onto a plurality of the multispectral images.

9. The method of claim 7, wherein the step of decomposing includes processing according to an image formation model.

10. The method of claim 9, wherein the image formation model includes a Bayesian model.

11. The method of claim 10, wherein an optimal image decomposition for at least one of a disease state, a specific materials, and a body constituent is achieved by maximizing a Bayesian a-posteriori distribution.

12. The method of claim 11, wherein the maximum-a-posteriori distribution is found by a multi-dimensional optimizing technique.

13. The method of claim 12, wherein the multi-dimensional optimizing technique includes simulated annealing.

14. The method of claim 9, wherein the image formation model includes a-priori information.

15. The method of claim 14, wherein the a-priori information comprises multi-spectral models of disease states or specific materials to be identified, together with results from repeated analysis of an increasing database of cases, and program instructions for the processing step to include use of a neural network.

16. The method of claim 9, wherein the image formation model includes heart cycle information provided by an EKG or ECG trace data, or other biorhythmic information.

17. The method of claim 9, wherein the image formation model includes respiratory cycle information provided by a pulmonary monitoring device.

18. The method of claim 9, wherein the image formation model includes body thickness data provided by a compression device or other positioning device.

19. The method of claim 9, wherein the image formation model includes a plurality of two-dimensional basis functions each associating spatial relationships within a given two-dimensional basis-function, and relationships across the plurality of two dimensional basis-functions are ascertained by using adaptive neighborhoods as cliques and associated measurement data.

20. The method of claim 9, wherein the step of processing includes using a multi-spectral model to identify the diagnostic condition selected from the group consisting of a disease state, a material, or a material feature by application of a multivariate model.

21. The method of claim 20, wherein the multivariate model includes an adaptive learning algorithm.

22. The method of claim 21, wherein the adaptive learning algorithm includes a neural network.

23. The method of claim 20, wherein the multivariate model includes a Bayesian model.

24. The method of claim 20, wherein the multivariate model associates a-priori information with the diagnostic condition.

25. The method of claim 24, wherein the a-priori information associates material that may constitute a threat with material multispectral characterization data of that material to ascertain the diagnostic condition.

26. The method of claim 24, wherein a-priori information associates a disease state with multispectral characterization data of that disease state to ascertain the diagnostic condition.

27. The method of claim 24, wherein the a-priori information comprises a self-training neural network that dynamically adjusts the multivariate model on the basis of additional multispectral data pertaining to at least one of a disease state and a material to increase diagnostic accuracy.

28. The method of claim 24, wherein the a-priori information associates spatial relationships with respect to image pixel data for at least one of a disease state and a material characterization, and the multivariate model uses a plurality of basis functions to identify relationships across decomposed basis-functions data by using adaptive neighborhoods as cliques and associated measure functions.

29. The method of claim 28, wherein use of the multivariate model includes maximizing a Bayesian a-posteriori distribution to ascertain the diagnostic condition as a disease state, material, or body constituent.

30. The method of claim 29, wherein the Bayesian a-posteriori maxima is found by a multi-dimensional optimizing technique.

31. The method of claim 30, wherein the multi-dimensional optimizing technique includes simulated annealing.

32. The method of claim 9, wherein the image formation model includes determination of the body outlined obtained from a first exact or approximate image reconstruction.

33. The method of claim 1, where the step of projecting includes changing spectrum after each source excursion and includes the step of weighting the projection data to compensate for artifacts that are introduced as a result of changing the spectral characteristics.

34. The method of claim 33, where the step of weighting is performed by under-scan weighting.

35. The method of claim 33, where the step of weighting is performed by over-scan weighting.

36. The method of claim 1, where the step of projecting includes a technique selected from the group consisting of electronically deflecting a source electron beam onto a different target location, mechanically adjusting a collimator aperture so that beam geometry with respect to the detector remains essentially as in a previous acquisition for one of the T sets of measurements, electronically selecting the detected projection data to identify a primary beam, and combinations thereof.

37. The method of claim 1, wherein the step of projecting includes scanning an X-ray projection system relative to an area of the body.

38. The method of claim 1, wherein the step of projecting includes scanning an X-ray source relative to an area of the body.

39. The method of claim 1, wherein

the step of projecting includes

selecting X-ray tube parameters from the group consisting of peak kilo-voltage, beam current, anode track and material, and focal spot size, and

filtering the X-ray radiation to isolate one of the respective M spectra,

the step of projecting includes conducting a source excursion at least partly circumscribing the body;

and

the step of detecting includes detecting the T sets of measurements and associated body attenuation at different times.

40. The method of claim 1, wherein

the step of processing includes pre-processing the data, extracting the signal, performing feature analysis, and using a multivariate model to analyze the multispectral projection data for automated identification of the diagnostic condition.

41. The method of claim 40, wherein the step of processing includes using decomposed and reconstructed tomographic images, and mapping the analytical results onto the tomographic image.

42. The method of claim 1, wherein the step of detecting includes positioning part of the plurality of detectors with respect to a primary beam projection to detect scattered X-ray radiation outside the projected primary beam.

43. The method of claim 42, wherein the step of detecting includes detecting Compton scattered X-ray radiation as input for the step of processing.

44. The method of claim 42, wherein the step of detecting includes detecting Rayleigh scattered X-ray radiation as input for the step of processing.

45. The method of claim 42, wherein the step of detecting includes detecting X-rays that have been scattered a multiplicity of times as input for the step of processing.

46. The method of claim 42, wherein the step of processing includes comparing detected scatter energy an expected scatter energy value, where a difference between the detected scatter energy and expected scatter energy is used as an input for improving line-integral calculations by scatter compensation and subsequent basis-function decomposition.

47. The method of claim 46, where the step of improving line integral calculations includes backprojecting the difference along a line-integral.

48. The method of claim 1, wherein the body is a living body.

49. The method of claim 1, wherein the body is an inanimate object.

50. The method of claim 1, wherein the body is a tissue sample.

51. The method of claim 1, wherein the step of projecting comprises changing a parameter selected from the group consisting of beam filtration, beam peak kilo-voltage, tube current, anode track material, focal spot size, and combinations thereof.

52. The method of claim 1, wherein the step of projecting comprises a technique selected from the group consisting of rotating a filter wheel, advancing a bow-tie filter that has a variable attenuation profile, and combinations thereof.

53. The method of claim 1, wherein the plurality of detectors has a row-column format and the T sets of measurements are at least as large in number as one of a number of the detector rows, and a number of segments of multiple detector rows.

54. The method of claim 1, including a step of positioning a body for imaging, the body selected from a group consisting of a human, an animal, in vivo tissue, a cell culture, a cell, a tissue sample, and an inorganic article.

55. The method of claim 1, including a step of post-processing to provide detector-specific energy response corrections to properly estimate incoming X-ray energy and photon count.

56. The method of claim 1, including step of performing a scout acquisition to define scan limits of the scan with respect to the patient longitudinal axis, to refine acquisition technique, and to get a first perspective of the body.

57. The method of claim 1, including step of performing a scout acquisition imaging procedure for use in substitution of a standard x-ray projection.

58. The method of claim 57, wherein the step of performing the scout acquisition includes multispectral imaging.

59. A process for imaging and determining two or more constituents of a body, comprising:

illuminating the body with X-ray radiation while adjusting, over time, spectra of the X-ray radiation and moving the X-ray source;

capturing the X-ray radiation with a moving detector array, over time and synchronously with the adjustment of the spectra, and

processing data from the detector array to image and determine the body constituents.

60. A system for imaging and determining constituents of a body, comprising:

(a) means for projecting X-ray radiation along paths through a body by scanning a beam of X-ray radiation with respect to the body in synchronicity with system configuration for selective emission of the X-ray radiation having M sets of spectral characteristics to provide T sets of measurements, the paths extending from an X-ray source to a detector commensurate with an x-ray source excursion in such a manner as to acquire sufficient data for at least one of a multispectral tomographic reconstruction and a multispectral tomosynthesis reconstruction, the paths extending from an X-ray source to a detector;

(b) means for detecting the X-ray radiation with a plurality of detectors that each define an X-ray path terminus;

(c) means for generating a plurality of signals representative of attenuation of the X-ray radiation on the X-ray paths, the plurality of signals allocated to one of the T sets of measurements;

(d) means for iterating through use of the above means (a) through (c) to acquire T sets of measurements in M sets of spectra in consequence of the projecting step (a) to provide multispectral projection data, the iterations being performed in such a manner that there is spatial commonality between respective ones of the T sets of measurements where each one of the T sets of measurements is substantially spatially aligned with at least a subset of another of the T sets of measurements; and

(e) means for processing the multispectral projection data to provide first information for a at least one of a multispectral tomographic and a multispectral tomosynthesis reconstruction, to obtain image data decomposed onto basis functions representative of body constituents, and to obtain analytical results indicative of a diagnostic condition.

61. The system of claim 60, wherein the means for projecting sequentially operates to project a number or all of the T sets of measurements on substantially the same radiation paths through the body.

62. The system of claim 60, wherein the means for projecting simultaneously operates to project all the T sets of measurements, and the means for detecting includes photon-counting and energy-discriminating detectors to associate measurements with each one of the M sets of spectral bands.

63. The system of claim 60, wherein the program instructions are operable to first optimally reduce the T sets of data into M sets of data by signal processing means, and to decompose the resulting M sets of measurements onto known projection basis-functions, and to reconstruct CT images for each basis-function set to obtain a stack of decomposed tomographic image basis-functions representative of body constituents for a given slice through the body.

64. The system of claim 60, where the program instructions are operable to first optimally reduce the T sets of data into M sets of data by signal processing means, and then reconstruct the multi-spectral projection data to obtain a stack of multi-spectral tomographic images for a given slice through the body, which is followed by decomposition of the stack of multi-spectral tomographic images onto a set of image basis-functions representative of body constituents for said slice through the body.

65. The system of claim 60, wherein the means for processing comprises computer equipment and program instructions to implement one of a filtered-backprojection approach or an iterative approach for scatter correction.

66. The system of claim 60, where the means for processing includes computer equipment and program instructions to implement iterative approaches in conjunction with the use of an image formation model, including a statistical model of data originating from the detection signals.

67. The system of claim 60, including means for directly acquiring and recording heart cycle information in the form of EKG trace data.

68. The system of claim 60, including means for directly acquiring and recording respiratory cycle information in the form of a pulmonary bellows or monitoring device data.

69. The system of claim 60, including means for directly acquiring and recording body thickness data.

70. The system of claim 60, wherein the means for processing includes means for performing a preliminary image reconstruction, from which a body outline is determined and provided as input to an image formation model.

71. The system of claim 70, wherein the image formation model includes computer instruction for the processing of a-priori information.

72. The system of claim 71, wherein the a-priori information comprises a multispectral model for at least one of a disease state and a specific material to be identified, and program instructions for implementing a neural network.

73. The system of claim 71, wherein the program instructions are configured to apply spatial relationships across basis-functions, using adaptive neighborhoods as cliques, and associated measure functions.

74. The system of claim 71, wherein the program instructions achieve an optimal tomographic or tomosynthesis image reconstruction and decomposition by maximizing the Bayesian a-posteriori distribution.

75. The system of claim 74, wherein a maximum-a-posteriori is found by a multi-dimensional optimizing technique.

76. The system of claim 75, wherein the multi-dimensional optimizing technique includes simulated annealing.

77. The system of claim 60, wherein the means for processing includes means for weighting the projection data to reduce or eliminate artifacts that would other wise be introduced as a result of the spectrum transitions to different ones of the T sets of measurements.

78. The system of claim 77, wherein the means for weighting is performed by scan under-weighting.

79. The system of claim 77, wherein the means for weighting is performed by scan over-weighting.

80. The system of claim 60, wherein the means for projecting comprises means for electronically deflecting a tube electron beam on a different target location, together with means for adjusting a collimator aperture so that the beam geometry with respect to the detector remains substantially as in a previous us of one of the T sets of measurements.

81. The system of claim 60 wherein the means for projecting comprises means for electronically deflecting a tube electron beam on a different target location, together with means for processing the detector signals to identify the primary beam location on the detector.

82. The system of claim 60 wherein the means for projecting includes means for scanning an X-ray projection system relative to an area of the body.

83. The system of claim 60, wherein the means for projecting includes means for scanning an X-ray source relative to an area of the body.

84. The system of claim 60, wherein the means for projecting includes a stationary X-ray projection system, and means for holding the detector array stationary while a narrow X-ray beam scans across the stationary detector to acquire the multi-spectral projection data.

85. The system of claim 84, wherein the X-ray projection position is changed in sequence after each projection acquisition to acquire a series of projection data sufficient for multi-spectral tomographic image reconstruction.

86. The system of claim 60, including means for concurrent movement of the source and detector image chain around the body while a narrow beam of X-ray is scanned across the detector.

87. The system of claim 86, wherein the movement includes at least one of a rotation and an excursion.

88. The system of claim 60, wherein the means for projecting includes means for selecting an X-ray tube parameters selected from the group consisting of peak kilo-voltage, beam current, anode track and material, focal spot size, and combinations thereof;

and means for filtering the X-ray radiation to isolate respective ones of the M sets of spectra.

89. The system of claim 60, wherein the program instructions are configured to analyze the projected, reconstructed, and decomposed multispectral data for the automated detection, characterization, and classification of a diagnostic condition selected form the group consisting of an anomaly, a disease state, and a presence of a specific material by application of data pre-processing techniques, signal extraction techniques, feature analysis, and a multivariate model.

90. The system of claim 60, wherein the program instructions include use of a multivariate model to assess the diagnostic condition.

91. The system of claim 90, wherein the multivariate model includes a-priori information.

92. The system of claim 91, wherein the a-priori information includes multispectral information pertaining to at least one of a disease state and a specific material to be identified, and the program instructions implement an adaptive learning algorithm.

93. The system of claim 92, wherein the adaptive learning algorithm includes a neural network.

94. The system of claim 92, wherein the program instructions are configured to apply spatial relationships within a given two-dimensional basis-function, and relationships across basis-functions, using adaptive neighborhoods as cliques, and associated measure functions.

95. The system of claim 94, wherein the program instructions achieve an optimal detection, characterization, and classification of disease states or specific materials by maximizing the Bayesian a-posteriori distribution.

96. The system of claim 90, wherein the multivariate model includes a Bayesian model.

97. The system of claim 96, wherein the program instructions find a Bayesian maximum-a-posteriori by a multi-dimensional optimizing technique.

98. The system of claim 96, wherein the multi-dimensional optimizing technique includes simulated annealing.

99. The system of claim 60, wherein the means for processing includes means for using the detection signals to prepare an image of the body, and for mapping the analytical results onto the image.

100. The system of claim 60, wherein the means for detecting includes means for positioning part of the detector array to detect scattered X-ray radiation outside a projected primary beam.

101. The system of claim 100, wherein the means for detecting includes means for detecting Compton scattered X-ray radiation as input for the means for processing.

102. The system of claim 100, wherein the means for detecting includes means for detecting Rayleigh scattered X-ray radiation as input for the step of processing.

103. The system of claim 100, wherein the means for detecting includes means for detecting X-rays that have been scattered a multiplicity of times as input for the step of processing.

104. The system of claim 100, wherein the program instructions are configured to process detected scatter energy by comparing a detected scatter energy value with an expected scatter energy value.

105. The system of claim 104, wherein a relationship between the detected scatter energy value and the expected scatter energy value is used as an input to improve line-integral calculations.

106. The system of claim 60, further comprising a display for displaying the decomposed X-ray projections onto multiple two-dimensional basis functions representative of body constituents.

107. The system of claim 106, wherein the two-dimensional basis functions are summed with specific weights prior to display.

108. The system of claim 107, wherein the program instructions allocate display color according to the sum of weights.

109. The system of claim 60, wherein the means for projecting includes an X-ray source that is selectively mounted in a fixed position and the means for detecting includes a detector array that is selectively retainable in a fixed position, the detector array having a spatial extent sufficient to image the body in a cross-sectional projection.

110. The system of claim 60, wherein the detectors having spatial extent sufficient to image both the projected primary radiation through the body and scattered radiation beyond the projected primary.

111. The system of claim 60, wherein the means for detecting and the means for projecting are linked in a mechanical assembly to provide relative motion such that a selectively fixed positional relationship is maintained between the means for projecting and the means for detecting concomitant with rotation of the mechanical assembly to a plurality of rotational positions about the body while collecting the multispectral data.

112. The system of claim 60, wherein the means for projecting moves on a pre-determined trajectory sufficient for exact or approximate tomographic or tomosynthesis image reconstruction, while the means for detecting remains in a fixed position and the body advances with respect to the means for detecting.

113. The system of claim 60, wherein the means for projecting and the means for detecting are coupled together in a fixed positional relationship to one another, and further comprising means for moving the body through the X-ray radiation, wherein

the means for projecting includes means for synchronously varying spectra of an X-ray source and

the means for processing includes means for weighting the multispectral projection data to reduce artifacts induced by spectral transitions between the M sets of spectra.

114. The system of claim 60, wherein the means for detecting includes an elongated detector array presenting a longitudinal axis, and the means for projecting is constructed and arranged to project the X-ray radiation in an elongated strip for illumination of a portion of the detector along the longitudinal axis.

115. The system of claim 60, wherein the detector array is in a row-column format and rows of the detectors capturing the X-ray radiation are narrower than a long axis of the body to be imaged.

116. The system of claim 60, wherein the X-ray source and detectors rotate or describe a path around a main axis of the body and wherein the body passes through the X-ray radiation, such that the T sets of measurements are acquired for each radiation path through the body after T or fewer rotations or path excursions where T is a divisor of (a) a number of detector rows or (b) a number of multiple detector row segments.

117. The system of claim 60, including a device to retain the body in a fixed position, wherein the means for projecting and the means for detecting are mechanically linked as an assembly enabling projection of the X-ray radiation on a selected area of the detectors during rotation of the assembly.

118. The system of claim 60, wherein the means for detecting includes an elongated detector array presenting a longitudinal axis, and the means for projecting X-ray beam projects the X-ray radiation onto a narrower area of the detector array than the longitudinal dimension.

119. The system of claim 60, wherein the X-ray source and detectors are mechanically assembled in a fixed geometric relationship wherein the single or mutli-row detectors are located on an arc centered at the X-ray source.

120. The system of claim 60, wherein the means for projecting is constructed and arranged to rotate an X-ray source around the body, and the means for detecting includes a detector array deployed in a fixed arc.

121. The system of claim 60, wherein the means for projecting and the means for detecting are mechanically linked by an arm that permits linear motion of the means for detecting, the means for projecting including a collimator assembly that is dynamically adjustable to maintain X-ray collimation of projected X-ray radiation on the means for detecting while the means for detecting is being moved by the arm.

122. The system of claim 60, including a feedback loop for selective positioning of the means for projecting and/or the means for detecting.

123. The system of claim 60, including means for performing a scout acquisition to define scan limits of the scan with respect to the patient longitudinal axis, to refine acquisition technique, and to get a first perspective of the body.

124. The system of claim 60, including means for performing a scout acquisition imaging procedure for use in substitution of a standard x-ray projection.

125. The system of claim 124, wherein the means for performing the scout acquisition includes multispectral imaging.

126. A dynamic multi-spectral X-ray imaging and analysis system, comprising:

an X-ray source for illuminating a body with X-ray radiation with adjustable spectra;

a plurality of detectors for detecting the X-ray radiation; and

means for moving the source and detectors with respect to the body while changing X-ray radiation spectrum to synchronize detection of the X-ray radiation with spectrum changes and body advance along the longitudinal system axis, wherein data from the detectors represents multi-spectral characteristics of the body.

127. The system of claim 126, further comprising post-processing means for processing data from the detectors to image and determine constituents of the body.

128. The system of claim 127, wherein the post-processing means include detector-specific energy response corrections to properly estimate incoming X-ray energy and photon count.