METHOD FOR CORRECTING ARTIFACTS IN A COMPUTED TOMOGRAPHY IMAGE DATA SET, COMPUTED TOMOGRAPHY FACILITY, COMPUTER PROGRAM AND ELECTRONICALLY READABLE DATA CARRIER

Abstract

For a computed tomography image data set of a recording region in which an at least substantially needle-shaped metal object is located, a method for correcting artifacts comprises: ascertaining an artifact data set describing, in an image space, at least one artifact caused by the at least substantially needle-shaped metal object, wherein the ascertaining uses prior knowledge about the at least one artifact; and subtracting the artifact data set from the computed tomography image data set. The computed tomography image data set being reconstructed from projection images, which are recorded at least partially such that the at least substantially needle-shaped metal object is irradiated at least substantially in the longitudinal direction.

Claims

1. A method for correcting artifacts in a computed tomography image data set of a recording region in which an at least substantially needle-shaped metal object is located, wherein the computed tomography image data set is reconstructed from projection images recorded at least partially such that the at least substantially needle-shaped metal object is irradiated at least substantially in a longitudinal direction, and wherein the method comprises: ascertaining an artifact data set describing, in an image space, at least one artifact caused by the at least substantially needle-shaped metal object, wherein the ascertaining is based on prior knowledge about the at least one artifact; and subtracting the artifact data set from the computed tomography image data set.

2. The method as claimed in claim 1, wherein during intervention monitoring with the at least substantially needle-shaped metal object as an intervention instrument, at least one of correction takes place for each recorded computed tomography image data set of a monitoring series, an image plane of the computed tomography image data set is a longitudinal extension plane of the at least substantially needle-shaped metal object, or the at least substantially needle-shaped metal object is an intervention needle.

3. The method as claimed in claim 1, wherein the ascertaining ascertains the artifact data set at least partially by at least one of ascertaining or adapting at least one progression function describing a progression of the at least one artifact that starts from a tip of the at least substantially needle-shaped metal object in at least one direction in the image space, the at least one of ascertaining or adapting based on image data, lying in an artifact region, of the computed tomography image data set.

4. The method as claimed in claim 3, wherein at least one of the at least one progression function is a transverse progression function perpendicular to the longitudinal direction of the at least substantially needle-shaped metal object in a longitudinal extension plane of the at least substantially needle-shaped metal object, and wherein the image data is chosen along at least one selection line that progresses at least substantially perpendicularly to the longitudinal direction of the at least substantially needle-shaped metal object.

5. The method as claimed in claim 4, wherein the transverse progression function is a Gaussian function.

6. The method as claimed in claim 4, wherein a longitudinal progression function along the longitudinal direction of the at least substantially needle-shaped metal object is used as a further progression function, and wherein the longitudinal progression function describes at least one parameter of a functional form chosen as the transverse progression function along the longitudinal direction.

7. The method as claimed in claim 6, wherein, for at least one of the ascertaining or adapting of the longitudinal progression function, image data of at least one image point of the at least one artifact is chosen in the computed tomography image data set, wherein the at least one image point lies adjacent to the tip of the at least substantially needle-shaped metal object as an artifact origin.

8. The method as claimed in claim 6, wherein a functional form of the longitudinal progression function to be at least one of adapted or ascertained is chosen from the prior knowledge about the at least one artifact, which is specific to the at least substantially needle-shaped metal object.

9. The method as claimed in claim 8, wherein, to ascertain the prior knowledge, at least one of analytical calculations of the at least one artifact or calibration measurements of at least one of the at least substantially needle-shaped metal object or a metal object of a same type are performed in a phantom.

10. The method as claimed in claim 1, wherein the ascertaining of the artifact data set comprises: at least one of choosing or determining, at least partially from reference data sets present in a database, the artifact data set for at least one of various reference metal objects or various parameters of at least one of the at least substantially needle-shaped metal object or the various reference metal objects.

11. The method as claimed in claim 10, wherein the reference data sets are derived from learning measurements of at least one of the at least substantially needle-shaped metal object or at least one reference metal object of a same type in a phantom.

12. The method as claimed in claim 10, wherein at least part of the ascertaining of the artifact data set is performed via the database based on recording parameters that deviate with regard to the reference data sets.

13. A computed tomography device including a control device configured to perform the method as claimed in claim 1.

14. A non-transitory computer-readable medium storing computer-executable instructions that, when executed at a control device of a computed tomography device, cause the computed tomography device to perform the method of claim 1.

15. A computed tomography device comprising: a memory storing computer-executable instructions; and at least one processor configured to executed the computer-executable instructions to cause the computed tomography device to ascertain an artifact data set describing, in an image space, at least one artifact caused by an at least substantially needle-shaped metal object, wherein the artifact data set is ascertained based on prior knowledge about the at least one artifact, and subtract the artifact data set from a computed tomography image data set of a recording region in which the at least substantially needle-shaped metal object is located, to correct for artifacts in the computed tomography image data set, wherein the computed tomography image data set is reconstructed from projection images recorded at least partially such that the at least substantially needle-shaped metal object is irradiated at least substantially in a longitudinal direction.

16. The method as claimed in claim 4, wherein the longitudinal extension plane corresponds to an image plane of the computed tomography image data set.

17. The method as claimed in claim 9, wherein the phantom is a structureless phantom.

18. The method as claimed in claim 17, wherein the structureless phantom is a water phantom.

19. The method as claimed in claim 11, wherein the phantom is a structureless phantom.

20. The method as claimed in claim 19, wherein the structureless phantom is a water phantom.

21. The method as claimed in claim 5, wherein a longitudinal progression function along the longitudinal direction of the at least substantially needle-shaped metal object is used as a further progression function, and wherein the longitudinal progression function describes at least one parameter of a functional form chosen as the transverse progression function along the longitudinal direction.

22. The method as claimed in claim 7, wherein a functional form of the longitudinal progression function to be at least one of adapted or ascertained is chosen from the prior knowledge about the at least one artifact, which is specific to the at least substantially needle-shaped metal object.

23. The method as claimed in claim 22, wherein the ascertaining of the artifact data set comprises: at least one of choosing or determining, at least partially from reference data sets present in a database, the artifact data set for at least one of various reference metal objects or various parameters of at least one of the at least substantially needle-shaped metal object or the various reference metal objects.

24. The method as claimed in claim 8, wherein the ascertaining of the artifact data set comprises: at least one of choosing or determining, at least partially from reference data sets present in a database, the artifact data set for at least one of various reference metal objects or various parameters of at least one of the at least substantially needle-shaped metal object or the various reference metal objects.

25. The method as claimed in claim 11, wherein at least part of the ascertaining of the artifact data set is performed via the database based on recording parameters that deviate with regard to the reference data sets.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Further advantages and details of the present invention are disclosed in the exemplary embodiments described below and by reference to the drawing, in which:

[0036] FIG. 1 shows a real situation in a recording region,

[0037] FIG. 2 shows the situation during ideal imaging without artifacts,

[0038] FIG. 3 schematically shows a real imaging result,

[0039] FIG. 4 shows an attenuation value progression in the transverse direction,

[0040] FIG. 5 shows an attenuation value progression in the longitudinal direction,

[0041] FIG. 6 shows a flow diagram of an exemplary embodiment of the method according to the present invention,

[0042] FIG. 7 shows a computed tomography facility according to an exemplary embodiment of the present invention, and

[0043] FIG. 8 shows the functional structure of a control facility of the computed tomography facility.

DETAILED DESCRIPTION

[0044] FIG. 1 schematically shows a situation in which an elongated metal object 1, here an intervention needle 2, is introduced into an object 3, here as part of a medical intervention into the body of a patient, wherein the object 3 has an X-ray-relevant structuring, for which by way of example a target structure 4, here a lesion, which is to be reached by the metal object 1, for example punctured by the intervention needle 2, is shown. At this point, however, it should be noted that the method is not restricted to interventions on a patient, but rather of course can also be used in other situations, for example in manufacturing or general processing procedures, in which an intervention on an object 3 is to take place via an elongated metal object 1, specifically during image monitoring. In the present case, the image monitoring takes place by way of computed tomography, wherein in the present case a rotation of the recording arrangement takes place in the plane of metal object 1 and target structure 4, meaning that some of the X-ray beams fully or at least largely cross the longitudinal extension of the metal object 1, consequently with an extremely strong attenuation occurring for extremely few projection images. This manifests itself in the reconstructed computed tomography image data set as an artifact, which is to be explained in further detail with regard to FIG. 2 and FIG. 3.

[0045] In this context, FIG. 2 schematically shows an ideal two-dimensional sectional image 5 of a computed tomography image data set, in which the object 3 can be clearly identified with the target structure 4 ahead of the tip 6 of the intervention needle 2, which is shown as bright, in front of a black background (no attenuation).

[0046] FIG. 3 shows the real situation in the form of a corresponding sectional image 7 of the reconstructed computed tomography image data set. Evidently, due to the artifact 8 originating at the tip 6 of the intervention needle 2, the target structure 4 can hardly be seen. The artifact 8 shows in the form of HU dimmed areas and represents a low-frequency signal, which always originates at the needle tip 6 and decreases in intensity as the distance from the needle tip 6 increases.

[0047] In order to explain this and the basic idea of the present invention in further detail, profile lines 9, 10, 11, 12 are shown in the transverse direction and in the longitudinal direction of the elongated metal object 1 in FIG. 2 and FIG. 3, wherein the attenuation value progression is schematically shown along the transverse lines in FIG. 4, and along the longitudinal lines in FIG. 5.

[0048] In FIG. 4, the progression 13 corresponds to that along the line 9 and the progression 14 corresponds to that along the line 11. Evidently, the progression 14 can be described as a superimposition of the progression 13 with a transverse progression 15 which describes the artifact. In this context, it can already be seen in FIG. 4 that the progression 15 can be very effectively mapped by a Gaussian function.

[0049] Accordingly, in FIG. 5 the progression 16 corresponds to the profile line 10, and the progression 17 corresponds to the profile line 12. Here too, it can be clearly seen that merely a reduction of the HU values is given by a longitudinal progression of the artifact 8 (which is not shown in further detail in FIG. 5 for clarity reasons).

[0050] Accordingly, the idea is now to correct the reconstructed computed tomography image data set, schematically indicated by the sectional image 7, in the direction of the ideal sectional image 5, by the artifact components being removed again to the greatest possible extent by raising the attenuation values, cf. arrow 18.

[0051] FIG. 6 shows the progression of a corresponding method according to an example embodiment of the present invention in the form of a flow diagram. Here, firstly the reconstructed computed tomography image data set 19 is provided. Using the computed tomography image data set 19 and prior knowledge 20 about the artifact 8, an artifact data set 21 describing the artifact 8 in the image space is described in a step S1. In a step S2, this can then be subtracted from the reconstructed computed tomography image data set 19, in order to obtain a corrected computed tomography image data set 22.

[0052] The artifact data set 21 consequently ultimately describes the transverse progression 15 in accordance with FIG. 4 as well as the corresponding longitudinal progression for FIG. 5, not shown in further detail for the sake of clarity, meaning that the step S2 corresponds to the operation indicated by the arrow 18.

[0053] In this context, two preferred approaches exist for enabling a specific implementation of the ascertaining of the artifact data set 21 in step S1. The first approach is substantially oriented toward what is shown in FIG. 4 and FIG. 5, and uses progression functions derived from the prior knowledge 20, specifically predefined progression forms, in order to allow a more rapid fitting on the basis of less image data of the reconstructed computed tomography image data set 19. This proceeds as follows in a particularly preferred exemplary embodiment.

[0054] In a first substep of step S1, the image data is selected, which forms the basis of the ascertaining of the progression functions. In this context, the selection can take place at least partially automatically and/or at least partially manually, for example by user marking. An automatic marking can be based, for example, on a segmentation of the metal object 1 in the reconstructed computed tomography image data set 19. In the present case, the following are selected as image data: image data of an image point, which marks the artifact origin adjacent to the tip 6 of the metal object 1, and image data along at least one selection line, which is transverse to the longitudinal direction marked by the profile lines 10 and 12 in the image plane, i.e. in parallel with the profile lines 9 and 11. For the selection line, in this context a less structured region of the object 3 is ideally used, therefore in the example ideally processed outside the target structure 4. Ideally, at least one of the at least one selection line is located 1 to 2 cm away from the tip 6 of the metal object 1. If a higher quality of the artifact data set 21 is intended, then a plurality of selection lines are used.

[0055] In a second substep of substep S1, in this embodiment a transverse progression function is ascertained or adapted for each of the at least one selection lines. Here, the prior knowledge 20 plays a decisive role for the first time, as according to this, by considering previous measurements here, in particular the calibration measurements yet to be discussed, the transverse progression of the artifact 8 can best be described by a Gaussian function. Ultimately, the transverse progression along the selection line, which in the structureless region most likely ought to correspond to the progression 15 in FIG. 4, is fitted to a Gaussian function, meaning that it is parametrized, for example by indicating at least the amplitude and the width. The parameters determined in this manner therefore define the transverse progression function for the corresponding selection line. It should be noted that, in principle, other functional forms can also be used for the transverse progression function, for example half a sine cycle.

[0056] If other selection lines are considered, then boundary conditions from the prior knowledge 20 relating thereto can also be introduced, for example such that the width of the Gaussian function is to increase with increasing distance from the tip 6, and the amplitude is to fall.

[0057] In a third substep of step S1, in this embodiment, the results for the transverse progression function are used in order to determine the longitudinal progression function. Ideally, this also has a functional form already predefined by the prior knowledge 20, wherein in a simple case it is possible to make an assumption regarding the linearity, but preferably the result of a spline fit to calibration measurements is used. In calibration measurements, the metal object 1 is measured in a preferably structureless phantom, here a water phantom, with the same recording parameters. Due to the structureless nature of the water phantom, which replaces the object 3, it is possible to directly derive the progressions for the artifact 8, as the anticipated value for the attenuation value (HU value) of the water is also known. Here, via a plurality of measurements, it is preferably possible to form a statistical average and the functional form can be ascertained by a spline interpolation in the longitudinal direction for the parameters of the transverse progression functions, in particular therefore the amplitudes and the widths of the Gaussian functions, which of course can likewise be determined for the calibration measurements.

[0058] Starting from the predefined functional form for the metal object 1 or a metal object of the same type, as can be derived from the calibration measurements, it is now possible in the third substep of step S1 to adapt this functional form to the image data via corresponding parameters of the longitudinal progression function, comprising at least the artifact origin (where it is possible to start from the image value there as the amplitude and from a very small width) and a selection line (where the parameters are indeed present as a result of the second substep for ascertaining the transverse progression function). For a more precise determination, it is of course possible for a plurality of selection lines to be considered, in order to obtain further supporting points for the adapting of the functional form and thus the ascertaining of the longitudinal progression function.

[0059] Since the transverse progression function and the longitudinal progression function are known, in a fourth substep the artifact data set 21 can be determined in a simple manner, by calculating which attenuation value is present for the artifact for each pixel or voxel on the basis of the longitudinal and transverse progression function.

[0060] In this context, it should also be noted at this point that a main application area will be two-dimensional sectional images, cf. the exemplary schematic sectional image 7, thus two-dimensional computed tomography image data sets 19, but of course an extension to three-dimensional computed tomography image data sets 19 can readily take place, for example, by considering transverse progression functions for two directions that are perpendicular to one another and perpendicular to the longitudinal direction or by assuming symmetry (particularly if the metal object 1 itself has rotational symmetry).

[0061] In a second, preferred variant for ascertaining the artifact data set 21, it is also possible to use a database with reference data sets, which ideally has been ascertained and compiled for various reference metal objects and parameters of the reference metal objects. These may be based on reference measurements which, as in the case of the calibration measurements, ideally can be performed with a structureless phantom, here a water phantom, wherein ideally a plurality of measurements are performed for each combination of reference metal object and parameters, in order to achieve a statistical averaging that ideally renders the shape of the artifact 8 as a reference data set. If a metal object 1 that matches a reference data set in terms of type and parameters is then used, then the corresponding reference data set can also then be retrieved from the database as the artifact data set 21. An interpolation is also possible for intermediate parameters. Should recording parameters have an influence, these of course can likewise be taken into consideration accordingly when constructing the database or by way of a corresponding conversion of reference data sets.

[0062] In addition to the two preferred embodiments mentioned here, which are able to provide correction in real time, further conceivable embodiments also exist, for example which use a trained artificial intelligence ascertaining function, for example a neural network.

[0063] FIG. 7 shows a schematic outline of a computed tomography facility 23 according to an example embodiment of the present invention. As is known in principle, this comprises a gantry 24, in which a recording arrangement with an X-ray emitter 25 and an X-ray detector 26 is rotatably mounted. The gantry 24 has a central opening, into which a patient can be introduced by a patient table 27 in such a manner that the recording area is located in the field of view of the recording arrangement.

[0064] With regard to the performing of an intervention with the use of the metal object 1 as the intervention instrument, in particular the intervention needle 2, it is also possible for an actuating mechanism 28 for the in particular robotic movement of the metal object 1 to be arranged on the patient table 27, meaning that a person performing the intervention does not have to work directly in the gantry 24.

[0065] The operation of the computed tomography facility 23 is controlled via a control facility 29, which is embodied to perform the method according to an example embodiment of the present invention.

[0066] To this end, FIG. 8 shows the functional structure of the control facility 29 in further detail. This comprises a storage means and/or device 30, in which for example prior knowledge 20 can be stored, for example in the form of the functional forms of the transverse and longitudinal progression functions and/or in the form of the database described. A recording unit 31, which is known in principle, controls the recording operation and can forward recorded projection images to a reconstruction unit 32, which provides the reconstructed computed tomography image data set 19, in the present case to an ascertaining unit 33.

[0067] The ascertaining unit 33 ascertains, as described in step S1, the artifact data set 21, meaning that the reconstructed computed tomography image data set 19 can be corrected via a correction unit 34 by subtracting the artifact data set 21 in accordance with step S2. The corrected computed tomography image data set 22 produced in this way can then be stored, for example, output via an interface and/or represented via an output unit on a representation facility of the computed tomography facility 23.

[0068] It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, components, regions, layers, and/or sections, these elements, components, regions, layers, and/or sections, should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and/or,” includes any and all combinations of one or more of the associated listed items. The phrase “at least one of” has the same meaning as “and/or”.

[0069] Spatially relative terms, such as “beneath,” “below,” “lower,” “under,” “above,” “upper,” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as “below,” “beneath,” or “under,” other elements or features would then be oriented “above” the other elements or features. Thus, the example terms “below” and “under” may encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly. In addition, when an element is referred to as being “between” two elements, the element may be the only element between the two elements, or one or more other intervening elements may be present.

[0070] Spatial and functional relationships between elements (for example, between modules) are described using various terms, including “on,” “connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitly described as being “direct,” when a relationship between first and second elements is described in the disclosure, that relationship encompasses a direct relationship where no other intervening elements are present between the first and second elements, and also an indirect relationship where one or more intervening elements are present (either spatially or functionally) between the first and second elements. In contrast, when an element is referred to as being “directly” on, connected, engaged, interfaced, or coupled to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (e.g., “between,” versus “directly between,” “adjacent,” versus “directly adjacent,” etc.).

[0071] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a,” “an,” and “the,” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the terms “and/or” and “at least one of” include any and all combinations of one or more of the associated listed items. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Expressions such as “at least one of,” when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list. Also, the term “example” is intended to refer to an example or illustration.

[0072] It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may in fact be executed substantially concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

[0073] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0074] It is noted that some example embodiments may be described with reference to acts and symbolic representations of operations (e.g., in the form of flow charts, flow diagrams, data flow diagrams, structure diagrams, block diagrams, etc.) that may be implemented in conjunction with units and/or devices discussed above. Although discussed in a particularly manner, a function or operation specified in a specific block may be performed differently from the flow specified in a flowchart, flow diagram, etc. For example, functions or operations illustrated as being performed serially in two consecutive blocks may actually be performed simultaneously, or in some cases be performed in reverse order. Although the flowcharts describe the operations as sequential processes, many of the operations may be performed in parallel, concurrently or simultaneously. In addition, the order of operations may be re-arranged. The processes may be terminated when their operations are completed, but may also have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, subprograms, etc.

[0075] Specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments. The present invention may, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.

[0076] In addition, or alternative, to that discussed above, units and/or devices according to one or more example embodiments may be implemented using hardware, software, and/or a combination thereof. For example, hardware devices may be implemented using processing circuitry such as, but not limited to, a processor, Central Processing Unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a System-on-Chip (SoC), a programmable logic unit, a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. Portions of the example embodiments and corresponding detailed description may be presented in terms of software, or algorithms and symbolic representations of operation on data bits within a computer memory. These descriptions and representations are the ones by which those of ordinary skill in the art effectively convey the substance of their work to others of ordinary skill in the art. An algorithm, as the term is used here, and as it is used generally, is conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of optical, electrical, or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0077] It should be borne in mind that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, or as is apparent from the discussion, terms such as “processing” or “computing” or “calculating” or “determining” of “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device/hardware, that manipulates and transforms data represented as physical, electronic quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

[0078] In this application, including the definitions below, the term ‘module’ or the term ‘controller’ may be replaced with the term ‘circuit.’ The term ‘module’ may refer to, be part of, or include processor hardware (shared, dedicated, or group) that executes code and memory hardware (shared, dedicated, or group) that stores code executed by the processor hardware.

[0079] The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules that are connected via interface circuits. For example, multiple modules may allow load balancing. In a further example, a server (also known as remote, or cloud) module may accomplish some functionality on behalf of a client module.

[0080] Software may include a computer program, program code, instructions, or some combination thereof, for independently or collectively instructing or configuring a hardware device to operate as desired. The computer program and/or program code may include program or computer-readable instructions, software components, software modules, data files, data structures, and/or the like, capable of being implemented by one or more hardware devices, such as one or more of the hardware devices mentioned above. Examples of program code include both machine code produced by a compiler and higher level program code that is executed using an interpreter.

[0081] For example, when a hardware device is a computer processing device (e.g., a processor, Central Processing Unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a microprocessor, etc.), the computer processing device may be configured to carry out program code by performing arithmetical, logical, and input/output operations, according to the program code. Once the program code is loaded into a computer processing device, the computer processing device may be programmed to perform the program code, thereby transforming the computer processing device into a special purpose computer processing device. In a more specific example, when the program code is loaded into a processor, the processor becomes programmed to perform the program code and operations corresponding thereto, thereby transforming the processor into a special purpose processor.

[0082] Software and/or data may be embodied permanently or temporarily in any type of machine, component, physical or virtual equipment, or computer storage medium or device, capable of providing instructions or data to, or being interpreted by, a hardware device. The software also may be distributed over network coupled computer systems so that the software is stored and executed in a distributed fashion. In particular, for example, software and data may be stored by one or more computer readable recording mediums, including the tangible or non-transitory computer-readable storage media discussed herein.

[0083] Even further, any of the disclosed methods may be embodied in the form of a program or software. The program or software may be stored on a non-transitory computer readable medium and is adapted to perform any one of the aforementioned methods when run on a computer device (a device including a processor). Thus, the non-transitory, tangible computer readable medium, is adapted to store information and is adapted to interact with a data processing facility or computer device to execute the program of any of the above mentioned embodiments and/or to perform the method of any of the above mentioned embodiments.

[0084] Example embodiments may be described with reference to acts and symbolic representations of operations (e.g., in the form of flow charts, flow diagrams, data flow diagrams, structure diagrams, block diagrams, etc.) that may be implemented in conjunction with units and/or devices discussed in more detail below. Although discussed in a particularly manner, a function or operation specified in a specific block may be performed differently from the flow specified in a flowchart, flow diagram, etc. For example, functions or operations illustrated as being performed serially in two consecutive blocks may actually be performed simultaneously, or in some cases be performed in reverse order.

[0085] According to one or more example embodiments, computer processing devices may be described as including various functional units that perform various operations and/or functions to increase the clarity of the description. However, computer processing devices are not intended to be limited to these functional units. For example, in one or more example embodiments, the various operations and/or functions of the functional units may be performed by other ones of the functional units. Further, the computer processing devices may perform the operations and/or functions of the various functional units without sub-dividing the operations and/or functions of the computer processing units into these various functional units.

[0086] Units and/or devices according to one or more example embodiments may also include one or more storage devices. The one or more storage devices may be tangible or non-transitory computer-readable storage media, such as random access memory (RAM), read only memory (ROM), a permanent mass storage device (such as a disk drive), solid state (e.g., NAND flash) device, and/or any other like data storage mechanism capable of storing and recording data. The one or more storage devices may be configured to store computer programs, program code, instructions, or some combination thereof, for one or more operating systems and/or for implementing the example embodiments described herein. The computer programs, program code, instructions, or some combination thereof, may also be loaded from a separate computer readable storage medium into the one or more storage devices and/or one or more computer processing devices using a drive mechanism. Such separate computer readable storage medium may include a Universal Serial Bus (USB) flash drive, a memory stick, a Blu-ray/DVD/CD-ROM drive, a memory card, and/or other like computer readable storage media. The computer programs, program code, instructions, or some combination thereof, may be loaded into the one or more storage devices and/or the one or more computer processing devices from a remote data storage device via a network interface, rather than via a local computer readable storage medium. Additionally, the computer programs, program code, instructions, or some combination thereof, may be loaded into the one or more storage devices and/or the one or more processors from a remote computing system that is configured to transfer and/or distribute the computer programs, program code, instructions, or some combination thereof, over a network. The remote computing system may transfer and/or distribute the computer programs, program code, instructions, or some combination thereof, via a wired interface, an air interface, and/or any other like medium.

[0087] The one or more hardware devices, the one or more storage devices, and/or the computer programs, program code, instructions, or some combination thereof, may be specially designed and constructed for the purposes of the example embodiments, or they may be known devices that are altered and/or modified for the purposes of example embodiments.

[0088] A hardware device, such as a computer processing device, may run an operating system (OS) and one or more software applications that run on the OS. The computer processing device also may access, store, manipulate, process, and create data in response to execution of the software. For simplicity, one or more example embodiments may be exemplified as a computer processing device or processor; however, one skilled in the art will appreciate that a hardware device may include multiple processing elements or processors and multiple types of processing elements or processors. For example, a hardware device may include multiple processors or a processor and a controller. In addition, other processing configurations are possible, such as parallel processors.

[0089] The computer programs include processor-executable instructions that are stored on at least one non-transitory computer-readable medium (memory). The computer programs may also include or rely on stored data. The computer programs may encompass a basic input/output system (BIOS) that interacts with hardware of the special purpose computer, device drivers that interact with particular devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, etc. As such, the one or more processors may be configured to execute the processor executable instructions.

[0090] The computer programs may include: (i) descriptive text to be parsed, such as HTML (hypertext markup language) or XML (extensible markup language), (ii) assembly code, (iii) object code generated from source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. As examples only, source code may be written using syntax from languages including C, C++, C #, Objective-C, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5, Ada, ASP (active server pages), PHP, Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, and Python®.

[0091] Further, at least one example embodiment relates to the non-transitory computer-readable storage medium including electronically readable control information (processor executable instructions) stored thereon, configured in such that when the storage medium is used in a controller of a device, at least one embodiment of the method may be carried out.

[0092] The computer readable medium or storage medium may be a built-in medium installed inside a computer device main body or a removable medium arranged so that it can be separated from the computer device main body. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave); the term computer-readable medium is therefore considered tangible and non-transitory. Non-limiting examples of the non-transitory computer-readable medium include, but are not limited to, rewriteable non-volatile memory devices (including, for example flash memory devices, erasable programmable read-only memory devices, or a mask read-only memory devices); volatile memory devices (including, for example static random access memory devices or a dynamic random access memory devices); magnetic storage media (including, for example an analog or digital magnetic tape or a hard disk drive); and optical storage media (including, for example a CD, a DVD, or a Blu-ray Disc). Examples of the media with a built-in rewriteable non-volatile memory, include but are not limited to memory cards; and media with a built-in ROM, including but not limited to ROM cassettes; etc. Furthermore, various information regarding stored images, for example, property information, may be stored in any other form, or it may be provided in other ways.

[0093] The term code, as used above, may include software, firmware, and/or microcode, and may refer to programs, routines, functions, classes, data structures, and/or objects. Shared processor hardware encompasses a single microprocessor that executes some or all code from multiple modules. Group processor hardware encompasses a microprocessor that, in combination with additional microprocessors, executes some or all code from one or more modules. References to multiple microprocessors encompass multiple microprocessors on discrete dies, multiple microprocessors on a single die, multiple cores of a single microprocessor, multiple threads of a single microprocessor, or a combination of the above.

[0094] Shared memory hardware encompasses a single memory device that stores some or all code from multiple modules. Group memory hardware encompasses a memory device that, in combination with other memory devices, stores some or all code from one or more modules.

[0095] The term memory hardware is a subset of the term computer-readable medium. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave); the term computer-readable medium is therefore considered tangible and non-transitory. Non-limiting examples of the non-transitory computer-readable medium include, but are not limited to, rewriteable non-volatile memory devices (including, for example flash memory devices, erasable programmable read-only memory devices, or a mask read-only memory devices); volatile memory devices (including, for example static random access memory devices or a dynamic random access memory devices); magnetic storage media (including, for example an analog or digital magnetic tape or a hard disk drive); and optical storage media (including, for example a CD, a DVD, or a Blu-ray Disc). Examples of the media with a built-in rewriteable non-volatile memory, include but are not limited to memory cards; and media with a built-in ROM, including but not limited to ROM cassettes; etc. Furthermore, various information regarding stored images, for example, property information, may be stored in any other form, or it may be provided in other ways.

[0096] The apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general purpose computer to execute one or more particular functions embodied in computer programs. The functional blocks and flowchart elements described above serve as software specifications, which can be translated into the computer programs by the routine work of a skilled technician or programmer.

[0097] Although described with reference to specific examples and drawings, modifications, additions and substitutions of example embodiments may be variously made according to the description by those of ordinary skill in the art. For example, the described techniques may be performed in an order different with that of the methods described, and/or components such as the described system, architecture, devices, circuit, and the like, may be connected or combined to be different from the above-described methods, or results may be appropriately achieved by other components or equivalents.

[0098] Although the present invention has been illustrated and described in detail by way of the preferred exemplary embodiment, the present invention is not restricted by the examples disclosed and other variations can be derived therefrom by a person skilled in the art without departing from the protective scope of the present invention.