Processing dark-field x-ray image data information

Abstract

An apparatus (10) for processing dark-field X-ray image data is provided. The apparatus includes a data interface (20) and a data processing unit (30). The data interface (20) is configured to provide dark-field X-ray image data to the data processing unit. The data processing unit (30) is configured to segment the image data to provide segmented image data of the lung, the segmented image data including at least a first lung segment and a second lung segment distinguishable therefrom, the first lung segment and the second lung segment being segmented into defined volumes. The data processing unit (30) is also configured to derive at least a first dark-field signal value assigned to the first lung segment and a second dark-field signal value assigned to the second lung segment from the image data. Furthermore, the data processing unit (30) is configured to derive a quantitative measure by comparing the first dark-field signal value and the second dark-field signal value with each other, the quantitative measure being output data for lung analysis.

Description

Technical Field

[0001] The present invention relates to dark-field X-ray imaging, and more particularly to apparatus for processing dark-field X-ray image data, methods for processing dark-field X-ray image data, X-ray imaging systems, computer program units, and computer-readable media. Background Technology

[0002] In medical applications such as lung analysis, there may be injuries, obstacles, or abnormalities that can be detected more easily or reliably using certain imaging techniques (compared to other imaging techniques). For example, computed tomography (CT) can more reliably detect some injuries, while other injuries cannot be detected reliably or at all.

[0003] In this regard, dark-field X-ray radiography is a promising imaging technique for detecting diseases, particularly lung diseases. However, it remains unclear how image data derived from dark-field X-ray radiography can be processed to obtain further information, especially information useful for lung analysis or diagnostic purposes. Summary of the Invention

[0004] Therefore, improved apparatus may be needed for processing dark-field image data. The object of the invention is achieved through the subject matter of the independent claims, wherein other embodiments are incorporated in the dependent claims.

[0005] According to a first aspect, an apparatus for processing X-ray image data (preferably dark-field X-ray image data) is provided. The apparatus includes: at least one data interface; and at least one data processing unit. The data interface is configured to provide dark-field X-ray image data to the data processing unit. The data processing unit is configured to segment the image data to provide segmented image data of the lung, the segmented image data including at least two lung segments, namely at least a first lung segment and a separable second lung segment, both the first and second lung segments being segmented into defined volumes. The data processing unit is configured to derive at least a first dark-field signal value assigned to the first lung segment and a second dark-field signal value assigned to the second lung segment from the image data itself and / or according to additional calculations, parameters, etc. The data processing unit is configured to derive a quantitative measure by comparing the first dark-field signal value and the second dark-field signal value with each other, the quantitative measure being output data for lung analysis.

[0006] In this manner, at least a semi-automatic means is provided for deriving quantitative measures from image data, which at least form the data basis for further lung analysis or diagnosis. The provided apparatus can allow differentiation of image data of subjects with lung injury from image data of less obvious subjects with little or no lung injury. In this regard, it has been found that some lung injuries, conditions, abnormalities, etc., of a subject can be detected more easily and / or more reliably by utilizing dark-field X-ray imaging techniques, especially if the lungs recorded in the image data are first segmented into two or more segments, and then the corresponding dark-field signal values ​​assigned to the respective lung segments are evaluated relative to the volume of the lung segments. For example, the apparatus according to the first aspect can be particularly suitable and / or sensitive for more reliably detecting emphysema, COPD, etc., or detecting emphysema already in its early stages, wherein emphysema cannot be detected or reliably detected by utilizing other imaging techniques (such as CT). It is also recognized in this disclosure that the specific selection of the lung segments regarding their position relative to each other and / or relative to the anatomy of the subject can also be a factor to be considered in order to derive quantitative measures for lung analysis and / or diagnosis.

[0007] In this specification, two segments are specified or referenced. However, it should be understood that although at least two segments are formed, the number of segments is not limited to two, and multiple segments can be formed, i.e., multiple segments. For example, the left and right lungs can be separated first, and then each lung (i.e., the left lung and / or the right lung) can be divided into two or more segments.

[0008] As used herein, image data segmentation can be understood as a computer-implemented process of dividing a digital image into multiple segments to simplify and / or alter the image's representation. The segmented image data derived from this segmentation process may be more meaningful to the reader and / or easier to analyze. For example, a lung captured in a digital image can be divided into two or more segments, which may also be referred to as separate groups of image objects and / or groups of pixels. Thus, the segmentation process can, for example, take advantage of the fact that pixels within a region are similar with respect to some characteristics or computational properties (such as color, intensity, or texture), while adjacent regions are significantly different with respect to the same characteristics. Several segmentation methods exist that can be utilized in this context, such as clustering methods, for example using the k-means algorithm, histogram-based methods, the use of convolutional neural networks trained for this purpose, etc.

[0009] The defined volume of a lung segment as used in this paper can be understood as follows: the volume of the corresponding segment is known, for example, from the segmentation process, or at least can be determined, preferably numerically, probabilistically, from its and / or image data.

[0010] As used herein, quantitative measures can be understood, for example, as indicators of potential lung damage, abnormalities, etc., recorded in image data. For instance, quantitative measures can be meaningful values, such as numerical values. For example, quantitative measures may include one or more of the following: the difference in dark-field signal values ​​between lung segments, the standard deviation of dark-field signal values ​​across all segments and / or regions of the lung, the normalized standard deviation of dark-field signal values ​​across all segments and / or regions of the lung, and / or the ratio of dark-field signal values ​​among lung segments. Quantitative measures can then be provided as output data for (further) lung analysis, which may also be part of a subsequent process preferably performed ex vivo. Based on quantitative measures, lung analysis can be performed at least semi-automatically using computer devices or manually by medical professionals (such as radiologists).

[0011] Each lung segment may have a dark field signal value, such as signal intensity, wherein the corresponding dark field signal value of each lung segment may be compared and / or evaluated relative to one or more other lung segments.

[0012] As used herein, X-ray image data can refer to at least one attenuated image, wherein the intensity of the image is proportional to the integral of the transmission properties of the tissue through which the X-rays have propagated (determined by density, dielectric constant of the material, etc.), such that the X-ray attenuated image can be a classical X-ray image, and / or can refer to X-ray information containing the scattered components of the applied X-rays. The latter can be obtained using interferometric detection methods, where other methods are known. Microstructures in the lungs can cause scattering of X-ray waves, where the scattered components have been integrated into the final X-ray attenuation value and are therefore inseparable. For example, using interferometric X-ray imaging techniques, the scattered components of the X-ray signal can be distinguished from the attenuation and phase components to provide additional information about the internal structure of the lungs.

[0013] According to an embodiment, the first lung segment and the second lung segment can be divided into segments having at least approximately equal volumes. In other words, after the segmentation process, at least two lung segments can have at least substantially equal or identical volumes. In this way, for example, by normalizing the dark field signal relative to the lung segment volume, the influence of object size can be mitigated. For example, by normalizing the dark field signal, information about the material constants of the lung, convolved with the length of the lung, can be determined, allowing healthy lung tissue to be distinguished from pathological tissue. In this way, by forming lung segments with the same volume, the dark field signal values ​​of the corresponding lung segments can be directly evaluated, i.e., compared with each other.

[0014] In embodiments, comparing the first dark-field signal value and the second dark-field signal value may include determining a ratio based on the at least two values ​​to derive a quantitative metric. For example, it has been found that subjects with lung injury (e.g., emphysema) typically have a different ratio than less observable subjects with little or no lung injury. For instance, subjects with emphysema and / or COPD tend to have a specific ratio of dark-field signal values ​​for the upper and lower lungs, such that this ratio allows for differentiation from subjects without lung injury. In particular, subjects with emphysema and / or COPD may have a higher upper-to-lower lung field ratio. That is, in this embodiment, the lung or lung field may be divided into two segments, wherein a first segment is assigned to the upper lung and a second segment is assigned to the lower lung, and wherein the terms upper and lower refer to the longitudinal axis of the subject's body. Based on this distinction between the upper and lower portions of the lung, the dark-field signal can be determined and calculated separately for each portion.

[0015] According to an embodiment, the ratio can be an indicator of the presence (optionally, the possibility of) a disease or other finding in the lungs. For example, the ratio could allow for the detection of emphysema.

[0016] In an embodiment, the first lung segment may be the upper lung field, and the second lung segment may be the lower lung field. The lung fields relative to the longitudinal axis of the object's body are defined as the upper and lower lung fields. As described above, objects with, for example, emphysema tend to have a specific ratio of dark field signal values ​​between the upper and lower lungs, such that segmenting the upper and lower lung fields allows for the differentiation of objects without lung damage.

[0017] According to an embodiment, the data processing unit can also be configured to estimate lung depth for multiple different locations within a corresponding lung segment, the lung depth being defined along the normal to the projection plane of the image data. The multiple different locations may correspond to, or may also be referred to as, multiple (particularly different) pixels of the image included in the image data. In this way, segments can be determined such that they have a defined (i.e., specific) volume and that volume is known, or they can be determined such that they have at least approximately the same volume. The volume can be calculated based on the area of ​​the lung segment that can be determined from the image data.

[0018] In an embodiment, the data processing unit is further configured to determine the corresponding segment volume based on the lung depth determination result and the area determination result of the image data, so as to obtain the defined segment volume, wherein the lung depth is defined along the normal of the projection plane of the image data.

[0019] According to an embodiment, the different locations are assigned to different pixels of the acquired image included in the image data. In this way, segments can be formed with pixel precision.

[0020] In one embodiment, the data processing unit is further configured to normalize the corresponding dark field signal value with respect to lung depth, which is defined along the normal to the projection plane of the image data. In this way, the dark field signal can be normalized with respect to the lung depth.

[0021] According to an embodiment, the data processing unit is further configured to determine the corresponding dark field signal value of the segment volume based on a lung depth determination, the lung depth being defined along the normal to the projection plane of the image data. In this way, the dark field signal can be normalized with respect to the lung depth.

[0022] In an embodiment, the data processing unit is further configured to separate the left and right lungs to provide the segmented image data of the lungs, wherein the first lung segment and the second lung segment are segmented for each lung. In other words, in the first step, the left and right lungs can be separated from each other, and in the second step, the left and / or right lungs can be segmented as described above.

[0023] According to a second aspect, an X-ray imaging system is provided. The system includes an image acquisition device and an apparatus according to a first aspect, the image acquisition device being configured to acquire X-ray image data of a target.

[0024] According to a third aspect, a method for processing X-ray image data (preferably dark-field X-ray image data) is provided. The method includes the following steps: receiving dark-field X-ray image data of a lung of a subject by a data processing unit; segmenting the image data by the data processing unit to provide segmented image data of the lung, the segmented image data including at least a first lung segment and a second lung segment distinguishable therefrom, both the first and second lung segments being segmented into defined volumes; deriving, by the data processing unit, at least a first dark-field signal value assigned to the first lung segment and a second dark-field signal value assigned to the second lung segment from the image data; and deriving a quantitative measure by the data processing unit by comparing the first dark-field signal value and the second dark-field signal value with each other, the quantitative measure being output data for lung analysis.

[0025] Preferably, the method is implemented by a computer. For example, the method can be performed by means of the apparatus according to the first aspect and / or by means of the system according to the second aspect.

[0026] According to a fourth aspect, a computer program unit is provided, which, when executed by a processor, is configured to perform the method according to a third aspect, and / or control the system according to a second aspect, and / or control the apparatus according to a first aspect.

[0027] According to a fifth aspect, a computer-readable storage or transmission medium is provided that stores or carries computer program units according to a fourth aspect.

[0028] Note that the above embodiments can be combined with each other, regardless of the aspects involved. Therefore, the method can be combined with structural features of devices and / or systems from other aspects, and similarly, the devices and / or systems can be combined with features of each other, and also with features described above regarding the method. Advantageously, the benefits provided by any of the above aspects and examples also apply to all other aspects and examples, and vice versa.

[0029] These and other aspects of the invention will be apparent and explained with reference to the embodiments described below. Attached Figure Description

[0030] Exemplary embodiments of this disclosure will be described in the following figures.

[0031] Figure 1 An example of an apparatus for processing dark-field X-ray image data according to an embodiment is shown.

[0032] Figure 2 An example of a system for processing dark-field X-ray image data according to an embodiment is shown.

[0033] Figure 3 The flowchart illustrates an example of a method for processing dark-field X-ray image data according to an embodiment.

[0034] Figure 4 An example of lung segmentation recorded in image data according to an embodiment is shown.

[0035] Figure 5 Another example of lung segmentation recorded in image data according to an embodiment is shown.

[0036] Figure 6 The diagram illustrates how the ratio of the dark field signal according to an embodiment can be used to process dark field X-ray image data.

[0037] List of reference numerals in the attached diagram:

[0038] 10 devices

[0039] 20 Data Interfaces

[0040] 30 Data Processing Units

[0041] 100 System

[0042] 110 Image Acquisition Equipment

[0043] 200 methods

[0044] 210 Method and Steps

[0045] 220 Method and Steps

[0046] 230 Methods and Steps

[0047] 240 Methods and Steps Detailed Implementation

[0048] Figure 1 An example of an apparatus 10 for processing dark-field X-ray image data is shown. Apparatus 10 includes a data interface 20 and a data processing unit 30. The data interface 20 may include means for data input and / or data output. For example, the data interface 20 may be configured to receive data from an image acquisition device 110 (see image acquisition device 110). Figure 2 The data processing unit 30 receives data (e.g., image data) and provides the received data to the data processing unit 30, and / or receives data from the data processing unit 30 and provides the received data to another entity for purposes such as representation of the processed data, further lung analysis, etc. The received image data may include X-ray attenuation information of the patient's chest. The data processing unit 30 is also configured to segment the image data to provide segmented image data of the lungs. The segmented image data includes at least a first lung segment and a second lung segment, wherein the segments are distinguishable from each other. The first lung segment and the second lung segment are segmented to each have a defined volume V, i.e., a known volume. The data processing unit 30 is configured to derive at least a first dark-field signal value DAX and / or NDAX assigned to the first lung segment and a second dark-field signal value DAX and / or NDAX assigned to the second lung segment from the image data. Furthermore, the data processing unit 30 is configured to derive quantitative measures by comparing the first dark-field signal value DAX and / or NDAX with the second dark-field signal value DAX and / or NDAX. The quantitative measure can then be provided as output data for further lung analysis, diagnosis, etc.

[0049] In the example, the first and second lung segments can be segmented into sections with at least approximately equal volumes. In other words, after the segmentation process, at least two lung segments can have at least substantially equal or identical volumes V. In this way, the influence of object size can be mitigated by normalizing the dark field signal relative to the corresponding lung segment volume V. For example, by normalizing the dark field signals DAX and / or NDAX, the material constant information of the lung, convolved with the length of the lung, can be determined, allowing healthy lung tissue to be distinguished from pathological tissue. In this way, by forming lung segments with identical volumes, the dark field signal values ​​of the corresponding lung segments can be directly evaluated, i.e., compared with each other.

[0050] In the example, first dark field signal values ​​DAX and / or NDAX and second dark field signal values ​​DAX and / or NDAX can be evaluated, for example, compared to each other, wherein the evaluation includes determining a ratio based on the at least two values ​​to derive a quantitative metric. For example, it has been found that subjects with lung injury (e.g., emphysema and / or COPD, etc.) typically have a different ratio than less obvious subjects without or with less lung injury. For example, subjects with emphysema tend to have a specific ratio of dark field signal values ​​for the upper lung to the lower lung, such that this ratio allows for differentiation from subjects without lung injury. In particular, for example, subjects with emphysema may have a higher upper lung to lower lung field ratio. That is, in this example, the lung or lung field can be divided into two segments, wherein a first segment is assigned to the upper lung and a second segment is assigned to the lower lung, and wherein the terms upper and lower refer to the longitudinal axis of the subject's body. Based on this distinction between the upper and lower portions of the lung, the dark field signal can be determined and calculated separately for each portion. In the example, the ratio could be an indicator of the minimum presence (optionally, the probability) of a disease or other finding in the lungs. For example, the ratio could allow for the detection of emphysema.

[0051] In the example, the first lung segment can be the upper lung field, and the second lung segment can be the lower lung field. The lung fields relative to the longitudinal axis of the object's body are defined as the upper and lower lung fields. As mentioned above, objects with, for example, emphysema tend to have a specific ratio of dark field signal values ​​between the upper and lower lungs, such that segmenting the upper and lower lung fields allows for the differentiation of objects without lung damage.

[0052] In the example, data processing unit 30 can also be configured to estimate lung depth for multiple different locations within a corresponding lung segment, the lung depth being defined along the normal to the projection plane of the image data. Lung depth can be estimated using image processing methods that achieve signal separation after acquiring X-ray attenuation and X-ray dark-field image information. The multiple different locations can correspond to, or may also be referred to as, multiple (particularly different) pixels of the image included in the image data. In this way, segments can be determined such that they have a defined (i.e., specific) volume and that volume is known, or they can be determined such that they have at least approximately the same volume. The volume can be calculated based on the area of ​​the lung segment that can be determined from the image data.

[0053] In the example, the data processing unit 30 is further configured to determine the corresponding segment volume based on the lung depth determination result and the area determination result of the image data, so as to obtain the defined segment volume, wherein the lung depth is defined along the normal of the projection plane of the image data.

[0054] In the example, the different locations are assigned to different pixels of the acquired image included in the image data.

[0055] In the example, the data processing unit 30 is further configured to normalize the corresponding dark field signal value with respect to the lung depth, which is defined along the normal of the projection plane of the image data. In this way, the dark field signal can be normalized with respect to the lung depth.

[0056] In the example, the data processing unit 30 is further configured to determine the corresponding dark field signal value of the segment volume based on the lung depth determination result, the lung depth being defined along the normal of the projection plane of the image data. In this way, the dark field signal can be normalized with respect to the lung depth.

[0057] In the example, the data processing unit 30 is further configured to separate the left and right lungs to provide the segmented image data of the lungs, wherein the first lung segment and the second lung segment are segmented for each lung. In other words, in the first step, the left and right lungs can be separated from each other, and in the second step, the left and / or right lungs L 左 L 右 It can be divided as described above.

[0058] Figure 2 An example of a system 100 for processing dark-field image data is shown. System 100 includes at least one image acquisition device 110, and as described above... Figure 1The apparatus 10 described, in conjunction with any of the examples or combinations thereof, for presenting dark-field information. The at least one image acquisition device 110 is configured to provide an X-ray attenuation image and a dark-field X-ray image. The data interface 20 is configured to output the X-ray attenuation image and / or the dark-field X-ray image, along with quantitative measurements.

[0059] In the example, at least one image acquisition device 110 includes a grating-based dark-field X-ray imaging device. In the example, at least one image acquisition device 110 includes an interferometer arrangement.

[0060] In this example, at least one image acquisition device 110 includes an X-ray imaging apparatus. For example, the device could be a radiographic system prepared for dark-field imaging.

[0061] In the example, at least one image acquisition device 110 can be configured to operate in a standard radiographic mode, where the intensity of transmitted radiation provides information about attenuation through the object. In the example, the same image acquisition device 110 can be used to acquire both attenuated and dark-field images. For example, the attenuated and dark-field image data can be the output of an evaluation of the same acquired data. This data can be multiple exposures (e.g., between 4 and 20) showing images with different phase positions of one of the gratings. A full-field system can be implemented using a grating as large as the detector.

[0062] In the example, at least one image acquisition device 110 can be configured to generate an attenuation image related to the detection of X-ray intensity values ​​in the inspection area with and without an object. In the example, the at least one image acquisition device is configured to generate a dark-field image related to the detection of X-ray stripe visibility in the inspection area with and without the object. In the example, the at least one image acquisition device is configured to generate any combination of these images. For example, the at least one image acquisition device can be configured to generate both an attenuation image and a dark-field image. In the example, both an attenuation image and a dark-field image can be generated simultaneously. In the example, the summed spatial lung volume information is the integral of the lung area (derived from pixel area data) and the intensity of each pixel value.

[0063] Figure 3 A method 200 for processing dark field image data is shown. Method 200 includes:

[0064] In step 210, the data processing unit 30 receives dark-field X-ray image data of the lungs of the subject.

[0065] In step 220, the data processing unit 30 segments the image data to provide segmented image data of the lung, wherein the segmented image data includes at least a first lung segment and a second lung segment that are distinguishable from each other, and wherein the first lung segment and the second lung segment are segmented into segments having defined volumes.

[0066] In step 230, the data processing unit 30 derives from the image data at least a first dark field signal value assigned to the first lung segment and a second dark field signal value assigned to the second lung segment.

[0067] In step 240, the data processing unit 30 derives a quantitative measure by comparing the first dark field signal value with the second dark field signal value, wherein the derived quantitative measure is output data for lung analysis.

[0068] In the example, the first lung segment and the second lung segment are divided into segments with at least approximately equal volumes.

[0069] In the example, the step of comparing the first dark field signal value and the second dark field signal value includes determining a ratio based on the two values ​​to derive the quantitative measure.

[0070] In the example, the ratio is used as an indicator of the presence of disease or other findings in the lungs of the subject.

[0071] In the example, the first lung segment is the upper lung field L. 上 Furthermore, the second lung segment is the lower lung field L. 左 The lung fields relative to the longitudinal axis of the body are defined as the upper lung field and the lower lung field.

[0072] In the example, the data processing unit 30 estimates the lung depth d for multiple different locations within the corresponding lung segment, the lung depth being defined along the normal of the projection plane of the image data.

[0073] In the example, the data processing unit 30 determines the corresponding segment volume V based on the lung depth d determination result and the area determination result of the image data to obtain the at least approximately equal segment volumes, wherein the lung depth L is defined along the normal of the projection plane of the image data.

[0074] In the example, the different positions p are assigned to different pixels of the acquired image recorded in the image data.

[0075] In the example, the data processing unit 30 normalizes the corresponding dark field signal value with respect to the lung depth d, wherein the lung depth is defined along the normal of the projection plane of the image data.

[0076] In the example, the data processing unit 30 determines the corresponding dark field signal value DAX of the segment volume based on the lung depth d, which is defined along the normal of the projection plane of the image data.

[0077] In the example, the data processing unit 30 will process the left lung L... 左 and right lung L 右 Separation to provide the segmented image data of the lungs, wherein, for each lung L 左 L 右 The first lung segment and the second lung segment are segmented.

[0078] Figure 4 An exemplary segmentation (e.g., separation) of the lungs recorded in image data is shown, wherein the lungs are segmented (e.g., separated) into the left lung L. 左 and right lung L 右 In addition, the left lung L Left and right lungs L 右 Divided (e.g., separated) into its upper part L 上 and the lower part L 下 Wherein, the corresponding upper and lower volumes V are defined or known, or preferably divided into equal parts. Figure 4 In the diagram, the divisions are indicated by dashed lines L1 and L2, where dashed line L1 indicates the division between the left and right lungs, and dashed line L2 indicates the division between the upper and lower lungs. 上 and lower lung L 下 The division.

[0079] Figure 5 Another exemplary segmentation (e.g., separation) of the lungs recorded in image data is shown, where the left lung... L Left and right lungs L 右 At least one of them is divided into more than an upper part and a lower part, that is, divided into n parts, wherein the division is indicated by a dashed line Ln.

[0080] In an exemplary embodiment, the data processing unit 30 segments (i.e., separates or divides) the lungs recorded in the image data into multiple parts, for example, four parts. First, the lungs are separated into the left lung and the right lung. Then, at least one or both of the left lung and the right lung are further segmented (i.e., separated or divided) into its upper part and lower part, or segmented into the medial part and the distal part, wherein each of the upper lung part and the lower lung part or the medial part and the distal part has a defined (i.e., known and / or determined) volume. In other words, the left lung and the right lung are segmented (i.e., separated or divided) into parts with respect to the lung volume. In another exemplary embodiment, the left lung and the right lung are segmented into more than two parts, for example, segmented into at least 3 or more parts. The parts may be of equal size with respect to the lung volume.

[0081] The right lung and the left lung may be represented as L, for example L 左 and L 右 . The volume V of each lung part can be estimated based on the lung depth d(p) for each position p (e.g., image pixel) in L (e.g., for example L 左 and / or L 右 ). The data processing unit 30 can then calculate the dark field value for each position p, which can be represented as DAX(p). This dark field value DAX(p) can be normalized with respect to the lung depth d(p), and the calculation of the normalized dark field value NDAX can be represented as:

[0082]

[0083] Then, the global normalized dark field value can be calculated, which can be represented as:

[0084]

[0085] where, ∑ L DAX(p) gives the total lung volume by integrating d(p) over L. By restricting L to L1 < L, the sub-volume is given for each sub-region L1 (e.g., only for the left lung L 左 and the right lung L 右 ).

[0086] Based on this, the lung field L 左 、L 右 can be divided into sub-fields with some given properties, such as the separation of the left lung and the right lung, and dividing at least one side of the lung into parts of equal size with respect to the lung volume by considering d(p).

[0087] For example, the quantitative measure can then include the dark field signal value DAX or NDAX in the left lung L 左 and the right lung L 右One or more of the following can be used: the difference between the values, the standard deviation of the dark field signal values ​​DAX or NDAX over all segments and / or regions of the lung, the normalized standard deviation (e.g., standard deviation or mean) of the dark field signal values ​​DAX or NDAX over all segments and / or regions of the lung, and / or the ratio of the dark field signal values ​​DAX or NDAX of lung segments. Quantitative measures can then be provided as output data for (further) lung analysis. Thus, (global) NDAX and, for example, the upper lung L... U and lower lung L L Both groups were statistically separated by the severity of their emphysema. That is, those with emphysema tended to have higher NDAx. 上 -NDAX 下 - ratio, while those without emphysema typically have lower NDAX. 上 -NDAX 下 -ratio.

[0088] Figure 6 A graph showing the number of subjects examined in the study, with different severity levels of emphysema and / or COPD given by spirometry. One point represents one subject. Points are defined by the global NDAX (which is plotted on the x-axis) and by the upper lung segment L... 上 and lower lung segment L 下 NDAX 上 -NDAX 下 - Ratio (which is plotted on the y-axis) positioning. In this graph, a higher NDAX... 上 -NDAX 下 - Ratio objects are represented by solid dots and have lower NDAX 上 -NDAX 下 - The objects of the ratio are represented by hollow dots. From Figure 4 As can be seen from this, NDAX, as a quantitative measure, is determined. 上 -NDAX 下 - The ratio provides a means of distinguishing subjects with emphysema and / or COPD from those without emphysema and / or COPD.

[0089] Further details on X-ray dark-field imaging can be found in the following papers: Pfeiffer, Franz et al., “Hard-X-ray dark-field imaging using a grating interferometer” (Nature Materials 7.2 (2008), pp. 134-137); and Yaroshenko, Andre et al., “Pulmonary emphysema diagnosis with a preclinical small-animal x-ray dark-field scatter-contrast scanner” (Radiology 269.2 (2013), pp. 427-433).

[0090] In another exemplary embodiment, a computer program or computer program unit is provided, characterized in that it is configured to run method steps of a method according to one of the foregoing embodiments on a suitable system.

[0091] Therefore, the computer program unit can be stored on the data processing unit, which may also be part of the embodiment. The data processing unit can be configured to perform or cause the execution of the steps of the methods described above. Furthermore, it can be configured to operate components of the device / or system described above. The computing unit can be configured to automatically operate and / or execute user commands. The computer program can be loaded into the working memory of the data processor. Therefore, the data processor can be equipped to perform a method according to one of the foregoing embodiments.

[0092] Furthermore, the computer program unit may be able to provide all the necessary steps to complete the process of an exemplary embodiment of the method as described above.

[0093] According to another exemplary embodiment of the present invention, a computer-readable medium, such as a CD-ROM, a USB stick, etc., is provided, wherein the computer-readable medium has computer program units stored on the computer-readable medium, the computer program units being described in the preceding sections.

[0094] Computer programs can be stored and / or distributed on suitable media, such as optical storage media or solid-state media supplied together with or as part of other hardware, but can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems.

[0095] However, computer programs can also be presented on networks, such as the World Wide Web, and can be downloaded from such networks to the working memory of a data processor. According to other exemplary embodiments of the invention, a medium is provided for making a computer program available for download, the computer program unit being arranged to perform a method according to one of the embodiments previously described according to the invention.

[0096] Note that embodiments of the present invention are described with reference to different subject matter. In particular, some embodiments are described with reference to method claims, while others are described with reference to apparatus claims. However, unless otherwise stated, those skilled in the art will infer from the above and below that any combination of features relating to different subject matter is also considered to be disclosed in this application, in addition to any combination of features belonging to one type of subject matter. However, all features can be combined to provide synergistic effects beyond the simple addition of features.

[0097] Although the invention has been illustrated and described in detail in the accompanying drawings and the foregoing description, such illustrations and descriptions should be considered illustrative or exemplary, and not restrictive. The invention is not limited to the disclosed embodiments. Those skilled in the art, through studying the drawings, the disclosure, and the claims, will understand and implement other variations of the disclosed embodiments in practicing the claimed invention.

[0098] In the claims, the word "comprising" does not exclude other elements or steps, and the words "a" or "an" do not exclude a plurality. A single processor or other unit can perform the functions of several items recited in the claims. Although some measures are recited in different dependent claims, this does not indicate that combinations of these measures cannot be advantageously used. No reference numerals in the claims should be construed as limiting the scope.

Claims

1. An apparatus (10) for processing dark-field X-ray image data information, comprising: Data interface (20); as well as Data processing unit (30); The data interface (20) is configured to provide dark-field X-ray image data to the data processing unit. The data processing unit (30) is configured to segment the dark-field X-ray image data to provide segmented dark-field X-ray image data of the lungs. The segmented dark-field X-ray image data includes at least a first lung segment and a second lung segment that can be distinguished from it. The first lung segment and the second lung segment are segmented into segments with defined volumes. The data processing unit (30) is configured to derive at least a first dark-field signal value assigned to the first lung segment and a second dark-field signal value assigned to the second lung segment from the dark-field X-ray image data, wherein... The data processing unit (30) is configured to derive a quantitative measure by comparing the first dark field signal value with the second dark field signal value, the quantitative measure being output data for lung analysis.

2. The apparatus according to claim 1, wherein, The first lung segment and the second lung segment are divided into segments having at least approximately equal volumes.

3. The apparatus according to claim 1 or 2, wherein, Comparing the first dark field signal value and the second dark field signal value involves determining a ratio based on these two values ​​to derive the quantitative measure.

4. The apparatus according to claim 3, wherein, The ratio is an indicator of the presence of disease or other findings in the lungs of the subject.

5. The apparatus according to any one of claims 1, 2, and 4, wherein, The first lung segment is the upper lung field, and the second lung segment is the lower lung field. The lung fields are defined as the upper and lower lung fields along the longitudinal axis of the object's body.

6. The apparatus according to any one of claims 1, 2, and 4, wherein, The data processing unit (30) is also configured to estimate lung depth for multiple different locations within a corresponding lung segment, the lung depth being defined along the normal to the projection plane of the dark field X-ray image data.

7. The apparatus according to any one of claims 1, 2, and 4, wherein, The data processing unit (30) is further configured to determine the volume of the corresponding lung segment based on the lung depth determination result and the area determination result of the dark-field X-ray image data, so as to obtain at least approximately equal volumes of the first lung segment and the second lung segment, wherein the lung depth is defined along the normal of the projection plane of the dark-field X-ray image data.

8. The apparatus according to claim 6, wherein, The different locations are assigned to different pixels in the acquired image included in the dark field X-ray image data.

9. The apparatus according to any one of claims 1, 2, 4 and 8, wherein, The data processing unit (30) is further configured to normalize the first dark field signal value and the second dark field signal value with respect to the corresponding lung depth, which is defined along the normal of the projection plane of the dark field X-ray image data.

10. The apparatus according to any one of claims 1, 2, 4 and 8, wherein, The data processing unit (30) is further configured to determine the first dark field signal value of the volume of the first lung segment and the second dark field signal value of the volume of the second lung segment based on the corresponding lung depth determination result, wherein the lung depth is defined along the normal of the projection plane of the dark field X-ray image data.

11. The apparatus according to any one of claims 1, 2, 4 and 8, wherein, The data processing unit (30) is further configured to separate the left and right lungs to provide the segmented dark-field X-ray image data of the lungs, wherein the first lung segment and the second lung segment are segmented for each lung.

12. An X-ray imaging system for providing dark-field X-ray image data information, comprising: An image acquisition device configured to acquire X-ray image data of an object; as well as The apparatus according to any one of the preceding claims.

13. A method for providing dark-field X-ray image data information, comprising: The data processing unit (30) receives dark-field X-ray image data of the lungs of the object; The data processing unit (30) segments the dark-field X-ray image data to provide segmented dark-field X-ray image data of the lung, the segmented dark-field X-ray image data including at least a first lung segment and a second lung segment that can be distinguished therefrom, the first lung segment and the second lung segment being segmented into defined volumes; The data processing unit (30) derives at least a first dark field signal value assigned to the first lung segment and a second dark field signal value assigned to the second lung segment from the dark field X-ray image data; and The data processing unit (30) derives a quantitative measure by comparing the first dark field signal value with the second dark field signal value, which is output data for lung analysis.

14. A computer program product configured, when executed by a processor, to perform the method according to claim 13, and / or control the apparatus (10) according to any one of claims 1 to 11, and / or control the system (100) according to claim 12.

15. A computer-readable medium storing a computer program product according to claim 14.

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