阅读说明：本技术 提供掩模图像 (Providing a masked image ) 是由 R.奥布勒 S.塔谢诺夫 于 2021-05-07 设计创作，主要内容包括：本发明涉及提供掩模图像。本发明涉及一种用于提供掩模图像的方法,包括：a)接收具有时间维度的医学图像数据,b)通过将傅立叶变换应用于图像数据来产生具有数据点的频率数据组,数据点分别具有频率值,其中,至少沿着时间维度应用傅立叶变换,c)基于至少一个频率阈值将频率数据组分割为两个子区域,d)通过将逆傅立叶变换应用于频率数据组的第一子区域和/或第二子区域来产生掩模图像,e)提供掩模图像。本发明还涉及一种用于提供差图像的方法、提供单元、医学成像设备和计算机程序产品。(The invention relates to providing a mask image. The invention relates to a method for providing a mask image, comprising: a) receiving medical image data having a time dimension, b) generating a frequency data set having data points by applying a fourier transformation to the image data, the data points each having a frequency value, wherein the fourier transformation is applied at least along the time dimension, c) dividing the frequency data set into two sub-regions on the basis of at least one frequency threshold, d) generating a mask image by applying an inverse fourier transformation to a first sub-region and/or a second sub-region of the frequency data set, e) providing the mask image. The invention further relates to a method for providing a difference image, a providing unit, a medical imaging device and a computer program product.)

1. A method for providing (PROV-MI) mask images, comprising:

a) receiving (REC-ID) medical Image Data (ID) having a time dimension,

b) generating (GEN-FD) a frequency data set (FD) with data points by applying a Fourier transform to the Image Data (ID), the data points each having a frequency value,

wherein the Fourier transform is applied at least along a time dimension,

c) dividing (SEG-FD) the frequency data group (FD) into two sub-areas (TB1, TB2) on the basis of at least one Frequency Threshold (FTH),

d) generating a (GEN-MI) Mask Image (MI) by applying an inverse Fourier transform to the first sub-region (TB1) and/or to the second sub-region (TB2) of the frequency data set (FD),

e) a (PROV-MI) Mask Image (MI) is provided.

2. Method according to claim 1, characterized in that in step d) the inverse fourier transform is applied to a first sub-region (TB1) and a second sub-region (TB2) of the frequency data group (FD), wherein the frequency value of at least one of the sub-regions (TB1, TB2) is adjusted.

3. The method according to claim 1 or 2, characterized in that the Fourier transform is also applied in step b) along at least one spatial axis,

wherein the division (SEG-FD) of the frequency data set (FD) in step c) is based on at least one first frequency threshold with respect to temporal frequencies and at least one second frequency threshold with respect to spatial frequencies.

4. The method according to any one of the preceding claims, further comprising:

a2) identifying a medical object (ID-MO) and/or an anatomical structure (ID-AS) in the Image Data (ID),

a3) determining data points in the frequency data set (FD) corresponding to the identified medical object and/or anatomical structure, excluding said corresponding data points from the segmentation (SEG-FD) in step c).

5. Method according to claim 4, characterized in that in step a3) a subset of data points of the frequency data set (FD) around the corresponding data point is additionally determined, which subset is excluded from the segmentation (SEG-FD) in step c).

6. The method according to claim 4 or 5, wherein step a) further comprises receiving object parameters (REC-OP) and/or structure parameters (REC-SP),

wherein the Object Parameter (OP) has information about a medical object and/or wherein the Structure Parameter (SP) has information about an anatomical structure,

wherein the corresponding data point is determined in step a3) based on the Object Parameter (OP) and/or the Structure Parameter (SP).

7. The method according to any of the preceding claims, wherein step a) further comprises registering (REG-ID) medical Image Data (ID).

8. The method according to one of the preceding claims, characterized in that the medical Image Data (ID) at least partially depict a common examination region (UB) of the examination object (31),

wherein step a) further comprises receiving (REC-SIG) a physiological signal and/or a motion Signal (SIG) of the examination object (31),

wherein at least one Frequency Threshold (FTH) in step c) is pre-given (DET-FTH) based on the physiological signal and/or the motion Signal (SIG).

9. A method for providing a (PROV-DI) difference image, comprising:

s1) medical Image Data (ID) having a time dimension are captured (ACQ-ID) by means of a medical imaging device,

s2) receiving (REC-MI) Mask Images (MI) by applying the method according to any one of the preceding claims to medical Image Data (ID),

s3) generating (GEN-DI) Difference Images (DI) by subtracting and/or multiplying the Mask Images (MI) with the medical Image Data (ID).

s4) provides (PROV-DI) Difference Images (DI).

10. A providing unit (PRVS) configured for performing the method according to any of the preceding claims.

11. Medical imaging device comprising a provision unit (PRVS) according to claim 10, wherein the medical imaging device is configured for capturing (ACQ-ID) and/or receiving (REC-ID) and/or providing (PROV-ID) medical image data.

12. A computer program product directly loadable into a Memory (MU) of a providing unit (PRVS), having program segments for performing all the steps of the method according to any of claims 1 to 9 when the program segments are executed by the providing unit (PRVS).

Technical Field

The invention relates to a method for providing a mask image, a method for providing a difference image, a providing unit, a medical imaging device and a computer program product.

Background

For the acquisition of changes over time on a body region of an examination object, for example the movement of a medical object on the body region, X-ray-based subtraction methods are frequently used. Changes over time in a body region of an examination subject may include, in particular, the propagation of contrast agent in the vascular system and/or the movement of surgical and/or diagnostic instruments.

In these X-ray-based subtraction methods, two X-ray images taken in a chronological manner and depicting the same body region are usually subtracted from one another, wherein treatment-and/or diagnosis-independent and/or disruptive components in the X-ray images, which in particular do not change over time, are reduced.

In methods such as Digital Subtraction Angiography (DSA), two phases of the acquisition are often distinguished. In the first phase, the mask phase, at least one X-ray image with the best image quality, in particular the maximum X-ray radiation dose, is usually taken. In a second phase, the filling phase, at least one second X-ray image is usually acquired, wherein at this point in time a change has occurred in the examined body region of the examination subject. In order to acquire such changes over time on the body region, a plurality of second X-ray images are often taken in succession in a very short time sequence. Then, by subtracting one of the second X-ray images in the second phase from the X-ray image in the first phase, changes over time on the body area can be made visible. Known DSA methods are often based on the assumption that the movement of different tissue regions and bone structures in the body region is uniform. In case of deviations between the respective movements DSA methods cannot often be used. Furthermore, in the mask phase, a high X-ray radiation dose load is disadvantageously imposed on the examination object.

Furthermore, image processing algorithms exist which often enhance the relevant spatial frequencies (Ortsfrequenz) in the medical image data and/or attenuate the spatial frequencies of interfering structures, e.g. bone structures, to highlight medical objects and/or specific anatomical structures, e.g. blood vessel parts. However, it is disadvantageous here that the medical object and/or the anatomical structure to be highlighted is often also attenuated or disturbing structures are enhanced, in particular in image regions in which mutual overlapping occurs.

Disclosure of Invention

The technical problem to be solved by the invention is therefore to enable masking of image portions having different states of motion.

According to the invention, the above technical problem is solved by the corresponding subject matter of the invention. Advantageous embodiments and suitable embodiments are the subject matter of the following description.

In a first aspect, the invention relates to a method, in particular computer-implemented, for providing a mask image. Here, in a first step a), medical image data having a temporal dimension are received. Furthermore, in a second step b), a frequency data set is generated by applying a fourier transformation to the image data, the frequency data set comprising data points each having a frequency value. Here, a fourier transform is applied at least along the time dimension. In a third step c), the frequency data set is divided into two subregions on the basis of at least one frequency threshold. Thereafter, in a fourth step d), a mask image is generated by applying an inverse fourier transformation to the first and/or second sub-region of the frequency data set. In a fifth step e), a mask image is provided.

The previously described steps a) to e) may advantageously be performed sequentially and/or at least partially simultaneously.

Receiving the image data in step a) may comprise, in particular, detecting and/or reading a computer-readable data memory and/or receiving from a data storage unit, for example a database. Furthermore, the image data may be provided by a providing unit of the medical imaging device.

The image data may in particular be two-dimensionally and/or three-dimensionally spatially resolved. Furthermore, the image data is time resolved. Advantageously, the image data may at least partially depict a common examination region of the examination object. The examination subject can be, for example, a human and/or animal patient and/or a Phantom (Phantom). The image data may comprise, for example, X-ray images, in particular projection images and/or ultrasound images and/or computed tomography images and/or magnetic resonance images and/or positron emission tomography images. Furthermore, the image data can depict the examination region at different points in time, in particular at the acquisition points in time. Thus, the image data is not only spatially resolved, but also temporally resolved. Thereby, it may advantageously be ensured that changes over time on the examination region, such as propagation motion of the contrast agent and/or motion of the medical and/or anatomical object, are mapped into the image data. Furthermore, the image data may have metadata, wherein the metadata may comprise information about operating parameters and/or acquisition parameters of the medical imaging device, for example.

In particular, the image data may comprise a plurality of single images. The image data, in particular the single image, may have a plurality of image points, in particular pixels and/or voxels. In this case, the image points of the image data can accordingly have a temporal intensity curve along the time dimension.

The fourier transformation for generating the frequency data set in step b) may comprise, for example, a windowed fourier transformation (gefenster fourier transform), in particular a short-time fourier transformation (english: short-time Fourier transform, STFT) and/or wavelet transform. The window fourier transformation may have, for example, a rectangular function and/or a Von Hann window function (Von-Hann-fensterfunkton) and/or a gaussian window function. Furthermore, the windowed Fourier transform may be implemented as a Fast Fourier Transform (FFT). The frequency data set can advantageously be generated by applying a fourier transform to the spatially and temporally resolved image data at least along the time dimension. The data points of the frequency data set can correspond accordingly to image points of the image data. Furthermore, in particular, the frequency data sets may be spatially resolved. Furthermore, the data points of the frequency data set can accordingly have frequency information about the time intensity curve of the respectively corresponding image point of the image data. The frequency information of the respective data point can have, for example, a frequency spectrum of the time intensity curve for the image point corresponding thereto. In case a short-time fourier transformation is applied to the image data, the short-time fourier transformation may comprise a window function for classifying the image data, in particular the time intensity curve of the image points, along the time dimension into time segments. In this case, the temporal section can be converted into a corresponding region of the frequency spectrum of the frequency data set by applying a short-time fourier transformation to the image data. In this way, transitions, in particular transition frequencies, between the different motion states mapped in time-resolved manner in the image data can be determined reliably and accurately, advantageously in the frequency data set, in particular by analyzing the frequency spectrum.

By applying the wavelet transform to the image data to generate the frequency data set, the resolution along the time dimension and the frequency resolution in the corresponding time segment or region of the spectrum corresponding thereto can be optimized. This can be achieved in particular by simultaneously shifting and scaling the window function in the wavelet transform. In particular, a short-time fourier transformation and/or a wavelet transformation is applied in step b), so that in particular a continuous adjustment of the time segment or the region of the frequency spectrum corresponding thereto is possible. This is advantageous in particular for the repeated execution of steps a) to e).

In step c), the frequency data set may be divided into two sub-regions based on at least one frequency threshold. In particular, the frequency threshold can be predefined as a transition frequency between two different motion states. In this case, the movement states can each have different movement speeds. The frequency value can describe the change over time of the time intensity curve of an image point of the image data. Furthermore, the segmentation may comprise a classification of sub-regions of the frequency data set, in particular of the data points, whose frequency values, for example frequency averages, respectively lie above and/or below a frequency threshold. In particular, the frequency data set may be classified by being divided into a first sub-region having frequency values below and/or equal to a frequency threshold and a second sub-region having frequency values above and/or equal to the frequency threshold. In this case, the data points of the first partial region can correspond in particular to image points of the image data which represent static and/or slowly changing segments of the examination region. Furthermore, the data points of the second partial region can correspond to image points of the image data which depict a comparatively rapidly changing section of the examination region. In this case, the distinction between static and/or slowly changing sections and comparatively rapidly changing sections can be based on a frequency threshold. In particular, the classification of the frequency data set into the first and second partial region can be adjusted by means of a frequency threshold.

In step d), a mask image may be generated by applying an inverse fourier transform to the first and/or second sub-region of the frequency data set. When applying an inverse fourier transformation to the first and second partial region of the frequency data set, it may be advantageous if the frequency value of at least one of the partial regions in step c) has been adjusted. The inverse fourier transformation in step d) may in particular comprise a wavelet synthesis if a frequency data set is generated in step b) by applying a wavelet transformation. Advantageously, the mask image has a plurality of image points, wherein each image point of the mask image corresponds to a data point of the frequency data set. The mask image may in particular have the same or smaller dimensions as the image data. Advantageously, the masked image may have masked and unmasked image areas. In this case, the image points of the respective image region can correspond to the data points of the respective partial region of the segmented frequency data set.

In the generation of the mask image by applying an inverse fourier transformation to the first sub-region of the frequency data set, the image points of the unmasked image region of the mask image may correspond to the data points of the first sub-region of the frequency data set. The unmasked image regions of the mask image can thus correspond in particular to image points of the image data which depict static and/or slowly changing segments of the examination region. Furthermore, the masked image regions of the mask image can correspond in particular to image points of the image data which depict comparatively rapidly changing segments of the examination region.

Similarly, when the mask image is generated by applying an inverse fourier transformation to the second sub-region of the frequency data set, the image points of the unmasked image region of the mask image may correspond to the data points of the second sub-region of the frequency data set.

Furthermore, the mask image may have a plurality of single mask images, wherein a single mask image corresponds to a respective single image of the image data. Further, the generating of the mask image in step d) may include: in particular along the time dimension, the single mask images are averaged, in particular adaptively and/or weighted averaged. The average mask image generated here can in particular be provided as a mask image in step e).

Providing the mask image in step e) may for example comprise: stored on a computer readable storage medium and/or displayed on a display unit and/or transmitted to a providing unit. In particular, a graphical display of the mask image may be displayed on the display unit. In particular, the mask image may be provided for the proposed method for providing a difference image.

The proposed method enables classification, in particular masking, of image portions having different states of motion based on an analysis, in particular a comparison, of frequency values obtained from the image data by means of a fourier transformation. The acquisition of the mask images can thereby advantageously be dispensed with, as a result of which the overall duration of the examination and/or the load on the examination subject, for example due to the X-ray dose, can be advantageously reduced. Furthermore, in particular in contrast to intensity-based masking, variable structures and/or medical objects, in particular anatomical structures and/or medical objects, depicted in the image data may advantageously be preserved.

In a further advantageous embodiment of the proposed method for providing a mask image, the inverse fourier transformation in step d) can be applied to the first and second sub-regions of the frequency data set. In particular, the frequency value of at least one subregion can be adjusted before the inverse fourier transformation is applied.

Here, the adjustment frequency value may include, for example: at least one subregion is filled with predetermined, in particular constant, frequency values. For example, the frequency value of at least one sub-region, in particular of at least one sub-region, can be padded (zero-padding) with zeros. Alternatively or additionally, the frequency values of the at least one subregion may be filled and/or adjusted, in particular scaled, in accordance with a predefined distribution function, in particular a window function and/or an attenuation function. This advantageously makes it possible to apply the inverse fourier transformation to the first and second partial regions of the frequency data record while preserving the segmentation. Furthermore, a mask image may be generated based on the adjusted frequency values of the at least one subregion such that the mask image has unmasked and masked image regions according to the segmentation in step c).

In a further advantageous embodiment of the proposed method for providing a mask image, the fourier transformation in step b) can also be applied along at least one spatial axis. The division of the frequency data set in step c) can be based on at least one first frequency threshold with respect to temporal frequencies and at least one second frequency threshold with respect to spatial frequencies.

The application of the fourier transformation in step b) can in particular be carried out in a stepwise manner (gettofelt) and/or in a multidimensional manner. For example, fourier transforms may be applied to the image data sequentially along the time dimension and along at least one spatial axis. Alternatively or additionally, the fourier transform may be constructed along the time dimension and along the at least one spatial axis in a multi-dimensional manner. The inverse fourier transformation in step d) can be configured in a similar manner to the fourier transformation. In particular, a fourier transform may be applied along at least one spatial dimension, in particular all spatial dimensions, of the image data.

By additionally applying a fourier transformation along at least one spatial axis, the frequency values of the data points of the frequency data set can describe temporal as well as spatial intensity changes of the image points of the image data. In particular, the frequency values of the data points of the frequency data set may accordingly have a temporal frequency and a spatial frequency, for example as a tuple (Tupel). In this case, a first frequency threshold value for the temporal frequency and a second frequency threshold value for the spatial frequency can be specified. The comparison conditions for the division of the frequency data set in step c) can advantageously have sufficient or necessary criteria in respect of the first and second frequency threshold values, respectively.

In the first configuration, the comparison condition for the division may have a sufficient criterion. The frequency data set can be classified by segmentation into a first subregion having frequency values below and/or equal to a first frequency threshold value or below and/or equal to a second frequency threshold value and into a second subregion having frequency values above and/or equal to the first frequency threshold value and above and/or equal to the second frequency threshold value. The data points of the first partial region can in particular correspond to image points of the image data which represent temporally or spatially stationary and/or temporally or spatially slowly changing segments of the examination region. Furthermore, the data points of the second partial region can correspond to image points of the image data which depict a temporally and spatially rapidly changing section of the examination region.

In a second embodiment, the comparison conditions for the segmentation may have the necessary criteria. The frequency data set can be classified by segmentation into a first subregion having frequency values below and/or equal to a first frequency threshold value and below and/or equal to a second frequency threshold value and into a second subregion having frequency values above and/or equal to the first frequency threshold value or above and/or equal to the second frequency threshold value. The data points of the first partial region can in particular correspond to image points of the image data which represent temporally and spatially static and/or temporally and spatially slowly changing segments of the examination region. Furthermore, the data points of the second partial region can correspond to image points of the image data which depict a temporally or spatially rapidly changing section of the examination region.

Thereby, temporal and spatial changes, in particular movements, in the examination region mapped in the image data can advantageously be segmented according to a predetermination of the first and second frequency threshold, for example the first and/or second sub-region can be segmented according to an Ellipsoid (Ellipsoid). In this case, a temporal change to be segmented, for example a change in the direction of movement and/or the intensity of the contrast agent flow, can advantageously be predetermined by distinguishing between a first frequency threshold with respect to the temporal frequency and a second frequency threshold with respect to the spatial frequency.

In a further advantageous embodiment of the proposed method for providing a mask image, the method may further comprise steps a2) and a 3). Here, in step a2), a medical object and/or an anatomical structure may be identified in the image data. Furthermore, in step a3), data points in the frequency data set corresponding to the identified medical object and/or anatomical structure may be determined. Here, the corresponding data points may be excluded from the segmentation in step c). Steps a2) and a3) may in particular be performed after step a) and before step b) of the proposed method.

The medical object can in particular be configured as a surgical and/or diagnostic instrument, for example as a catheter and/or as a guide wire and/or as an endoscope. Furthermore, the medical object may be configured as a contrast agent, in particular a contrast agent bolus (kontrasmititelbolus), arranged in the examination region. Furthermore, the anatomical structure may have, for example, a vessel structure, in particular a vessel segment, and/or an organ, in particular a hollow organ, and/or a tissue boundary. In particular, the medical object may be at least partially arranged in the anatomy.

Identifying the medical object and/or the anatomical structure in the image data in step a2) may for example comprise: a map of the medical object and/or the anatomical structure in the segmented image data (Abbildung). In this case, the segmentation of the medical object and/or the anatomical structure may be based in particular on a comparison of the image values of the image points with a predetermined threshold value. Alternatively or additionally, the medical object and/or the anatomical structure may be identified, for example, on the basis of a shape, in particular a contour. Alternatively or additionally, the medical object and/or the anatomical structure may be identified from at least one marker structure, which is mapped in the image data. Further, identifying the medical object and/or the anatomical structure may comprise: the image points of the image data are annotated, for example by user input. In particular, image points in the image data which map the medical object and/or the anatomical structure can be identified in step a 2).

In step a3), data points in the frequency data set can be identified which correspond to the image points in the image data identified in step a 2). This can be done in particular when applying a fourier transformation according to a mapping rule between image points of the image data and data points of the frequency data set.

Advantageously, the corresponding data points may be excluded from the segmentation in step c). The image regions, in particular the image points, identified in step a3) can be predefined as unmasked image regions of the mask image. Thereby, it may advantageously be achieved that the medical object and/or the anatomical structure identified in step a2) remains as unmasked image areas when applying, in particular subtracting and/or multiplying, the mask image.

Advantageously, steps a2) and a3) may be performed after step a) and before step b).

In a further advantageous embodiment of the proposed method for providing a mask image, a subset of data points of the frequency data set around the corresponding data point can additionally be determined in step a3), which subset is excluded from the segmentation in step c).

In this case, for example, a subset of the data points of the frequency data set around the corresponding data point can be determined from a distribution function, in particular a spatial distribution function. Alternatively or additionally, the subset may comprise data points of the frequency data set which lie within a predefined spatial distance from the corresponding data point. Alternatively or additionally, a subset of image points may be determined whose image points lie within a predetermined spatial distance from the image point identified in step a 2). Thereafter, a corresponding data point can be determined based on the identified image point, and a subset of data points can be determined based on the subset of image points. Thus, a safety region (safety margin) can advantageously be determined around the image points depicting the medical object and/or the anatomical structure, in particular a spatial safety region, which is excluded from the segmentation, in particular from a later masking. Similar to the corresponding data points, partial regions of the data points of the frequency data set can advantageously be predefined as unmasked image regions of the mask image. It is thereby advantageously possible to apply, in particular subtract and/or multiply, the mask image, while also holding the image of the medical object and/or the anatomical structure as an unmasked image region, in particular completely.

In a further advantageous embodiment of the proposed method for providing a mask image, step a) may further comprise receiving object parameters and/or structure parameters. The object parameters can have information about the medical object and/or the structural parameters can have information about the anatomical structure. Furthermore, the corresponding data points may be determined in step a3) on the basis of the object parameters and/or the structure parameters.

Receiving the object parameters and/or the structural parameters may comprise in particular: collect and/or read computer-readable data storage and/or receive from a data storage unit, such as a database. The object parameters and/or the structural parameters may also be provided by a providing unit of the medical imaging device. Alternatively or additionally, the object parameters and/or the structural parameters may be acquired in dependence of a user input on the input unit by a user.

Advantageously, the object parameters can have information about the medical object, for example at least one operating parameter and/or material property and/or shape property and/or information about a marking structure arranged on the medical object. Furthermore, the structure parameter may have information about the anatomical structure, for example a tissue parameter and/or a physiological parameter and/or information about a marker structure arranged on the anatomical structure and/or information about a contrast agent arranged in the anatomical structure. Furthermore, the structural parameters may have geometric information about the anatomical structure, such as a centerline and/or a volumetric network model and/or spatial extension information. In this way, it is advantageously possible to identify image points of the image data which depict the medical object and/or the anatomical structure in a particularly reliable and computationally efficient manner. For example, after identifying at least a part of the medical object and/or the anatomical structure, based on the object parameters and/or the structural parameters, for example by virtual complementing () To identify the remaining images. Corresponding data points of the frequency data set may then be identified based on the identified image points of the image data.

Alternatively or additionally, corresponding data points of the frequency data set may be identified based on a comparison of the respective frequency values with the object parameters and/or the structure parameters.

By determining the corresponding data points based on the object parameters and/or the structure parameters in step a3), a reliable, simultaneous calculation can be effectively ensured that an image of the medical object and/or the anatomical structure is also preserved when applying, in particular subtracting and/or multiplying, the mask image.

In a further advantageous embodiment of the proposed method for providing a mask image, step a) may further comprise registering medical image data, in particular a single image. In this case, the image data, in particular the individual images, can be registered with respect to one another along the time dimension. Here, the registration of the image data may comprise a rigid and/or non-rigid transformation of the single images, for example relative to a reference single image and/or relative to each other. Alternatively or additionally, the registration of the image data may comprise a motion correction, in particular a motion correction based on physiological motion signals of the examination object. Thereby, deviations of the individual images with respect to one another, for example due to movements of the examination object, can advantageously be reduced. Thereby, the accuracy and reliability of the segmentation of the frequency data set in step c) may advantageously be improved.

In a further advantageous embodiment of the proposed method for providing a mask image, the medical image data may at least partially depict a common examination region of the examination object. Here, step a) may also comprise receiving physiological signals and/or motion signals of the examination subject. Furthermore, at least one frequency threshold in step c) may be predefined on the basis of the physiological signal and/or the motion signal.

Receiving the physiological signal and/or the motion signal may comprise, in particular: collect and/or read computer-readable data storage and/or receive from a data storage unit, such as a database. Furthermore, the physiological signal and/or the motion signal may be provided by a provision unit of the medical imaging device and/or by a sensor unit for monitoring the examination object.

The physiological signal can have, for example, a cardiac signal, in particular a pulse signal and/or a respiratory signal of the examination subject. Furthermore, the motion signal can have spatially and temporally resolved motion information of at least a part of the examination object, in particular of the examination region. The frequency threshold value can advantageously be predefined on the basis of the physiological signal and/or the motion signal such that the frequency value of the data points of the frequency data set corresponding to a change over time, in particular a motion, of the examination region, which changes over time at least partially follows the physiological signal and/or the motion signal, is greater than and/or equal to the frequency threshold value. Thereby, it may advantageously be achieved that the image area of the image data depicting the change over time of the examination area corresponds to an unmasked image area of the mask image. It can thus be advantageously ensured that also changes over time on the examination region, which follow at least partially the physiological signal and/or the motion signal, are maintained when applying, in particular subtracting and/or multiplying, the mask image.

In a second aspect, the invention relates to a method for providing a difference image. In a first step s1), medical image data having a time dimension are acquired by means of a medical imaging device. In a second step s2), a mask image is received by applying an embodiment of the proposed method for providing a mask image to the medical image data. In a third step s3), a difference image is generated by subtracting and/or multiplying the mask image and the medical image data. Thereafter, in a fourth step s4) a difference image is provided.

The advantages of the proposed method for providing a difference image substantially correspond to the advantages of the proposed method for providing a mask image. Features, advantages, or alternative embodiments mentioned herein may also be applied to other claimed subject matter as well, and vice versa.

The medical imaging device used for recording the medical image data in step s1) may be embodied, for example, as a medical X-ray device, in particular as a C-arm X-ray device and/or a computed tomography device (CT) and/or an ultrasound examination device and/or a positron emission tomography device (PET). Furthermore, the medical image data acquired in step s1) may be provided for step a) of the proposed method for providing a mask image. Generating the difference image in step s3) may include: the mask image is subtracted from and/or multiplied by the medical image data, in particular image-point-by-image and/or single-image-by-single-image. Thereby, the masked image regions of the mask image can advantageously be removed from the medical image data. The difference image advantageously has unmasked image areas of the mask image.

Providing the difference image in step s4) may for example comprise: stored on a computer readable storage medium and/or displayed on a display unit and/or transmitted to a providing unit. In particular, a graphical display of the difference image may be displayed on the display unit.

In a third aspect, the invention relates to a providing unit comprising a computing unit, a storage unit and an interface. Such a provision unit is preferably configured for carrying out the previously described method for providing a mask image and/or for providing a difference image according to the invention and aspects thereof. The provision unit is configured for performing these methods and aspects thereof by configuring the interface, the storage unit and the calculation unit for performing the respective method steps.

In particular, the interface may be configured for performing steps a), a2), a3) and/or e) of the proposed method for providing a mask image. Furthermore, the interface may be configured for performing steps s2) and s4) of the proposed method for providing a difference image. Furthermore, the computing unit and/or the storage unit may be configured for carrying out the remaining steps of the proposed method.

The advantages of the proposed provision unit substantially correspond to the advantages of the proposed method for providing a mask image and/or for providing a difference image. Features, advantages, or alternative embodiments mentioned herein may also be applied to other claimed subject matter as well, and vice versa.

In a fourth aspect, the invention relates to a medical imaging device comprising the proposed providing unit. The medical imaging device, in particular the provision unit, is designed to carry out the proposed method for providing a mask image and/or for providing a difference image. In particular, the medical imaging device may be configured as a medical X-ray device, in particular as a C-arm X-ray device and/or as a computed tomography device (CT) and/or as an ultrasound examination device and/or as a positron emission tomography device (PET). The medical imaging device can also be designed for recording and/or receiving and/or providing medical image data and/or mask images and/or difference images.

The advantages of the proposed medical imaging device substantially correspond to the advantages of the proposed method for providing a mask image and/or for providing a difference image. Features, advantages, or alternative embodiments mentioned herein may also be applied to other claimed subject matter as well, and vice versa.

In a fifth aspect, the invention relates to a computer program product with a computer program, which can be loaded directly into a memory of a providing unit, with program segments for performing all the steps of the proposed method for providing a mask image and/or for providing a difference image when the program segments are executed by the providing unit. Here, the computer program product may comprise software with source code which also has to be compiled and bound or only has to be interpreted, or executable software code which only has to be loaded into the provision unit for execution. By means of the computer program product, the method for providing a mask image and/or the method for providing a difference image can be performed in a fast, identically repeatable and robust manner by means of the providing unit. The computer program product is configured such that the method steps according to the invention can be executed by means of the providing unit.

The computer program product is stored, for example, on a computer-readable storage medium or on a network or server, from where it can be loaded into a processor of the providing unit, which is directly connected to the providing unit or can be constructed as part of the providing unit. Furthermore, the control information of the computer program product can be stored on an electronically readable data carrier. The control information of the electronically readable data carrier can be designed such that, when the data carrier is used in the provision unit, the control information of the electronically readable data carrier executes the method according to the invention. Examples of electronically readable data carriers are DVD, magnetic tape or USB stick, which store electronically readable control information, in particular software. All embodiments according to the invention of the method described above can be carried out when these control information are read by the data carrier and stored in the provision unit.

The invention may also relate to a computer-readable storage medium and/or an electronically-readable data carrier, on which program segments are stored which are readable and executable by a providing unit for carrying out all the steps of the method for providing a mask image and/or for providing a difference image when the program segments are executed by the providing unit.

A largely software-based implementation has the advantage that the supply unit which has been used up to now can also be modified in a simple manner by software updating in order to operate in the manner according to the invention. In addition to computer programs, such computer program products may optionally also comprise additional components, such as documents and/or additional components, and hardware components, such as hardware keys (dongles, etc.) for using the software.

Drawings

Embodiments of the invention are illustrated in the drawings and described in detail below. In different figures, the same reference numerals are used for the same features.

Figures 1 to 4 show schematic views of different embodiments of the proposed method for providing a mask image,

figure 5 shows a schematic view of an embodiment of the proposed method for providing a difference image,

figure 6 shows a schematic view of the proposed provision unit,

fig. 7 shows a schematic view of a medical C-arm X-ray device for exemplary use in the proposed medical imaging device.

Detailed Description

Fig. 1 schematically shows an advantageous embodiment of the proposed method for providing a mask image. Here, in a first step a), a medical image data ID may be received, the REC-ID having a time dimension. Furthermore, in the second step b), a frequency data set FD of GEN-FD data points, each having a frequency value, can be generated by applying a fourier transformation to the image data ID. Here, a fourier transform may be applied at least along the time dimension. In a third step c), the frequency data set FD may be partitioned SEG-FD into two sub-areas TB1 and TB2 based on at least one frequency threshold FTH. Here, a segmented frequency data set SFD may be provided. Thereafter, in a fourth step d), a GEN-MI mask image MI may be generated by applying an inverse fourier transform to the first sub-region TB1 and/or to the second sub-region TB2 of the frequency data set FD. Furthermore, a PROV-MI mask image MI may be provided in the fifth step e).

The fourier transformation used in step b) for generating the GEN-FD frequency data set FD may comprise, for example, a windowed fourier transformation, in particular a short-time fourier transformation (english: short-time Fourier transform, STFT for short) and/or wavelet transform. The windowed fourier transformation may have, for example, a rectangular function and/or a von hann window function and/or a gaussian window function. In addition, the windowed Fourier transform may be implemented as a Fast Fourier Transform (FFT).

Furthermore, the frequency data set FD may be classified by segmenting SEG-FD into a first sub-zone TB1 and a second sub-zone TB2, the first sub-zone TB1 having frequency values smaller than and/or equal to at least one frequency threshold FTH, the second sub-zone TB2 having frequency values larger than and/or equal to the frequency threshold FTH. In this case, the data points of the first partial region TB1 can correspond in particular to image points of the image data ID which represent static and/or slowly changing segments of the examination region UB, for example bone structures. Furthermore, the data points of the second partial region TB2 may correspond to image points of the image data ID which depict a comparatively rapidly changing section of the examination region UB, for example a medical object and/or a contrast agent and/or a moving anatomical structure arranged therein.

Advantageously, the mask image MI may have masked and unmasked image areas. In this case, the image points of the respective image region can correspond to the data points of the respective partial regions TB1, TB2 of the segmented frequency data set SFD.

In generating the GEN-MI mask image MI by applying an inverse fourier transformation to the first sub-region TB1 of the frequency data set FD, the image points of the unmasked image region of the mask image MI may correspond to the data points of the first sub-region TB1 of the frequency data set FD. The unmasked image regions of the mask image MI can thus correspond in particular to image points of the image data ID which depict temporally static and/or slowly changing sections of the examination region UB. Furthermore, the masked image regions of the mask image MI can correspond in particular to image points of the image data ID which depict comparatively rapidly changing sections of the examination region UB.

Furthermore, in particular, the inverse fourier transformation in step d) can be applied to the first subregion TB1 and to the second subregion TB2 of the divided frequency data set SFD, wherein the frequency value of at least one of the subregions TB1 and/or TB2 is adjusted.

Furthermore, a fourier transform may be applied along at least one spatial axis in step b). The segmentation SEG-FD of the frequency data set in step c) may be based on at least one first frequency threshold with respect to temporal frequencies and at least one second frequency threshold with respect to spatial frequencies.

Fig. 2 shows a schematic view of another advantageous embodiment of the proposed method for providing a PROV-MI mask image. Here, the proposed method may further comprise steps a2) and a 3). Step a2) may comprise identifying a medical object ID-MO and/or an anatomical structure ID-AS in the image data ID. Furthermore, corresponding data points in the frequency data set FD, which data points correspond to the identified medical object and/or anatomical structure, can be identified in step a 3). Advantageously, these corresponding data points may be excluded from the segmentation SEG-FD in step c).

Furthermore, a subset of data points of the frequency data set FD around the corresponding data point may additionally be determined in step a3), which subset is excluded from the segmentation SEG-FD in step c).

Fig. 3 schematically shows another advantageous embodiment of the proposed method for providing a PROV-MI mask image. Here, step a) may also comprise receiving the object parameters REC-OP and/or the structure parameters REC-SP. The object parameters OP can have information about the medical object. Furthermore, the structural parameters SP may have information about the anatomical structure. Advantageously, the corresponding data point may be determined in step a3) on the basis of the object parameter OP and/or the structure parameter SP.

Fig. 4 shows a schematic view of another advantageous embodiment of the proposed method for providing a PROV-MI mask image. Here, step a) may further comprise registering the REG-ID medical image data, wherein the registered medical image data RID may then be provided for step a2) and/or step b). Advantageously, the medical image data ID can in particular depict a common examination region of the examination object along a time dimension with a plurality of individual images. Here, step a) may further comprise receiving a physiological signal and/or a motion signal SIG of the REC-SIG examination subject. Furthermore, at least one frequency threshold FTH in the DET-FTH step c) can be predefined on the basis of the physiological signal and/or the motion signal SIG.

Fig. 5 schematically shows an advantageous embodiment of the proposed method for providing a difference image. In a first step s1, medical image data ID with an ACQ-ID having a time dimension can be recorded by means of a medical imaging device. Furthermore, a PROV-ID medical image data ID may be provided for step a) of the proposed method V1 for providing a PROV-MI mask image. By applying the proposed method V1 for providing a PROV-MI mask image to the image data ID, the REC-MI mask image MI can be received in step s 2). Then, a GEN-DI difference image DI may be generated in step s3) by subtracting and/or multiplying the mask image MI by the medical image data ID. The PROV-DI difference image DI may be provided in a fourth step s 4).

Fig. 6 schematically shows a proposed providing unit PRVS comprising an interface IF, a calculation unit CU and a storage unit MU. The provision unit PRVS may be configured for carrying out the proposed method for providing a PROV-MI mask image and/or for providing a PROV-DI difference image by the interface IF, the calculation unit CU and the storage unit MU being configured for carrying out the respective method steps.

Here, the interface IF may be configured for performing steps a), a2), a3) and/or e) of the proposed method for providing a PROV-MI mask image. Furthermore, the interface IF may be constructed for performing steps s2) and s4) of the proposed method for providing a PROV-DI difference image. Furthermore, the calculation unit CU and/or the storage unit MU may be configured for performing the remaining steps of the proposed method.

The provision unit PRVS may be, in particular, a computer, a microcontroller or an integrated circuit. Alternatively, the providing unit PRVS may be a real or virtual union of the computers (real union english term is "Cluster" and virtual union english term is "Cloud"). The provision unit PRVS may also be constructed as a virtual system (virtualization) executing on a real computer or a real or virtual union of computers.

The interface IF may be a hardware or software interface (e.g. PCI bus, USB or Firewire). The computation unit CU may have hardware or software components, for example a microprocessor or the english abbreviation of the so-called PFGA ("Field Programmable Gate Array"). The Memory unit MU can be implemented as a non-persistent working Memory (Random Access Memory, abbreviated to RAM) or as a persistent mass storage (hard disk, USB disk, SD card, solid-state disk).

The interface IF may in particular comprise a plurality of sub-interfaces which carry out the different steps of the respective method. In other words, an interface IF may also be understood as a plurality of interfaces IF. The computing unit CU may in particular comprise a plurality of sub-computing units which perform the different steps of the respective method. In other words, the calculation unit CU may also be understood as a plurality of calculation units CU.

Fig. 7 schematically shows a medical C-arm X-ray device 37 exemplarily for the proposed medical imaging device. The medical C-arm X-ray device 37 can advantageously comprise the proposed provision unit PRVS, in particular for controlling the medical C-arm X-ray device 37. Here, the medical C-arm X-ray device 37, in particular the proposed providing unit PRVS, is configured for performing the proposed method for providing a PROV-MI mask image and/or for providing a PROV-DI difference image.

Here, the medical C-arm X-ray device 37 further comprises a detector unit 34 and an X-ray source 33. For recording the medical image data ID, the arm 38 of the C-arm X-ray device 37 can be mounted so as to be movable about one or more axes. Furthermore, the medical C-arm X-ray device 37 may comprise a movement device 39, for example a wheel system and/or a rail system, which enables the C-arm X-ray device 37 to move in space.

For recording the medical image data ID of the examination region UB of the examination subject 31 arranged on the patient support 32, the provision unit PRVS can transmit the signal 24 to the X-ray source 33. Subsequently, the X-ray source 33 may emit a beam of X-ray radiation, in particular a cone-beam and/or a fan-beam and/or a parallel beam. When the beam of X-ray radiation hits a surface of the detector unit 34 after interaction with an examination region UB to be imaged of the examination object 31, the detector unit 34 may send a signal 21 to the providing unit PRVS. The provision unit PRVS may receive REC-ID medical image data ID, for example, depending on the signal 21.

Furthermore, the medical C-arm X-ray device 37 may comprise an input unit 42, for example a keyboard and/or a display unit 41, for example a monitor and/or a display. The input unit 42 may preferably be integrated into the display unit 41, for example in case of a capacitive and/or resistive input display. The input of the operator on the input unit 42 here enables a control, in particular a supplementary control, of the medical C-arm X-ray device 37, in particular of the proposed method. For this purpose, the input unit 42 can, for example, send the signal 26 to the provision unit PRVS.

Furthermore, the display unit 41 may be configured for displaying information and/or a graphical display of information of the C-arm X-ray device 37 and/or the provision unit PRVS and/or other components. For this purpose, the provision unit PRVS may for example send a signal 25 to the display unit 41. The display unit 41 may in particular be configured for displaying a graphical display of the medical image data ID and/or the frequency data set FD and/or the mask image MI and/or the difference image DI.

The schematic representations contained in the drawings described do not reflect dimensions or size ratios.

Finally, it should again be pointed out that the method described in detail above and the device shown are only embodiments which can be modified in different ways by a person skilled in the art without departing from the scope of the invention. Furthermore, the use of the indefinite article "a" or "an" does not exclude that a related feature may also be present in plural. The terms "unit" and "element" also do not exclude that an associated component is made up of a plurality of cooperating sub-components, which may also be spatially distributed, if desired.