METHOD FOR PROVIDING A VIRTUAL, NON-CONTRAST IMAGE DATA SET

Description

[0001] The invention relates to a method for providing a virtual, non-contrast image dataset of a patient based on a multiphase CT angiography image dataset (mCTA image dataset) of the patient, which comprises at least three CT image datasets that depict an imaging area of ​​the patient at three different time points relative to the administration of a contrast agent. The invention further relates to an associated device for providing a virtual, non-contrast image dataset of a patient, a computed tomography device, a computer program product, and a computer-readable storage medium.

[0002] In the case of an acute stroke, several examinations must be performed to determine the cause and severity of the stroke. Typically, a computed tomography (CT) scan without contrast is performed first to determine whether there is bleeding in the patient and to look for early signs of a stroke (e.g., assessment of hyperdense middle cerebral artery signs, the Alberta Stroke Program Early CT Score, or ASPECTS, etc.). If no bleeding is detected, a computed tomography angiography (CTA) is performed, followed by a computed tomography perfusion (CTP) scan.In CTP (cardiac perfusion tapping), the area of ​​interest is repeatedly scanned at multiple time points over a specific period, for example, every 1.5 seconds for 40 seconds. This generates a time-resolved 3D dataset that shows the flow of contrast agent in the vessels of the area of ​​interest, particularly the brain. CTA can locate the clot, while CTP can provide information about the severity of the stroke and answer the question of whether there is still an area of ​​viable cells (penumbra) adjacent to a central necrotic zone that could be salvaged by therapy. In some hospitals, this perfusion CT scan is replaced by multiphase CTA (mCTA). In mCTA, after the initial CTA scan from the aortic arch to the vertex, typically only two further scans are performed, focusing solely on the brain.These three scans provide a significantly lower temporal resolution than a CTP, but more information about the distribution of contrast agent in the brain than a CTA alone.

[0003] It is possible to use mCTA data quantitatively and generate result images from them, similar to those used in the analysis of CT perfusion scans. This includes so-called "perfusion maps," e.g., CBF (cerebral blood flow), which indicates how much volume of blood (ml) flows per mass of tissue (g) per time (min), CBV (cerebral blood volume), which indicates how much volume of blood (ml) is present per mass of tissue (g), or TTP (time-to-peak), also called time to maximum hyperdensity, which indicates how much time a contrast agent bolus takes to accumulate maximally in a specific tissue region.

[0004] Document US20170265829A1 discloses a method for color-coded visualization of the different phases of blood flow in mCTA images.

[0005] Document US20220087631A1 discloses a method for functional perfusion mapping based on mCTA images.

[0006] Examinations using mCTA are not limited to stroke patients. There are other applications as well, for example, for tumor assessment.

[0007] However, creating these perfusion image datasets, i.e., perfusion maps, requires a base image dataset of the diagnostically relevant area, which corresponds to a non-contrast CT image dataset, i.e., a CT image dataset without the presence of contrast agent. With CTP, this is generally not a problem, as the scan starts even before the contrast agent has reached the area of ​​interest.

[0008] While a CT image dataset without prior administration of contrast agent is often available, as previously described, and is subsequently referred to as a native, non-contrast CT image dataset because it is frequently the first scan performed on a stroke patient, it is generally acquired under different parameters than the subsequent mCTA scans. This initial scan, the native, non-contrast CT image dataset, is typically performed at a higher X-ray tube voltage, for example, 120 kVp, to achieve optimal image quality for soft tissue. In contrast, CTA scans are generally performed at a relatively lower X-ray tube voltage, for example, 80–100 kVp, to achieve better contrast for visualizing the vascular system.Furthermore, special image filters are often applied to native, non-contrast CT image datasets, whereas different filters are usually applied to CTA image datasets.

[0009] Despite these differences, the native, non-contrast image dataset is currently generally used as the base image dataset for calculating perfusion maps based on mCTA image datasets. However, this leads to problems and artifacts due to the different acquisition parameters.

[0010] The object of the invention is therefore to provide a method that allows an improved, non-contrast image dataset of a patient to be made available, for example, for optional, subsequent processing of the patient's multiphase CT angiography image dataset (mCTA image dataset). It is also an object to provide an associated device, a computed tomography device, a computer program, and a computer-readable storage medium.

[0011] The problem is solved by the method and the devices described in the independent claims. Advantageous and inventive embodiments are the subject of the dependent claims and the following description.

[0012] The invention relates to a method for providing a virtual, non-contrast CT image dataset of a patient.

[0013] A virtual, non-contrast CT image dataset corresponds to an image dataset that depicts the patient's area of ​​interest without the influence of a contrast agent on the image values. The virtual, non-contrast CT image dataset is referred to as virtual, in contrast to the native, non-contrast CT image dataset, because the latter is not based on the acquisition of measurement data acquired without contrast agent administration, but rather is subsequently generated by a processing unit based on measurement data acquired with contrast agent administration.

[0014] The CT image datasets provided and acquired within the framework of the procedure, including the minimum intensity image dataset, can each be a three-dimensional image dataset (3D image dataset). A 3D image dataset allows a three-dimensional, specifically spatially three-dimensional, representation of the patient's imaging area. A 3D image dataset can also be represented as multiple slice image datasets. Each slice image dataset comprises one slice of the 3D image dataset at a position along a defined axis. Each slice image dataset then allows a two-dimensional, specifically spatially two-dimensional, representation of the respective slice.

[0015] Advantageously, such a 3D image dataset comprises several voxels, in particular pixels. Each voxel can preferably represent one image value, in particular a CT image value in HU (Hounsfield Units) or an analogous intensity value. Similarly, a tomographic image dataset can comprise several pixels, in particular pixels. Each pixel can preferably represent one image value, in particular a CT image value in HU (Hounsfield Units) or an analogous intensity value. The at least three CT image datasets can be identical, in particular with respect to their image resolution.

[0016] The patient can be an animal and / or a human patient. Furthermore, the patient's imaging area can encompass an anatomical and / or spatial region of the patient, including a predetermined tissue area and / or a spatial region necessary for diagnosis. The imaging area can include a body region, such as the head or the thorax.

[0017] Providing the at least three CT image datasets of the mCTA image dataset can, for example, include acquiring and / or reading a computer-readable data storage device and / or receiving data from a storage unit. Furthermore, the method according to the invention can also include acquiring the at least three CT image datasets of the mCTA image dataset using a CT scanner, which can then be provided for the subsequent steps of the method. Output can, for example, include output to a downstream processing unit, which further processes the provided virtual, non-contrast CT image dataset, for example, by generating perfusion maps. Output can also include output to a display unit, e.g., comprising a monitor, which allows the image dataset to be displayed to a user.

[0018] The at least three CT image datasets, as well as the virtual, non-contrast CT image dataset and the minimum intensity CT image dataset, all depict at least the patient's imaging area. The imaging area specifically encompasses the area of ​​the patient that is to be evaluated in a subsequent assessment using the image information obtained from the mCTA image dataset. However, the acquisition areas of the at least three CT image datasets for which measurement data were acquired may, but do not necessarily, differ, provided they each encompass the imaging area. For example, the acquisition area of ​​the first of the at least three CT image datasets may be different, and in particular more extensive, than the second and / or third of the at least three CT image datasets.The imaging area can correspond to the smallest of the acquisition areas of the at least three CT image datasets with respect to the spatial extent depicted. However, it can also be only a section of it.

[0019] For example, the imaging area specifically includes a patient's brain. Advantageously, a virtual, non-contrast CT image dataset can be used for further processing of the mCTA image dataset, e.g. .for brain perfusion imaging datasets. Thus, improved stroke diagnostics can be achieved based on such a virtual, non-contrast CT image dataset when used in the provision of perfusion imaging datasets. For example, the acquisition area of ​​the first CT image dataset can encompass at least the patient from the aortic arch to the vertex, and the acquisition area of ​​the second and / or the third CT image dataset can depict at least the patient from the skull base to the vertex. For example, the imaging area corresponds to the acquisition area of ​​the second and / or third CT image dataset or at least a portion thereof. In other embodiments, the imaging area can also depict a different body region of the patient.

[0020] Imaging studies using contrast agents, such as intravenously administered iodine-based contrast agents, are performed to improve the visualization of blood vessels, organ tissue, tumorous structures, or hemorrhages. Further diagnostically relevant information can be obtained from the temporal course of the contrast agent distribution. The at least three CT image datasets depict the patient's imaging area at three different time points relative to, i.e., specifically, after, the administration of the contrast agent. Thus, each of the at least three CT image datasets depicts the state of the contrast agent distribution in the patient at different time points following the administration of the contrast agent.

[0021] For example, the first of at least three CT image datasets in the mCTA image set depicts the state of contrast agent distribution in the patient at the peak of an arterial phase. For example, the second of at least three CT image datasets depicts the state of contrast agent distribution in the patient at the peak of the venous phase. For example, the third of at least three CT image datasets depicts the state of contrast agent distribution in the patient during a late venous phase. Different phases can also be selected for different applications.

[0022] The respective timing of the measurement data acquisition for the at least three CT image datasets can be based on empirical values ​​or, for example, at least partially determined via bolus tracking or test bolus methods.

[0023] The relative time interval between the first and second CT image datasets of at least three CT image datasets, and the relative time interval between the second and third CT image datasets of at least three CT image datasets, is at least 5 seconds in preferred configurations. For example, the acquisition of the second CT image dataset starts 7, 8, or 10 seconds after the start of the first CT image dataset, and the acquisition of the third CT image dataset starts 7, 8, or 10 seconds after the start of the second CT image dataset. A time interval of 7–10 seconds is particularly advantageous for mCTA in the context of stroke diagnostics. This may differ for other applications. In particular, the time delay between the respective data acquisition starts is selected such that the CT image datasets are suitable for subsequent diagnostics.The relative time interval between the first CT image data set and the second CT image data set, or the relative time interval between the second CT image data set and the third CT image data set, can differ among at least three CT image data sets.

[0024] The minimum intensity image dataset at least depicts the imaging area. Therefore, with regard to the image, it can correspond to the smallest of the three CT image datasets in terms of spatial extent. However, it can also depict only a portion of it.

[0025] The image values ​​of the minimum intensity image dataset are each based on the minimum value among the image values ​​of the spatially corresponding pixels in the at least three CT image datasets. That is, the image value of a pixel in the minimum intensity image dataset is based on the image value of the spatially corresponding pixel in the image area of ​​the CT image dataset with the minimum value. Specifically, they can correspond to this minimum value. However, they can also be based on and adjusted to this minimum value. For example, to create the minimum intensity image dataset, a preliminary image dataset can be generated whose pixels are assigned the minimum image value from one of the at least three CT image datasets.Locally corresponding means that locally corresponding pixels in the at least three CT image datasets, and also in the minimum intensity image dataset, depict the same image area of ​​the patient. This procedure can therefore be understood as a minimum intensity projection based on the mCTA image dataset along the time axis. According to the invention, the minimum value in a CT image dataset corresponds, due to the usual representation of a CT image dataset, to the value that corresponds to the highest transmission of X-rays, i.e., the lowest absorption.

[0026] The rationale behind the inventive procedure is as follows: Since the at least three CT image datasets depict the imaging area at different times relative to the administration of the contrast agent, it is assumed that for each imaged area or tissue type within the imaging area, there is an image in one of the three CT image datasets in which this area is not affected by the contrast agent. Therefore, the image area of ​​the CT image dataset for which this applies can be used as a non-contrast image for that area. It can then further be assumed that the respective minimum image value for a corresponding pixel or voxel in the at least three CT image datasets corresponds to the value that is not affected by the contrast agent, since the contrast agent would lead to an increase in the image value relative to a baseline value.

[0027] Applied to the example of a stroke patient, this means: In healthy brain tissue, the contrast agent reaches the tissue and vessels very early, so that the first of at least three CT image datasets will show an effect of contrast agent in the brain parenchyma. However, in these parts of the brain, no contrast agent will be present in the last of the at least three CT image datasets, as it will have already been washed out. The penumbra around the core of the infarct will show contrast enhancement in the image datasets with a delay relative to healthy tissue. Consequently, it will appear unaffected in the first of the at least three CT image datasets. The affected brain tissue of the core of the infarct will always appear unaffected by contrast agent, as it does not reach this area.Consequently, for each tissue area, at least one image is available that is not affected by contrast agent, on which a virtual, non-contrast CT image dataset can be based, which can then be provided, for example, as a base image for optional further processing of the mCTA image dataset.

[0028] Advantageously, the method according to the invention can provide a non-contrast CT image dataset based on measurement data corresponding to the acquisition conditions used for the at least three CT image datasets. Advantageously, diagnostics and further processing of the image datasets can be performed based on the improved non-contrast image dataset, thus achieving improved quality. Advantageously, it may be possible to dispense with a further dose application in order to generate a non-contrast CT image dataset from the patient that corresponds to the mCTA image dataset and is based on measurement data acquired under the same parameters as the mCTA image dataset.

[0029] According to an advantageous embodiment, the method also includes the following step: Performing motion correction on at least three CT image datasets, thereby providing motion-corrected CT image datasets, and forming the minimum intensity image dataset based on the motion-corrected CT image datasets.

[0030] The proposed method can be particularly advantageous when the at least three CT image datasets depict the patient or the imaging area in an unchanged position and condition (apart from the contrast agent distribution). Due to movements of the imaged structures between individual acquisitions, disruptive motion artifacts can occur in the minimum intensity image dataset. As a consequence of these movements, the resulting virtual, non-contrast CT image dataset may be unusable for subsequent processing and diagnosis. Advantageously, motion correction can account for patient movements between acquisitions for the at least three CT image datasets, reduce their impact, and thus provide an improved minimum intensity image dataset.

[0031] For motion correction, as can be used within the framework of this method, there are various possibilities known to those skilled in the art. The aim is, in particular, to register and transform the structures of the at least three CT image datasets relative to one another, so that a minimum intensity image dataset can be advantageously calculated based on the corrected at least three CT image datasets. In particular, a known rigid registration can be used. For example, a method as described in Zhang L, Chefd'hotel C, Bousquet G "Group-wise motion correction of brain perfusion images", (2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010, pp. 832-835, doi: 10.1109 / ISBI.2010.5490115.) can also be used.However, other methods can also be used which at least reduce artifacts caused by patient movement in the minimum intensity image dataset.

[0032] According to an advantageous training variant of the procedure, the following steps are also included: Providing a native, non-contrast CT image dataset that depicts the patient's imaging area without contrast agent administration, and correcting the minimum intensity image dataset using the native, non-contrast CT image dataset, thereby providing a corrected minimum intensity image dataset, and where The output virtual, non-contrast image dataset is based on the corrected minimum intensity image dataset.

[0033] The native, non-contrast CT image set is acquired under different acquisition parameters than those of the at least three CT image sets of the multiphase CT angiography image set. This applies particularly to operating parameters of the X-ray source, such as X-ray tube voltage and / or current. For example, the native, non-contrast CT image set may have been acquired at a higher X-ray tube voltage than the at least three CT image sets to achieve optimal image quality for soft tissue. The native, non-contrast CT image set is generally acquired before the at least three CT image sets and depicts the patient's imaging area before and without any influence from the contrast agent.

[0034] As previously described, the direct use of the native, non-contrast CT image dataset is only of limited use for further processing of the mCTA image dataset, for example, to create perfusion maps, due to differing acquisition parameters. However, it can be advantageously used to correct the minimum intensity image dataset in order to provide an improved virtual, non-contrast CT image dataset. For example, noisy regions in the at least three CT image datasets of the mCTA imageset can cause a distortion towards a local underestimation of tissue density due to the calculation of the minimum during the creation of the minimum intensity image dataset.Such underestimated regions in the virtual, non-contrast CT image dataset can (erroneously) result in locally higher contrast agent uptake when the image datasets are further processed into perfusion maps based on the virtual, non-contrast CT image dataset. Advantageously, a correction can be performed based on a comparison of the native, non-contrast CT image dataset and the minimum intensity image dataset. For example, a comparison of the contrast of existing structures or image areas to surrounding structures or areas can be used to perform a correction. This is particularly useful if the comparison of the ratios in the native, non-contrast CT image dataset and the minimum intensity image dataset shows an unexpectedly large discrepancy. If a ratio or contrast is similar in both image datasets, orWithin a given expected range, the probability is greater that there is no artifact, but rather that the image values ​​are actually as they appear and the structures are reproduced correctly.

[0035] In this case, too, a motion correction can be performed before a comparison to account for possible movements of the patient between the acquisition of the native, non-contrast CT image dataset and the at least three CT image datasets underlying the minimum intensity image dataset.

[0036] In advantageous embodiments of this method variant, the correction step includes identifying image areas in the minimum intensity image dataset that exceed a certain noise value or fall below a certain intensity value, and determining at least one correction value for the identified image areas in the minimum intensity image dataset based on a comparison of these specific image areas with spatially corresponding image areas in the native, non-contrast CT image dataset.

[0037] It is also possible that, for the correction, image areas in at least one of the at least three CT image datasets are determined which exceed a certain noise value or which fall below a certain intensity value, and based on a comparison between spatially corresponding image areas in the minimum intensity image dataset and in the native, non-contrast CT image dataset, at least one correction value is determined for the determined image areas in the minimum intensity image dataset.

[0038] Based on the noise level or a low intensity value, image areas can be identified that are more likely to lead to a distorted calculation of the minimum intensity image dataset. Advantageously, after identifying these areas with an increased probability of distorted image value determination for the minimum intensity image dataset, a review and, if necessary, correction can be performed.

[0039] As an alternative to determining image areas based on the minimum intensity image dataset or at least one of the at least three CT image datasets, correction can also include identifying image areas that may require correction in a perfusion image dataset created based on the uncorrected minimum intensity image dataset. This variant of the procedure then involves creating a perfusion image dataset based on the at least three CT image datasets of the multiphase CT angiography image dataset and the patient's uncorrected minimum intensity image dataset, identifying image areas in the perfusion image dataset with locally increased contrast enhancement, and determining at least one correction value based on a comparison between spatially corresponding image areas in the minimum intensity image dataset and spatially corresponding image areas in the native, non-contrast CT image dataset.

[0040] As previously described, noisy regions in the at least three CT image datasets of the mCTA image set can cause a distortion towards a local underestimation of tissue density due to the calculation of the minimum during the creation of the minimum intensity image set. Such underestimated regions in the preliminary, uncorrected minimum intensity image set can (erroneously) result in locally higher contrast enhancement when the image datasets are further processed into perfusion maps. Areas with locally higher contrast enhancement in a perfusion image set therefore represent areas where there is an increased probability of miscalculation of the values ​​in the minimum intensity image set. These are identified and subjected to review and, if necessary, correction based on a comparison with the native, non-contrast CT image set.

[0041] In the variants described above, the correction value can be based, in particular, on the ratio of image values ​​in specific image areas of the minimum intensity image dataset or the native, non-contrast CT image dataset to image values ​​in the vicinity of those specific image areas. The comparison can specifically involve comparing a contrast value between existing structures or image areas to surrounding structures or areas. If a ratio in both image datasets is similar or within an expected range, the probability is higher that this is not an artifact, but rather the image values ​​are actually as they appear.If the ratio differs significantly or lies outside expectations, the image values ​​in the previously defined image areas of the minimum intensity image dataset are adjusted using at least one correction value, for example, in such a way that the resulting ratio is similar to or the same as in the native, non-contrast CT image dataset, or lies within the expected range. Adjusting when an adjustment should be made, for example, within an expected range, and to what extent, can be based on empirical data or measurement series. The measurement of phantoms can also be used for this purpose.

[0042] The invention also relates to a method for providing a perfusion image data set, wherein a multiphase CT angiography image data set of the patient comprising at least three CT image data sets, which depict an imaging area of ​​the patient at three different time points relative to a contrast agent administration, is provided.

[0043] In particular, based on the mCTA image dataset and the advantageously generated virtual, non-contrast CT image dataset, so-called "perfusion maps" can be generated, e.g., CBF (cerebral blood flow), which indicates how much volume of blood (ml) flows per mass of tissue (g) per time (min), or CBV (cerebral blood volume), which indicates how much volume of blood (ml) is present per mass of tissue (g), and / or further parameters can be derived from them. Methods for calculating the parameters are known to those skilled in the art and can be applied in the same way here. Advantageously, however, an improved perfusion image dataset can be generated using the virtual, non-contrast CT image dataset, which can be less artifact-prone.This allows for the advantageous provision of high-quality image data sets and values, which improves subsequent diagnosis.

[0044] The invention further relates to a device for providing a virtual, non-contrast image data set of a patient.

[0045] Such a device for providing a virtual, non-contrast image dataset of a patient can, in particular, be configured to execute the previously described methods according to the invention for providing a virtual, non-contrast image dataset of a patient and their aspects. The device can be configured to execute the methods and their aspects by providing the interfaces and the computing unit to perform the corresponding method steps.

[0046] The device can further be configured to generate and provide a perfusion image dataset based on a provided multiphase CT angiography image dataset of the patient comprising at least three CT image datasets which depict an imaging area of ​​the patient at three different time points relative to a contrast agent administration, and based on a virtual, non-contrast CT image dataset of the imaging area provided according to one of the preceding claims based on the at least three CT image datasets.

[0047] The device or computing unit can be, in particular, a computer, a microcontroller, or an integrated circuit. Alternatively, it can be a real or virtual cluster of computers (a real cluster is called a "cluster," a virtual cloud is called a "cloud"). The device can also be designed as a virtual system running on a real computer or a real or virtual cluster of computers (virtualization).

[0048] An interface can be a hardware or software interface (for example, PCI bus, USB, or FireWire). A computing unit can consist of hardware or software elements, such as a microprocessor or an FPGA (Field Programmable Gate Array).

[0049] The interfaces can, in particular, comprise multiple sub-interfaces. In other words, the interfaces can also comprise a multitude of interfaces. The processing unit can, in particular, comprise multiple sub-processing units that execute different steps of the respective procedures. In other words, the processing unit can also be understood as a multitude of processing units.

[0050] The device can also include a storage unit. A storage unit can be implemented as non-persistent working memory (Random Access Memory, or RAM) or as persistent mass storage (hard drive, USB flash drive, SD card, solid state disk).

[0051] The advantages of the proposed device essentially correspond to the advantages of the proposed method for providing a virtual, non-contrast image dataset of a patient. Features, advantages, or alternative embodiments mentioned therein can likewise be transferred to the device and vice versa.

[0052] The invention further relates to a computed tomography (CT) device comprising a device for providing a virtual, non-contrast image dataset of a patient as previously described. The CT device comprises at least one X-ray source and a detector arranged opposite it, both mounted on a rotatable gantry. The CT device is configured to acquire multiple projection datasets from different projection angles during a relative rotational movement between an X-ray source and a patient positioned between the X-ray source and the detector. Based on the multiple projection datasets, CT image datasets can be reconstructed and made available for further processing.The computed tomography device is specifically designed to provide at least three CT image datasets of the mCTA image dataset, which depict the patient's imaging area at three different time points relative to the administration of contrast medium.

[0053] The computed tomography device is advantageously designed to carry out an embodiment of the proposed method for providing a virtual, non-contrast image data set of a patient.

[0054] The advantages of the proposed computed tomography device essentially correspond to the advantages of the proposed method for providing a virtual, non-contrast image dataset of a patient. Features, advantages, or alternative embodiments mentioned therein can likewise be transferred to the device and vice versa.

[0055] The invention further relates to a computer program product comprising a computer program that can be directly loaded into a memory of a device for providing a virtual, non-contrast image data set of a patient, with program sections to execute all steps of an inventive method for providing a virtual, non-contrast image data set of a patient when the program sections are executed by the device.

[0056] A computer program product can be a computer program or comprise a computer program. This allows the method according to the invention to be executed quickly, identically, and robustly. The computer program product is configured so that it can execute the method steps according to the invention using the device. The device must have the necessary prerequisites, such as sufficient working memory, a suitable graphics card, or a suitable logic unit, so that the respective method steps can be executed efficiently. The computer program product is stored, for example, on a computer-readable medium or on a network or server, from where it can be loaded into a processing unit of the device.

[0057] The invention further relates to a computer-readable storage medium on which readable and executable program sections are stored by a device for providing a virtual, non-contrast image data set of a patient, in order to execute all steps of an inventive method for providing a virtual, non-contrast image data set of a patient as described above when the program sections are executed by the device.

[0058] Examples of computer-readable storage media include a DVD, a magnetic tape, a hard drive or a USB stick, on which electronically readable control information, especially software, is stored.

[0059] A largely software-based implementation has the advantage that even previously used devices and computing units can be easily retrofitted via a software update to operate in the manner of the invention. In addition to the computer program itself, a computer program product may optionally include additional components such as... . Documentation and / or additional components, as well as hardware components, such as... . Hardware keys (dongles etc.) for using the software are included.

[0060] In addition to the previously described method according to the invention for providing a virtual, non-contrast CT image dataset, it is alternatively possible to generate such a virtual, non-contrast CT image dataset based on the use of a trained function. Accordingly, a device could also be provided which is configured to execute such a method as described below. The device can be configured to execute the methods and their aspects by providing suitable interfaces and a computing unit configured to perform the corresponding method steps.

[0061] Such a procedure would then at least include the following: A multiphase CT angiography image dataset of the patient comprising at least three CT image datasets, which depict an imaging area of ​​the patient at three different time points relative to the administration of contrast agent, is provided via an interface; a virtual, non-contrast CT image dataset is generated by applying a trained function to the mCTA image dataset using a computing unit, wherein at least one parameter of the trained function is adapted to a comparison between a virtual, non-contrast training image dataset and a non-contrast comparison image dataset, wherein the generated virtual, non-contrast training image dataset and the non-contrast comparison image dataset are linked together, and the generated virtual, non-contrast CT image dataset is subsequently output via another interface.

[0062] An explanation of what can be included in a virtual, non-contrast training image dataset and a non-contrast comparison image dataset will be provided in more detail below.

[0063] A trained function can preferably be implemented using an artificial intelligence system, i.e., through a machine learning process. An artificial intelligence system can be defined as a system for the artificial generation of knowledge from experience. Such a system learns from examples during a training phase and can generalize after the training phase is complete. The use of such a system can include the recognition of patterns and regularities in the training data. After the training phase, the optimized, i.e., .A trained algorithm can, for example, derive a virtual, non-contrast CT image dataset from a previously unknown mCTA image dataset. The artificial intelligence system can be based on an artificial neural network or another machine learning method. In particular, after the training phase, a trained function based on an artificial intelligence system can enable the highly reliable and time-efficient automated determination of the virtual, non-contrast CT image dataset.

[0064] A trained function maps input data to output data. The output data can depend on one or more parameters of the trained function. These parameters can be determined and / or adjusted through training. Determining and / or adjusting the parameters can be based on a pair of training input data and corresponding comparison output data, where the trained function is applied to the training input data to generate the training output data. Specifically, determining and / or adjusting the parameters can be based on a comparison of the training output data and the training comparison data. In general, a trainable function—that is, a function with one or more parameters that have not yet been adjusted—is also referred to as a trained function.

[0065] Other terms for a trained function include trained mapping rule, mapping rule with trained parameters, function with trained parameters, artificial intelligence-based algorithm, and machine learning algorithm. An example of a trained function is an artificial neural network, where the edge weights of the artificial neural network correspond to the parameters of the trained function. The term "neural network" can also be used instead of "neural network." In particular, a trained function can also be a deep artificial neural network (DAN). Within this alternative approach, a neural network in the form of a so-called U-Net can be used (see, for example: Ronneberger O, Fischer P and Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation). http: / / arxiv.org / abs / 1505.04597) can be used. However, other architectures known from the prior art can also be used, in particular those based on a deep learning model with an encoder-decoder architecture.

[0066] The trained function can be trained, in particular, using backpropagation. First, training output data can be determined by applying the trained function to training input data. Then, a deviation between the training output data and the training comparison data can be determined by applying an error function to both. Furthermore, at least one parameter, in particular a weight, of the trained function, especially of the neural network, can be iteratively adjusted based on a gradient of the error function with respect to that parameter. This advantageously minimizes the deviation between the training output data and the training comparison data during the training of the function.

[0067] Advantageously, the trained function, particularly the neural network, has an input layer and an output layer. The input layer can be configured to receive input data, while the output layer can be configured to provide output data. Both the input layer and / or the output layer can each comprise multiple channels, particularly neurons.

[0068] The input data for the trained function can include at least the three CT image datasets of a patient's mCTA image dataset. The output data can include, in particular, the virtual, non-contrast CT image dataset.

[0069] To train the function, training data must be provided. This training data comprises the previously mentioned virtual, non-contrast training image dataset as training output data and the non-contrast comparison image dataset as training comparison data.

[0070] For training, the training input data is provided, the trained function is applied to the provided training input data to obtain training output data, and finally, based on a comparison of the training output data with corresponding training comparison data, parameters of the trained function are adjusted. The training input data and the training comparison data are linked. In particular, annotated training input data can be used.

[0071] Training input data, which is used to determine training output data by applying the trained function to the training input data during the training phase, and training comparison data can be generated or provided in various ways.

[0072] In a conveniently simple form, the training input data can correspond to mCTA image datasets acquired on patients or with phantoms, with training comparison data being provided as an additional non-contrast image dataset. This dataset was acquired under the same acquisition parameters as the measured mCTA image dataset but without the influence of contrast agent. This represents a conveniently simple and direct training option. However, this approach is disadvantageous because it requires additional measurements and thus increases the effort involved, especially considering that a comprehensive set of training data should be available. Furthermore, this is associated with additional radiation exposure, particularly when measurements are taken on patients.

[0073] In another variant, the training data can first be recreated based on existing CTP data. After an initial training phase with such recreated data, a second training phase could involve fine-tuning using actual mCTA image datasets and corresponding non-contrast CT image datasets. This is advantageous because a significantly smaller amount of training data would be required for fine-tuning, thus reducing the effort and additional radiation exposure compared to training directly based on mCTA image datasets.

[0074] This means that in this variant, simulated mCTA image datasets based on CTP image datasets are provided as training input datasets, and likewise a simulated non-contrast image dataset based on CTP image datasets is provided as training comparison datasets.

[0075] The reconstructed mCTA image datasets based on the CTP image datasets are based on the premise that a subset of a time-resolved CTP image dataset, assigned to selected time points, represents similar information to the at least three CT image datasets of an mCTA image dataset. This means that at least three image datasets from a CTP image dataset are specifically selected, which essentially correspond to the depicted phases of an mCTA image dataset. In contrast, one of the earliest image datasets, particularly the first image dataset, from a CTP image dataset can be used as a training control dataset, in which there is usually no contrast agent present in the imaging area of ​​interest. Based on these image data, a preliminary training of the trained function can be performed in an initial training phase.

[0076] Subsequently, a second training phase allows for fine-tuning. Here, the pre-trained function is further trained using provided, measured mCTA image datasets as training input data and corresponding non-contrast CT image datasets as training comparison data. The advantage is that only a small amount of mCTA image data and corresponding non-contrast CT image datasets are required for fine-tuning, while widely available, existing CTP image datasets can be used for the majority of the training.

[0077] In another approach, such fine-tuning can potentially be avoided by taking further steps to support transferability to mCTA image datasets. One such step could involve training the function to output the difference between the input and the desired output instead of the output itself (known as residual learning). This can advantageously not only reduce unwanted complexity in general but, in particular, prevent the trained function from reproducing the appearance of perfusion image datasets in addition to removing the contrast agent contrast. In this approach, training is also performed using simulated mCTA image datasets based on CTP image datasets and simulated non-contrast image datasets based on CTP image datasets, employing a residual learning method.For example, see Shen, Wei and Liu, Rujie "Learning Residual Images for Face Attribute Manipulation" (https: / / doi.org / 10.48550 / arxiv.1612.05363).

[0078] In all the variants described here for using a trained function, a native, non-contrast image dataset, as is commonly available, can also be used as an auxiliary variable as an additional input dataset. Despite differing acquisition parameters compared to the mCTA image datasets, this provides additional complementary patient information, such as improved soft tissue contrast. This information can be used as an additional input dataset for the trained function, leading to more accurate imaging. If such an input is planned, it must be taken into account during the training of the function.

[0079] Furthermore, within the scope of the invention, features described in relation to different embodiments of the invention and / or different claim categories (method, use, device, system, arrangement, etc.) can be combined to form further embodiments of the invention. For example, a claim relating to a device can also be further developed with features described or claimed in connection with a method, and vice versa. Functional features of a method can be implemented by appropriately designed physical components.

[0080] The use of the indefinite articles "a" or "an" does not preclude the possibility that the characteristic in question may be present multiple times. The use of the expression "to exhibit" does not preclude the possibility that the terms linked by the expression "to exhibit" may be identical. For example, the computed tomography device exhibits the computed tomography device. The use of the expression "unit" does not preclude the possibility that the object to which the expression "unit" refers may have several components that are spatially separated from one another.

[0081] In the context of this application, the expression "based on" can be understood in particular as meaning "using". Specifically, a formulation stating that a first feature is generated (alternatively: determined, ascertained, etc.) based on a second feature does not preclude the possibility that the first feature may be generated (alternatively: determined, ascertained, etc.) based on a third feature.

[0082] The invention is explained below with reference to exemplary embodiments and the accompanying figures. The representation in the figures is schematic, greatly simplified, and not necessarily to scale. They show: Fig. 1 a method for providing a virtual, non-contrast image dataset of a patient in a first embodiment, Fig. 2 a method for providing a virtual, non-contrast image dataset of a patient in a second embodiment, Fig. 3 a method for providing a virtual, non-contrast image dataset of a patient in a third embodiment, Fig. 4 a device for providing a virtual, non-contrast image dataset of a patient, and Fig. 5 a computed tomography device comprising a device for providing a virtual, non-contrast image data set of a patient.

[0083] Fig. 1 shows a schematic sequence of a method for providing a virtual, non-contrast image data set D v,non of a patient 39 in a first embodiment.

[0084] The procedure comprises step S1 of providing a multiphase CT angiography image dataset of the patient 39, comprising at least three CT image datasets D1, D2, D3, which depict a region of the patient at three different time points relative to the administration of contrast medium. The procedure further comprises step S2 of generating a minimum intensity image dataset Dmin of the region based on the at least three CT image datasets D1, D2, D3, wherein the image value of a pixel in the minimum intensity image dataset Dmin is based on the minimum value among the image values ​​of the spatially corresponding pixels in the region of the at least three CT image datasets D1, D2, D3.

[0085] The procedure further includes the step of outputting S3 of the virtual, non-contrast image data set D v,non based on the minimum intensity image data set D min .

[0086] The imaging area of ​​patient 39, which is captured by at least three CT image datasets D1, D2, D3, as well as by the virtual, non-contrast CT image dataset Dv,non and the minimum intensity image dataset Dmin, can encompass an anatomical and / or spatial area of ​​patient 39, including a predetermined tissue region and / or a spatial area necessary for diagnosis. For example, in the case of a stroke patient, the imaging area specifically includes the brain of patient 39. In other applications, the imaging area may differ.

[0087] The acquisition areas of the respective image datasets may differ. For example, when applying the procedure in the field of stroke diagnostics, the acquisition area of ​​the first CT image dataset D1 of at least three CT image datasets D1, D2, D3 differs from an acquisition area of ​​the second CT image dataset D2 and / or third CT image dataset D3 of at least three CT image datasets D1, D2, D3, particularly in that the acquisition area of ​​the first CT image dataset D1 encompasses at least patient 39 from the aortic arch to the vertex, while the acquisition area of ​​the second CT image dataset D2 and / or the acquisition area of ​​the third CT image dataset D3 depicts at least patient 39 from the skull base to the vertex.

[0088] In the case of different imaging areas, the procedure may in particular include restricting the CT image datasets to the desired imaging area of ​​the virtual, non-contrast image dataset D v,non when creating the minimum intensity image dataset D min.

[0089] If, as described above, the mCTA image dataset is provided such that the acquisition area of ​​the first CT image dataset D1 encompasses at least patient 39 from the aortic arch to the vertex, and the acquisition area of ​​the second CT image dataset D2 and the acquisition area of ​​the third CT image dataset D3 encompass at least patient 39 from the skull base to the vertex, then a preliminary image dataset Dbase can be generated for creating the minimum intensity image dataset Dmin. This preliminary image dataset will only have the size of the second and third CT image datasets combined. The minimum intensity image dataset can then be generated by populating the preliminary image dataset Dbase with the respective image values ​​determined by a minimum intensity projection based on the mCTA image dataset and providing it as the minimum intensity image dataset Dmin.This means that the image values ​​Vbase can essentially correspond to a min (V1, V2, V3), where the Vx pixel values ​​at corresponding positions in Dx correspond to xe{base,1,2,3}. "Locally corresponding" here means that locally corresponding pixels in at least the three CT image datasets D1, D2, D3, and also in the minimum intensity image dataset Dmin, each depict the same image area of ​​patient 39. This procedure applies equally to other acquisition and imaging areas.

[0090] The at least three CT image datasets D1, D2, and D3 each depict the state of contrast agent distribution in the patient at different time points relative to the contrast agent administration. In an advantageous variant of the procedure, the relative time interval between the first CT image dataset D1 and the second CT image dataset D2 of the at least three CT image datasets D1, D2, and D3, and the relative time interval between the second CT image dataset D2 and the third CT image dataset D3 of the at least three CT image datasets D1, D2, and D3, is at least 5 seconds. For mCTA in the context of stroke diagnostics, a time interval of 7–10 seconds is particularly advantageous. Specifically, the time delay between the respective data acquisition starts is selected such that the CT image datasets D1, D2, and D3 are suitable for subsequent diagnostics.In contrast, the time difference between image data sets of a CTP is only 1 to 2 s or less.

[0091] Since the at least three CT image datasets D1, D2, and D3 depict the imaging area at different times relative to the administration of contrast agent, it is assumed that for each imaged area or tissue type within the imaging area, there is an image in one of the three CT image datasets D1, D2, and D3 in which this area or tissue is not affected by contrast agent. Therefore, the image area of ​​the CT image dataset for which this applies can be used as a non-contrast image for that area. It can then further be assumed that the respective minimum image value for a corresponding pixel or voxel in the at least three CT image datasets D1, D2, and D3 corresponds to the value that is not affected by contrast agent, since the contrast agent would lead to an increase in the image value relative to a baseline value.

[0092] Advantageously, this provides a virtual, non-contrast CT image dataset that is compatible with the provided mCTA image dataset.

[0093] Based on the provided multiphase CT angiography image dataset of the patient and the associated virtual, non-contrast image dataset D v,non, an improved perfusion image dataset can then be generated in further steps, in particular so-called "perfusion maps", e.g. CBF or CBV.

[0094] Fig. 2 A second embodiment shows a method for providing a virtual, non-contrast image dataset D v,non of a patient 39. The S1, S2, and S3 can essentially be the ones described in the section on Fig. 1 The steps S1, S2 and S3 are explained.

[0095] This variant of the procedure still includes step S4 of performing a motion correction on at least three CT image datasets D1, D2, D3, thereby providing motion-corrected CT image datasets M1, M2, M3. Based on these, the minimum intensity image dataset Dmin is then generated in the subsequent step S2.

[0096] For motion correction as can be used within the framework of this procedure, there are various possibilities known to those skilled in the art. Examples have already been mentioned in the general description. In particular, a known rigid registration of at least three CT image datasets D1, D2, and D3 can be used.

[0097] Advantageously, motion correction can take into account movements of the patient 39 between the measurement data acquisition for at least three CT image data sets D 1 , D 2 , D 3 and reduce their effect, thus providing an improved minimum intensity image data set D min.

[0098] Fig. 3 show another variant of a method for providing a virtual, non-contrast image dataset D v,non of a patient 39. In addition to the one in Fig. 2 In the described variant, this training also includes step S5 of providing a native, non-contrast CT image dataset D n,non, which depicts an area of ​​the patient 39 before contrast administration and which was acquired under acquisition parameters that differ from those of the mCTA image dataset. In a further step, S6, the minimum intensity image dataset D min is corrected using the native, non-contrast CT image dataset D n,non, thereby providing a corrected minimum intensity image dataset D min, korr.

[0099] The virtual, non-contrast image data set D v,non, output in step S5, is then based on the corrected minimum intensity image data set D min, korr .

[0100] In this case, too, a motion correction can be performed before a comparison to account for possible movements of the patient between the acquisition of the native, non-contrast CT image dataset and the at least three CT image datasets D 1 , D 2 , D 3 underlying the minimum intensity image dataset D min.

[0101] The correction step S6 can include identifying image areas in the minimum intensity image dataset D min that exceed a certain noise level or fall below a certain intensity level, and determining at least one correction value based on a comparison of these identified image areas with spatially corresponding image areas in the native, non-contrast CT image dataset D n,non. This correction value is then applied to the uncorrected minimum intensity image dataset D min.

[0102] It is also possible that, instead of using the minimum intensity image dataset D min for correction, image areas in at least one of the at least three CT image datasets D 1 , D 2 , D 3 , are determined which exceed a certain noise value or which fall below a certain intensity value, and based on a comparison between spatially corresponding image areas in the minimum intensity image dataset D min and in the native, non-contrast CT image dataset D n,non, at least one correction value for the determined image areas in the minimum intensity image dataset D min is determined.

[0103] Alternatively, the correction step S6 can also include creating a perfusion image dataset based on at least three CT image datasets D1, D2, D3 of patient 39's mCTA image dataset, identifying image areas in the perfusion image dataset with locally increased contrast enhancement, and determining at least one correction value based on a comparison between spatially corresponding image areas in the minimum intensity image dataset Dmin and spatially corresponding image areas in the native, non-contrast CT image dataset Dn,non. This correction value is then applied to the uncorrected minimum intensity image dataset.

[0104] Regardless of the method used to determine the image areas, the correction value can be based, in particular, on the ratio of image values ​​in the defined image areas of the minimum intensity image dataset Dmin or the native, non-contrast CT image dataset Dn,non to image values ​​in the vicinity of the defined image areas of the minimum intensity image dataset Dmin or the native, non-contrast CT image dataset Dn,non. The comparison can specifically involve comparing a contrast value between existing structures or image areas to surrounding structures or areas. If a ratio in both image datasets is similar or within an expected range, the probability is higher that this is not an artifact, but rather that the image values ​​are actually as they appear.If the ratio differs significantly or is outside the expected range, the image values ​​in the previously determined image areas of the minimum intensity image dataset D min are adjusted by means of at least one correction value, for example in a way that results in a ratio similar to or the same as in the native, non-contrast CT image dataset or is within the expected range.

[0105] Fig. 4 shows a device 45 for providing a virtual, non-contrast image data set D v,non of a patient comprising a first interface IF1, configured to provide at least one multiphase CT image dataset of the patient, comprising at least three CT image datasets D1, D2, D3, each depicting an imaging area of ​​the patient at three different time points relative to contrast agent administration; a computing unit CU, configured to generate a minimum intensity image dataset Dmin of the imaging area based on the at least three CT image datasets D1, D2, D3, wherein the image value of a pixel of the minimum intensity image dataset Dmin is based on the minimum value among the image values ​​of the spatially corresponding pixels in the imaging area in the at least three CT image datasets D1, D2, D3; and a second interface IF2, configured to output the virtual non-contrast image dataset Dv,non based on the minimum intensity image dataset Dmin.

[0106] The device 45, or the computing unit CU, can be, in particular, a computer, a microcontroller, or an integrated circuit. Alternatively, it can be a real or virtual cluster of computers (a real cluster is called a "cluster," and a virtual cloud is called a "cloud"). The device 45 can also be configured as a virtual system running on a real computer or a real or virtual cluster of computers (virtualization).

[0107] An interface IF1, IF2 can be a hardware or software interface (for example, PCI bus, USB, or FireWire). A computing unit CU can have hardware or software elements, for example, a microprocessor or a so-called FPGA (English acronym for "Field Programmable Gate Array").

[0108] The interfaces IF1 and IF2 can, in particular, comprise multiple sub-interfaces. In other words, the interfaces IF1 and IF2 can also comprise a multitude of interfaces. The processing unit CU can, in particular, comprise multiple sub-processing units that execute different steps of the respective procedures. In other words, the processing unit CU can also be considered a multitude of processing units.

[0109] The device 45 can also include a storage unit MU. A storage unit can be implemented as non-persistent working memory (Random Access Memory, or RAM) or as persistent mass storage (hard drive, USB stick, SD card, solid state disk).

[0110] Such a device 45 for providing a virtual, non-contrast image data set D v,non of a patient 39 can in particular be configured to execute the previously described methods according to the invention for providing a virtual, non-contrast image data set D v,non of a patient 39 and their aspects. The device 45 can be configured to execute the methods and their aspects by providing the interfaces IF1, IF2 and the computing unit CU to execute the corresponding method steps.

[0111] The device can also be connected to a CT scanner 32, which is designed to acquire measurement data for an mCTA image dataset.

[0112] Fig. 5Figure 1 shows a CT scanner 32 according to the invention. The CT scanner has a gantry 33 with a rotor 35. The rotor 35 comprises at least one X-ray source 37, in particular an X-ray tube, and, opposite it, at least one X-ray detector 2. The X-ray detector 2 and the radiation source 37 are rotatable about a common axis 43 (also called the axis of rotation). The patient 39 is positioned on a patient table 41 and is movable along the axis of rotation 43 through the gantry 33. In general, the patient 39 can, for example, be an animal patient and / or a human patient.

[0113] Typically, measurement data in the form of multiple (raw) projection data sets of the patient 39 are acquired from a variety of projection angles during a relative rotational movement between the X-ray source 37 and the patient 39, while the patient 39 is moved continuously or sequentially through the gantry 33 by means of the patient table 41. Subsequently, an image data set of the imaging area can be reconstructed based on the projection data sets using a mathematical procedure, for example, including a filtered back projection or an iterative reconstruction method.

[0114] The CT scanner 32 comprises a computer system 45. The computer system 45 may also include a reconstruction unit for reconstructing image datasets based on the measurement data acquired by the imaging scanner 32. The computer system 45 may also include a control unit for controlling the CT scanner. In particular, the computer system 45 includes a device 45 for providing a virtual, non-contrast CT image dataset.

[0115] The device 45, which is included in the computer system 45, for providing a virtual, non-contrast image data set D v,non of a patient, is in particular designed to carry out a method according to the invention for generating a result image data set as described above.

[0116] Furthermore, an input device 47 and an output device 49 are connected to the computer system 45. The input device 47 and the output device 49 can, for example, enable user interaction, such as manual configuration, confirmation, or triggering of a process step. For example, the output device 49 can display at least three CT image data sets, the minimum intensity image data set, or the virtual, non-contrast CT image data set on a monitor for the user.

Claims

1. Method for providing a virtual, noncontrast image dataset (Dv,non) of a patient (39) comprising the steps - provision (S1) of a multiphase CT angiography image dataset of the patient (39) comprising at least three CT image datasets (D1, D2, D3) that map an imaging area of the patient at three different points in time relative to an administration of contrast agent, characterised by: - formation (S2) of a minimum intensity image dataset (Dmin) of the imaging area on the basis of the at least three CT image datasets (D1, D2, D3), wherein the image value of an image point of the minimum intensity image dataset (Dmin) is in each case based on the minimum value from among the image values of the image points, locally corresponding in the imaging area, in the at least three CT image datasets (D1, D2, D3), wherein the minimum value corresponds to the value that corresponds to the highest transmission of X-ray radiation, and thus the least absorption, - output (S3) of the virtual, noncontrast image dataset (Dv,non) on the basis of the minimum intensity image dataset (Dmin), wherein the virtual, noncontrast CT image dataset (Dv,non) is generated by means of a computing unit on the basis of measured data that has been captured with the administration of contrast agent.

2. Method according to claim 1, wherein the relative time interval between the first CT image dataset in time (D1) and the second CT image dataset (D2) of the at least three CT image datasets (D1, D2, D3) or the relative time interval between the second CT image dataset in time (D2) and the third CT image dataset (D3) of the at least three CT image datasets (D1, D2, D3) is at least 5 s.

3. Method according to one of the preceding claims, wherein the imaging area comprises the brain of the patient (39).

4. Method according to one of the preceding claims, wherein an acquisition area of the first CT image dataset (D1) of the at least three CT image datasets (D1, D2, D3) differs from an acquisition area of the second CT image dataset (D2) and / or third CT image dataset (D3) of the at least three CT image datasets (D1, D2, D3).

5. Method according to claim 4, wherein the acquisition area of the first CT image dataset (D1) at least comprises the patient (39) from the aortic arch to the crown, and wherein the acquisition area of the second CT image dataset (D2) and / or the acquisition area of the third CT image dataset (D3) at least maps the patient (39) from the base of the skull to the crown.

6. Method according to one of the preceding claims, also comprising the step: - performance (S4) of a motion correction of the at least three CT image datasets (D1, D2, D3), as a result of which motion-corrected CT image datasets (M1, M2, M3) are provided, and wherein the minimum intensity image dataset (Dmin) is formed on the basis of the motion-corrected CT image datasets (M1, M2, M3).

7. Method according to one of the preceding claims, also comprising the steps - provision (S5) of a native, noncontrast CT image dataset (Dn,non) that maps an imaging area of the patient prior to administration of contrast agent, and - correction (S6) of the minimum intensity image dataset (Dmin) using the native, noncontrast CT image dataset (Dn,non), as a result of which a corrected minimum intensity image dataset (Dmin,corr) is provided, and wherein the virtual, noncontrast image dataset (Dv,non) that is output is based on the corrected minimum intensity image dataset (Dmin,corr).

8. Method according to claim 7, wherein the step of correction (S6) comprises ascertaining image areas in the minimum intensity image dataset (Dmin) or at least one of the at least three CT image datasets, that exceed a particular noise level value or that fall below a particular intensity value, and ascertaining a correction value on the basis of a comparison between image areas in each case locally corresponding thereto in the minimum intensity image dataset (Dmin) and in the native, noncontrast CT image dataset (Dn,non).

9. Method according to claim 7, wherein the step of correction (S6) comprises creating a perfusion image dataset on the basis of the at least three CT image datasets (D1, D2, D3) of the multiphase CT angiography image dataset of the patient (39), identifying image areas of the perfusion image dataset with locally increased contrast agent absorption, and ascertaining a correction value on the basis of a comparison between image areas locally corresponding thereto in the minimum intensity image dataset (Dmin) and locally corresponding image areas in the native, noncontrast CT image dataset (Dn,non).

10. Method according to one of claims 8 or 9, wherein the correction value is based on a ratio of the image values in the determined image areas of the minimum intensity image dataset (Dmin) or of the native, noncontrast CT image dataset (Dn,non) to image values in a surrounding area of the determined image areas of the minimum intensity image dataset (Dmin) or of the native, noncontrast CT image dataset (Dn,non).

11. Method for providing a perfusion image dataset, wherein - a multiphase CT angiography image dataset of the patient (39) comprising at least three CT image datasets (D1, D2, D3) that map an imaging area of the patient at three different points in time relative to an administration of contrast agent is provided, - according to one of the preceding claims a virtual, noncontrast CT image dataset (Dv,non) of the imaging area is provided on the basis of the at least three CT image datasets (D1, D2, D3), and - a perfusion image dataset is generated and provided on the basis of the at least three CT image datasets (D1, D2, D3) and the virtual, noncontrast CT image dataset (Dv,non).

12. Apparatus (45) for providing a virtual, noncontrast image dataset (Dv,non) of a patient comprising - a first interface (IF1), designed to provide at least one multiphase CT image dataset of the patient, comprising at least three CT image datasets (D1, D2, D3), which in each case map an imaging area of the patient at three different points in time relative to an administration of contrast agent, - a computing unit (CU), designed to form a minimum intensity image dataset (Dmin) of the imaging area on the basis of the at least three CT image datasets (D1, D2, D3), wherein the image value of an image point of the minimum intensity image dataset (Dmin) is in each case based on the minimum value from among the image values of the image points, locally corresponding in the imaging area, in the at least three CT image datasets (D1, D2, D3), wherein the minimum value corresponds to the value that corresponds to the highest transmission of X-ray radiation, and thus the least absorption, and - a second interface (IF2), designed to output the virtual noncontrast image dataset (Dv,non) on the basis of the minimum intensity image dataset (Dmin), wherein the virtual, noncontrast CT image dataset (Dv,non) is generated by means of a computing unit on the basis of measured data that has been captured with the administration of contrast agent.

13. Computed tomography device (32) having an apparatus (45) according to claim 12.

14. Computer program product having a computer program which can be loaded directly into a memory (MU) of an apparatus (45) for providing a virtual, noncontrast image dataset (Dv,non) of a patient, with program sections in order to execute all steps of the method according to one of claims 1 to 11, if the program sections are executed by the apparatus (45).

15. Computer-readable storage medium, on which are stored program sections which can be read and executed by an apparatus (45) for providing a virtual, noncontrast image dataset (Dv,non) of a patient, in order to execute all steps of the method according to one of claims 1 to 11, if the program sections are executed by the apparatus (45).