Calibration method for CT (Computed Tomography) imaging

Abstract

A method for calibration in the context of a CT imaging mechanism in which projection data is acquired over a series of angular positions of a CT scanner about the body, and in which at least two angularly displaced X-ray sources (e.g., X-ray tube focal spots) are alternately activated as the scanner is orbitally rotated about the body. The alternately enabled source may be a focal spot or may be other sources. It is suggested to detect a spatial transformation (i.e., a spatial shift) in all or part of a 2D attenuation pattern measured from successive first and second projections respectively corresponding to the first and second X-ray sources. The concept is using the displacement in the measured projection pattern to infer the separation between the X-ray sources. It may be determined whether the separation is within allowable operational constraints.

Description

Technical Field

[0001] The present invention relates to the field of CT imaging, and more particularly to alternating dual-source CT imaging, such as dual focal-spot imaging. Background Art

[0002] In prior art medical CT systems, dual focal-spot (DFS) acquisition is commonly used to increase spatial resolution and reduce aliasing. In DFS acquisition, during the entire orbit rotation of the CT scanner around the examination area, the position of the X-ray tube focal spot (i.e., the X-ray source position) alternates between two adjacent positions at a high frequency. Often, the source is configured such that the focal spot jumps back and forth around the geometric center of the system by ±0.25 times the radial sampling interval of the detector between successive detector readings. Additionally, there may also be jumps in the z-direction. In fact, when the X-ray scanning device rotates around a 3D object, two datasets are acquired simultaneously, one corresponding to the first focal spot and one corresponding to the second focal spot.

[0003] The dual focal-spot (DFS) acquisition method is described in detail in the following article: M. Kachelriess et al., Flying Focal Spot (FFS) in Cone-Beam CT (IEEE TNS, 53(3), 2006).

[0004] The dual focal-spot (DFS) acquisition method is also described in document US4637040.

[0005] System reliability is an important consideration in modern CT systems. However, CT systems are also very complex machines, and occasional downtime is inevitable. Therefore, it is desirable to establish an automatic module for fault detection and fault prediction in order to warn the user of potential errors that may lead to imaging, minimize system downtime by actively triggering the visit of a field service engineer, and provide accurate diagnostic information to allow for a quick identification of the root cause of system problems.

[0006] Part of a CT system that requires accurate calibration is the X-ray source positioning.

[0007] In the context of dual focal-spot imaging, if the accuracy of the focal spot position changes over time and deviates from the expected value, the image quality degrades. Specifically, the spatial resolution will decrease, and aliasing artifacts may result. However, the decalibration of the X-ray tube focal spot tends to occur gradually, such that it may not be noticed in the degraded image quality until the artifacts are significant.

[0008] It should be noted that although the reference to dual focal spot acquisition presents this problem, the same problem will apply to other types of dual-source CT imaging, including those that may not use two focal spots on the X-ray tube but use different technical means to implement alternating sources. SUMMARY OF THE INVENTION

[0009] The present invention is defined by the claims.

[0010] According to an example in accordance with an aspect of the present invention, there is provided a computer-implemented method for determining a calibration state of an X-ray point source within a dual-point source computed tomography (CT) acquisition device (e.g., a dual focal spot acquisition device).

[0011] The method includes: receiving dual-point source CT data, the dual-point source CT data including X-ray detector measurement data for each X-ray projection in a series of X-ray projections, the measurement data corresponding to measurement data acquired using an X-ray generator of the CT acquisition device, the X-ray generator of the CT acquisition device providing alternately enabled first and second X-ray sources, which correspond, for example, to a first X-ray tube focal spot and a second X-ray tube focal spot. The sources are moved relative to each other along the axis of rotation of the generator's orbit within the CT acquisition device. For each X-ray projection, the measurement data includes a 2D attenuation pattern corresponding to measured attenuation values of a 2D array of detector elements.

[0012] The method includes performing a point source calibration check, the point source calibration check including: using the measurement data to determine a shift of all or part of the attenuation pattern between a first successive projection and a second successive projection, each of the first and second projections being generated using a different one of the two point sources. The method preferably further includes: determining whether the shift is within a predefined expected range. For example, the expected range may correspond to an expected range in the case of an expected distance or displacement between two X-ray point sources (within the X-ray generator).

[0013] Thus, embodiments of the present invention propose to check the calibration of dual point sources, in particular to check the separation distance or displacement between the point sources. The inventors propose to achieve this by detecting a spatial transformation between all or part of the points within the 2D attenuation pattern measured by a detector having a first point source and a second point source. This shift in the measured attenuation pattern maps to the separation between the point sources and thus provides a metric by which the target parameter of the point source separation can be probed.

[0014] The first and second X-ray point sources are moved relative to each other within the reference frame of the X-ray generator, i.e., they are moved relative to each other on or within the X-ray generator. Their displacement is preferably a displacement in the direction of the gantry's orbital axis. Thus, the generator carries an X-ray generating device operable to generate two point sources that are moved relative to each other, and wherein the orbital rotation of the generator moves each of these point sources around a series of orbital angular positions to acquire first measurement data and second measurement data.

[0015] The X-ray generator may include at least one X-ray tube.

[0016] The first point source and the second point source may generally correspond to the first X-ray tube focal spot and the second X-ray tube focal spot within a dual-focus CT acquisition device. Alternatively, the method is also applicable to any other type of dual-point source system.

[0017] Although the above refers to attenuation patterns, this may equally well be an intensity pattern representing the radiation reaching the detector (i.e., attenuation is the logarithm of the expected zero-attenuation intensity minus the logarithm of the measured intensity for a given source intensity and system geometry). Intensity and attenuation are related by Beer’s law and are thus effectively interchangeable. Accordingly, in the present disclosure, without any change to the principles of the present invention, a reference to a 2D attenuation pattern may be equivalently understood as corresponding to intensity. An attenuation pattern measured from a particular source position is also referred to as a ‘projection’.

[0018] A shift of all or part of the above-mentioned attenuation pattern means, for example, a spatial shift, i.e., a transformation, such as a translation or a rotation. Thus, in some embodiments, determining the shift may include determining a transformation, such as a rotation or a translation, of all or part of the attenuation pattern of a first successive projection and a second successive projection.

[0019] In some embodiments, determining the shift includes: determining a translation of all or part of the attenuation pattern in the direction of the orbital rotation axis of the generator.

[0020] In some embodiments, determining the shift includes: identifying characteristic (spatial) points in the respective 2D attenuation patterns of the first projection and the second projection, and determining the translation of the characteristic points between the first projection and the second projection. By using characteristic points, this makes the analysis easier because only a single point needs to be tracked rather than the shift in all projections.

[0021] In some embodiments, the characteristic points may be defined in each 2D attenuation pattern based on a function of attenuation values weighted according to the attenuation value positions in the 2D pattern (meaning the positions of the corresponding detector elements).

[0022] In some embodiments, the characteristic point may be defined as the attenuation center along at least one dimension of each 2D attenuation pattern, and the attenuation center is an attenuation simulation of the centroid.

[0023] By way of example, the attenuation center along at least one dimension of the attenuation pattern may be defined by the following equation:

[0024]

[0025] where p’(j) = ∑ i p(i,j),

[0026] where i is the index of the detector element row, and j is the index of the detector element column, and

[0027] where p(i,j) is the attenuation measurement of the detector element in row i and column j.

[0028] Thus, it can be seen that this reflects the equation of the centroid, but the mass is replaced by the attenuation measured at a given detector pixel, and the spatial coordinate is provided by the row index j.

[0029] As a complement or alternative to the method of detecting the shift of the characteristic point in the attenuation pattern, another method is to perform registration between at least patches of each of the first projection and the second projection.

[0030] Thus, in some embodiments, determining the shift includes: identifying characteristic sub-regions of the 2D attenuation patterns of each of the first projection and the second projection, and performing translational registration between the characteristic sub-regions of the two projections.

[0031] The idea here is to detect the translation by analytical translational registration of small patches of two successive projections. Methods for doing this are known, and examples will be described in the detailed description.

[0032] In some embodiments, the method further includes rebinning the measurement data of each of the projections into a parallel beam geometry before performing the calibration check. In a preferred embodiment, wedge beam rebinning is performed. Wedge beam rebinning is described, for example, in section II.A of the following article: Shechter et al.: The frequency split method for helical cone-beam reconstruction (Med Phys. August 2004; 31(8):2230-6).

[0033] In some embodiments, the method may include: performing an imaged object alignment check for each projection prior to a calibration check. In particular, if the first and second projections are not truncated, the accuracy of the calibration check is improved, which means that each projection completely contains the target imaged object.

[0034] To achieve this, in some embodiments, the imaged object alignment check may include first identifying measured attenuation values in a 2D attenuation pattern corresponding to a predefined boundary region of an array of detector elements. The alignment check may also include determining whether each of the measured attenuation values in the boundary region falls within a predefined null range corresponding to a zero attenuation measurement result. The alignment check may also include: in response to determining that not all of the measured attenuation values in the boundary region fall within the null range, generating an alert message and / or aborting the calibration check.

[0035] As a supplement or alternative to the method outlined above, in some embodiments, determining the shift includes: using a trained machine learning algorithm that is trained to receive measurement data of a first projection and a second projection as input and generate, as output, a value of the shift between the 2D attenuation patterns of the first projection and the second projection.

[0036] In some embodiments, determining the shift of all or part of the attenuation pattern of the first projection and the second projection is performed indirectly based on detection operations performed in the image domain.

[0037] For example, in some embodiments, determining the shift in all or part of the attenuation pattern of the first projection and the second projection is performed by: reconstructing a first image using only the X-ray projections in a series of X-ray projections acquired using a first X-ray point source among X-ray point sources; reconstructing a second image using only the X-ray projections in a series of X-ray projections acquired using a second X-ray point source among X-ray point sources; and detecting the rotation between the first image and the second image. In particular, the first image and the second image may be axial images, which means that the images in the rotation plane of the scanner are perpendicular to the z-axis.

[0038] In some embodiments, the method may include calculating an actual separation or spacing or displacement between the first X-ray point source and the second X-ray point source based on a determined translation or rotation in all or part of the attenuation pattern between a first successive projection and a second successive projection. In some embodiments, this may be the spatial distance between the sources. In some embodiments, it may be the angular separation / displacement between the point sources.

[0039] In some embodiments, the method includes determining a deviation of the distance between a first and a second X-ray point source from a target distance between the first and the second X-ray point source. The method may further include communicating with an image reconstruction module to apply an additional correction step to a reconstruction algorithm included by the reconstruction module based on the determined deviation.

[0040] In some embodiments, the method may further include supplying CT data to the reconstruction module and applying a reconstruction algorithm with an additional correction step to the CT data.

[0041] In some embodiments, the method further includes: generating a status report indicating a difference between a calculated separation or spacing or displacement (e.g., distance) between a first X-ray point source and a second X-ray point source and a predefined target separation or spacing or displacement between the first X-ray point source and the second X-ray point source, and transmitting the status report to a server computer. For example, this may be a computer of a maintenance or repair team.

[0042] In some embodiments, the method further includes storing a measured separation or spacing or displacement (e.g., distance) between the first X-ray point source and the second X-ray point source in a data log.

[0043] In some embodiments, the method includes generating an alert signal for transmission to a user interface or another computing device in response to the determined shift being outside the expected range.

[0044] Another aspect of the invention is a computer program product comprising computer program code configured to cause a processor to perform a method according to any of the embodiments described in this document or according to any of the claims in this application when run on the processor.

[0045] Another aspect of the invention is a processing device including: an input / output section; and one or more processors configured to perform a method. The method includes: receiving dual-point source CT data at the input / output section, the dual-point source CT data including X-ray detector measurement data for each X-ray projection in a series of X-ray projections, the measurement data corresponding to measurement data acquired by an X-ray generator of a CT acquisition device, the X-ray generator of the CT acquisition device providing alternately enabled first and second X-ray point sources (which correspond to a first X-ray tube focal spot and a second X-ray tube focal spot), the point sources moving relative to each other along an axis of rotation of an orbit of the generator within the CT acquisition device, and wherein the measurement data includes, for each X-ray projection, a 2D attenuation pattern corresponding to measured attenuation values of a 2D array of detector elements.

[0046] The method further includes performing a point source calibration check, the point source calibration check including: using the measurement data to determine a relative shift between all or part of the attenuation patterns of a first successive projection and a second successive projection, each of the first projection and the second projection being generated using a different one of two point sources. The method may further include determining whether the shift is within a predefined expected range. The predefined expected range may correspond, for example, to the expected range for a given expected distance between two X-ray point sources.

[0047] Another aspect of the invention is a system including: a dual point source CT acquisition device; and a processing device as described above or according to any embodiment described in this document or according to any claim of this application.

[0048] These and other aspects of the invention will be apparent and elucidated with reference to the (one or more) embodiments described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] For a better understanding of the invention, and to more clearly show how the invention may be implemented, reference will now be made, by way of example only, to the accompanying drawings, in which:

[0050] Figure 1 Schematically illustrates an example CT acquisition device according to one or more embodiments;

[0051] Figure 2 Schematically illustrates a dual focal spot imaging protocol according to prior art methods;

[0052] Figure 3 Schematically illustrates the fan shape of an X-ray beam generated by a single X-ray focal spot source (single X-ray point source) according to prior art CT scanning methods;

[0053] Figure 4 Schematically illustrates the fan shape of an X-ray beam generated according to dual focal spot CT scanning operations;

[0054] Figure 5 Outlines the steps of an example method according to one or more embodiments of the invention;

[0055] Figure 6 Schematically outlines the components and processing flow of an example processing device according to one or more embodiments of the invention;

[0056] Figure 7 Illustrates the 2D attenuation pattern for an example projection captured by a 2D detector;

[0057] Figure 8 Illustrates a graph of the calculated shift of characteristic points of the first and second projections on a series of successive frames during scanning;

[0058] Figure 9 Illustrated is the registration of patches for the selection of two successive projections for detecting shifts in the selected patches;

[0059] Figure 10 Schematically illustrated are the sampling positions of an X-ray point source in calibrated and uncalibrated scenarios;

[0060] Figure 11 Additional illustration of the geometry of the CT apparatus is provided;

[0061] Figure 12 Illustrated is the presence of rotation in the image domain caused by drift separated by an X-ray point source;

[0062] Figure 13 Illustrated is the absence of rotation in the image domain in the case of a well-calibrated dual X-ray source;

[0063] Figure 14 Illustrated is the process of checking that a projection is not truncated by checking for zero attenuation at the lateral detector region; and

[0064] Figure 15 Illustrated are the detector measurements of projections in r, φ space; and

[0065] Figure 16 Illustrated is the mapping of the measured rays into r, φ space. Detailed Description

[0066] The present invention will be described with reference to the accompanying drawings.

[0067] It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems, and methods, are intended for illustrative purposes only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems, and methods of the present invention will be better understood from the following description, the appended claims, and the drawings. It should be understood that the drawings are merely schematic and are not drawn to scale. It should be understood that the same reference numerals are used throughout the drawings to indicate the same or similar components.

[0068] The present invention provides a method for calibration in the context of a CT imaging mechanism, wherein projection data is acquired at a series of angular positions of a CT scanner around a body, and wherein at least two angularly displaced X-ray sources (e.g., X-ray tube focal spots) are alternately enabled while the scanner rotates around the body along an orbit. The alternately enabled sources can be focal spots or can be other sources. It is proposed to detect a spatial transformation (e.g., a spatial shift) in all or part of the 2D attenuation patterns measured from successive first and second projections respectively corresponding to a first X-ray source and a second X-ray source. The concept is to use this shift in the measured projection patterns between two projections to infer the separation between the X-ray sources. It can be determined whether the separation is within allowable operating constraints.

[0069] To aid in understanding the upcoming description, Figure 1 An example computed tomography (CT) imaging system 100, such as an X-ray CT scanner, is illustrated.

[0070] The imaging system 100 includes a substantially stationary gantry 102 and a rotating gantry 104. The rotating gantry 104 is rotatably supported by the stationary gantry 102 and rotates along an orbit around a longitudinal axis or z-axis of an examination region 106 that axially extends through the center of the gantry.

[0071] A patient support 120, such as a couch, supports an object or subject, such as a human patient, in the examination region. The support 120 is configured to move the object or subject to load, scan, and / or unload the object or subject.

[0072] A radiation source or generator 108, such as an X-ray tube, is rotatably supported by the rotating gantry 104. The radiation generator 108 rotates with the rotating gantry 104 and emits radiation through the examination region 106.

[0073] A radiation-sensitive detector array 110 forms an angular arc across the examination region 106 opposite the radiation generator 108. The detector array 110 includes one or more rows of detectors extending in the z-axis direction, which detect the radiation passing through the examination region 106 and generate projection data indicative thereof.

[0074] For cone-beam scanning, the radiation source generates an X-ray cone beam, and the detector array is a 2D detector array. The rotation of the radiation generator and the detector around the examination region 106 (which receives the body to be scanned) can be described as an orbital rotation, wherein the trajectory of the rotation is described as an orbital angular rotation axis or path.

[0075] A general computing system or computer can be used as the operator console 112 and can include one or more input devices 114 (such as a mouse, keyboard, etc.) and output devices 116 (such as a display monitor, film, etc.). The console 112 allows an operator to control the operation of the system 100.

[0076] The reconstruction device 118 processes projection data and reconstructs image data, such as volumetric image data. This data can be displayed via one or more display monitors of the one or more output devices 116.

[0077] The reconstruction device 118 in a conventional system can employ filtered backprojection (FBP) reconstruction, noise reduction reconstruction algorithms (such as iterative reconstruction) in the image domain and / or projection domain, and / or other algorithms. It should be understood that the reconstruction device 118 can be implemented by one or more microprocessors that run one or more computer-readable instructions encoded or embedded on a computer-readable storage medium (such as physical memory and other non-transitory media). Additionally or alternatively, the one or more microprocessors can execute one or more computer-readable instructions carried by a carrier wave, signal, and other transient (or non-transient) media.

[0078] As briefly discussed above, in order to increase spatial resolution and sampling, a dual focal spot (DFS) acquisition method has been developed. In dual focal spot acquisition, the position of the X-ray tube focal spot (i.e., the X-ray point source position) alternates at a high frequency between two adjacent positions during the entire orbit rotation of the CT scanner. In fact, when the X-ray scanning device rotates around a 3D object, two data sets are acquired simultaneously, one corresponding to the first focal spot position and one corresponding to the second focal spot position. Each focal spot forms an effective X-ray point source. The terms 'focal spot' and 'point source' can be used interchangeably in this disclosure.

[0079] This is Figure 2 schematically illustrated. This shows a portion of the arc of rotation of the X-ray generator around the body being scanned. The X-ray focal spot positions fs0, fs1 of each measured sample are shown as a solid circle and an open circle respectively. Measurement samples of the two focal spots are sequentially acquired at each of a series of regular angular intervals Δα r in. The regular angular interval Δα r is commonly referred to in the art as the 'angular sampling' interval of the system. For further elaboration, for each pair of two focal spots, there is a geometric center point or midpoint between them. This midpoint is labeled fs2 in Figure 4 . The control system is such that these centers or midpoints are at equidistant angular intervals Δα rIn other words, since the entire gantry moves around the patient at a constant speed, the positions of the center points between each pair of foci are angularly equally distributed along the rotation path.

[0080] The two X-ray focal spots are preferably at a fixed distance from each other relative to the X-ray generator (e.g., relative to the tube housing of the X-ray tube of the generator). The reference herein to "the point sources moving relative to each other along the rotation axis of the generator within the CT acquisition device" can be understood to relate, for example, to this distance between fs0 and fs1.

[0081] It should also be noted that for a constant rotation speed of the detector and a constant sampling rate (e.g., one detector readout per x degrees of gantry rotation), a series of focal spot positions is not angularly equally distributed (e.g., as is apparent from Figure 2 ). However, the corresponding center points between each pair of focal spot positions fs0, fs1 are angularly equally distributed.

[0082] There are different ways to create alternating X-ray point sources. Both can be provided by a single X-ray tube having a pair of filaments. Alternatively, they can be provided by a radiation source having a pair of X-ray tubes, each tube including a different point radiation source. However, preferably, they can be provided by a deflection module (e.g., electrostatic or electromagnetic) for deflecting an electron beam between at least two different focal spots on a target electrode.

[0083] The fan shape of the X-ray beam 48 produced by a single focal spot 46 is schematically shown in Figure 3 wherein the plane of the 2D detector is illustrated by the arc 50.

[0084] By comparison, Figure 4 schematically illustrates the respective fan shapes of the X-ray beams 48a, 48b produced by each of a pair of focal spots fs0, fs1.

[0085] Embodiments of the present invention are based on performing a calibration check to determine whether the separation distance between two focal spots is as expected. The basic method proposed to achieve this is to determine a spatial shift or transformation of all or part of the attenuation patterns detected for a first successive projection and a second successive projection acquired by the CT system, wherein the first projection is acquired using a first focal spot (fs0) and the second projection is acquired using a second focal spot (fs1). In other words, it is proposed to compare successive projections acquired from the first and second focal spots and detect a translation between them (or parts, or sections, or points therein), and use this as an indicator of the separation between the focal spots. The shift parameter can be directly evaluated as a proxy for the separation between the focal spots, or the shift parameter can be mapped to an estimate of the true separation between the focal spots.

[0086] One example method is to identify characteristic points in the corresponding 2D attenuation patterns of a first projection and a second projection, and determine the shift or transformation of the characteristic points between the first projection and the second projection. For example, in a preferred method, the characteristic point is the attenuation center point, by which is meant an analogue of the centroid for attenuation. Another alternative characteristic point is the maximum attenuation point of the projection or the maximum attenuation point within a preselected patch of the projection.

[0087] Another example method is to perform spatial registration between at least a portion of the first projection and the second projection, e.g., perform spatial registration between selected spatial patches of each of the first projection and the second projection. For example, this can be translational registration or rotational registration.

[0088] Another example method is to use a trained machine learning algorithm to detect the shift.

[0089] Figure 5 The steps of an example method 10 in accordance with one or more embodiments of the present invention are outlined in block diagram form. These steps will be outlined before being further explained in the form of example embodiments.

[0090] Method 10 may be implemented by a computer or a processor.

[0091] This method is for determining the calibration state of a dual point source of an X-ray generator within a dual point source computed tomography (CT) acquisition device (e.g., a dual focal spot acquisition device).

[0092] Method 10 includes receiving 12 dual point source CT data, which includes X-ray detector measurement data for each X-ray projection in a series of X-ray projections. The measurement data corresponds to measurement data acquired using the X-ray generator of the CT acquisition device, and the X-ray generator of the CT acquisition device provides alternately enabled first and second X-ray point sources, which for example correspond to a first X-ray tube focal spot and a second X-ray tube focal spot. The point sources are moved relative to each other along the axis of rotation of the generator within the CT acquisition device. For each X-ray projection, the measurement data includes a 2D attenuation pattern corresponding to measured X-ray attenuation values (or alternatively, measured X-ray intensity values) of a 2D array of detector elements.

[0093] The method further includes performing a point source calibration check 14, such as a focal spot calibration check. The calibration check 14 includes using measurement data to determine 20 a shift of all or part of the attenuation pattern between a first successive projection and a second successive projection, each of the first and second projections being generated using a different one of two point sources. The calibration check may also include determining 22 whether the shift is within a predefined expected range (i.e., comparing the shift to the expectation). For example, the expected range may correspond to an expected range for a given expected separation or distance between two X-ray point sources.

[0094] The method may further include generating a data output based on the result of the calibration check, such as an indication as to whether the shift is within the expected range.

[0095] As described above, the method may also be embodied in hardware, such as in the form of a processing unit configured to perform the method according to any example or embodiment described in this document or according to any claim of this application.

[0096] For further assistance in understanding, Figure 6 A schematic representation of an example processing device 32 configured to perform the method according to one or more embodiments of the present invention is presented. The processing device is shown in the context of a system 30. The processing device 32 may be provided separately as an aspect of the present invention, and the system also forms another aspect of the present invention. The system need not include all of the components shown, but may include only a subset of them.

[0097] The processing device 32 includes an input / output section 34 or communication interface and one or more processors 36. The one or more processors 36 are configured to perform the method according to any example or embodiment described in this document or according to any claim of this application.

[0098] The system 30 in this example further includes a CT acquisition device / system 40, such as a dual point source CT acquisition device, such as a dual focal spot (DFS) acquisition device. The CT device generates dual point source CT data 42 received at the input / output section of the processing device 32.

[0099] As described above, the present invention can also be implemented in software form. Accordingly, another aspect of the present invention is a computer program product comprising computer program instructions or code which, when run by a processing device 32 including one or more processors 36, are configured to cause the processing device to perform a method according to any of the embodiments described in the present disclosure or according to any of the claims of the present application. Optionally, the system 30 or the processing device 32 may also include a memory 38 storing computer program instructions configured to cause one or more processors 36 of the above-described processing device 32 to perform a method according to any of the embodiments of the present invention. The memory 38 may also store one or more registers for use in the method. By way of example, the memory 38 may store values for the predefined expected range.

[0100] The implementation details of one or more example embodiments will now be outlined in more detail.

[0101] As described above, detecting potential misalignment in an X-ray focal spot is valuable as it allows remedial service work to be requested or queued for action and avoids continuing to acquire imaging data that may contain artifacts that could skew the results. An embodiment may be performed, for example, as a runtime check applied to measurement data acquired from a CT acquisition system.

[0102] In addition, in some embodiments, the projection data may be adjusted, or the reconstruction algorithm may be adjusted, to compensate for any detected misalignment of the focal spot. This may be applied, for example, as a temporary stopgap measure while waiting for repair.

[0103] Reference Figure 7 , which shows an example 2D attenuation pattern of a single X-ray projection acquired using a single X-ray point source out of two X-ray point sources of an X-ray generator. The 2D attenuation pattern corresponds to the measured attenuation values of a 2D array detector (meaning a detector having a 2D array of detector elements). Embodiments of the present invention propose: acquiring at least first and second such attenuation patterns for a first successive projection and a second successive projection, one corresponding to a first X-ray point source and one corresponding to a second X-ray point source; and detecting a shift of all or part of the attenuation pattern of the first successive projection and the second successive projection.

[0104] When comparing two such successive projections with each other, a slight shift of the object being imaged, e.g., from left to right, results. This shift in the detector attenuation pattern can be used to infer the separation between the point sources.

[0105] In particular, according to at least one set of embodiments, it is proposed to determine the shift of the attenuation pattern between a first projection and a second projection by determining a translation in the direction of the orbit rotation axis of the generator. For example, this corresponds to detecting a shift inFigure 7 A shift in the direction marked 'x' in the exemplary detector attenuation pattern shown. This is, for example, the direction of increasing detector columns, i.e., the direction of detector column index j.

[0106] A particularly effective way to do this is to identify common characteristic points in the corresponding attenuation patterns of the first projection and the second projection, and then detect the translation of said characteristic points between the first projection and the second projection. In a very simple example, the characteristic point can be the point of maximum attenuation (minimum intensity) or the point of minimum attenuation (maximum intensity). Given a very short time interval between the first projection and the second projection (assuming a high-frequency alternation between the first point source and the second point source), then a reasonable assumption is that the point of maximum or minimum attenuation in each projection should correspond to the same physical path through the object being imaged, and thus its shift can be regarded as reflecting the shift in the position of the point source between the two projections.

[0107] A more robust method is to identify the characteristic points defined in each 2D attenuation pattern based on a function of the attenuation values in the attenuation pattern weighted by the intensity value positions (meaning detector element positions) in the 2D pattern.

[0108] A simple and powerful example is to identify the characteristic point in each projection attenuation pattern that is the 'attenuation center' point. This means the point that is an analogue of the centroid but for intensity or attenuation (depending on which of these the projection pattern represents).

[0109] This is based on the inventor's recognition that a jump in the X-ray source position results in a good approximation of such a shift in the 'center of gravity' of the projection.

[0110] Here it means that the characteristic point can be defined as the attenuation center along at least one dimension of each 2D attenuation pattern, and the attenuation center is the intensity analogue of the centroid.

[0111] In particular, the mathematical expression for the attenuation center along at least one dimension of the attenuation pattern can be derived as follows.

[0112] Let p(i,j) be the projection value, where i is the detector row and j is the column. Of interest is the attenuation center only in the j direction, so the first step is to take the average over the rows:

[0113] p’(j) = ∑ i p(i,j)

[0114] The next step is to calculate the attenuation center as:

[0115]

[0116] Thus, this is an analogue of the centroid but for attenuation.

[0117] The result is a value per detector column index j corresponding to the attenuation center of the projection. It can be non-integer, but this is immaterial for comparing values between a first projection and a second projection.

[0118] This attenuation center value can be calculated for the first and second projections and these values compared to determine translation along the detector column index j direction.

[0119] Thus, by calculating the attenuation center of each projection and calculating the difference value of subsequent readings, it can be measured whether the focal spot position should be so. In other words, the translation of the attenuation center between the first projection and the second projection can be compared to a predefined expected range to see if it falls within that range.

[0120] By way of an example, it can be checked whether the focal spot separation is actually within a predefined range that is a target multiple of the angular sampling interval Δα r of.

[0121] Figure 8 Illustrated is a graph of the calculated shift of the attenuation center points of the first and second projections over a series of successive frames during a scan. In this example, the difference in the attenuation centers of two readings within each frame of a dual focal spot acquisition is shown. The x-axis indicates the frame number. The y-axis indicates the difference in the attenuation center between the first projection and the second projection in units of the detector angular sampling interval Δα r of.

[0122] Since this is a well-calibrated clinical system, the difference is very close to the target value (in this example, 0.5 times the detector angular sampling interval).

[0123] As a complement or alternative to detecting a shift in characteristic points, according to some embodiments, the separation between point source positions can be performed based on detecting the registration between patches of a first projection and a second projection (corresponding to a first point source and a second point source respectively).

[0124] In other words, in some embodiments, determining the shift can include: identifying characteristic sub-regions of the 2D attenuation pattern in each of the first projection and the second projection, and performing translational registration between the characteristic sub-regions of the two projections.

[0125] In some embodiments, the registration can be performed analytically. Example embodiments will now be explained.

[0126] Refer to Figure 9 .

[0127] The general principle is to select a region of interest in a first projection n and then seek to determine (preferably with sub-pixel accuracy) where that region is in a subsequent (second) projection n + 1. Figure 9Detector patterns 70a, 70b of the measurements of projection n and projection n+1 are shown respectively.

[0128] The algorithm for implementing the registration process can proceed to the following steps.

[0129] The first step is to select or identify the region of interest 72a in projection n. By way of example, this can be the region of interest with the maximum average intensity or attenuation.

[0130] The next step can be to upsample the region of interest by a factor m (e.g., using an interpolation method such as the cubic spline method).

[0131] The next step can be to upsample projection n+1 by the same factor m.

[0132] The next step can be to compute the convolution of the upsampled region of interest and the upsampled projection n+1. In other words, compute the correlation of projection n+1 with the region of interest in projection n until a constant scaling.

[0133] The next step is to find the position within the convolution of the maximum value. Its position is an indicator of how much the "template" structure defined as the region of interest 72a has moved in the subsequent projection 70b.

[0134] For completeness, Figure 9 (Bottom) shows the second projection (n+1) 70b and illustrates the shifted position 72b of the region of interest 72a selected in the first projection n 70a, as determined by the above process. In this case, the shift is actually not visible as it is on the order of less than one pixel (i.e., sub-pixel level). For this very reason, upsampling of the two patches is valuable; this allows detection of sub-pixel shifts.

[0135] Another method for determining that all or part of the first attenuation pattern is registered with the second attenuation pattern is to utilize a trained machine learning algorithm (such as a convolutional neural network (CNN)) to predict the X-ray point source position difference.

[0136] In other words, in some embodiments, a trained machine learning algorithm is proposed to determine the shift of the attenuation pattern between two projections. The trained machine learning algorithm is trained to receive the measurement data of the first projection and the second projection as inputs and generate the value of the shift between the 2D attenuation patterns of the first projection and the second projection as an output. The training data used when training the algorithm can be obtained, for example, from projections acquired from a dual-point-source CT system using a deliberately mis-calibrated system, where the distance between the two point sources is known before the training data is acquired. The point source spacing can be adjusted iteratively, and the 2D attenuation patterns are measured for multiple pairs of projections for each point source spacing. The attenuation pattern pairs acquired for each spacing can be labeled with the known separation distance (i.e., the ground truth) corresponding to each spacing. Then, these can be used to train a machine learning algorithm such as a CNN.

[0137] According to one or more embodiments, the shift in all or part of the attenuation pattern (in the projection domain) can be determined indirectly by first transferring the measurement data into the image domain and detecting the rotation in the image domain.

[0138] Specifically, according to one or more embodiments, determining the shift of all or part of the attenuation pattern of the first projection and the second projection can be performed by the following operations: reconstructing a first image using only the X-ray projections in a received series of X-ray projections acquired using a first X-ray point source among the X-ray point sources; reconstructing a second image using only the X-ray projections in a received series of X-ray projections acquired using a second X-ray point source among the X-ray point sources; and detecting the rotational shift between the first image and the second image. More specifically, the first and second images can be axial (slice) images. The concept underlying this embodiment is that if the projection domain data is shifted translationally with respect to the detector for each projection, the resulting reconstructed (axial) image slices will have an overall rotation around the z-axis of the scanner.

[0139] To further illustrate this embodiment, now refer to Figure 10 and Figure 11 . Figure 10 Illustrates the positions of two X-ray point sources (e.g., X-ray tube focal spots) fs0 and fs1 at different sampling points around the axis of rotation of the orbit of the X-ray generator within the CT acquisition device in calibrated and uncalibrated scenarios. Figure 10 (Left) shows a scenario (‘Scenario A’), where the X-ray sources fs0 and fs1 are perfectly calibrated and thus their positions are as expected. Figure 10(Right) shows a scenario (‘Scenario B’) where the X-ray sources fs0 and fs1 have drifted from their intended positions. Thus, Scenario B represents an uncalibrated scenario where the true positions of the X-ray sources fs0 and fs1 have become dimensionally shifted along the axis of rotation of the track. The sampling points of the first focal spot fs0 are shown using solid circles, and the sampling points of the second focal spot fs1 are shown using hollow circles.

[0140] Thus, within the axial reference frame, in fact, the entire sampling pattern of fs0 rotates counterclockwise and the entire sampling pattern of fs1 rotates clockwise. When projected onto the detector, this appears as a translational shift in each projection along the direction of increasing detector columns (i.e., Figure 7 the x-axis as shown). However, over the entire projection dataset of a single axial slice, this appears in the dataset as a rotation about the z-axis. The z-axis is in the direction into the page from Figure 10 the perspective of. Figure 10 The direction of the z-axis is shown using a cross symbol. This indicates the z-direction but does not represent the true position of the z-axis, which would instead be at the center of rotation of the system.

[0141] Therefore, if image reconstruction is performed only on the fs0 data, the resulting axial image is slightly rotated for the uncalibrated scenario ( Figure 10 right side) compared to the calibrated scenario ( Figure 10 left side). The same is true for reconstructions using only the actual fs1 data, but the direction of rotation is opposite. Thus, based on the reconstructed images, it is possible to determine whether the focal spot deflection or separation is correct.

[0142] In fact, quantitative analysis is possible. This can be further understood from Figure 11 . Figure 11 shows the geometry of a dual point source (dual focal spot) system and illustrates the projections of the two point sources fs0 and fs1 onto the detector array 110.

[0143] In this figure, R FD = the focus-to-detector distance (meaning the distance from the X-ray tube focal spot to the detector). R F is the focus-to-isocenter distance (meaning the distance from the X-ray tube focal spot to the imaging isocenter).

[0144] The angle Δα d is the angular separation between the two focal spots fs1 and fs2 relative to the rotation isocenter. Using the small angle approximation (where tan(Δα d / 2) ≈ Δα d / 2), this corresponds to a spatial separation distance d1 of approximately d1 = R F Δα d between the two focal spots.

[0145] Angle Δβ d is the angle between the rays (corresponding center points) to adjacent detector pixels (detector elements). In this figure, two adjacent detector pixels are shown, labeled n and n+1.

[0146] The projections of the corresponding rays from the first focal spot fs0 and the second focal spot fs1 onto the detector are shown. Again, using the small angle approximation mentioned above, the separation distance d2 between these ray projections on the detector is approximately equal to d2 = (R FD -R F )Δα d . Thus, the separation distance d1 between the focal spots fs0, fs1 can be mapped to the corresponding separation distance d2 of the focal spot projections on the detector 110 through the relationship d1 / d2 = R F / (R FD -R F ).

[0147] According to Figure 11 the specific example system illustrated, the target focal spot deflection distance d1 = R F Δα d (i.e., the target distance between the two focal spots) is set according to the preferred constraint that the corresponding projection rays passing through the isocenter from the first (fs0) and second (fs1) focal spots are separated on the detector by the distance.

[0148] Thus, in this example, the target spacing d2 between the projections of the two focal spots (or point sources) on the detector is set to be approximately equal to and the angular separation of the rays from the two focal spots with respect to the X-ray generator is Δβ d / 2.

[0149] Again using the small angle approximation, whereby Δα d ≈sinΔα d ≈tanΔα d , then the angular separation between the two focal spots with respect to the rotational isocenter along the rotation axis of the generator is approximately equal to

[0150] In operation, during imaging, the gantry rotates at a presumed constant speed and acquires projections ( r not shown in Figure 11 ) at an angular sampling rate of Δα.

[0151] According to the above, considering both the sampling rate and the focal spot separation, for a single (first) projection, the total focal spot deflection along the rotation axis of the generator will correspond to:

[0152]

[0153] where α 0,fs0 is the angular position of the first projection obtained using fs0, α 0,fs1 is the angular position of the first projection obtained using fs1, and Δα r is the angular sampling density of the frame.

[0154] In the context of an exemplary embodiment of the present invention, it is proposed to detect the separation distance d1≈R between the focal spots fs0, fs1 by detecting the relative rotation between a first image reconstructed using only X-ray projections acquired using a first X-ray point source (fs0) in an X-ray point source and a second image reconstructed using only X-ray projections acquired using a second X-ray point source (fs1) in the X-ray point source F Δα d drift.

[0155] Reference Figure 10 where, in fact, the entire sampling pattern of fs0 rotates counterclockwise the entire sampling pattern of fs1 rotates clockwise by the same angle.

[0156] Figure 12 and Figure 13 illustrate the rotation effect in the image domain. Figure 12 illustrates an axial image of a scene where the focal spot is not calibrated (scene B in Figure 10 ), Figure 13 illustrates an axial image of a scene where the focal spot is calibrated (scene A in Figure 10 ). Figure 12 (a) shows an axial image reconstructed using only projections acquired using fs0, Figure 12 (b) shows an exemplary axial image reconstructed using only projections acquired using fs1. In Figure 12 and Figure 13 each of, image (c) shows the difference between image (a) and image (b).

[0157] In Figure 12 the difference image of (c), edges are visible (indicated by arrows 92a, 92b), which is a characteristic of the slight rotation of the two images (a) and (b) relative to each other. This indicates that the data acquired using fs0 and fs1 are slightly inconsistent.

[0158] In contrast, in Figure 13 the difference image of (c), the edges do not appear as strongly, indicating less rotation between the two images.

[0159] As described above, in operation, a first image reconstructed using only fs0 data is compared with a second image reconstructed using only fs1 data, and a relative rotation between the two is detected, for example, by forming a difference image or by performing rotational registration between the two. The detected rotation is quantified by a rotation metric, such as degrees of rotation or a more indirect rotation metric. This is then compared with the expectation of the relative rotation to see if the relative rotation falls within the expected range of values of the rotation metric. The expected range typically depends on how the (one or more) reconstruction algorithms are configured. When generating Figure 12 and Figure 13 example images, the corresponding reconstruction algorithm is employed, which is configured according to the expected positioning of the focal spot corresponding to the calibration position. For this purpose, an image reconstructed from projection data acquired in the calibration state shows little rotation in the difference image because the true focal spot position matches the expected position. Thus, in this case, the expected range of rotation between the first image and the second image can be a range centered on a zero value because if the focal spot is properly calibrated, the reconstruction algorithm is configured such that the resulting first and second images should be exactly rotationally aligned. However, this is not necessary. In other cases, the reconstruction algorithm can be configured differently, meaning that the expected range of rotation between the first image and the second image in the case of perfect calibration can be non-zero.

[0160] According to any embodiment of the present invention, the method can be improved by performing an initial preprocessing of the measurement data corresponding to each projection in order to rearrange the data into a parallel beam geometry. In a preferred example, a wedge beam rearrangement is performed. In wedge rebinning, a cone beam is converted into a wedge beam. Wedge beam rearrangement is described, for example, in section II.A of the following article: Shechter et al.: The frequency split method for helical cone-beam reconstruction (Med Phys. August 2004; 31(8):2230-6).

[0161] This kind of parallel beam rearrangement adds some additional computational cost, but can improve robustness. This can be understood from the fact that embodiments of the present invention are based on the shift between a first X-ray source and a second X-ray source in the attenuation pattern detected at the detector. The attenuation pattern is naturally a projection of the attenuation distribution of the object being scanned. In addition, the attenuation center of the attenuation pattern measured at the detector is actually the projection of the attenuation center of the object projected onto the (parallel beam) detector. Since the centroid of the object being imaged is typically close to the isocenter of the system, it hardly moves once projected onto the parallel beam detector. For this reason, the parallel beam geometry is useful and increases robustness.

[0162] According to any embodiment of the present invention, it may be advantageous to initially perform an object alignment check before starting the calibration check to verify that the imaged object is fully contained within the projection span and is not truncated. Such a test is actually typically already integrated into many modern CT systems.

[0163] Referring Figure 14 , the process for checking object alignment (i.e., non-truncation) for a given projection may include the following steps.

[0164] The process may include identifying measured attenuation values in a measured 2D attenuation pattern of a projection corresponding to a predefined boundary region of an array of detector elements. Figure 14 Example boundary regions of an example measured projection attenuation pattern are illustrated using boxes 82a, 82b. The boundary region may be defined, for example, as corresponding to an attenuation pattern region formed by a predefined number of detector columns (i.e., outermost detector columns) on each far side of the 2D attenuation pattern.

[0165] The process may further include determining whether each of the measured attenuation values in the boundary regions 82a, 82b falls within a predefined null range corresponding to a zero attenuation measurement result. In other words, checking whether the boundary region is empty. If it is not empty, this potentially indicates that the object is truncated in the projection and spills over the sides of the projection boundary. The process may further include: in response to determining that not all of the measured attenuation values in the boundary regions 82a, 82b fall within the null range, generating an alert message and / or aborting the calibration check.

[0166] In some embodiments, the method may further include calculating an actual distance between a first X-ray point source and a second X-ray point source based on a determined translation in all or part of the attenuation patterns of a first successive projection and a second successive projection. This may be based on a pre-formed look-up table or transfer function that allows mapping from a calculated shift in all or part of the attenuation pattern and a separation distance between the two point sources. The look-up table or transfer function may be empirically pre-determined by a simple calibration operation or a machine learning algorithm may be trained to output a value of the separation distance, as already described above.

[0167] In fact, if the geometry of the system is known, an equation or function may be used to calculate the separation distance. In particular, referring again to Figure 11 , using the small angle approximation, the separation distance d1 along the axis of generator rotation between two point sources fs0, fs1 is approximately equal to d1≈R F Δα d , where Δα d is the angular separation between two focal spots fs1 and fs2 relative to the rotation isocenter, and R Fis the distance from the focal spot to the isocenter (meaning the distance from the X-ray tube focal spot to the imaging isocenter). The distance d2 between the ray projections of fs0 and fs1 on the detector 110 is approximately equal to d2≈(R FD -R F )Δα d . Therefore, by simply substituting Δα d , the distance shift d2 detected at the detector 110 (i.e., the change in (R FD -R F )Δα d ) can be converted into a corresponding shift in the separation distance d1 between the focal spots along the rotation axis of the generator (i.e., the corresponding change in R F Δα d ). For example, d1 / d2 = R F / (R FD -R F ).

[0168] Here (refer to Figure 11 ): R FD = the distance from the focal spot to the detector (meaning the distance from the X-ray tube focal spot to the detector); R F is the distance from the focal spot to the isocenter (meaning the distance from the X-ray tube focal spot to the imaging isocenter).

[0169] According to some embodiments, a response action can be performed after performing a calibration check.

[0170] Some embodiments propose to perform a correction.

[0171] In particular, the method can include determining the deviation of the distance between the first and second X-ray point sources from the target distance between the first and second X-ray point sources. The method can also include communicating with an image reconstruction module to apply additional correction steps to a reconstruction algorithm included by the reconstruction module based on the determined deviation. Optionally, the method can further include supplying CT data to the reconstruction module and applying the reconstruction algorithm with corrections to the CT data.

[0172] By way of further illustration of some of the concepts discussed above, Figure 15 it is illustrated how a CT device uses standard rφ diagram sampling line integrals. For further clarity, Figure 16 it is illustrated the mapping of the measured rays into r, φ space. Each measured (intensity or attenuation) value or sample (measured at the detector) is represented by a point in the diagram of Figure 15 .

[0173] Refer to Figure 15 , assuming that each sample corresponds to the projection of a single straight ray from the source to the point where the measurement is made on the detector. Refer to Figure 16, the value r corresponds to the distance of the ray to the rotational isocenter. The value φ corresponds to the angle of the ray with the x-axis, where the x-axis is the Cartesian x-axis of the plane defined by the fan-shaped ray. Within this geometry, the previously mentioned angular sampling interval Δα r (often referred to as the radial sampling density or simply 'radial sampling') is defined as the difference in the radial value r of adjacent detector measurements as measured at the rotational isocenter.

[0174] All points from a single projection lie on an oblique line. For subsequent projections, the point shift is equal to the amount of the angular sampling interval. Figure 15 The inclined measurement lines of the first projection in this figure corresponding to, for example, the first point source fs0 and the inclined measurement lines of the first projection in this figure corresponding to the second point source fs1 are indicated by arrows. The samples collected using the point source fs0 are shown as solid gray points. The samples collected using fs1 are shown as white points. Figure 15 The angular sampling interval Δα is also illustrated r , which corresponds to the separation distance between the two point sources fs0, fs1.

[0175] As explained above, the original samples are preferably rearranged into a parallel beam geometry and preferably a wedge beam geometry. Figure 15 The vertical lines of the samples in indicate the rearranged sample points. The rearrangement may involve interpolation from the original measurement points to the rearranged points. The rearranged points are shown as black squares.

[0176] The reconstruction algorithm is configured to receive as input a set of attenuation measurements from the detector array for each projection from alternating point sources. Each attenuation measurement may be labeled or annotated as corresponding to a measurement point in r, φ space. This may involve mapping the measurement points of each projection from the detector array geometry to the corresponding ray geometry in r, φ space.

[0177] The drift of the separation distance between the X-ray point sources causes a shift in the r coordinate of the measurements for each projection. The measurement points for each fs0 projection are shifted slightly upward, and the measurement points for each fs1 projection are shifted slightly downward. At the same time, the points are shifted slightly to the right and left respectively, see Figure 2 .

[0178] Therefore, in order to implement correction in the reconstruction, this can be implemented by applying correction to the measurement data before applying the reconstruction algorithm. In particular, in order to compensate for the shift in the separation between the point sources, this may include adjusting the r and φ coordinates of the measurement points of at least one of the two point sources by an interval amount equal to the total detected change in the separation between the two focal points.

[0179] Regarding calibration, it should be noted that the appearance of aliasing artifacts and the resolution loss at the incorrectly calibrated focal spot position have two causes. The first cause is that the actual spatial position of the focal spot does not match the theoretically optimal position. This part is not corrected by the above process. However, the second cause is that the reconstruction algorithm assumes that the actual position matches the optimal position. Therefore, knowledge of the actual position allows for calibration to compensate for geometric variations, thereby minimizing the negative impact of mis-calibration.

[0180] In some embodiments, the method may further include generating a status report indicating the difference between the calculated distance between the first X-ray point source and the second X-ray point source and a predefined target distance between the first X-ray point source and the second X-ray point source, and transmitting the status report to a server computer.

[0181] In some embodiments, the system further includes a data log, and the method further includes storing the measured distance between the first X-ray point source and the second X-ray point source in the data log. In this way, the system automatically records information about the estimated focal spot position, and a red flag can be issued if the position is outside the acceptable bounds.

[0182] In some embodiments, the method may further include generating an alert signal for transmission to a user interface or another computing device in response to the determined shift being outside the expected range.

[0183] The above-described embodiments of the present invention employ a processing device. A processing device generally may include a single processor or multiple processors. It may be located in a single containing device, structure, or unit, or it may be distributed among multiple different devices, structures, or units. Thus, a reference to a processing device being adapted or configured to perform a particular step or task may correspond to the performance of that step or task by any one or more of a plurality of processing components, either individually or in combination. Those skilled in the art will understand how such a distributed processing device may be implemented. The processing device includes a communication module or input / output section for receiving data and outputting data to additional components.

[0184] One or more processors of the processing device may be implemented in software and / or hardware in various ways to perform the various functions required. A processor generally employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform the required functions. The processor may be implemented as a combination of dedicated hardware for performing some functions and one or more programmed microprocessors and associated circuitry for performing other functions.

[0185] Examples of circuits that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field programmable gate arrays (FPGAs).

[0186] In various embodiments, a processor may be associated with one or more storage media, such as volatile and non-volatile computer memories such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when run on one or more processors and / or controllers, perform the desired functions. The various storage media may be fixed within the processor or controller or may be transportable such that one or more programs stored thereon can be loaded into the processor.

[0187] By studying the drawings, the disclosure, and the appended claims, those skilled in the art can understand and realize variations of the disclosed embodiments when practicing the claimed invention. In the claims, the word "comprising" does not exclude other elements or steps, and the words "a" or "an" do not exclude a plurality.

[0188] A single processor or other unit may implement the functions of several items recited in the claims.

[0189] Although specific measures are recited in mutually different dependent claims, this does not indicate that a combination of these measures cannot be used advantageously.

[0190] A computer program may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium provided together with or as part of other hardware, but the computer program may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0191] If the term "adapted to" is used in a claim or the specification, it should be noted that the term "adapted to" is intended to be equivalent to the term "configured to".

[0192] Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. A computer-implemented method (10) for determining the calibration state of an X-ray tube point source within a dual point-source computed tomography (CT) acquisition apparatus, the method comprising: Receiving (12) dual point-source CT data, the dual point-source CT data including X-ray detector measurement data for each X-ray projection in a series of X-ray projections, the measurement data corresponding to measurement data acquired by an X-ray generator of the CT acquisition apparatus, the X-ray generator of the CT acquisition apparatus providing alternately enabled first and second X-ray point sources, the point sources being displaced relative to each other along an axis of rotation of the generator's orbit within the CT acquisition apparatus, and wherein the measurement data for each X-ray projection includes a 2D attenuation pattern corresponding to measured attenuation values of a 2D array of detector elements; Performing (14) a point-source calibration check, the point-source calibration check including: Using the measurement data to determine (20) a shift of all or part of the attenuation pattern between a first successive projection and a second successive projection, each of the first and second projections being generated using a different one of the two point sources; and Determining (22) whether the shift is within a predefined expected range.

2. The method according to claim 1, wherein, Determining the shift includes: determining a translation in the direction of the axis of rotation of the generator's orbit.

3. The method according to claim 1 or 2, wherein Determining the shift includes: identifying characteristic points in the respective 2D attenuation patterns of the first and second projections and determining a translation of the characteristic points between the first and second projections.

4. The method according to claim 3, wherein, Defining the characteristic points in each 2D attenuation pattern based on a function of attenuation values weighted by intensity value positions in the 2D pattern.

5. The method according to claim 4, wherein The characteristic points are defined as attenuation centers along at least one dimension of each 2D attenuation pattern, the attenuation centers being an attenuation analog of a centroid.

6. The method according to claim 5, wherein The attenuation center along at least one dimension of the attenuation pattern is defined by the following equation: where p'(j) = ∑ i p(i, j), where i is an index of a detector element row and j is an index of a detector element column, and where p(i,j) is the measured value of the detector element in row i, column j.

7. The method according to claim 1 or 2, wherein Determining the shift includes: identifying a characteristic sub-region of the 2D attenuation pattern of each of the first and second projections and performing translational registration between the characteristic sub-regions of the two projections.

8. The method according to any one of claims 1-7, further comprising, prior to the calibration check, performing an imaged object alignment check for each projection, the imaged object alignment check including: Identifying the measured attenuation values in the 2D attenuation pattern corresponding to a predefined boundary region of the array of detector elements; Determining whether each of the measured attenuation values in the boundary region falls within a predefined null range corresponding to a zero attenuation measurement result; and Generating an alert message and / or aborting the calibration check in response to determining that not all of the measured attenuation values in the boundary region fall within the null range.

9. The method according to any one of claims 1-8, wherein, Determining the shift includes: using a trained machine learning algorithm that is trained to receive the measurement data of the first projection and the second projection as inputs and generate, as an output, a value of the shift between the 2D attenuation pattern of the first projection and the 2D attenuation pattern of the second projection.

10. The method according to any one of claims 1-9, wherein Determining the shift of all or part of the attenuation patterns of the first projection and the second projection is performed by: Reconstructing a first image using only the X-ray projections in a received series of X-ray projections acquired using the first X-ray point source among the X-ray point sources; Reconstructing a second image using only the X-ray projections in a received series of X-ray projections acquired using the second X-ray point source among the X-ray point sources; And Detecting a rotation between the first image and the second image.

11. The method according to any one of claims 1-10, further comprising: Calculating an actual distance between the first X-ray point source and the second X-ray point source based on the determined translation or rotation in all or part of the attenuation patterns of the first successive projection and the second successive projection.

12. A computer program product comprising computer program code configured to, when run on a processor, cause the processor to perform the method according to any one of claims 1-11.

13. A processing device (32) comprising: An input / output unit (34); And One or more processors (36) configured to execute a method that includes: Receiving, at the input / output unit, dual-point source CT data including X-ray detector measurement data (42) for each X-ray projection in a series of X-ray projections, the measurement data corresponding to measurement data acquired using an X-ray generator of a CT acquisition device (40), the X-ray generator of the CT acquisition device providing an alternately enabled first X-ray point source and a second X-ray point source that are moved relative to each other along an axis of rotation of the generator's orbit within the CT acquisition device, and wherein the measurement data for each X-ray projection includes a 2D attenuation pattern corresponding to measured attenuation values of a 2D array of detector elements; Performing a point source calibration check that includes: Using the measurement data to determine a shift of all or part of the attenuation patterns of a first successive projection and a second successive projection, each of the first projection and the second projection being generated using a different one of the two point sources; and Determining whether the shift is within a predefined expected range.

14. A system (30) comprising: A dual-point source CT acquisition device (40); And The processing device (32) according to claim 13.

Images