Subtraction Imaging

Abstract

To further improve the accuracy of masking the image, an apparatus for digital subtraction imaging is provided, having an image data input, a data processor, and an output interface. 3D image data of a region of interest of an object having a 3D representation of the object based on reconstruction from a plurality of 2D projection images and a 2D live X-ray image of the region of interest are received and registered, and a matching pose of the 3D image data corresponding to the 2D live X-ray image is determined. A digital reconstruction radiograph is calculated from the 3D image data based on the determined matching pose to generate a 2D mask image. Current data associated with the 2D live X-ray image is used to obtain a fitted 2D mask image, comprising 2D live acquisition parameters and / or data of the 2D live X-ray image. The produced fitted 2D mask is an image subtracted from the 2D live X-ray image to provide a digital image highlighting changes in the region of interest.

Description

[Technical field]

[0001] The present invention relates to subtraction imaging, and in particular to an apparatus for digital subtraction imaging, an imaging system for digital subtraction imaging, and a method for digital subtraction imaging. [Background technology]

[0002] X-ray imaging is used to visualize regions of interest within a subject. However, X-ray based imaging techniques, such as fluoroscopy, computed tomography, etc., are limited with respect to blood vessels because the X-ray attenuation properties of blood and adjacent tissues are very similar. To visualize the vasculature, a radiopaque contrast agent can be injected into the blood vessels to increase the X-ray attenuation within the vessels. To visualize the blood vessels more clearly without scattering from the surrounding tissues that can reduce the visibility of the vessels, a digital subtraction angiography (DSA) image is calculated by subtracting a pre-contrast mask image from a contrast-enhanced image of the same structure. However, due to patient motion, the alignment of the mask image (typically uninjected) and the live image (typically filled with contrast agent) is not perfect. The net effect is a suboptimal subtraction in the presence of what are usually referred to as motion artifacts. US10682112B2 describes the suppression of independent motions in a series of 3D X-ray fluoroscopic images using a 3D preoperative volume, where a digitally reconstructed radiographic image of the 3D-2D registration of the (preoperative) three-dimensional volume of the structures to be removed from the X-ray images is generated. This includes the projection of the structures onto the correct viewing plane as well as their registration. The digitally reconstructed radiographic image is subtracted from the X-ray image to generate a structure-suppressed fluoroscopic image without the undesired / interfering motions of the suppressed structures. However, it has been shown that artifacts can still occur. Summary of the Invention [Problem to be solved by the invention]

[0003] Therefore, there may be a need to further improve the accuracy of masking the image. [Means for solving the problem]

[0004] The object of the present invention is solved by the subject matter of the independent claims, further embodiments are incorporated in the dependent claims. It is noted that the below described aspects of the invention also apply to an apparatus for digital subtraction imaging, an imaging system for digital subtraction imaging and a method for digital subtraction imaging.

[0005] According to the present invention, an apparatus for digital subtraction imaging is provided. The apparatus comprises an image data input, a data processor, and an output interface. The image data input is configured to receive 3D image data of a region of interest of the object, the 3D image data having a 3D representation of the object based on reconstruction from a plurality of 2D projection images of the region of interest. The image data input is also configured to receive a 2D live X-ray image of the region of interest. The data processor is further configured to register the 3D image data and the 2D live X-ray image, and a matching pose of the 3D image data corresponding to the 2D live X-ray image is determined. The data processor is also configured to calculate digitally reconstructed radiography from the 3D image data based on the determined matching pose to generate a 2D mask image. The data processor is also configured to use current data associated with the 2D live X-ray image to obtain an adapted 2D mask image for generation of the 2D mask image. The data associated with the 2D live X-ray image comprises at least one of the following groups: 2D live acquisition parameters and data of the 2D live X-ray image. The data processor is further configured to subtract the generated fitted 2D mask image from the 2D live X-ray image.Further, the output interface is configured to provide a digital image highlighting changes in the region of interest.

[0006] In effect, the adaptation compensates for the fact that the Digitally Reconstructed Radiographic (DRR) image and the live image result from different data and formation processes, the adaptation reduces artifacts in the subtraction image and thus provides visualization of the current situation in the form of the subtraction image with improved accuracy.

[0007] According to one example, the image data input is configured to receive an angiographic contrast injection 2D live X-ray image of the region of interest as a 2D live X-ray image. The data processor is configured to subtract the generated fitted 2D mask image from the angiographic contrast injection 2D live X-ray image. The output interface is configured to provide a digital subtraction angiogram representative of the vascular structure within the region of interest.

[0008] According to one example, the image data input is configured to receive projection data of a region of interest comprising a plurality of 2D projection images to obtain an adapted 2D mask image. The image data input is also configured to receive 2D data characteristics from the acquisition of a 2D live X-ray image. Furthermore, the data processor is configured to adapt a reconstruction of the projection data of the region of interest based on the 2D data characteristics to generate adapted 3D image data comprising an adapted 3D representation of the object. The data processor is also configured to use the adapted 3D image data for the calculation of a digital reconstruction radiograph.

[0009] According to an option, the data processor is configured to also use the adapted 3D image data for registration.

[0010] According to one example, the image data input is configured to receive 2D data characteristics from the acquisition of the 2D live X-ray image to obtain the adapted 2D mask image, and the data processor is configured to adapt the calculation of the digital reconstruction radiography from the 3D image data based on the 2D data characteristics to generate the adapted 2D mask image.

[0011] According to one example, the 2D data characteristics are based on 2D live acquisition parameters having at least one of the following groups: i) spectrum, resolution, and x-ray imaging energy. Additionally or alternatively, the 2D data characteristics are based on image data of the 2D live x-ray image having at least one of the following groups: ii) resolution and contrast.

[0012] According to one example, for 3D image data, the adapted reconstruction comprises reconstructed attenuation values ​​that correspond to a given average photon energy expected to be used for the 2D live X-ray images.

[0013] According to an option, the data processor is configured to segment the segmented 3D image into regions of bone and soft tissue types, such that each tissue type has an individually adjusted attenuation value for the expected photon energy.

[0014] According to another option, the data processor is configured to apply different spectrally related factors for the digitally reconstructed radiograph.

[0015] According to a further option, the 3D image data is vessel-free.

[0016] According to one example, the data processor is configured to estimate a pose of the object in the 2D live X-ray image. For registration of the 3D image data and the 2D live X-ray image, the data processor is configured to align a 3D representation of the object in the 3D image data to the estimated pose. Additionally or alternatively, for reconstruction of projection data of the region of interest, the data processor is configured to take the estimated pose into account for the reconstruction.

[0017] According to one example, the data processor is configured to provide a 2D scatter estimate or a 3D scatter estimate using the 2D live X-ray image as an input. The data processor is configured to add the scatter signal to the calculated digital reconstruction radiograph.

[0018] According to one example, as a correction for the reconstruction of the 3D image data, the data processor is configured to: i) provide a virtual heel effect correction. Additionally or alternatively, the data processor is configured to: ii) provide a virtual inverse detector gain correction.

[0019] According to one example, a display is provided for presenting a digital representation of the vascular structure within the region of interest.

[0020] According to the invention there is also provided an imaging system for digital subtraction imaging, the system comprising an X-ray imaging device having an X-ray source and an X-ray detector, and an apparatus for digital subtraction imaging according to one of the previous examples, the X-ray imaging device being configured to generate a 2D live X-ray image.

[0021] According to the invention, a method for digital subtraction imaging is also provided. The method comprises the steps of: receiving 3D image data of a region of interest of an object, the 3D image data having a 3D representation of the object based on a reconstruction from a plurality of 2D projection images of the region of interest; receiving a 2D live X-ray image of the region of interest; registering the 3D image data with the 2D live X-ray image, where a matching pose of the 3D image data corresponding to the 2D live X-ray image is determined; and calculating a digital reconstruction radiograph from the 3D image data based on the determined matching pose to generate a 2D mask image. To generate the 2D mask image, current data associated with the 2D live X-ray image is used to obtain an adapted 2D mask image, the data associated with the 2D live X-ray image comprising at least one of the following groups: 2D live acquisition parameters and data of the 2D live X-ray image. Further provided are the steps of subtracting the generated adapted 2D mask image from the 2D live X-ray image and providing a digital image highlighting changes in the region of interest.

[0022] The field of application is neurology, in particular neurointervention. Another suitable field is spine-related intervention.

[0023] According to one embodiment, the 3D generated mask image is adapted to the live image. To that end, in one example, live image characteristics are extracted and introduced into the reconstruction process to adapt the generated 3D volume to the live image. Alternatively or additionally, the matching digitally reconstructed radiographic image is adapted to the content and characteristics of the live image. This (potentially dual) mechanism targets at least spectral, scattering, and resolution adaptation.

[0024] According to one embodiment, two main sources of adaptations are provided: in a first option, one adaptation is provided at the time of 3D reconstruction, i.e. during the 3D reconstruction, and in a second option, additionally or alternatively, one adaptation is provided at the time of digitally reconstructed radiography generation, i.e. during the calculation of the digitally reconstructed radiography. According to one embodiment, these two sources are combined when used together, but can also be used independently, e.g. in the absence of the other.

[0025] The adaptation is particularly suitable for use in interventional X-ray imaging systems, e.g., fixed and mobile imaging systems, for various X-ray based procedures using contrast enhanced imaging, including, but not limited to, intravascular procedures, e.g., stenosis detection, aneurysm embolization, neurological stroke treatment, and the like.

[0026] According to another aspect, an adaptation of the mask image generation to the current live situation is optionally provided for other subtractive imaging techniques, especially in roadmapping or device visualization. For example, during the maneuvering of a device in the vasculature, the fluoroscopic image is visualized after subtraction to remove the background content and leave only the device visible. In another example, a vascular footprint acquired at a previous intervention time is superimposed during the device manipulation for guidance purposes. It should be noted that any movement occurring between the two steps of the imaging technique (previous moment vs. live moment) will degrade the subtraction process. Therefore, adaptation is also suitable in these cases.

[0027] According to a further aspect, an apparatus for digital subtraction imaging is provided, comprising an image data input, a data processor, and an output interface. 3D image data of a region of interest of an object having a 3D representation of the object based on reconstruction from a plurality of 2D projection images is received. Furthermore, a 2D live X-ray image of the region of interest is received. The 3D image data and the 2D live X-ray image are registered, and a matching pose of the 3D image data corresponding to the 2D live X-ray image is determined. A digital reconstruction radiography is calculated from the 3D image data based on the determined matching pose to generate a 2D mask image. For the 2D mask image, current data associated with the 2D live X-ray image is used to obtain an adapted 2D mask image, the data associated with the 2D live X-ray image comprising 2D live acquisition parameters and / or data of the 2D live X-ray image. The generated adapted 2D mask is image subtracted from the 2D live X-ray image to provide a digital image highlighting changes in the region of interest.

[0028] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0029] Exemplary embodiments of the invention are described below with reference to the following drawings: [Brief description of the drawings]

[0030] [Figure 1] 1 illustrates a schematic diagram of an example of an apparatus for digital subtraction imaging. [Diagram 2] 1 illustrates an example of an imaging system for digital subtraction imaging. [Diagram 3] 1 illustrates the basic steps of an example method for digital subtraction imaging. [Figure 4] 1 shows an example of a workflow for digital subtraction imaging. [Diagram 5] 1 illustrates another example of a workflow for digital subtraction imaging. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0031] Certain embodiments will now be described in detail with reference to the accompanying drawings. In the following description, like drawing reference numbers are used for like elements in different drawings. Matters defined herein, such as detailed configurations and elements, are provided to aid in a comprehensive understanding of the exemplary embodiments. Also, well-known functions or configurations will not be described in detail since they would obscure the embodiments in unnecessary detail. Furthermore, phrases such as "at least one of," when preceding a list of elements, modify the entire list of elements and do not modify individual elements of the list.

[0032] The term "subject" may also be referred to as an individual. A "subject" may also be referred to as a patient, although it should be noted that this term does not indicate whether any disease or disorder is actually present in the subject.

[0033] 1 shows a schematic diagram of an example of an apparatus 10 for digital subtraction imaging. The apparatus 10 has an image data input section 12, a data processor 14, and an output interface 16.

[0034] Optionally, a display 18 is also provided for presenting a digital image representative of the vascular structure within the subject's region of interest. Dashed arrow 20 indicates a data connection providing data for display 18.

[0035] The image data input 12 is configured to receive 3D image data 22 of a region of interest of the object, the 3D image data comprising a 3D representation of the object based on reconstruction from a plurality of 2D projection images of the region of interest. The image data input 12 is also configured to receive a 2D live X-ray image 24 of the region of interest.

[0036] The data processor 14 is configured to register the 3D image data and the 2D live X-ray image, and a matching pose of the 3D image data corresponding to the 2D live X-ray image is determined. The data processor 14 is also configured to calculate a digital reconstruction radiograph from the 3D image data based on the determined matching pose to generate a 2D mask image. For the generation of the 2D mask image, the data processor 14 is further configured to use current data associated with the 2D live X-ray image to obtain an adapted 2D mask image, the data associated with the 2D live X-ray image comprising at least one of the following groups: 2D live acquisition parameters and data of the 2D live X-ray image. The data processor 14 is further configured to subtract the generated adapted 2D mask image from the 2D live X-ray image.

[0037] The output 16 interface is configured to provide a digital image, indicated by arrow 20, which highlights changes in the region of interest.

[0038] The surrounding frame 26 illustrates the option that the image data input 12, the data processor 14, and the output interface 16 can be located within a common structure, such as a housing, however, they can also be provided as separate components.

[0039] 2 shows an example of an imaging system 50 for digital subtraction imaging. The system 50 comprises an X-ray imager 52 having an X-ray source 54 and an X-ray detector 56. Further, an example of an apparatus 10 for digital subtraction imaging according to one of the above and following examples is provided. The X-ray imager 52 is configured to generate a 2D live X-ray image.

[0040] The X-ray imaging device 52 has a support structure 58 that is movably suspended from a ceiling structure. Another option is to provide a mobile X-ray imaging device or a fixed but mobile system supported on the floor. In the illustrated example, a movable C-arm 60 is provided that carries the X-ray source 54 and the X-ray detector 56. A subject 62 having a region of interest is shown, the subject being positioned on an adjustable subject support table 64. A bedside controller 66 is also shown. Provided above are lighting equipment 68 and display equipment 70.

[0041] An example of an apparatus 10 for digital subtraction imaging is provided in the context of a workstation or console 72, shown in the foreground. The console 72 has a display 74, a keyboard 76, and a mouse 78. Additionally, a control console 80 and a graphics tablet 82 are shown.

[0042] An imaging system for digital subtraction imaging is also referred to as an imaging system for digital subtraction angiography.

[0043] Optionally, the X-ray imager is also configured to generate a plurality of 2D projection images for reconstruction of 3D image data.

[0044] 3 shows the basic steps of an example of a method 100 for digital subtraction imaging. The method 100 comprises the following steps: In a first step 102, 3D image data of a region of interest of an object is received. The 3D image data comprises a 3D representation of the object based on a reconstruction from a number of 2D projection images of the region of interest. In a second step 104, a 2D live X-ray image of the region of interest is received. In a third step 106, the 3D image data and the 2D live X-ray image are registered: a matching pose of the 3D image data corresponding to the 2D live X-ray image is determined. In a fourth step 108, a digital reconstruction radiograph is calculated from the 3D image data based on the determined matching pose to generate a 2D mask image. To generate the 2D mask image, current data associated with the 2D live X-ray image is used to obtain an adapted 2D mask image, the data associated with the 2D live X-ray image comprising at least one of the following groups: 2D live acquisition parameters and data of the 2D live X-ray image. In a fifth step 110, the generated fitted 2D mask image is subtracted from the 2D live X-ray image. In a sixth step 112, a digital image is provided that highlights the changes in the region of interest.

[0045] Optionally, a 2D live X-ray image is provided as an angiographic contrast injection 2D live X-ray image of the region of interest. For subtraction, the generated fitted 2D mask image is subtracted from the angiographic contrast injection 2D live X-ray image. To provide a digital image, a digital subtraction angiogram representing the vascular structure within the region of interest is provided.

[0046] The method for digital subtraction imaging may also be referred to as a method for digital subtraction angiography.

[0047] In one example of this method, a plurality of 2D projection images are provided as a set of images forming a scan of an image. In one example, a plurality of scans, i.e., two or more sets of images, each having a plurality of 2D projection images, are provided.

[0048] A computed digital reconstruction radiograph is an approximation of the 2D image that an x-ray system would produce when imaging a 3D volume under the provided poses.

[0049] The 3D image data is also referred to as 3D reconstruction data. The 3D image data provides projection data for the generation of images such as 2D projection images, i.e. X-ray images, but in this case a virtual projection of the 3D image data onto a virtual detector plane. Therefore, the 3D image data is also referred to as projection data.

[0050] The 2D live X-ray image is also referred to as 2D live data.

[0051] The 3D image data is also referred to as a 3D image dataset.

[0052] The 3D image data is provided as 3D reconstruction data, i.e. as a 3D data set representative of the region of interest.

[0053] In one example, the 3D reconstruction data is a 3D data set representing the region of interest. For example, in neurointervention, this can be a 3D representation of the head, reconstructed from a rotational scan performed without contrast injection and generating so-called projection data. In this case, the 3D data represents the anatomy of the head of bones and soft tissues, but does not include vascular parts (which are invisible on X-rays). Without contrast injection, these cannot be distinguished from the surrounding soft tissues using X-rays.

[0054] In another scenario, a 3D scan with contrast-enhanced vasculature is provided, and 2D live (fluoroscopy) data is provided without contrast, which is used to create a virtual vascular roadmap on the fluoroscopy data, allowing navigation without further contrast injection.

[0055] The 2D live X-ray images are provided, i.e. acquired, by angiographic acquisition.

[0056] In one example, the 2D projection scans are provided as multiple scans of a rotational C-arm scan, hi another example, the 2D projection scans are provided as multiple scans of a CT scan.

[0057] In one variation, a C-arm scan is provided without contrast.

[0058] In one example, current data associated with a 2D live X-ray image has 2D data characteristics. In one example, certain required characteristics of the 2D data, such as an X-ray spectrum, used to generate the live image or its resolution are collected. The 2D data characteristics can be derived from acquisition parameters from the system or directly from the live image content itself.

[0059] In one example, a 2D live x-ray image is provided as a vascular contrast injection 2D live x-ray image of the region of interest.

[0060] In another example, a 2D live x-ray image is provided without contrast as a fluoroscopic image with the device inserted, for example subtraction can then help highlight the device during intravascular navigation.

[0061] 1, in an example of an apparatus not shown in detail, an image data input 12 is configured to receive an angiographic contrast injection 2D live X-ray image of the region of interest as a 2D live X-ray image. A data processor 14 is configured to subtract the generated fitted 2D mask image from the angiographic contrast injection 2D live X-ray image. An output interface 16 is configured to provide a digital subtraction angiogram representative of the vascular structure in the region of interest. The apparatus 10 for digital subtraction imaging may also be referred to as an apparatus for digital subtraction angiography.

[0062] In one example of the apparatus, shown as an option in Fig. 1, the image data input 12 is configured to receive projection data of a region of interest comprising a plurality of 2D projection images to obtain an adapted 2D mask image. The image data input 12 is also configured to receive 2D data characteristics 28 from the acquisition of a 2D live X-ray image. The data processor 14 is configured to adapt the reconstruction of the projection data of the region of interest based on the 2D data characteristics 28 to generate adapted 3D image data having an adapted 3D representation of the object. The data processor 14 is further configured to use the adapted 3D image data for the calculation of a digital reconstruction radiograph.

[0063] Optionally, the data processor 14 is configured to also use the adapted 3D image data for the registration.

[0064] In one example, an adapted 3D reconstruction is provided, which allows for a specific adaptation to generate a volume that is more capable of delivering a 2D-live compatible digitally reconstructed radiographic image, which provides compatibility with a given volume of the targeted anatomical structure through the adapted digitally reconstructed radiography idea.

[0065] In one example of an apparatus, shown as an additional or alternative option in Figure 1, the image data input 12 is configured to receive 2D data characteristics 30 from the acquisition of 2D live X-ray images to obtain an adapted 2D mask image. The data processor 14 is configured to adapt the calculation of a digitally reconstructed radiograph from the 3D image data based on the 2D data characteristics 30 to generate the adapted 2D mask image.

[0066] In one example, for adapted DRR generation, the digital reconstruction radiography can be adapted to the live image content and characteristics. For example, a 3D or 2D scatter estimator is used to add scatter signals to the digital reconstruction radiography. The 2D approach uses an angiogram image as input, rather than the DRR (digital reconstruction radiography) or 3D reconstruction, since the 2D approach may include scattering structures that are not in the field of view of the 3D reconstruction image, such as the subject support, also referred to as the patient table.

[0067] Further improvements that can make digital reconstruction radiography and angiography more consistent are the virtual heel effect and virtual inverse detector gain correction. For both effects, calibration data from the image reconstruction pipeline is used. However, in one example, the digital reconstruction radiography process also includes spectral adaptation or resolution restoration as post-processing. In one example, the adapted digital reconstruction radiography calculation receives both the live image and its characteristics as input.

[0068] Optionally, the reconstruction of the projection data of the region of interest and the calculation of the digital reconstruction radiography are adapted based on the 2D data characteristics to obtain an adapted 2D mask image.

[0069] In one example of the device, not shown in detail, the 2D data characteristics are based on 2D live acquisition parameters having at least one of the following groups: i) spectrum, resolution, and x-ray imaging energy. Alternatively or additionally, the 2D data characteristics are based on image data of a 2D live x-ray image having at least one of the following groups: ii) resolution and contrast.

[0070] The fit using the 2D data characteristics provides a calibrated alignment between the data acquisition (of multiple 2D images) for the 3D data and the data acquisition for the 2D live data.

[0071] During the adapted 3D reconstruction, characteristics of the 2D live data, such as the spectrum, are used in one example to improve 3D / 2D compatibility. As an example, the compatibility of the 3D reconstruction is improved by using a specific beam hardening correction method during the reconstruction of the 3D attenuation values. For example, it may be beneficial if the reconstructed attenuation values ​​correspond to the average photon energy expected to be used by the 2D live data acquisition. This average photon energy may differ from the average photon energy during the rotational acquisition used for the 3D reconstruction, for example, because the X-ray spectra used to generate the 3D data and the 2D data may be different.

[0072] For this purpose, the 3D image can be further segmented into bone and soft tissue regions, for example by using thresholds or model-based segmentation, so that each tissue type has an individually adjusted attenuation value for the expected photon energy. As an effect, X-ray spectral discrepancies between the projected and live data are addressed.

[0073] In further examples, other adaptations of the 3D reconstruction, such as scatter correction (wherein the scatter effects are removed during reconstruction) and spatial resolution mismatches, are provided.

[0074] In one example, a dual energy or spectral 3D acquisition is provided and one or more reconstructions for a particular energy and / or material type are created and combined into a final matched 3D image.

[0075] In one example, when the projection data does not allow for an ideal 3D reconstruction resolution to match the 2D live data, neural network-based super-resolution methods are used to further improve the 3D image resolution.

[0076] In an example of an apparatus not shown in detail, for 3D image data the adapted reconstruction has reconstructed attenuation values ​​corresponding to a given average photon energy expected to be used for the 2D live X-ray images.

[0077] Further, optionally, the data processor 14 is configured to segment the segmented 3D image into regions of bone and soft tissue types, such that each tissue type has an attenuation value individually tailored to the expected photon energy.

[0078] Further, as another option, alternatively or additionally, data processor 14 is configured to apply different spectrally related factors for digitally reconstructed radiographs.

[0079] Further, as another option, alternatively or additionally, the 3D image data is blood vessel free.

[0080] As a result, for virtual projection in 2D, the 3D image data is provided as adapted 3D image data, which is optimized for digital reconstruction to generate a digitally reconstructed radiography. The adapted 3D image data is less suitable for 3D viewing purposes compared to standard 3D reconstructions, but can be improved in terms of creating a mask image.

[0081] The average photon energy is typically different from the average photon energy during the rotational acquisition used (as the X-ray spectra used to generate the 3D and 2D data are usually different).

[0082] Optionally, for segmentation, threshold-based or model-based segmentation is provided.

[0083] In one example, a predetermined average photon energy is provided in lieu of beam hardening correction.

[0084] In one example, for reconstruction of projection data of the region of interest, an adapted reconstruction is provided, including beam hardening correction during reconstruction. The 3D image data is separated into at least two different absorption rate material types. For digital reconstruction radiography, different spectral related factors are applied. The 3D image data is vessel-less. The term "vessel-less" refers to 2D or 3D image data in which the vessels of the vasculature cannot be identified based on the image. Thus, the 3D image is provided without identifiable vessels.

[0085] For example, a first absorption rate material is assigned to portions representing bone and a second absorption rate material is assigned to portions representing tissue.

[0086] In one example, the 3D image data is based on contrast-injected X-ray images, and portions of the image that exhibit contrast are removed for volume generation.

[0087] As an example, external objects such as blood vessels filled with contrast agent, or removable medical devices, or one or several ECG cables, may be removed from the 3D data if they are not present in the 2D live data and are not of interest for a particular application.

[0088] Beam hardening is one example of a specific adaptation provided to generate volumes for which it is more feasible to deliver 2D-live compatible digitally reconstructed radiological images.

[0089] In one example of an apparatus not shown in detail, the data processor 14 is configured to estimate a pose of the object in the 2D live X-ray image. For registration of the 3D image data with the 2D live X-ray image, the data processor 14 is configured to align a 3D representation of the object in the 3D image data to the estimated pose. Alternatively or additionally, for reconstruction of projection data of the region of interest, the data processor 14 is configured to take the estimated pose into account for the reconstruction.

[0090] The term "pose" refers to the spatial arrangement of the imaging device relative to the subject being imaged. Thus, the term pose refers to spatial discrimination. The term "pose" refers to the subject's pose, i.e. the arrangement of movable body parts or segments relative to each other, such as a closed or open jaw, and the geometric arrangement of the X-ray source and X-ray detector relative to each other and to the subject.

[0091] In one example, a matching pose of the 3D data corresponding to the 2D live image is determined from the 3D reconstruction data and the live image. In one example, this is provided as an example of 3D-2D registration, with the possible constraint that this operation may ignore the presence of injected structures (vessels) in one or the other data source (usually the 2D live data).

[0092] For pose estimation, a number of computer vision or machine learning methods are offered to solve this task. In one example, the reconstruction operation is adapted and external radiopaque markers implanted in the patient are used to make this operation faster or more robust. For example, markers are present in both the 3D and 2D live data, and a geometric transformation that matches the projections from 3D to their life positions in 2D determines the target pose.

[0093] In an example of the apparatus not shown in detail, to find a matching pose, the data processor 14 is configured to provide at least one of the following group: i) pose estimation based on system-based geometric registration, ii) segmentation-based pose estimation, iii) a constraint that the presence of contrast-injected vasculature is ignored in the 2D live X-ray images and / or the 3D image data, and iv) generation of multiple digitally reconstructed radiographic images for different projection directions of the 3D image data and use of the multiple digitally reconstructed radiographic images to identify possible matches with the 2D live images.

[0094] The vasculature injected with contrast agent represents blood vessels.

[0095] In one example, radiopaque markers are provided attached to the subject for matching poses, and these markers are present in both the 3D image data and the 2D live x-ray images. A geometric transformation that matches the projections from 3D to positions in 2D determines the target pose.

[0096] In one example, an external radiopaque marker is temporarily implanted in the subject.

[0097] In one example, an image-based registration is provided.

[0098] In one example, registration of the 3D image data and the 2D live image is provided depending on the target anatomical structure: for rigid anatomical structures, a rigid transformation is provided; for non-rigid structures, an elastic deformation is provided.

[0099] In one example, the 3D image data is provided as at least two different sub-volumes, the sub-volumes being adjustable in spatial orientation relative to one another.

[0100] For example, respective sub-volumes that can be articulated within the 3D data to compensate for the subject's jaw movement are identified.

[0101] In an example of an apparatus not shown in detail, for mask generation, the data processor 14 is arranged to invert the transfer function of the 3D reconstruction plus projection process. In other words, the inverse of the transfer function of the "3D reconstruction + projection" process is provided. This has the effect that a reprojection is provided that matches as well as possible with the live image.

[0102] For example, both radiometry and full spatial spectrum are adapted.

[0103] In addition, this should be done on a spatially-varying basis.To achieve this goal, blind or informed deconvolution / restoration techniques are presented.

[0104] In one example of an apparatus, not shown in detail, the data processor 14 is configured to provide a 2D or 3D scatter estimate using a 2D live X-ray image as input. The data processor 14 is configured to add the scatter signal to a calculated digitally reconstructed radiograph.

[0105] This provides the advantage that scattering structures that are not within the field of view of the 3D reconstructed image can be included, for example the patient table.

[0106] This provides a further improvement that can make the DRR and angiograms more consistent.

[0107] In one example, 2D scattering estimation uses angiogram images as input.

[0108] In an example of an apparatus not shown in detail, as corrections for the reconstruction of 3D image data, the data processor 14 is configured to provide i) a virtual heel effect correction, and additionally or alternatively, the data processor 14 is configured to provide ii) a virtual inverse detector gain correction.

[0109] Heel effect correction and detector gain correction are used to generate a virtually uniform X-ray intensity. Due to the heel effect, the radiation intensity of X-rays varies slightly for each radiation angle. The X-ray detection efficiency of the detector varies from pixel to pixel due to various factors such as the internal detector configuration. In the 2D live data, these effects may or may not be corrected. Furthermore, if corrections are applied, they may be simplifications of the real effects and therefore may not be completely accurate. For DRR generation, these correction steps can be applied inversely, where no corrections are used for the 2D live data. Thus, in the DRR, virtual non-uniformities can be generated similarly to the 2D live data. Instead, the heel effect and detector gain can be considered during DRR generation simulating the physical reality of the system, and then simplified corrections of the 2D live data are applied to achieve the same corrections with limited accuracy that are created on the DRR.

[0110] In one example, for both effects, calibration data from the image reconstruction pipeline is used.

[0111] In one example, if the digitally reconstructed radiography does not geometrically match the live image after restoration, a 2D registration process may be provided. As an example, at least some of the out-of-plane motion is corrected by a fitting step. Thus, the subsequent 2D registration process can provide good results even in the presence of severe patient motion. Subtraction can then be applied to generate a clean and improved digital subtraction angiogram.

[0112] Returning to FIG. 3, in an example method not shown in detail, the following steps are performed to obtain an adapted 2D mask image: receiving projection data of a region of interest having a plurality of 2D projection images; receiving 2D data characteristics from an acquisition of a 2D live X-ray image; adapting a reconstruction of the projection data of the region of interest based on the 2D data characteristics to generate adapted 3D image data having an adapted 3D representation of the object; is provided. The adapted 3D image data is used for the calculation of a digital reconstruction radiograph.

[0113] Optionally, the adapted 3D image data is also used for registration.

[0114] In one example of the method, to obtain the fitted 2D mask image, the following steps are performed: receiving 2D data characteristics from an acquisition of a 2D live X-ray image; adapting a calculation of a digital reconstruction radiograph from the 3D image data based on the 2D data characteristics to generate an adapted 2D mask image; is provided.

[0115] In one example, the 2D data characteristics are based on 2D live acquisition parameters having at least one of the following group: i) spectrum, resolution, and x-ray imaging energy. Additionally or alternatively, the 2D data characteristics are based on image data of the 2D live x-ray image having at least one of the following group: ii) resolution and contrast.

[0116] In one example of the method, for 3D image data, the adapted reconstruction has reconstructed attenuation values ​​that correspond to a given average photon energy expected to be used for the 2D live X-ray image.

[0117] In an option of the method, the 3D image is segmented into regions of bone and soft tissue types such that each tissue type has an individually adjusted attenuation value for the expected photon energy. In another option, different spectral correlation factors are applied for digitally reconstructed radiography. In a further option, the 3D image data is blood vessel free.

[0118] In one example of the method, a step of estimating a pose of the object in the 2D live X-ray image is provided. Furthermore, i) for the registration of the 3D image data with the 2D live X-ray image, a 3D representation of the object in the 3D image data is aligned to the estimated pose. Additionally or alternatively, ii) for the reconstruction of projection data of the region of interest, the estimated pose is taken into account for the reconstruction.

[0119] In one example method, to find a matching pose, i) Pose estimation based on system-based geometric alignment; ii) segmentation-based pose estimation; iii) the constraint that the presence of contrast-injected vascular structures is ignored in the 2D live X-ray images and / or 3D image data; and iv) generating a plurality of digitally reconstructed radiographic images for different projection directions of the 3D image data, and using the plurality of digitally reconstructed radiographic images to identify possible matches with the 2D live image; At least one of the groups is provided. In one example of the method, for mask generation, the transfer function of the 3D reconstruction plus projection process is inverted.

[0120] In one example of the method, a 2D scatter estimate is provided using a 2D live X-ray image, e.g., an angiogram image, as input. In another example, a 3D scatter estimate is provided. The scatter signal is added to a computed digital reconstruction radiograph.

[0121] In one example of the method, i) a virtual heel effect correction, and / or ii) a virtual inverse detector gain correction are provided as corrections for the reconstruction of 3D image data.

[0122] 4 shows an example workflow 200 for digital subtraction imaging. To start with, 2D live data 202 and 3D reconstruction data 204 are provided. The 2D live data 202 is used 206 for a pose estimation procedure 208. The 3D reconstruction data 204 is also used 210 for the pose estimation procedure 208.

[0123] Optionally, as indicated by the dashed line, projection data 212 is provided and used (214) for an adapted 3D reconstruction procedure 216, leading to adapted 3D reconstruction data 204' (218).

[0124] The pose estimation procedure 208 is coupled (220) to a DRR generation 222 that also receives (224) input from the 3D reconstruction data 204 or the adapted 3D reconstruction data 204'. The DRR generation 222 is coupled (226) to a transfer function inversion procedure 228.

[0125] Optionally, as indicated by the dashed line, the 2D live data 202 is also fed 230 to the transfer function inversion procedure 228 .

[0126] The transfer function inversion procedure 228 is connected (232) to a subtraction procedure with possible 2D alignment 234, to which the 2D live data 202 is also provided (236). As a result of the subtraction procedure with possible 2D alignment 234, a result 240 is provided (238), which may be displayed, for example, as a subtraction image, i.e. an adapted subtraction image.

[0127] 5 shows another example of a workflow 300 for digital subtraction imaging. Starting so to speak, 2D live data 302, 2D live acquisition parameters 304, and projection data 306 are provided. The projection data 306 is used (308) in an adaptive 3D reconstruction procedure 310. The 2D live acquisition parameters 304 are provided (312) to a 2D data characterization procedure 314 that provides data such as spectrum, resolution, etc., and also receives (316) the 2D live data 302. The 2D data characterization procedure 314 is connected (318) to the adaptive 3D reconstruction procedure 310. The adaptive 3D reconstruction procedure 310 leads (320) to 3D reconstruction data 322 that is provided (324) to a pose estimation procedure 326. The pose estimation procedure 326 also receives (328) the 2D live data 302.

[0128] Optionally, as indicated by the dashed line, the pose estimation procedure is connected (330) back to the adapted 3D reconstruction procedure 310, providing a looped procedure for optimization in determining pose. The connecting arrow 330 from the pose estimation 326 to the 3D reconstruction 310 indicates that the fit of the 3D reconstruction to the live data can be refined once the pose is known. In that case, not only the live data characteristics but also the actual live data content can drive the fit process. In one example, an iterative reconstruction method is used with the aligned live data as an additional input. The reconstruction parameters are iteratively optimized to maximize the similarity of the DRR to the aligned live data. For example, the root mean square difference in selected regions of the live data is used to measure this similarity.

[0129] The pose estimation procedure 326 is further connected (332) to an adapted DRR procedure 334 that receives (336) the 2D live data 302, and is also connected (338) to the 2D data characterization procedure 314. The adapted DRR procedure 334 is connected (340) to a subtraction + 2D registration procedure 344, which also receives (342) the 2D live data 302. As a result of the subtraction + 2D registration procedure 344, a result 348 is provided (346), which may be displayed, for example, as a subtraction image, i.e., an adapted subtraction image.

[0130] Note that in Figures 4 and 5, square cornered boxes represent data and round cornered boxes represent procedures, i.e., processing steps or data collection / provision steps. Arrows represent application of processing steps to input data to generate output data. Dashed arrows represent optional application of processing steps to input data to generate output data.

[0131] It should further be noted in Figure 5 that frames 310, 314 and 334 offer fundamentally different adaptation options: frames 306 and 322 relate to a 3D context, whereas frames 304 and 302 relate to a live 2D context.

[0132] In another example, a computer program is provided having computer readable code or instructions that, when executed by a processor, enables the processor to perform a method of one of the preceding examples, for example on a suitable system.

[0133] In one example, a computer program or program element for controlling an apparatus according to one of the above examples is provided, which program or program element is configured to perform the method steps of one of the above method examples when executed by a processing unit.

[0134] Thus, the computer program elements may be stored in a computing unit or distributed across one or more computing units that may be part of an embodiment of the present invention. This computing unit may be configured to execute or direct the execution of the steps of the above-mentioned method. Furthermore, it may be configured to operate the components of the above-mentioned apparatus. The computing unit may be configured to operate automatically and / or to execute a user's order. The computer program may be loaded into a working memory of a data processor. The data processor may thus be equipped to execute the method of the present invention.

[0135] In another example to be claimed, a computer readable medium has stored thereon the computer program of the previous example.

[0136] Aspects of the invention may be embodied in a computer program product, which may be a collection of computer program instructions stored on a computer-readable storage device that may be executed by a computer. The instructions of the invention may be any interpretable or executable code mechanism, including, but not limited to, a script, an interpretable program, a dynamic link library (DLL), or a Java class. The instructions may be provided as a complete executable program, a partial executable program, a modification (e.g., an update) to an existing program, or an extension (e.g., a plug-in) to an existing program. Additionally, parts of the processing of the invention may be distributed across multiple computers or processors.

[0137] As described above, a processing unit, e.g., a controller, performs the control method. This controller can be implemented in various ways using software and / or hardware to perform the various functions required. A processor is one example of a controller that employs one or more microprocessors that can be programmed using software (e.g., microcode) to perform the functions required. However, a controller may be implemented with or without a processor, or as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.

[0138] Examples of controller components that may be used in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field programmable gate arrays (FPGAs).

[0139] This exemplary embodiment of the invention encompasses both computer programs that use the invention from the outset, and computer programs that convert existing programs into programs that use the invention by means of an update.

[0140] Moreover, the computer program element may be capable of providing all the steps required to fulfill the procedures of the exemplary embodiments of the methods described above.

[0141] According to a further exemplary embodiment of the present invention, a computer readable medium, such as a CD-ROM, is presented, having stored thereon a computer program element, which is described by the previous section. The computer program may be stored and / or distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems.

[0142] However, the computer program may also be presented over a network such as the World Wide Web and can be downloaded from such a network into the working memory of a data processor. According to a further exemplary embodiment of the invention, a medium for making available for downloading a computer program element is provided, this computer program element being configured to perform a method according to one of the aforementioned embodiments of the invention.

[0143] It should be noted that the embodiments of the present invention are described with reference to different subject matters. In particular, some embodiments are described with reference to method type claims, and other embodiments are described with reference to device type claims. However, those skilled in the art will understand from the above and the following description that, unless otherwise notified, any combination of features belonging to one type of subject matter, as well as any combination between features relating to different subject matters, is considered to be disclosed in the present application. However, all features can be combined to provide a synergistic effect higher than the simple sum of the features.

[0144] While the invention has been illustrated and described in detail in the drawings and the foregoing description, such illustration and description are to be considered as illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the dependent claims.

[0145] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be interpreted as limiting the scope.

Claims

1. An image data input unit; A data processor; An output interface; 1. An apparatus for digital subtraction imaging, comprising: the image data input is configured to receive 3D image data of a region of interest of an object, the 3D image data comprising a 3D representation of the object based on a reconstruction from a plurality of 2D projection images of the region of interest, the image data input being configured to receive a 2D live X-ray image of the region of interest; the data processor is configured to register the 3D image data with the 2D live X-ray image, a matching pose of the 3D image data corresponding to the 2D live X-ray image is determined, the data processor calculates a digital reconstruction radiograph from the 3D image data based on the determined matching pose using current data associated with the 2D live X-ray image to generate a fitted 2D mask image, the data associated with the 2D live X-ray image comprising at least one of the following groups: 2D live acquisition parameters and data of the 2D live X-ray image, and the data processor is configured to subtract the generated fitted 2D mask image from the 2D live X-ray image. the output interface is configured to provide a digital image highlighting changes in the region of interest. Device.

2. the image data input unit is configured to receive a vascular contrast injection 2D live X-ray image of the region of interest as the 2D live X-ray image; the data processor is configured to subtract the generated fitted 2D mask image from the vascular contrast injection 2D live X-ray image; the output interface is configured to provide a digital subtraction angiogram representative of a vascular structure within the region of interest.

2. The apparatus of claim 1.

3. To obtain the fitted 2D mask image, the image data input is configured to receive the projection data of the region of interest comprising the plurality of 2D projection images and to receive 2D data characteristics from the acquisition of the 2D live X-ray images; the data processor is configured to adapt the reconstruction of the projection data of the region of interest based on the 2D data characteristics to generate adapted 3D image data having an adapted 3D representation of the object; the data processor is configured to use the adapted 3D image data for calculation of the digital reconstruction radiograph; The data processor is configured to also use the adapted 3D image data for the registration.

3. Apparatus according to claim 1 or 2.

4. To obtain the fitted 2D mask image, the image data input is configured to receive 2D data characteristics from the acquisition of the 2D live X-ray image; the data processor is configured to adapt the calculation of the digitally reconstructed radiograph from the 3D image data based on the 2D data characteristics to generate an adapted 2D mask image.

4. Apparatus according to any one of claims 1 to 3.

5. The 2D data characteristics include: i) 2D live acquisition parameters having at least one of the following group: spectrum, resolution, X-ray imaging energy; and / or ii) image data of said 2D live X-ray image having at least one of the group of resolution and contrast; The device according to claim 3 or 4, which is based on

6. For the 3D image data, the adapted reconstruction has reconstructed attenuation values ​​corresponding to a predetermined mean photon energy expected to be used for the 2D live X-ray images; the data processor is configured to segment the 3D image into regions of bone and soft tissue types, such that each tissue type has an individually tailored attenuation value for the expected photon energy; the data processor is configured to apply different spectral related factors for the digitally reconstructed radiograph; The 3D image data is free of blood vessels.

6. Apparatus according to any one of claims 1 to 5.

7. the data processor is configured to estimate a pose of the object within the 2D live X-ray image; i) for registration of the 3D image data and the 2D live X-ray image, the data processor is configured to align the 3D representation of the object in the 3D image data to the estimated pose; and / or ii) for the reconstruction of the projection data of the region of interest, the data processor is configured to take into account the estimated pose for the reconstruction; 7. Apparatus according to any one of claims 1 to 6.

8. To find a matching pose, the data processor i) pose estimation based on system-based geometric registration; ii) segmentation-based pose estimation; iii) the constraint that the presence of contrast-injected vasculature is ignored in the 2D live X-ray images and / or the 3D image data; iv) generating a plurality of digitally reconstructed radiographic images for different projection directions of the 3D image data and using the plurality of digitally reconstructed radiographic images for identifying possible matches with the 2D live image; configured to provide at least one of the following groups:

8. Apparatus according to any one of claims 1 to 7.

9. 9. The apparatus according to claim 1, wherein for generating the adapted 2D mask image, the data processor is configured to invert a transfer function of the reconstruction process.

10. The data processor includes: 2D scatter estimation using the 2D live x-ray image as input; or 3D scattering estimation, configured to provide the data processor is configured to add scatter signals to the calculated digitally reconstructed X-ray image.

10. Apparatus according to any one of claims 1 to 9.

11. As a correction for the reconstruction of the 3D image data, the data processor i) Virtual Heel Effect Correction, and / or ii) Virtual inverse detector gain correction; configured to provide 11. Apparatus according to any one of claims 1 to 10.

12. The apparatus, display, and the display is provided for presenting the digital image; 12. Apparatus according to any one of claims 1 to 11.

13. an x-ray imaging device having an x-ray source and an x-ray detector; An apparatus for digital subtraction imaging according to any one of claims 1 to 12, 1. An imaging system for digital subtraction imaging, comprising: the x-ray imaging device is configured to generate the 2D live x-ray image; Imaging system.

14. 1. A method for digital subtraction imaging, comprising: receiving 3D image data of a region of interest of an object, the 3D image data comprising a 3D representation of the object based on a reconstruction from a plurality of 2D projection images of the region of interest; receiving a 2D live X-ray image of the region of interest; registering the 3D image data with the 2D live X-ray image, where a matching pose of the 3D image data corresponding to the 2D live X-ray image is determined; calculating a digital reconstruction radiogram from the 3D image data based on the determined matching pose using current data associated with the 2D live X-ray image to generate a fitted 2D mask image, the data associated with the 2D live X-ray image comprising at least one of the following groups: 2D live acquisition parameters and data of the 2D live X-ray image; subtracting the generated fitted 2D mask image from the 2D live X-ray image; providing a digital image highlighting the changes in the region of interest; The method according to claim 1,

15. A computer program comprising computer readable code or instructions which, when executed by a processor, enables said processor to carry out the method of claim 14.

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