METHOD AND APPARATUS FOR PROVIDING A PERFUSION IMAGE DATA SET OF A PATIENT

Abstract

A first image data set and at least one second image data set are captured, and a contrast-enhanced image data set and a non-contrast image data set are determined based on the first and the at least one second image data set. The perfusion image data set is calculated based on a ratio of image values of the contrast-enhanced image data set and locally corresponding image values of the non-contrast image data set, and the perfusion image data set is provided via a second interface.

Claims

1. A method for providing a perfusion image data set of a patient, the method comprising: capturing, via a first interface, a first image data set and at least one second image data set, wherein the first image data set represents a first X-ray attenuation distribution of the patient corresponding to a first X-ray quantum energy distribution, and the at least one second image data set represents at least one second X-ray attenuation distribution of the patient corresponding to at least one second X-ray quantum energy distribution, wherein the first image data set and the at least one second image data set are recorded with contrast medium administration, or the first image data set is recorded with contrast medium administration and represents a third X-ray attenuation distribution of the patient with contrast medium, and the at least one second image data set is recorded without contrast medium administration and represents a fourth X-ray attenuation distribution of the patient without contrast medium; determining, via a computing unit, a contrast-enhanced image data set and a non-contrast image data set based on the first image data set and the at least one second image data set; calculating, via the computing unit, the perfusion image data set based on a ratio of image values of the contrast-enhanced image data set and locally corresponding image values of the non-contrast image data set; and providing the perfusion image data set via a second interface.

2. The method as claimed in claim 1, wherein the capturing comprises: capturing the first image data set representing the first X-ray attenuation distribution of the patient corresponding to the first X-ray quantum energy distribution and the at least one second image data set representing the at least one second X-ray attenuation distribution of the patient corresponding to the at least one second X-ray quantum energy distribution, wherein the first image data set and the at least one second image data set are recorded with contrast medium administration, and the determining includes a base material decomposition based on the first image data set and the at least one second image data set.

3. The method as claimed in claim 2, wherein the base material decomposition is based on at least base materials including the contrast medium used and water or a tissue material, and a non-contrast image corresponds to a water image data set or tissue image data set resulting from the base material decomposition.

4. The method as claimed in claim 2, wherein the calculating comprises: scaling, by a scaling factor, the ratio of the image values of the contrast-enhanced image data set and the non-contrast image data set.

5. The method as claimed in claim 4, wherein the scaling factor is dependent on an X-ray photon energy.

6. The method as claimed in claim 4, wherein the scaling factor is dependent on at least one of the specific attenuation coefficients $\frac{\mu }{\rho }$ for the base materials on which the base material decomposition is based with an energy-dependent absorption coefficient of a respective base material μ for X-rays and a respective density ρ.

7. The method as claimed in claim 6, wherein the scaling factor includes a quotient of the specific attenuation coefficients $\frac{\mu }{\rho }$ for the base materials on which the material decomposition is based.

8. The method as claimed in claim 1, wherein the perfusion image data set maps perfusion of the lung parenchyma.

9. An apparatus for providing a perfusion image data set of a patient, the apparatus comprising: a first interface configured to capture a first image data set and at least one second image data set, wherein the first image data set represents a first X-ray attenuation distribution of the patient corresponding to a first X-ray quantum energy distribution, and the at least one second image data set represents at least one second X-ray attenuation distribution of the patient corresponding to at least one second X-ray quantum energy distribution, wherein the first image data set and the at least one second image data set are recorded with contrast medium administration, or the first image data set is recorded with contrast medium administration and represents a third X-ray attenuation distribution of the patient with contrast medium, and the at least one second image data set is recorded without contrast medium administration and represents a fourth X-ray attenuation distribution of the patient without contrast medium; processing circuitry configured to determine a contrast-enhanced image data set and a non-contrast image data set based on the first image data set and the at least one second image data set, and calculate the perfusion image data set based on a ratio of image values of the contrast-enhanced image data set and locally corresponding image values of the non-contrast image data set; and a second interface configured to provide the perfusion image data set.

10. A medical imaging device comprising: the apparatus as claimed in claim 9; and at least one X-ray source opposite at least one X-ray detector.

11. A non-transitory computer program product including a computer program that, when executed by at least one processor at an apparatus for providing a perfusion image data, causes the apparatus to perform the method as claimed in claim 1.

12. A non-transitory computer-readable storage medium storing program sections that, when executed by at least one processor at an apparatus for providing a perfusion image data set, causes the apparatus to perform the method as claimed in claim 1.

13. The method as claimed in claim 3, wherein the calculating comprises: scaling, by a scaling factor, the ratio of the image values of the contrast-enhanced image data set and the non-contrast image data set.

14. The method as claimed in claim 13, wherein the scaling factor is dependent on an X-ray photon energy.

15. The method as claimed in claim 14, wherein the scaling factor is dependent on at least one of the specific attenuation coefficients $\frac{\mu }{\rho }$ for the base materials on which the base material decomposition is based with an energy-dependent absorption coefficient of a respective base material μ for X-rays and a respective density ρ.

16. The method as claimed in claim 5, wherein the scaling factor is dependent on at least one of the specific attenuation coefficients $\frac{\mu }{\rho }$ for the base materials on which the base material decomposition is based with an energy-dependent absorption coefficient of a respective base material μ for X-rays and a respective density ρ.

17. The method as claimed in claim 2, wherein the perfusion image data set in particular maps perfusion of the lung parenchyma.

18. The method as claimed in claim 3, wherein the perfusion image data set in particular maps perfusion of the lung parenchyma.

19. The method as claimed in claim 4, wherein the perfusion image data set in particular maps perfusion of the lung parenchyma.

20. The method as claimed in claim 6, wherein the perfusion image data set in particular maps perfusion of the lung parenchyma.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The following explains exemplary embodiments with reference to the attached figures. The representation in the figures is schematic, greatly simplified and not necessarily true to scale. The figures show:

[0070] FIG. 1 a schematic view of different scenarios of the perfusion of lung tissue,

[0071] FIG. 2 a schematic depiction of the course of a method for providing a perfusion image data set,

[0072] FIG. 3 a schematic representation of an apparatus for providing a perfusion image data set, and

[0073] FIG. 4 a schematic representation of an exemplary medical imaging device.

DETAILED DESCRIPTION

[0074] FIG. 1 is a schematic view of different scenarios of the perfusion of lung tissue in order to represent the deficits of a conventional evaluation of the perfusion of the lung parenchyma based on the conventional lung PBV value. In the previous conventional lung PBV approach, the contrast map resulting from base material decomposition shows how much “contrast medium per volume” (for example, mg/ml) has accumulated in the tissue and ignores the density, or amount, of the lung parenchyma. Thus, although it is known where more iodine has accumulated and where less, except for cases with large differences, it is not known whether the perfusion of the lung parenchyma is locally lower or higher.

[0075] The first row represents a lung volume LV with vessels a that are normally perfused at the microscopic level. Mapping via the conventional PBV value produces a first lung PBV value PBV1 with a first perfusion level at the macroscopic level.

[0076] The second row represents a lung volume LV with vessels a that are normally perfused at the microscopic level, but, for example, in a state of exhalation, while the first row reflects a state of inhalation so that, with mapping via the conventional lung PBV value, a higher number of normally perfused vessels per volume unit leads to a second lung PVB value PBV2 having a second perfusion level, which is higher than the first, at the macroscopic level, although there is normal perfusion of the vascular structure. Therefore, the previous determination of lung perfusion based solely on contrast medium concentration per volume does not always provide information as to whether a higher concentration of contrast medium is attributable to more parenchyma (higher density) or to lower perfusion. Quantification is also problematic.

[0077] The third row shows a lung volume LV for example, in a state of inhalation, which, due to inflammation, for example as the result of a viral infection, has increased perfused vascular structures b. Here, mapping via the conventional lung PBV value could lead to the same, or at least similar, lung PBV2 value as with normally perfused tissue in a state of exhalation (second row), i.e., two different scenarios with different perfusion levels at the microscopic level can lead to the same perfusion level at the macroscopic level based on the conventional lung PBV value. No differentiation between the states is possible on this basis.

[0078] In addition, as represented in the fourth row, there may also be depositions c, for example of fluids, which can additionally change the composition of the lung tissue. In this case, once again, mapping via the conventional lung PBV value could lead to the same, or at least similar, PBV2 value as for normally perfused tissue in a state of exhalation (second row). Here, once again, differentiation would not be possible.

[0079] In such cases, the previously used division of the results of an examination of the perfusion of the lung parenchyma into a purely functional part (contrast-enhanced image) has its limitations. Embodiments of the present invention described here with the linkage of both types of information potentially enables the compensation of such influences and the provision of distinguishable or potentially quantifiable results.

[0080] FIG. 2 is a schematic depiction of the course of the method according to one or more example embodiments of the present invention for providing a perfusion image data set.

[0081] In a first alternative of the schematic course of the method, the step S1 comprises capturing a first image data set of the patient 39, wherein the first image data set is recorded with contrast medium administration and represents a first X-ray attenuation distribution (also referred to as a third X-ray attenuation distribution) of the patient 39 with contrast medium, and a second image data set of the patient 39, wherein the second image data set is recorded without contrast medium administration and represents a first X-ray attenuation distribution (also referred to as a fourth X-ray attenuation distribution) of the patient 39 without contrast medium.

[0082] In a second alternative of the course of the method, the step S1 comprises capturing a first image data set representing a first X-ray attenuation distribution of the patient 39 corresponding to a first X-ray quantum energy distribution and at least one second image data set representing at least one second X-ray attenuation distribution of the patient 39 corresponding to at least one second X-ray quantum energy distribution via a first interface IF1, wherein the first and second image data set are recorded with contrast medium administration.

[0083] The step S2 comprises determining a contrast-enhanced image data set and a non-contrast image data set via a computing unit CU.

[0084] In the first alternative, the contrast data set can be determined by subtracting the second image data set from the first image data set, optionally after registering the image data sets to one another, in the image area. The result of the subtraction then corresponds to the contrast data set. The non-contrast image data set can be determined directly based on the first image data set without contrast medium administration, i.e., it can in particular correspond directly thereto.

[0085] In the second alternative, the determination of a contrast-enhanced image data set and a non-contrast image data set via a computing unit CU can in particular comprise base material decomposition based on the first and the at least one second image data set. In one embodiment, the base material decomposition is based on at least the base materials comprising the contrast medium used and water or the contrast medium used and a tissue material, in particular a soft tissue material. In other embodiments, it is also possible to differentiate between more than two base materials, for example the contrast medium, water and fat. The contrast data set can substantially result directly from the base material decomposition and maps the local contrast medium concentration (amount of contrast medium per volume) in the patient. In simple embodiments, the non-contrast image can correspond directly to the base material image set resulting from the base material decomposition, which, in the case of base material decomposition into contrast medium and a second material, is assigned to the second material. For example, the non-contrast image can correspond to a water image data set. This is in particular advantageous if the region of interest relating to the material composition can be sufficiently well described in terms of its morphology based on the second material. In other variants, the non-contrast image data set can also be provided otherwise, so that different materials in the relevant image region are taken into account. In particular, a generally known approach for calculating virtual non-contrast images (so-called VNC images) can be used here. However, if the perfusion image data set is, for example, used to map the perfusion of the lung parenchyma in the context of an advantageously simple assumption and implementation, the water image data set resulting from base material decomposition into contrast medium and water can, for example, be selected as the non-contrast image data set in order to obtain a suitable perfusion image data set.

[0086] The step S3 comprises calculating S3 the perfusion image data set based on a ratio of image values of the contrast-enhanced image data set and locally corresponding image values of the non-contrast image data set via the computing unit CU.

[0087] The step S4 comprises providing S4 the perfusion image data set via a second interface IF2.

[0088] In a particularly simple and time-efficient embodiment, the final perfusion image data set corresponds directly to the quotient of the contrast-enhanced image data set and the non-contrast image data set, or the respective locally corresponding image values.

[0089] However, in particular in connection with the above-described second alternative, the calculation S3 of the perfusion image data set can moreover also comprise scaling the ratio of the image values of the contrast-enhanced image data set and the non-contrast image data set by a scaling factor. The scaling factor can in particular also be dependent on an X-ray photon energy L. The scaling factor can, for example, be dependent on at least one of the specific attenuation coefficients

[00003]$\frac{\mu }{\rho }$

for the base materials on which the base material decomposition is based with the respective absorption coefficient of the base material μ for X-rays at an energy L at a defined X-ray photon energy and the respective density ρ. In particular, the scaling factor can comprise a quotient of the specific attenuation coefficients

[00004]$\frac{\mu }{\rho }$

of the at least two base materials at a specific X-ray photon energy and serve to generate a virtual monoenergetic perfusion image data set for this X-ray photon energy. Possible implementations of a scaling factor are explained in more detail below.

[0090] For example, according to one variant, a perfusion image data set PD for a defined X-ray photon energy L can be calculated as follows:

[00005]$PD\left(L\right)=\frac{b_I}{b_w}.Math.\mathrm{hu}m\left(L\right)$

[0091] Here, b.sub.w [in HU] and b.sub.l [in g/cm3] correspond by way of example to the base material image of water or tissue b.sub.w and the base material image of the contrast medium b.sub.l, for example iodine, from base material decomposition into these two materials. If the base material decomposition is based on other base materials, corresponding base material images should be used. In particular in the area of the lung, which, as already described, is a particularly advantageous application of the method according to one or more example embodiments of the present invention, the choice of water as the base material can be a suitable approximation and enable simple provision, since the relevant region of the lung, in particular the density of the lung tissue, is thus described sufficiently well and few other materials, such as fat or also calcifications, play a role.

[0092] hum(L) corresponds to a scaling factor dependent on an X-ray photon energy L and based on the specific attenuation coefficients of the base materials

[00006]$\frac{\mu }{\rho }$

at an X-ray photon energy L for the base materials on which the base material decomposition is based, for example water and the contrast medium, with the respective energy-dependent absorption coefficient of the base material μ for X-rays and the respective density ρ. Here, the scaling factor in particular comprises a quotient of the specific attenuation coefficients of the two base materials at the energy L:

[00007]$\mathrm{hum}\left(L\right)\propto \frac{{\left(\frac{\mu }{\rho }\right)}_I\left(L\right)}{{\left(\frac{\mu }{\rho }\right)}_W\left(L\right)}$

[0093] Specifically, the scaling factor can, for example, be defined as follows:

[00008]$\mathrm{hum}\left(L\right)=\frac{{\left(\frac{\mu }{\rho }\right)}_I\left(L\right)}{{\left(\frac{\mu }{\rho }\right)}_W\left(L\right)}.Math.1000\mathrm{HU}$

[0094] The scaling coefficient at different energies L can, for example, be held in a retrievable tabular form or provided based on a functional relationship. For example, the functional relationship can be based on a fit to known reference data for the respective base materials (for example, using the NIST X-ray attenuation database, https://physics.nist.gov/). The application of the scaling factor at a selected X-ray photon energy L then enables the perfusion image data set for this X-ray photon energy to be calculated.

[0095] To a certain extent, the energy-dependent scaling factor enables a representation of the perfusion image data set in the sense of a virtual monoenergetic perfusion image data set at a defined energy level L in a similar style as the known representation of virtual monoenergetic images VMI(L) at an energy level L. In the case of virtual monoenergetic images VMI, the definition at an energy level, and the thus associated fixed attenuation behaviors of all materials, standardizes the representation in order to eliminate differences in CT-acquisition parameters. This is in particular interesting in the context of the possible standardization of all recordings by new technologies (for example, photon-counting detectors). For the generation of virtual monoenergetic images, reference can be made, for example, to DE 10 2015 204 450 A1.

[0096] A virtual monoenergetic image can be calculated in a simplified manner from a linear combination of the base material images determined. For example, a virtual monoenergetic image can be assembled in a simplified manner based on two-material decomposition into the two materials water or tissue and contrast medium, for example iodine as follows:

VMI(L)=b.sub.w+b.sub.l.Math.hum(L)

[0097] wherein, according to the above calculation of the perfusion image data set, b.sub.w [in HU] and b.sub.l [in g/cm3] correspond by way of example to the base material images of water or tissue and contrast medium from base material decomposition. The function hum(L) enables CT values per concentration at the defined energy L to be calculated. If the base material decomposition is based on other base materials, corresponding base material images should be used. If, in the context of an X-ray imaging application with contrast medium administration based on the first and second image data set, virtual monoenergetic image data sets are calculated anyway, the intermediate results of the monochromatization, b.sub.w and b.sub.l, can be used for the calculation of the perfusion data set.

[0098] As with the provision of virtual monoenergetic image data sets, an X-ray photon energy to be used for the calculation can be defined for the calculation of the perfusion image data set, or it is also possible for different energies L to be repeatedly selected for which a perfusion image data set is calculated. In particular, a method can also comprise a step of selecting an X-ray photon energy. Here, a user can then, for example, select, for example via an input unit such as a keyboard, an X-ray photon energy for which the perfusion image data set is to be calculated. The selection could also be defined automatically based on other parameters, for example patient-specific parameters, such as a patient's weight or size.

[0099] In addition to the aforementioned scaling using hum(L), other types of scaling may also be desirable in the context of the method. For example, scaling with a HU-free background can be useful:

[00009]$PD\left(L\right)=\frac{b_I.Math.1000\mathrm{HU}}{b_w.Math.{\left(\frac{\mu }{\rho }\right)}_W\left(L\right)}$

wherein b.sub.w [in HU] and b.sub.l [in g/cm3] correspond to the base material images of water and contrast medium.

[0100] Furthermore, subsequent calculations of a perfusion image data set, according to one or more example embodiments of the present invention, on already created virtual monoenergetic images with two different energy levels (L.sub.1 and L.sub.2) are also feasible. This is possible because each VMI basically only represents a linear combination of the contrast medium, mapped by b.sub.l, and water or the tissue, mapped by b.sub.w, and the base materials can be extracted again by the explicit knowledge of the linear factors.

[00010]$PD\left(L\right)=\frac{b_I}{b_w}.Math.\mathrm{hu}m\left(L\right)=\frac{\left(\mathrm{VMI}\left(L_1\right)-\mathrm{VMI}\left(L_2\right)\right).Math.\mathrm{hu}m\left(L\right)}{\left(\mathrm{VMI}\left(L_2\right).Math.\mathrm{hu}m\left(L_1\right)-\mathrm{VMI}\left(L_1\right).Math.\mathrm{hu}m\left(L_2\right)\right)}$

[0101] with the virtual monoenergetic image data set at an energy L.sub.1 or L.sub.2 (in keV), the scaling factor hum(L) at a selectable energy L for the perfusion image data set PD(L) or the respective energies L.sub.1 or L.sub.2.

[0102] In the above formulas, there would be no explicit mention of the use of pseudo densities [in HU] instead of the actual HU values in the image data sets, which corresponds to the addition of 1000 HU, in order to avoid divisions by zero, in particular when water is used as the base material and is calculated in HU units.

[0103] FIG. 3 is a schematic representation of an apparatus 20 for providing a perfusion image data set of a patient 39.

[0104] The apparatus 45 comprises a first interface IF1 embodied to capture a first image data set of the patient 39 representing a first X-ray attenuation distribution of the patient 39 corresponding to a first X-ray quantum energy distribution and at least one second image data set of the patient 39 representing at least one second X-ray attenuation distribution of the patient 39 corresponding to at least one second X-ray quantum energy distribution. Alternatively, the first interface IF1 can be embodied to capture a first image data set of the patient 39, wherein the first image data set is recorded with contrast medium administration and represents a first X-ray attenuation distribution (also referred to as a third X-ray attenuation distribution) of the patient 39 with contrast medium, and a second image data set of the patient 39, wherein the second image data set is recorded without contrast medium administration and represents a first X-ray attenuation distribution (also referred to as a fourth X-ray attenuation distribution) of the patient 39 without contrast medium.

[0105] Furthermore, the apparatus 45 comprises a computing unit CU embodied to determine a contrast-enhanced image data set and a non-contrast image data set based on the first and the at least one second image data set, and to calculate the perfusion image data set based on a ratio of image values of the contrast-enhanced image data set and locally corresponding image values of the non-contrast image data set.

[0106] Furthermore, the apparatus 45 comprises a second interface IF2 embodied to output the perfusion image data set.

[0107] Furthermore, the apparatus 45 comprises a memory unit MU. The memory unit MU can be used to hold captured image data sets or a calculated perfusion image data set in a retrievable manner. A memory unit MU can be implemented as a non-permanent random-access memory (RAM) or as a permanent mass storage device (hard disk, USB stick, SD card, solid state disk).

[0108] Such an apparatus 45 for providing a perfusion image data set can in particular be embodied to execute the above-described method, according to one or more example embodiments of the present invention, for providing a perfusion image data set and the aspects thereof. The apparatus can be embodied to execute the method and the aspects thereof in that the interfaces If1, If2 and the computing unit CU are embodied to execute the corresponding method steps.

[0109] In the exemplary embodiment shown, the apparatus 45 is connected to a medical imaging device 32. The apparatus 45 can, for example, be connected to the imaging device 32 via a network. In particular, the apparatus can also be comprised by the imaging device 32. The imaging device 32 can, for example, be a computed tomography device.

[0110] The network can be a local area network (LAN) or a wide-area network (WAN). An example of a local area network is an intranet, an example of a wide-area network is the internet. The network can in particular also be embodied as wireless, in particular as a WLAN (wireless LAN, commonly known as WiFi) or as a Bluetooth connection. The network can also be embodied as a combination of the above examples.

[0111] Furthermore, communication between the apparatus 45 and an imaging device 32 can also take place offline, for example by an exchange of data carriers.

[0112] FIG. 3 shows a medical imaging device 32 in the form of a computed tomography device.

[0113] The CT device has a gantry 33 with a rotor 35. The rotor 35 comprises at least one X-ray source 37, in particular an X-ray tube, and opposite thereto at least one X-ray detector 36. The X-ray detector 36 and the radiation source 37 can be rotated about a common axis 43 (also called the axis of rotation). The patient 39 is supported on a patient support 41 and can be moved through the gantry 33 along the axis of rotation 43. In general, the patient 39 can, for example, be a veterinary patient and/or a human patient.

[0114] The CT device 32 comprises a computer system 20 comprising an apparatus 45 for providing a perfusion image data set. The computer system 20 can also comprise a reconstruction unit 42 for reconstructing image data sets based on the data determined by the imaging device 32. The computer system 20 can also have a control unit 48 for actuating the imaging device.

[0115] Furthermore, an input facility 47 and an output facility 49 are connected to the computer system 20. The input facility 47 and the output facility 49 can, for example, enable interaction, for example manual configuration, confirmation or triggering of a method step by a user. For example, computed tomography projection data sets and/or a two-dimensional image data set or a three-dimensional image data set can be displayed to the user on the output apparatus 49 comprising a monitor.

[0116] Usually, measurement data in the tabular form of a plurality of (raw) projection data sets of the patient 39 are recorded from a plurality of projection angles during a relative rotational movement between the radiation source and the patient, while the patient 39 is moved continuously or sequentially through the gantry 33 via the patient support 41. Subsequently, a slice image data set can be reconstructed for a respective z-position along the axis of rotation within an examination area based on the projection data sets via a mathematical method, for example comprising filtered back projection or an iterative reconstruction method.

[0117] The imaging device 32 is in particular embodied to generate at least the first and the second image data set according to one or more example embodiments of the present invention. The apparatus for providing a perfusion image data set comprised by the computer system 45 is then in particular embodied, based on these image data sets, to perform a method according to one or more example embodiments of the present invention for providing a perfusion image data set.

[0118] The X-ray detector 36 can correspond to a spectrally separating X-ray detector, for example a quantum-counting detector or a two-layer detector. The imaging device 32 can also be embodied for so-called “kV switching”, wherein the X-ray source emits different emission spectra in rapid succession in the direction of an X-ray detector 36. The X-ray device can also be embodied as a so-called “twin-beam” device.

[0119] In other embodiments, the imaging device can also comprise two X-ray source-detector systems operating at different emission spectra. In this case, the imaging device comprises two X-ray sources and two X-ray detectors, wherein each detector is configured to record the X-rays emitted by one of the X-ray sources. This is also referred to as a dual-source X-ray imaging apparatus. At least one of the two X-ray sources can also comprise a filter for improving the spectral separation of the emitted X-rays, in particular a tin filter.

[0120] 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”.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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®.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Although the present invention has been shown and described with respect to certain example embodiments, equivalents and modifications will occur to others skilled in the art upon the reading and understanding of the specification. The present invention includes all such equivalents and modifications and is limited only by the scope of the appended claims.