X-ray image data is generated based on weights derived from position-dependent changes in the underlying material.

Abstract

This application relates to an X-ray imaging method, preferably a CT-X-ray imaging method, for generating image data of an examination area of ​​an object to be examined. In this method, first X-ray projection measurement data is acquired from the examination area using a first X-ray energy spectrum, and at least second X-ray projection measurement data is acquired using a second X-ray energy spectrum, the second X-ray energy spectrum being different from the first X-ray energy spectrum. Furthermore, prior image data is reconstructed based at least on the first X-ray projection measurement data, and the location-related distribution of X-ray attenuation values ​​in the examination area is determined. Basic material decomposition is performed based on the first X-ray projection measurement data and at least the second X-ray projection measurement data. The location-related weights of the basic materials are determined according to the location-related distribution of the X-ray attenuation values. Finally, a complete image of the examination area is obtained by reconstructing location-related, virtual, and basic material-weighted image data.

Description

Technical Field

[0001] This invention relates to an X-ray imaging method for generating image data of an area to be examined in an object. The invention also relates to an image data generation apparatus. Furthermore, this invention relates to a computed tomography (CT) system. Background Technology

[0002] Modern imaging methods often generate two-dimensional or three-dimensional image data, which can be used to visualize the object being inspected and can also be used for other applications.

[0003] In many cases, imaging methods are based on the acquisition of X-ray radiation, which produces so-called projective measurement data. For example, projective measurement data can be acquired using a computed tomography (CT) system. In a CT system, a combination of an X-ray source mounted on a gantry and opposing X-ray detectors typically rotates around a measurement chamber in which the subject (hereinafter generally referred to as the patient without limitation) is located. The center of rotation (also called the "isocenter") coincides here with the so-called system axis z. During one or more rotations, the patient is fluoroscopically examined with X-rays from the X-ray source, during which projective measurement data, or X-ray projective measurement data, is acquired by means of the opposing X-ray detectors.

[0004] The resulting projection measurement data is particularly relevant to the configuration of the X-ray detector. X-ray detectors typically have multiple detection units, usually arranged in a regular pixel array. The acquisition units generate detection signals for the X-ray radiation incident on the detection units, and these signals are analyzed at specific time points regarding the intensity and spectral distribution of the X-ray radiation to obtain conclusions about the object being examined and generate projection measurement data.

[0005] In CT scans, tube voltage is typically matched to patient parameters, such as height and body size, and to the type of examination planned. For example, natural imaging can be performed without contrast agents, or examinations of solid organs such as the liver can be performed with the aid of contrast agents or CT angiography.

[0006] In CT imaging with spectral-resolution CT data, virtual monoenergetic image data is calculated using multimaterial decomposition, also known as basic material decomposition. The CT data is generated, for example, using a photon-counting detector, a dual-source CT system, or a CT system with two X-rays. Here, for example, a monoenergetic CT image reconstructed at an average X-ray energy of 65 keV roughly corresponds to a 120 kV image reconstructed from a classical image. Such an average X-ray energy value is also simply referred to as the keV value below. The energy range used to calculate this virtual monoenergetic image data is approximately between 45 keV and 190 keV. The energy selection used to calculate the virtual monoenergetic image data can be used to calculate image sequences with different material contrasts, thereby providing an improved basis for subsequent diagnosis.

[0007] The problem with creating such image sequences is that there is no globally universally valid keV value in an image that guarantees optimal image quality across all regions of that image. For example, contrast formation is regionally dependent on the corresponding organ. For instance, a suitable keV value for the liver is unfavorable for imaging arteries connected to the liver because it produces excessively high contrast there. For example, a constant, so-called center / width window value for the entire image causes vessels to "stop" and only emit white light. This means that the X-ray attenuation value (also known as the HU value) of the vessels far exceeds the maximum value of the X-ray attenuation value for the defined window.

[0008] Currently, numerous additional monoenergetic sequences are generated, and physicians jump between sequences while diagnosing images, depending on the region being diagnosed in the image. Alternatively, it is possible to reconstruct CT system images in a way that allows interactive calculation of monoenergetic images at the workstation. In this way, during diagnosis, the image display is adapted by manipulating a slider called a "slider," which changes the keV value and triggers a recalculation of the image. Thus, physicians can readjust the keV value as needed. However, frequent image recalculation requires high computational resources, placing high demands on the computer units of the CT system. Therefore, when switching keV values, physicians typically have to wait longer until the corresponding image is recalculated and displayed. Summary of the Invention

[0009] Therefore, there is a purpose to propose an X-ray imaging method and a corresponding image data generation device that can achieve simplified image display of extended body regions while maintaining good image quality.

[0010] This objective is achieved by the X-ray imaging method, image data generating apparatus, and computed tomography system according to the present invention for generating image data of the examination area of ​​the object to be examined.

[0011] In the X-ray imaging method, preferably CT-X-ray imaging method, for generating image data of the examination area of ​​the object to be examined according to the present invention, first X-ray projection measurement data is first acquired from the examination area of ​​the object using a first X-ray energy spectrum and at least second X-ray projection measurement data is acquired using a second X-ray energy spectrum, wherein the second X-ray energy spectrum is different from the first X-ray energy spectrum.

[0012] For example, X-ray projection measurement data can be received from the data storage of a computed tomography (CT) system or the data network of a clinic system. However, X-ray projection measurement data can also be acquired directly within the scope of CT measurement methods and further processed using the method according to the invention. In this case, for example, a so-called multi-energy measurement method, preferably a dual-energy measurement method, can be used, in which X-rays with different X-ray energy spectra are emitted toward the area to be examined, the X-rays are partially absorbed by the area to be examined, and the transmitted portion of the X-rays is subsequently detected by different X-ray detectors.

[0013] X-ray detectors can, but are not necessarily, spectrally resolved. In the case of dual sources—that is, methods using two separate X-ray sources with different X-ray energies, or kV switching devices that switch the voltage of the X-ray sources between different values—conventional X-ray detectors are also used. Here, recording is performed using different X-ray energy spectra. Alternatively, spectral data can be recorded using a spectrally resolved X-ray detector. In this case, it is sufficient to record using only one X-ray with a unique X-ray energy spectrum. Unlike the aforementioned methods that emit X-rays with different X-ray energy spectra, energy separation is achieved at the X-ray detector in this case. However, this method can often be combined with a spectral X-ray detector.

[0014] Furthermore, prior image data is reconstructed at least based on the first X-ray projection measurement data. Alternatively, a hybrid image can be generated from the first and second X-ray projection measurement datasets, corresponding to either a classic CT image or the original image. In such a hybrid image, image data from different X-ray spectra are typically mixed in an approximately equally weighted manner. This hybrid image typically corresponds to a 120 kV image, which is contrast-optimized for water, particularly regarding the contrast-to-noise ratio. Therefore, prior image data is generated before the actual multi-energy image reconstruction to prepare for subsequent image reconstruction.

[0015] Then, the location-related distribution of X-ray attenuation values ​​in the examination area is obtained based on the reconstructed prior image data. For this purpose, local X-ray attenuation values ​​can be obtained for each partial region of the examination area, for example. Preferably, each partial region with a separately constrained attenuation value bandwidth is identified and segmented. In extreme cases, each partial region may also consist of only one image point or one image pixel. The region bandwidth associated with the attenuation values ​​for each image point is then used as a reference value so that, after base material decomposition based on the first X-ray projection measurement data and at least the second X-ray projection measurement data, the location-related or region-specific weights of the base material are determined according to the obtained location-related distribution of the X-ray attenuation values, which is necessary for reconstructing image data based on base material decomposition. Finally, an overall image of the examination area is generated by reconstructing location-related, for example, region-weighted, virtual base material-weighted image data and their combination in the proposed overall image. For this purpose, a partial image with location-specific partial image data can first be generated by reconstructing location-related, differently-weighted, virtual base material-weighted image data, and finally, the overall image can be generated by combining the partial image data.

[0016] Methods for reconstructing or computing virtual, material-weighted image data, especially pseudo-monoenergy image data, are known from Alvarez RE and Macovski A. “Energy-selective reconstructions in x-ray computed tomography,” Phys. Med. Biol. 21, 733-744 (1976).

[0017] The assessment of an advanced image-based technique to calculate virtual monoenergetic computed toographic images from a dual-energy examination to improve the contrast-to-noise ratio in examinations using iodinated contrast media by KLGrant et al., Investigative Radiology 2014; 00:00-00, describes a particularly suitable method for reconstructing or calculating such reconstructed image data, especially pseudo-monoenergetic image data.

[0018] In the reconstruction and combination of image data reconstructed in different locations, filtering and smoothing methods can be used to coordinate transition areas between different regions or reduce the impact of noise.

[0019] In cases where the underlying material is weighted, for example, by using so-called keV levels based on location or region, this locally optimized image generation with different weights achieves improved visualization of strongly different material contrasts in a single image. Advantageously, physicians can perform a complete diagnosis based on a single series of images without having to jump back and forth between different reconstructions. Furthermore, the number of images required for examination is reduced, saving resources in terms of computation time for image reconstruction, storage space required for image data, and management of image data. keV values ​​can also vary continuously. In this way, artifacts caused by discontinuities in keV value variation can be avoided.

[0020] The image data generating apparatus according to the present invention has a control unit for manipulating one or more X-ray sources of a CT system to generate X-rays having a first X-ray energy spectrum and a second X-ray energy spectrum different from the first X-ray energy spectrum. Preferably, different X-ray energy spectra are generated using a first or a second average energy, respectively. The two energy values ​​can be preset standard values.

[0021] The image data generating apparatus according to the present invention further includes a projection measurement data acquisition unit, used to acquire first X-ray projection measurement data from the inspection area of ​​the object being inspected using a first X-ray energy spectrum and to acquire at least second X-ray projection measurement data using a second X-ray energy spectrum.

[0022] The prior image reconstruction unit is also part of the image data generation apparatus according to the present invention, which is used to reconstruct prior image data based at least on first X-ray projection measurement data.

[0023] Furthermore, the image data generation apparatus according to the present invention includes a value determination unit for determining the location-related distribution of X-ray attenuation values ​​in the inspection area.

[0024] The image data generation apparatus according to the invention further includes a weighting unit for determining the position-related weights of the base material based on the position-related distribution of X-ray attenuation values. For this purpose, preferably, an appropriate third X-ray energy spectrum is determined based on the X-ray attenuation of the corresponding region. This third X-ray energy spectrum has a region-specific, appropriate third average energy, preferably a single third energy value, also referred to as a keV value, wherein the third X-ray energy spectrum or average energy can be used for weighting in the reconstructed base material-weighted image data.

[0025] Spectrally variable weights can be used instead of constant weights in material-based reconstructions. The weights can be obtained as any functional relation w = f(E(keV), x, y), where x and y are position coordinates, and E is the spectral energy in keV. Instead of multiplying the weights by the corresponding X-ray attenuation of the material fraction, a convolution of the spectrally variable weights with spectrally resolved X-ray attenuation can also be performed. This requires a detector with high-resolution X-ray counting. This allows for improved reconstruction accuracy with fewer artifacts.

[0026] The image generation unit, also part of the image data generation apparatus according to the invention, is used to generate a holistic image of the examination area by reconstructing location-dependent, virtual, base material-weighted image data with varying weights. Preferably, region-by-region reconstruction of preferred pseudo-monoenergy image data is performed based on acquired first and at least second X-ray projection measurement data, which are associated with a corresponding third X-ray energy spectrum having a corresponding third average energy (preferably a unique third energy value). The image generation unit combines the reconstructed image data of the regions to form a holistic image. For this purpose, the function of the image generation unit can also be divided into a partial image generation unit and a holistic image generation unit. The partial image generation unit first generates a partial image with location-specific partial image data by reconstructing location-dependent, virtual, base material-weighted image data with varying weights, and the holistic image generation unit ultimately generates a holistic image by combining the partial image data. The image data generation apparatus according to the invention shares the advantages of the X-ray imaging method according to the invention.

[0027] The computed tomography system according to the present invention includes an image data generation apparatus according to the present invention. The image data generation apparatus according to the present invention can, in particular, be part of the control apparatus of the computed tomography system. The computed tomography system according to the present invention shares the advantages of the image data generation apparatus according to the present invention.

[0028] Most of the main components of the image data generation apparatus according to the present invention can be configured as software components. This particularly relates to the prior image reconstruction unit, value extraction unit, decomposition unit, weighting unit, and image generation unit. However, in principle, especially when rapid computation is involved, these components can also be partially implemented in software-supported hardware, such as in the form of an FPGA. Similarly, when only receiving data from other software components is involved, the required interface can be designed as a software interface, for example. However, the interface can also be designed as a hardware-based interface that is manipulated by appropriate software.

[0029] The software implementation scheme has significant advantages, particularly in that the existing computer units or control devices of the computed tomography (CT) system can be easily upgraded via software to operate according to the invention. Furthermore, this objective is achieved through a corresponding computer program product that can be directly loaded into the storage device or control device of the CT system's computer unit and includes program segments to execute all steps of the method according to the invention when the computer program is executed in the CT system's computer unit or control device.

[0030] In addition to computer programs, such computer program products may include additional components, such as documentation and / or additional parts, as well as hardware components, such as hardware keys (dongles, etc.) for using the software.

[0031] Computer-readable media, such as memory sticks, hard disks, or other transportable or fixedly mounted data carriers, can be used to transport to and / or store at the computer unit of a computed tomography (CT) system, wherein program segments of a computer program that can be read and executed by the computer unit are stored on the data carrier. The computer unit may, for example, have one or more microprocessors that work together.

[0032] The following description contains particularly advantageous designs and modifications of the invention. Specifically, a claim of one claim class can be modified by analogy to a dependent claim of another claim class. Furthermore, within the scope of the invention, various features of different embodiments and claims can be combined to form new embodiments.

[0033] Preferably, determining the position-related weights involves automatically determining an appropriate third X-ray spectrum with a suitable third average energy (preferably a unique third energy value) based on the determined position-related distribution of the X-ray attenuation values. This third average energy value, or the keV value associated with it, is then used as the basis for reconstructing, preferably pseudo-monoenergy, image data of the region in each segment or region. Image data is reconstructed based on the acquired first and at least second X-ray projection measurement data, using the corresponding third X-ray spectrum with the corresponding third average energy (preferably a unique third energy value). The third energy value or third X-ray spectrum can now be different for each segment or region and advantageously matched to the contrast performance or HU value in the corresponding region to improve the local image quality in the various parts or segments of the examined region. Advantageously, for selecting the third X-ray spectrum, prior known information about the correlation between the X-ray attenuation values ​​and the keV value suitable for optimal image quality, or the X-ray spectrum suitable therefor, is used. Details of this are explained further below.

[0034] The third X-ray spectrum preferably has a unique X-ray energy. That is, preferably, a single-energy X-ray spectrum exists as the third X-ray spectrum, where the third "average" energy then represents the individual energy of the single-energy X-ray spectrum. If the third X-ray spectrum having a third average energy is mentioned below, it should also particularly preferably include the specific embodiment in which a pseudo-single-energy X-ray image is generated based on the individual energy. Thus, the "average" energy corresponds to that individual energy value or keV value.

[0035] When generating virtual monoenergetic image data, also known as pseudo-monoenergetic image data, the acquired projection measurement data is decomposed in the original data space, or the image data reconstructed from it is decomposed in the image data space. For example, in the case of using an iodine contrast agent, it is decomposed into iodine / calcium components and water / soft tissue components, and then the attenuation value (HU value) is calculated for the corresponding voxel using a tabulated value of X-ray energy (keV) selected by the user. Preferably, the acquired first and / or second projection measurement data are projection measurement data generated in the presence of a contrast agent. For example, contrast agents can make blood vessels or blood-soaked tissue components particularly well visible.

[0036] In one embodiment of the X-ray imaging method according to the invention, a position-dependent third energy value, or keV value, is obtained by applying a lookup table to the position-dependent distribution of X-ray attenuation values ​​derived from prior image data. Advantageously, the correlation between the keV value and the X-ray attenuation value distribution can be determined experimentally or model-based and stored in a table that can be accessed at any time without computational or experimental repetition costs.

[0037] Alternatively, the location-dependent third energy value can be obtained by using an objective function that maps the X-ray attenuation value to an appropriate keV value. This objective function can, for example, be determined empirically. Advantageously, the correlation between the obtained X-ray attenuation value and the keV value can be determined theoretically or based on a model without experimental procedures.

[0038] In particular, the selection of a corresponding third energy value or keV value results in an improved contrast-to-noise ratio compared to an image display with a first or second energy value.

[0039] Preferably, the organ regions being imaged are segmented and classified in the prior image data. Then, specific base material weights are determined based on the distribution of X-ray attenuation values ​​at different locations within different segments. Here, for example, a region-specific, appropriate third average energy can be obtained based on the classification of each segment by organ type. However, for the overall image, segment-specific, virtual base material-weighted image data is reconstructed separately. Advantageously, prior known information about the optimal display of different organs can be used when selecting the third average energy. For example, it is known that the liver can be displayed with particularly high detail and contrast at an average energy value of 45 keV, the kidney at 55 keV, the artery at 70 keV, and the vein at 65 keV.

[0040] Besides attenuation values, spectral information can also be used in the reconstruction of virtual, material-weighted image data. Specifically, in addition to prior image data, spectral information can be used to determine a region-specific, appropriate third mean energy. For example, this information can be provided as a so-called dual-energy ratio. Spectral information can be advantageously used to identify specific exogenous materials in the examined area, such as materials from prostheses, and to determine a region-specific, suitable third mean energy based on the identified material to reduce image artifacts, particularly those caused by metallic components in the patient's body. For example, it can be used to identify vascular stents in coronary angiography. Typically, metals either produce artifacts or negatively impact the image impression, making high keV values ​​undesirable in image areas covered by metal.

[0041] In the reconstruction of image data with segment-specific weighting and virtual base material weighting, the third energy value preferably changes continuously according to location in at least one segment, and a continuous transition is generated in the boundary region between at least two segments by making the third energy values ​​or keV values ​​of the two segments approximate each other in the boundary region.

[0042] This processing method is similar to a smoothing approach applied to the transitions between regions or segments. Therefore, compensation is made to the division of the inspection area into segments, so that jumps in average energy values, and consequently, jumps in image contrast, are avoided, especially in the transition regions between segments. Advantageously, this type of modification further improves image quality.

[0043] When reconstructing location-dependent, virtual, underlying material-weighted image data, at least one of the following material decomposition methods can be advantageously used:

[0044] -Based on the material decomposition of iodine and water

[0045] - Material decomposition of virtual, non-contrast images

[0046] - Material decomposition of virtual non-calcium images

[0047] - Combine materials that are decomposed differently in different areas of the inspection area.

[0048] The material breakdown of iodine and water clearly shows the blood vessels loaded with contrast agent.

[0049] The material decomposition of the virtual non-contrast image involves material decomposition based on iodine and water, where the virtual non-contrast image is then reconstructed based on the base materials through appropriate weighting. The non-contrast image here corresponds to the water content of the base materials.

[0050] Material decomposition of the non-calcium image first involves decomposition based on iodine and calcium. Then, as described above, the calcium image is subtracted from the standard mixed image, resulting in an image without a calcium component. Advantageously, structures obscured or shimmering by very dense, strongly absorbed calcium are better visible.

[0051] Combining different material decompositions for different regions of the inspection area allows for image reconstruction that is particularly effective in matching the material distribution of the region.

[0052] In a particular variant of the X-ray imaging method according to the invention, for recording without a contrast agent, the highest possible energy value is selected as the third average energy value in the low-density region. Advantageously, in this processing method, hardening artifacts and metal artifacts can be reduced. Since no contrast agent is used in this case, selecting a lower average energy does not produce a better contrast-to-noise ratio. Therefore, in this case, it is advantageous to use a higher third average energy value or keV value to generate pseudo-monoenergy image data, wherein the aforementioned artifacts are reduced, thereby achieving improved image quality. Attached Figure Description

[0053] The present invention will now be explained in more detail with reference to the accompanying drawings and embodiments. The drawings show:

[0054] Figure 1 A flowchart illustrating an X-ray imaging method according to an embodiment of the present invention is shown.

[0055] Figure 2 A block diagram of an image data generation apparatus according to an embodiment of the present invention is shown.

[0056] Figure 3 A schematic diagram of a computed tomography system according to an embodiment of the present invention is shown.

[0057] Figure 4 A priori X-ray image of the patient's torso is shown.

[0058] Figure 5 Showing based on Figure 4 The target keV image generated from the prior image shown.

[0059] Figure 6 An overall image is shown, generated by an X-ray imaging method according to an embodiment of the present invention. Detailed Implementation

[0060] Figure 1 Flowchart 100 is shown, which explains a CT imaging method according to an embodiment of the present invention using a so-called dual-energy technique that generates contrast-enhanced image data of a patient.

[0061] In a dual-energy imaging method, two sets of projection measurement data, PMD1 and PMD2, are recorded. These sets of projection measurement data are transmitted through X-rays with different energy spectra R. E1 R E2 The X-rays produced have different average energy values ​​E1 and E2 in their energy spectrum. To produce X-rays with different energy spectra R... E1 R E2 X-rays, for example, can be obtained using two X-ray sources 15a and 15b (see... Figure 3 X-ray sources emit X-rays with different average X-ray energies E1, E2, or X-ray energy spectra R. E1 R E2 X-rays.

[0062] Within the scope of imaging methods, in step 1.I, two different X-ray sources first generate first X-ray energy spectra R with different first X-ray energy spectra. E1 Second X-ray energy spectrum R E2 X-rays. X-ray energy spectrum R E1 R E2 With the help of the relatively low first tube voltage LU of 80kVRE1 And the relatively high second tube voltage HU of 150kV RE2 Generated by the voltages LU of the first and second transistors. RE1 and HU RE2 An X-ray tube is excited to generate X-rays with preset first and second average energy values ​​E1 and E2. The average energy values ​​E1 and E2 of the generated X-ray spectrum correspond to the first X-ray energy spectrum R. E1 Approximately 45 keV, and for the second X-ray spectrum R E2 Approximately 80 keV. Furthermore, in step 1.I, the X-rays generated by the two X-ray sources are also collected by two X-ray detectors 16a and 16b positioned opposite the corresponding X-ray sources (see...). Figure 3 The imaging method described above is also known as a dual-energy CT measurement method. Figure 1 The method used to generate first and second projection measurement datasets PMD1 and PMD2, the first and second projection measurement datasets being associated with corresponding different X-ray energy spectra R E1 R E2 Related.

[0063] Then, in step 1.II, the prior image data A-BD is reconstructed based on the first X-ray projection measurement data PMD1 recorded at a lower first energy E1 = 45 keV. The reconstruction of the prior image data A-BD is significantly less costly than that based on the two image datasets PMD1 and PMD2.

[0064] In step 1.III, the X-ray attenuation value R-HU of the area is calculated based on the prior image data A-BD in the inspection area. Figure 4 The image shows a view of a CT image recorded using a low energy of 45 keV on the patient's torso. Such images are suitable as a basis for subsequent segmentation based on the observed HU values ​​and artifacts. For example, areas with particularly weak absorption, such as the lungs, can also be identified.

[0065] In step 1.IV, the area to be inspected is segmented based on the obtained X-ray attenuation value R-HU. Here, the segment or region R is determined and classified according to the obtained X-ray attenuation value.

[0066] Then, in step 1.V, for each region, an appropriate third X-ray energy spectrum R with a region-specific, appropriate third average energy E3 or corresponding keV value is automatically determined based on the separately obtained region-specific X-ray attenuation value R-HU. E3In this specific example, determination is achieved using a lookup table that associates the HU value R-HU of the desired region with the appropriate keV value for image reconstruction. Alternatively, segments can be determined organ-specifically, and organ-specific keV values ​​can be obtained for each organ. For this purpose, a target keV image is generated based on prior image data A-BD, as shown in... Figure 5 As shown in the image.

[0067] Then, in step 1.VI, based on the acquired first and second X-ray projection measurement data PMD1, PMD2 and the segment-specific weight W(E3), the virtual monoenergetic image data V-BD of each segment is reconstructed using the target keV value E3 obtained respectively.

[0068] Finally, in step 1.VII, the overall image G-BD is generated from the virtual monoenergetic image data V-BD based on the region.

[0069] In addition, strong smoothing of image data is performed on the boundary regions between each segment or region R to reduce strong noise and / or strong jumps between individual target keV values ​​or energy E3 in the image regions.

[0070] The important point here is not to perform or plan smoothing on individual parts of the image, but rather to smooth and the resulting smooth transitions involve only a weighted function W(E3) composed of individual parts. A characteristic of the weighted function can be considered as its sufficient smoothness.

[0071] exist Figure 2 An image data generating apparatus 20 according to an embodiment of the present invention is schematically shown.

[0072] The image data generation device 20 includes an input interface 25, through which information is acquired regarding the absorption behavior of the field of view (FOV) of the patient's area to be examined, particularly the dimensional parameter value A, as well as information regarding the contrast agent KM administered to the patient prior to the imaging method and the type of imaging method AB used. The acquired data A, AB, and KM are transmitted from the input interface 25 to the energy dispersive spectroscopy (EDS) acquisition unit 26.

[0073] The energy spectrum acquisition unit 26 uses an automatic keV algorithm to obtain the first X-ray energy spectrum R based on the input keV value. E1 Second X-ray energy spectrum R E2 The corresponding average energy values ​​are E1 and E2.

[0074] The control unit 27, also part of the image data generation device 20, now generates a control signal AS based on the received energy values ​​E1 and E2, which is transmitted to the associated CT system 1 (see...). Figure 3 ) control interface 34 (see Figure 3 Furthermore, the obtained first and second average energy values ​​E1 and E2 are transmitted to the reconstruction unit 24 for further interpretation.

[0075] Figure 2 The image data generation device 20 shown also includes a projection measurement data acquisition unit 21. The projection measurement data acquisition unit 21 is used to acquire data with different X-ray energy spectra R from the inspection area FOV of the object being inspected O or from a database during the actual imaging process. E1 R E2 X-ray projection measurement data PMD1 and PMD2 are obtained and stored in a database. During imaging, X-ray projection measurement data PMD1 and PMD2 are generated as follows: A first and second X-ray energy spectrum R is applied to the FOV of the examination area. E1 R E2 The X-rays are transmitted by X-ray detectors that are separated from each other (see X-ray detectors). Figure 3 The X-ray projection measurement data (PMD1 and PMD2) generated by the X-ray detectors and acquired by the projection measurement data acquisition unit 21 are then forwarded to the previously mentioned image data reconstruction unit 24, from which the pseudo-monoenergy image data V-BD is reconstructed. The first X-ray projection measurement data (PMD1) is also additionally transmitted to the prior image reconstruction unit 22a, which is designed to reconstruct the prior image data A-BD based on the first X-ray projection measurement data (PMD1). The prior image data A-BD is transmitted to the value determination unit 22b, which is designed to determine the X-ray attenuation value R-HU for each segment of the inspection area FOV. The X-ray attenuation value R-HU for each segment is transmitted to the energy value determination unit 23, which is designed to determine the keV value E3 for each segment based on the X-ray attenuation value R-HU determined for each segment.

[0076] The calculated average keV value E3 is transmitted to the previously mentioned reconstruction unit 24. Reconstruction unit 24 includes a decomposition unit 24a that performs base material decomposition based on the acquired first and second X-ray projection measurement data PMD1, PMD2, where first and second X-ray attenuation values ​​are calculated for the base materials, such as iodine and water, respectively. An iodine image J-BD is generated to a certain extent using the first X-ray attenuation value, and a non-contrast image NK-BD is generated using the second X-ray attenuation value. Weighting unit 24b, also part of reconstruction unit 24, now calculates a weighting factor W(E3) for each segment or each keV value E3 associated with each segment, and then uses this weighting factor to weight the X-ray attenuation values ​​of the individual base materials or the iodine image J-BD and the non-contrast image NK-BD.

[0077] The associated weighting factor can be calculated from the keV value E3 using a physical NIST table. Here is a lookup table that establishes a physical correlation between the keV value and the weight. This physical correlation can be calculated from the material breakdown and the associated energy effects, comparing Compton effect scattering with light effect scattering.

[0078] The weighting factor W(E3) is transmitted to the sub-image generation unit 24c, which is also part of the reconstruction unit 24 and generates virtual monochromatic image data V-BD of the region by weighted combination of the iodine image J-BD and the non-contrast image NK-BD.

[0079] Therefore, virtual monoenergetic image data V-BD of the region is generated according to the following formula:

[0080] I V-BD =W(E3)·I J-BD +(1-W(E3))·I NK-BD (1)

[0081] Here, the weight W(E3) is a function of the virtual energy E3 or keV value, and the X-ray attenuation value I V-BD I K-BD I NK-BD The X-ray attenuation values ​​are the virtual monoenergetic image data V-BD, the iodine image data J-BD, and the non-contrast image data NK-BD for the region. Reconstruction unit 24 includes filtering and smoothing functions, which adapt the weighting factor W(E3) or keV value E3 to harmonize the boundary regions between segments and reduce noise accordingly. In other words, the keV value E3 can also vary within a segment. For example, the keV values ​​E3 of adjacent segments can be close to each other at the segment boundary lines or boundary surfaces, thus avoiding artifacts caused by jumps in the keV value E3 within the segment boundary regions.

[0082] A filtering method is described in DE 10 2011 083 727 A1. Filtering methods for reducing noise in X-ray images are also described in German Patent and Trademark Office applications 10 2015 223 601.4 and 10 2015 223 606.4.

[0083] The region-reconstructed image data V-BD is transmitted to the overall image generation unit 28, which is designed to generate an overall image G-BD based on the region's virtual single-image data V-BD.

[0084] The combined, filtered, large-scale noise-free overall image data G-BD is then transmitted to output interface 29, from which the overall image data G-BD is output, for example, to the data storage unit (see...). Figure 3 The data is either stored in the data storage unit 32 or transmitted to the display unit, where the overall image data is graphically displayed.

[0085] exist Figure 3 The image shows a computed tomography (CT) system 1, also known as CT system 1. The computed tomography (CT) system includes... Figure 2The image data generation device 20 is shown. The CT system 1, designed as a dual-energy CT system, is essentially composed of a conventional scanning unit 10. Within the scanning unit, a projection measurement data acquisition unit 5 rotates around a measurement chamber 12 at a gantry 11. The projection measurement data acquisition unit has two X-ray detectors 16a and 16b and two X-ray sources 15a and 15b opposite to the detectors 16a and 16b. A patient support device 3 or patient table 3 is positioned in front of the scanning unit 10. The upper part 2 of the patient support device or patient table can be moved toward the scanner 10 along with the patient O located thereon, so that the patient O can move relative to the detector system 16a and 16b through the measurement chamber 12. The scanning unit 10 and the patient table 3 are controlled by a control device 30, from which acquisition control signals AS are obtained via a conventional control interface 34 to operate the entire system in a conventional manner according to a preset measurement protocol. In the case of helical acquisition, a spiral orbit is obtained during measurement by the movement of patient O along the z-direction and the simultaneous orbiting of X-ray sources 15a and 15b relative to patient O, where the z-direction corresponds to the system axis z longitudinally through the measurement space 12. Here, detectors 16a and 16b always travel together relative to X-ray sources 15a and 15b in parallel to acquire projection measurement data PMD1 and PMD2, which are then used to reconstruct volumetric and / or slice image data. Similarly, a sequential measurement method can also be performed, where the required projection measurement data PMD1 and PMD2 are acquired near a fixed position in the z-direction and then during one, partial, or multiple rotations at the relevant z-position to reconstruct a cross-sectional image at said z-position or to reconstruct image data from projection measurement data at multiple z-positions. In principle, the method according to the invention can also be used in other CT systems, such as CT systems with only one unique X-ray source and detectors with opposing X-ray counting, or CT systems with a single X-ray source with kV switching capability, or CT systems with X-ray detectors forming a complete loop. For example, the method according to the invention can also be applied to systems with a variable patient table and a gantry that moves in the z-direction (so-called sliding gantry).

[0086] The projection measurement data PMD1 and PMD2 (hereinafter also referred to as raw data) acquired by detectors 16a and 16b are transmitted to the control device 30 via the raw data interface 33. Then, after appropriate preprocessing if necessary, the raw data is further processed in the image data generation device 20, which in this embodiment is implemented in the control device 30 as software on a processor. The image data generation device 20 reconstructs the overall image data G-BD based on the raw data PMD1 and PMD2 using the method according to the invention. This implementation structure of the image data generation device 20 is described in... Figure 2This is shown in detail in the text.

[0087] Then, the overall image data G-BD generated by the image data generating device 20 is stored in the data storage unit 32 of the control device 30 and / or output to the screen of the control device 30 in a normal manner. The overall image data can also be transmitted via... Figure 3 The data is fed into a network connected to the computed tomography system 1, such as a radiographic information system (RIS), and stored in an accessible mass storage device or as an image output at a connected printer or film station. Therefore, the data can be further processed in any manner and then stored or output. Appropriate control parameters or control signals AS are also determined via the image data generation device 20 based on previously input data, particularly the size parameter value A of the object being examined O, and information about the imaging type. The control signals are then transmitted to the aforementioned control interface 34. From there, the control signals AS are transmitted directly to the units involved in the imaging process, such as X-ray sources 15a and 15b, X-ray detectors 16a and 16b, the patient bed 3, etc.

[0088] Additionally, in Figure 3 The image also shows a contrast agent injection device 35, by which a contrast agent can be injected into the patient O beforehand (i.e., before the start of the CT imaging procedure). The improved contrast can then be used to obtain the aforementioned virtual monoenergetic image data V-BD based on the contrast agent image and a second base material image, such as water or bone.

[0089] The components of the image data generating apparatus 20 can be implemented primarily or entirely as software elements on a suitable processor. In particular, the interfaces between these components can also be configured purely in software. All that is required is the availability of access to appropriate storage areas where data can be temporarily stored and retrieved and updated at any time.

[0090] exist Figure 4 The image shown is a typical image 40 of a patient's coronal section. This illustration is made using a window with a center of 60 HU and a window width of 400 HU, corresponding to a lower threshold of -140 HU and an upper threshold of 260 HU. Here, in particular, the lung (L) can be identified in rich detail; the lung is identifiable in the upper left of the image and includes low HU values. The relatively air-rich tissue of the lung (L) with particularly low density absorbs X-rays relatively well at low energy, making this area appear relatively bright. The kidney (N) contains a large amount of blood in its delicate vascular structure and therefore also absorbs significantly more intense X-rays. Figure 4The kidneys are clearly visible and appear very bright. The HU value of the kidneys is already very high at the first energy E1 = 45 keV, placing them at the edge of the window selected for detailed visualization of the lungs (L), and arguably almost "hitting" the upper threshold of the window, thus displaying them at maximum brightness. For example, the lung window falls within the X-ray attenuation range of -1000 HU to 200 HU. The kidney window, on the other hand, includes values ​​from approximately -120 HU to 240 HU, thus still falling within the range of the selected window. For more detailed visualization of the kidneys (N), a higher keV value, E3 = 55 keV, is more advantageous. Blood vessels and arteries contain particularly large amounts of blood with corresponding iodine contrast agents, thus absorbing X-rays even more strongly than the kidneys (N). At 45 keV, a window between approximately -300 HU and 500 HU is suitable for blood vessels and arteries. Therefore, a higher keV value, E3 = 65 keV, is suitable for the area encompassed by the background (H). If you want to display bones with hip joint HG and spine WS, where the upper limit threshold of the window is about 1300 HU and the bones are particularly dense and absorb X-rays extremely strongly, then an E3 = 150 keV value is suitable.

[0091] exist Figure 5 The middle shows Figure 4 The so-called target keV image 50 of the torso shown is divided into segments S1, S2, S3, and S4. Figure 5 The first segment S1, shown in the upper left of the image, includes the lung (L) and is suitable for a keV value of 45 keV. The second segment S2, adjacent to the right of the first segment S1, is divided into two regions, including the kidney (N). A keV value of 55 keV is suitable for the second segment S2. The third segment S3 includes a background (H) with large blood vessels, and should be displayed at a keV value of 65 keV. The fourth segment S4 includes the skeleton, with the hip joint (HG) in the lower left and right of the image and the spine (WS) in the middle of the image. The fourth segment S4 should now be displayed at a keV value of 150 keV. The image shows a smooth, blurred transition between segments S1, S2, S3, and S4, achieved by low-pass filtering the original organ mapping. This blurred transition is intended to represent the generation of virtual monoenergetic image data with continuous transitions at keV values ​​in the region, to achieve a smooth transition between the individual segments S1, S2, S3, and S4 in the subsequent overall image G-BD and to reduce noise effects and artifacts.

[0092] exist Figure 6 It is shown in Figure 4The view 60 of the torso shown comprises image regions with different organ-related keV weights. Here, the whole is displayed using the same window with a center of 60 HU and a width of 400 HU, which corresponds to a lower threshold of -140 HU and an upper threshold of 260 HU.

[0093] Here, the lung (L) is again shown in the upper left of the image and can be identified with particularly rich detail. The lung segment is shown using a keV value of 45 keV. The relatively low-density, relatively air-rich tissue of the lung absorbs X-rays relatively well at low energy, making the area appear relatively bright. The kidney (N) is shown using a keV value of 55 keV, thus making the kidney appear less bright and showing slightly more tissue structure. Furthermore, the background (H) is shown using a keV value of 65 keV. This allows for more significant identification of the structure and extension of blood vessels.

[0094] The skeleton, with the hip joints (HG) at the lower left and right sides of the image and the spine (WS) at the center of the image, is displayed using a keV value of 150 keV. Now, the structure is also more clearly visible within the skeleton because artifacts are reduced through lower brightness, and the uniformly defined range of HU values ​​for the window can be better used for structural display.

[0095] Finally, it should be reiterated that the above-described methods and apparatus are merely preferred embodiments of the present invention, and that those skilled in the art can modify them without departing from the scope of the invention. Therefore, the X-ray imaging method and image data generating apparatus for generating image data of an examination area are explained primarily based on a system for recording medical image data. However, the present invention is not limited to applications in the medical field, but in principle, it can also be used to record images for other purposes. For completeness, it should also be noted that the use of the indefinite article "a" or "an" does not preclude the repetition of related features. Similarly, the term "unit" does not preclude the unit from being composed of multiple components, which may, if necessary, be spatially distributed.

Claims

1. An X-ray imaging method (100) for generating image data (G-BD) of an area of ​​view (FOV) of an object (O) to be examined, comprising the following steps: - From the inspection area (FOV), using the first X-ray energy spectrum (R E1 The first X-ray projection measurement data (PMD1) was acquired and the second X-ray energy spectrum (R) was used. E2 Acquire at least a second X-ray projection measurement (PMD2), and a second X-ray energy spectrum (R E2 ) is different from the first X-ray energy spectrum (R E1 ), - Reconstruct prior image data (A-BD) based at least on the first X-ray projection measurement data (PMD1). - Based on the prior image data (A-BD), determine the location-related distribution of X-ray attenuation values ​​(R-HU) in the examination area (FOV). - Perform basic material decomposition based on the first X-ray projection measurement data (PMD1) and at least the second X-ray projection measurement data (PMD2). - Calculate the position-related weights (W(E3)) of the base material based on the position-related distribution of the X-ray attenuation values ​​(R-HU). - By reconstructing location-related, differently weighted, virtual, and base material-weighted image data (V-BD), a global image (G-BD) of the inspection area (FOV) is generated. in - In the prior image data (A-BD), the imaging organ regions (S1, S2, S3, S4) are segmented and classified. - Based on the distribution of the X-ray attenuation value (R-HU) at different locations in different segments (S1, S2, S3, S4), specific base material weights (W(E3)) are determined respectively. - For the overall image (G-BD), segment-specific, virtual base material-weighted image data (V-BD) are reconstructed.

2. The method according to claim 1, wherein calculating the position-related weights (W(E3)) comprises: - Based on the position-dependent distribution obtained from the X-ray attenuation value (R-HU), obtain the position-dependent third X-ray energy spectrum (Ri) with the position-dependent third average energy (E3). E3 ), - Based on the obtained position-related third X-ray energy spectrum (R E3 ) Calculate the position-related weights (W(E3)).

3. The method of claim 2, wherein the position-dependent third X-ray energy spectrum (R... E3 Each of these includes a third energy value (E3) associated with a single location.

4. The method of claim 3, wherein the position-related third energy value (E3) is obtained by applying a lookup table to the obtained position-related distribution of the X-ray attenuation value (R-HU).

5. The method of claim 3, wherein the location-related third energy value (E3) is obtained by using an objective function that maps the X-ray attenuation value (HU) to an appropriate third energy value (E3).

6. The method of claim 1, wherein the specific base material weight (W(E3)) is determined based on the classification of each of the segments (S1, S2, S3, S4) according to organ type (L, N, H, HG, WS).

7. The method according to any one of claims 1 to 6, wherein the reconstruction of the virtual base material weighted image data (V-BD) is performed based on the spectral information of the prior image data (A-BD).

8. The method of claim 7, wherein the spectral information is used to identify exogenous materials and to determine specific base material weights based on the identified materials for reconstructing the virtual base material weighted image data (V-BD).

9. The method according to any one of claims 3 to 5, wherein when reconstructing segment-specific, virtual base material-weighted image data (V-BD), - The third energy value (E3) changes continuously according to position in at least one segment (S1, S2, S3, S4), and - In the boundary region between at least two segments (S1, S2, S3, S4), a continuous transition is generated by making the third energy value (E3) of the two segments approximate each other in the boundary region.

10. The method according to any one of claims 1 to 6, wherein one of the following basic material decompositions is used when reconstructing the virtual basic material weighted image data (V-BD): - Based on the material decomposition of iodine and water - Based on iodine and the breakdown of bone materials - Combinations of different material decompositions for different regions of the inspection area (FOV).

11. An image data generating apparatus (20), comprising: - A control unit (27) for manipulating one or more X-ray sources of a CT system (1) to generate an X-ray spectrum having a first X-ray energy spectrum (R E1 ) and different second X-ray energy spectra (R E2 X-rays, - A projection measurement data acquisition unit (21) for using a first X-ray energy spectrum (R) from the inspection area (FOV) of the object under inspection (O). E1 The first X-ray projection measurement data (PMD1) was acquired and the second X-ray energy spectrum (R) was used. E2 Acquire at least a second X-ray projection measurement (PMD2). - A priori image reconstruction unit (22a) for reconstructing priori image data (A-BD) based at least on the first X-ray projection measurement data (PMD1). - A value-determining unit (22b) is used to determine the location-related distribution of X-ray attenuation values ​​(R-HU) in the inspection area based on the first X-ray projection measurement data (PMD1). - A decomposition unit (24a) for performing basic material decomposition based on the first X-ray projection measurement data (PMD1) and at least the second X-ray projection measurement data (PMD2). - A weighting unit (24b) for determining the position-related weights of the base material based on the position-related distribution of the X-ray attenuation values ​​(R-HU). - An image generation unit (24c, 28) is used to generate a global image (G-BD) of the inspection area (FOV) by reconstructing location-related, differently weighted, virtual, base material-weighted image data (V-BD). The image data generating device is designed as follows: - In the prior image data (A-BD), the imaging organ regions (S1, S2, S3, S4) are segmented and classified. - Based on the distribution of the X-ray attenuation value (R-HU) at different locations in different segments (S1, S2, S3, S4), specific base material weights (W(E3)) are determined respectively. - For the overall image (G-BD), segment-specific, virtual base material-weighted image data (V-BD) are reconstructed.

12. A computed tomography system (1) having an image data generation device (20) according to claim 11.

13. A computer program product comprising a computer program capable of being directly loaded into a storage unit of a computer unit, the computer program having program segments for performing all steps of the method according to any one of claims 1 to 10 when the computer program is run in the computer unit.

14. A computer-readable medium storing a program segment executable by a computer unit so as to perform all steps of the method according to any one of claims 1 to 10 when the computer unit runs the program segment.

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