Identifying image artifacts by means of machine learning

A method is for producing an identification unit for identifying image artifacts automatically. In an embodiment, the method includes providing a learning processing apparatus; providing an initial identification unit; providing a first image data library including artifact reference acquisitions containing image artifacts; and training the identification unit using the image artifacts. An identification method is for identifying image artifacts automatically in an image acquisition. In an embodiment, the identification method includes: providing a trained identification unit; providing an image acquisition produced via a medical imaging system; inspecting the image acquisition for image artifacts by the identification unit; and labeling the ascertained image artifacts.

PRIORITY STATEMENT

The present application hereby claims priority under 35 U.S.C. § 119 to German patent application number DE 102017217550.8 filed Oct. 2, 2017, the entire contents of which are hereby incorporated herein by reference.

FIELD

Embodiments of the invention generally relate to a method for producing an identification unit for identifying image artifacts automatically in an image acquisition that was produced by way of a medical imaging system, and to an identification unit and to a learning processing apparatus for this purpose. Embodiments of the invention also generally include an identification method for using the identification unit to identify image artifacts automatically in an image acquisition that was produced by way of a medical imaging system, and also generally includes a control device for controlling a medical imaging system and a corresponding medical imaging system. In the context of embodiments of the invention, an image acquisition is a digital image, so comprises or consists of image data.

BACKGROUND

In imaging techniques in medicine, the creation of each pixel of an image acquisition usually relies on evaluating a single measurement variable or a single physical measurement principle. For instance, computed tomography (CT) detects as a single measurement variable the local X-ray attenuation by the patient. This issue also arises in other imaging techniques such as magnetic resonance imaging or ultrasound examination, for example.

Although sometimes radiation in two or more energy bands is employed, in particular for identifying different substances in tissue, for instance in multi-energy acquisitions, this does not alter the fundamental problem that always only one underlying physical principle, e.g. X-ray attenuation, is used.

Interference effects can cause image artifacts to appear in the image acquisition, which cannot be easily eliminated because of the lack of redundancy in the measured values. In the worst case, these artifacts can cause an incorrect positive diagnosis or an incorrect negative diagnosis.

CT images of lung parenchyma (lung perfused blood volume-Lung PBV), for example, often comprise motion artifacts in the region of the heart, which present themselves as dark zones of apparently no perfusion. Images of the thoracic cavity, in particular aforethe images of the lung parenchyma, may also comprise rib shadows caused by scattered radiation. They present themselves as typical dark zones emanating from the ribs and can be misidentified as regions of reduced iodine absorption. A high contrast-agent density in the vena cava can produce dark bar artifacts in images, which can be misidentified in iodine images as regions of low iodine absorption. In spectral CT acquisitions of the abdomen, the hepatic dome, for example, likewise because of scattering, often exhibits an apparently increased iodine absorption. Further frequently occurring image artifacts are, inter alia, streaks, ripple patterns, partial volume effects or pseudo-enhancement.

SUMMARY

The inventors have discovered that these image artifacts cannot be recognized in an automated manner using conventional methods, and therefore artifacts in image acquisitions must be recognized and manually tagged by a diagnostician, for instance a physician.

At least one embodiment of the present invention provides an alternative, more convenient identification method and a corresponding identification unit and a control device for controlling a medical imaging installation automatically, which avoid the disadvantages described above and allow image artifacts to be recognized reliably and in an automated manner. At least one embodiment of the invention is also directed to producing the identification unit and corresponding processing apparatuses.

Embodiments are directed to a method, an identification unit, a learning processing apparatus, an identification method, a control device, and a medical imaging system.

The method according to an embodiment of the invention for producing an identification unit for identifying image artifacts automatically in an image acquisition from a medical imaging system comprises:providing a learning processing apparatus, the learning processing apparatus being designed via an algorithm to recognize graphical elements in image acquisitions;providing on, or in, the learning processing apparatus, an initial identification unit designed to be trained by way of machine learning;providing a first image data library including artifact reference acquisitions produced via a medical imaging system, the artifact reference acquisitions including image artifacts; andtraining the identification unit according to a machine learning principle, based upon recognizing the image artifacts of the artifact reference acquisitions of the first image data library.

An identification unit, according to an embodiment of the invention, is for identifying image artifacts automatically in an image acquisition from a medical imaging system. An embodiment of the identification unit includes one which has been produced by a method according to an embodiment of the invention. The identification unit according to an embodiment of the invention includes one produced according to the machine learning principle from an initial identification unit, with the training carried out on the basis of recognizing image artifacts in artifact reference acquisitions of a provided first image data library. The recognition is one performed by way of the recognition algorithm by a provided learning processing apparatus that comprised an initial identification unit which has been trained.

A learning processing apparatus according to an embodiment of the invention comprises a processor and a data storage device containing instructions which, on being executed, allow the processor to capture reference acquisitions provided to the processing apparatus, to recognize image objects in the reference acquisitions as objects, and to identify in accordance with the method according to an embodiment of the invention, image artifacts in these image objects as artifact objects, and to train an initial identification unit according to an embodiment of the method.

A learning processing apparatus of an embodiment of the invention comprises:a processor; anda data storage device storing instructions which, upon being executed, allow the processor to at least:capture reference acquisitions provided to a processing apparatus,recognize image objects in reference acquisitions as objects, andidentify image artifacts in the image objects as artifact objects, and to train an initial identification unit according to a machine learning principle, based upon recognizing the image artifacts of the artifact reference acquisitions of a first image data library of the identification unit.

An identification method, according to an embodiment of the invention for identifying image artifacts automatically in an image acquisition from a medical imaging system, comprising:providing an identification unit;providing an image acquisition produced via a medical imaging system;inspecting, via the identification unit, the image acquisition for image artifacts; andby processing the image acquisition, at least one oflabeling image artifacts ascertained during the inspecting,reducing image artifacts ascertained during the inspecting, andeliminating image artifacts ascertained during the inspecting.

A control device according to an embodiment of the invention for a medical imaging system is designed to perform an identification method according to an embodiment of the invention.

A medical imaging system according to an embodiment of the invention comprises a control device according to an embodiment of the invention.

In this respect, at least one embodiment of the invention is also achieved by a corresponding non-transitory computer program product comprising a computer program, which can be loaded directly into a memory device of a control device and/or of a processing system and which contains program segments, in order to perform the method according to an embodiment of the invention when the program is executed. The computer program product may comprise in addition to the computer program, if applicable, extra elements such as e.g. documentation and/or extra components, including hardware components, such as e.g. hardware keys (dongles etc.) for using the software.

For transfer to the control device and/or to the processing system, and/or for storage on, or in, the control device and/or the processing system, a non-transitory computer-readable medium, for instance a memory stick, a hard disk or any other portable or permanently installed data storage medium can be used, on which are stored the program segments of the computer program, which program segments can be downloaded and executed by a processing unit. For this purpose, the processing unit can comprise, for example, one or more interacting microprocessors or the like.

Therefore also preferred is an identification unit in the form of a non-transitory computer program product comprising a computer program which can be loaded directly into a memory device of a processing system or of a control device of a medical imaging system and which contains program segments in order to perform the identification method according to at least one embodiment of the invention when the computer program is executed in the processing system or the control device.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

Most of the aforementioned components, in particular the identification unit, can be implemented in full or in part in the form of software modules in a processor of a suitable control device or of a processing system. An implementation largely in software has the advantage that even control devices and/or processing systems already in use can be easily upgraded by a software update in order to work in the manner according to at least one embodiment of the invention.

A solution to the problem described above is very complex, and identifying image artifacts is not easily possible. In addition, it is not possible to produce easily an identification unit. Therefore an embodiment of the invention includes not only the identification unit and a method for identifying image artifacts using this identification unit, but also producing the identification unit and the corresponding processing apparatus.

In addition, the identification also presents the opportunity to eliminate image artifacts, and/or to control a medical imaging device in such a way that on an artifact being recognized, it makes a second image acquisition using other acquisition parameters in order to achieve an artifact-free image acquisition of the region, or subject, concerned. This is also part of at least one embodiment of the invention.

The method according to an embodiment of the invention for producing an identification unit for identifying image artifacts automatically in an image acquisition from a medical imaging system comprises:

Providing a Learning Processing Apparatus.

Embodiments of the learning processing apparatus are described in more detail further below. An embodiment of the learning processing apparatus is designed by using an algorithm (also referred to as a “recognition algorithm”) to recognize graphical elements in the image acquisition or in image data of the image acquisitions. Graphical elements refer here, for example, to patterns, graphical primitives and/or continuous surfaces or structures or the like. For instance, these can also include more complex structures such as organs, bones, vessels, etc., for example, or even artifacts.

Providing an Initial Identification Unit.

The initial identification unit is the subsequent identification unit, but which has not yet been trained or has not yet been optimally trained. It is provided on, or in, the learning processing apparatus and is designed to be trained by way of machine learning (by the processing apparatus). The recognition algorithm may here also be part of the (initial) identification unit itself, for instance.

Providing a First Image Data Library.

This first image data library can also be called an “artifact library” and comprises in particular spectral, artifact reference acquisitions from a medical imaging system, which artifact reference acquisitions comprise image artifacts. The reference acquisitions are image acquisitions, i.e. comprise or consist of image data. The first image data library may be, for example, a database which contains artifact reference acquisitions and has a data link to the learning processing apparatus. A suitable medical imaging system may be, for example, a computed tomography (CT) machine, a magnetic resonance imaging (MRI) machine, an ultrasound device, an X-ray system for angiography, a system for interventional radiology or another X-ray system.

Training the Identification Unit.

The identification unit is trained in this step according to a machine learning principle on the basis of recognizing the image artifacts of the artifact reference acquisitions of the first image data library. The recognition is performed via the recognition algorithm.

“An image acquisition from a medical imaging system” refers here to an image acquisition of an object under examination (also called the “subject”), for instance of an organ, part of the body and/or region of a patient, which image acquisition has been produced by the medical imaging system. This can involve two-dimensional images or image data, volume image data or even an image dataset composed of a plurality of image data, for instance a stack of two-dimensional image data.

Since image artifacts are normally not intelligible to a computer, information about the artifacts can be stored in the form of (computer-intelligible) objects. In this regard, the identification unit can contain, for example, objects that correspond to the image artifacts. These are referred to below as “artifact objects”. For the purpose of identifying image artifacts, initially unknown structures, for example, in examined image acquisitions can be compared with these artifact objects. In the event of a match with an artifact object, the structure concerned would be labeled as an image artifact.

An identification unit, according to an embodiment of the invention, is for identifying image artifacts automatically in an image acquisition from a medical imaging system. An embodiment of the identification unit includes one which has been produced by a method according to an embodiment of the invention. The identification unit according to an embodiment of the invention includes one produced according to the machine learning principle from an initial identification unit, with the training carried out on the basis of recognizing image artifacts in artifact reference acquisitions of a provided first image data library. The recognition is one performed by way of the recognition algorithm by a provided learning processing apparatus that comprised an initial identification unit which has been trained.

A learning processing apparatus according to an embodiment of the invention comprises a processor and a data storage device containing instructions which, on being executed, allow the processor to capture reference acquisitions provided to the processing apparatus, to recognize image objects in the reference acquisitions as objects, and to identify in accordance with the method according to an embodiment of the invention, image artifacts in these image objects as artifact objects, and to train an initial identification unit according to an embodiment of the method.

An identification method according to an embodiment of the invention for identifying image artifacts automatically in an image acquisition from a medical imaging system comprises:

Providing an Identification Unit.

The identification unit is a unit that has been trained as described above. In this context, training of an initial identification unit can also be performed in particular first.

Providing an Image Acquisition.

The image acquisition is an image acquisition from a medical imaging system. This system preferably works according to the same principle as that which has acquired the reference acquisitions, in order that the artifacts are also comparable with one another.

Inspecting the Image Acquisition for Image Artifacts.

The inspection is performed by the identification unit.

Labeling the Ascertained Image Artifacts.

The image artifacts can be labeled simply using tags, although it is also possible to substitute artifact objects for the image artifacts and hence to add computer-intelligible elements to the resultant image. The labeling can be performed directly in the image acquisition or in an additional representation, for instance in an additional image layer.

Alternatively or additionally, the ascertained image artifacts can be eliminated or at least reduced by processing the image acquisition.

A control device according to an embodiment of the invention for a medical imaging system is designed to perform an identification method according to an embodiment of the invention.

A medical imaging system according to an embodiment of the invention comprises a control device according to an embodiment of the invention.

Most of the aforementioned components, in particular the identification unit, can be implemented in full or in part in the form of software modules in a processor of a suitable control device or of a processing system. An implementation largely in software has the advantage that even control devices and/or processing systems already in use can be easily upgraded by a software update in order to work in the manner according to at least one embodiment of the invention.

In this respect, at least one embodiment of the invention is also achieved by a corresponding non-transitory computer program product comprising a computer program, which can be loaded directly into a memory device of a control device and/or of a processing system and which contains program segments, in order to perform the method according to an embodiment of the invention when the program is executed. The computer program product may comprise in addition to the computer program, if applicable, extra elements such as e.g. documentation and/or extra components, including hardware components, such as e.g. hardware keys (dongles etc.) for using the software.

For transfer to the control device and/or to the processing system, and/or for storage on, or in, the control device and/or the processing system, a non-transitory computer-readable medium, for instance a memory stick, a hard disk or any other portable or permanently installed data storage medium can be used, on which are stored the program segments of the computer program, which program segments can be downloaded and executed by a processing unit. For this purpose, the processing unit can comprise, for example, one or more interacting microprocessors or the like.

Therefore also preferred is an identification unit in the form of a non-transitory computer program product comprising a computer program which can be loaded directly into a memory device of a processing system or of a control device of a medical imaging system and which contains program segments in order to perform the identification method according to at least one embodiment of the invention when the computer program is executed in the processing system or the control device.

An identification unit is preferred in the form of a computer-readable medium, on which are stored program segments which can be downloaded and executed by a processing unit in order to perform all the steps of the identification method according to an embodiment of the invention when the program segments are executed by the processing unit. The identification unit in the form of this computer-readable medium can also exist as hardware, for instance as a programmed EPROM.

Further, particularly advantageous embodiments and developments of the invention are given in the claims and in the following description, where the claims in one category of claims can also be developed in a similar way to the claims and passages of the description in another category of claims, and in particular individual features of different example embodiments or variants can also be combined to create new example embodiments or variants. In particular, the identification unit according to at least one embodiment of the invention can also be developed in a similar way to the method claims or embodiments in passages of the description.

An embodiment of the method according to the invention comprises providing a second image data library, which can also be called a “clear-acquisitions library”. This clear-acquisitions library comprises (preferably spectral) clear reference acquisitions from a medical imaging system. These clear reference acquisitions comprise substantially no image artifacts. Since it is always possible to find some kind of image artifacts, systemic to the acquisition technology (e.g. image noise), in real acquisitions, the expression “substantially” is used here to refer to image acquisitions that according to current standards can be considered as usable acquisitions containing at most minimal image artifacts, or to those parts of artifact-affected image acquisitions that meet these standards.

In this case, training the identification unit is preferably based additionally on recognizing image components of the clear reference acquisitions of the second image data library. As a result, image components contained solely in the artifact reference acquisitions can be tagged explicitly as image artifacts, and preferably image components contained solely in the clear reference acquisitions of the second image data library can be tagged explicitly as artifact-free image objects.

An embodiment of the method according to the invention is also preferred in which the artifact reference acquisitions of the first image data library, and preferably also the clear reference acquisitions of the second image data library, are acquisitions of an object in the form of dual-energy (spectral) computed tomography acquisitions or multi-energy computed tomography acquisitions. Thus in particular they comprise acquisitions at at least two different acquisition energies, e.g. high kV, low kV. Alternatively or additionally, it is preferred that the reference acquisitions comprise acquisitions decomposed according to material, e.g. in the form of a two-material decomposition or three-material decomposition.

An embodiment of the method according to the invention is also preferred in which the provided artifact reference acquisitions of the first image data library, and preferably also the clear reference acquisitions of the second image data library, comprise already labeled image objects. It is preferred here that artifact reference acquisitions contain image artifacts labeled as artifact objects. Clear reference acquisitions contain preferably image objects containing no artifacts and labeled as non-artifact objects. This has the advantage that the learning step can be performed more efficiently.

An embodiment of the method according to the invention is also preferred in which a first base-element library is created from a set of artifact reference acquisitions, in particular from a large number of spectral CT result images, for instance resulting from material decomposition or similar computational techniques. This first base-element library comprises artifact objects as base elements. Also a second base-element library is preferably created using clear reference acquisitions. This second base-element library comprises non-artifact objects as base elements. The artifact objects and non-artifact objects have been recognized automatically via the identification unit or have been tagged by prior labeling, as was described earlier. The labeling can have been performed manually or automatically.

As an alternative, or in addition, to the aforementioned base-element libraries (which can also be called a dictionary), it is also possible to generate a base-element library that contains different instances of image noise as base elements.

The previous section containing preferred embodiments is concerned with producing an identification unit according to the invention. A trained identification unit is assumed below, which can be used to identify image artifacts.

Preferably, the image acquisitions containing recognized image artifacts can be used again as artifact reference acquisitions, and the image acquisitions without image artifacts as clear reference acquisitions. Thus once an identification has been made, additional training of the identification unit or of another identification unit can be performed according to a method described above.

An embodiment of the identification method according to the invention is preferred that can be used additionally to achieve a reduction in image artifacts in an image acquisition from a medical imaging system. In this case, the identification unit has been trained additionally by clear reference acquisitions, as already described above. This preferred embodiment comprises the step of eliminating or at least reducing the ascertained image artifacts by processing (graphically) the image acquisition. In particular, the effect of the processing is that image objects that have been identified as image artifacts are removed and/or replaced by corresponding non-artifact objects.

An embodiment of the identification method according to the invention is preferred in which a first base-element library, which comprises artifact objects as base elements, is provided, and preferably also a second base-element library, which comprises non-artifact objects as base elements, is provided. The base-element libraries have already been described above. As part of this preferred method, at least one (preferably spectral) combination image is generated in a (standard) optimization process by combinations of the base elements of a base-element library. In this process, a combination image composed of image artifacts is generated, and preferably also a complementary combination image containing reduced artifacts is generated. These combination images exist preferably in the form of a combination-image group, wherein the combination image composed of image artifacts can be separated from the combination image containing reduced artifacts. For instance, the combination image composed of image artifacts and the combination image containing reduced artifacts can exist as different layers in a combination-image group.

As another preferred embodiment of the aforementioned embodiment of the identification method according to an embodiment of the invention, the combination image comprises an image from the group: calcium image, iodine image, virtual non-contrast image, water image and soft-tissue image, iron image, contrast-agent image and a mono-energy image.

An embodiment of the identification method according to the invention is also preferred that can be used alternatively or additionally for controlling a medical imaging system. This embodiment comprises the additional steps:

Providing a Control Data Library.

In this control data library are held control datasets for a medical imaging system that are computationally linked to artifact objects. The control datasets can be configured in particular in such a way that the image artifacts to which they are computationally linked are suppressed when the control dataset is used.

Selecting a Control Dataset.

The control dataset is selected here on the basis of at least one identified image artifact in an inspected image.

Using the Selected Control Dataset.

The selected control dataset is here used for re-acquisition of the subject (or object under examination) of the inspected image acquisition. The body region acquired in the original image acquisition is thereby acquired again using a control dataset optimized for suppressing these artifacts, resulting in an improved image acquisition. It is hence unnecessary to repeat the entire examination because of artifact-affected images, since when reduced quality is ascertained, an image is re-acquired immediately using optimized control.

In the following explanations it is assumed that the medical imaging system or imaging installation is a computed tomography system. In principle, however, the method can also be used on other imaging installations.

FIG. 1shows a schematic diagram of a simple embodiment of the method according to an embodiment of the invention for producing an identification unit according to an embodiment of the invention.

In step I, a learning processing apparatus7is provided, the learning processing apparatus7being designed via an algorithm to recognize graphical elements in image acquisitions or in image data of the image acquisitions (see alsoFIG. 3on this subject).

In step II, an initial identification unit6ais provided, which is designed to be trained by way of machine learning, and basically constitutes the untrained identification unit6. This initial identification unit6ais provided on, or in, the learning processing apparatus7, so for instance as a database that has a data link to this learning processing apparatus7, as shown in the figure, or provided as a data structure directly in this learning processing apparatus7.

In step III, a first image data library BB1is provided, comprising artifact reference acquisitions AR from a medical imaging system1(i.e. artifact reference acquisitions AR that have been produced via a medical imaging system1, preferably the same type of medical imaging system1as was used to produce the image acquisitions to be inspected, so for instance a CT system for inspecting CT acquisitions, etc.), wherein the artifact reference acquisitions AR comprise image artifacts BA.

The circle at which the three arrows denoted by I, II, III terminate is here the output state at which the three previous aspects have been provided. Now, in order to produce the identification unit6, the initial identification unit6amust be trained.

This training of the identification unit6takes place in step VI according to the machine learning principle on the basis of recognizing the image artifacts BA of the artifact reference acquisitions AR of the first image data library BB1. In order to make clear that the identification unit6is trained, a schematic artifact object AO has been shown in the identification unit6in this example.

FIG. 2shows a schematic diagram of another example embodiment of the method according to the invention for producing an identification unit according to an embodiment of the invention. This figure is an extension ofFIG. 1, with the steps I, II and III again being performed here. Unlike the method shown inFIG. 1, the identification unit6is trained not just by image artifacts BA but also by artifact-free clear reference images KR.

Thus unlikeFIG. 1, the additional step IIIa is performed, in which a second image data library BB2is provided, comprising clear reference acquisitions KR from a medical imaging system1, which clear reference acquisitions KR comprise substantially no image artifacts BA, preferably no image artifacts whatsoever.

Training the identification unit6in step IVa is consequently based on recognizing image components of the clear reference acquisitions KR of the second image data library BB2. In order to make clear that this identification unit6is trained both by image artifacts and additionally by clear reference acquisitions, in this example both an artifact object AO and a non-artifact object NO have been shown schematically in the identification unit6.

Base-element libraries BEB1, BEB2can be produced using the artifact objects AO and the non-artifact objects NO respectively. Shown on the left is a first base-element library BEB1, which comprises as base elements BE, artifact objects AO recognized by the identification unit6. Shown on the left is a second base-element library BEB2, which comprises as base elements BE, non-artifact objects NO recognized by the identification unit6.

FIG. 3shows a schematic diagram of a preferred learning processing apparatus7. This learning processing apparatus7comprises a processor8and a data storage device9, which are depicted here as blocks. The data storage device9contains instructions which, on being executed, allow the processor8to capture reference acquisitions AR, KR provided to the processing apparatus, to recognize image objects BO in the reference acquisitions AR, KR as objects, and to identify in accordance with one of the mentioned methods, image artifacts BA in these image objects BO as artifact objects AO, and to train an initial identification unit6a, for instance according to an embodiment of the method according to the invention shown inFIGS. 1 and 2.

FIG. 4shows a flow diagram of a possible sequence of a method according to an embodiment of the invention for identifying image artifacts BA in an image acquisition B from a medical imaging system1such as shown inFIG. 5, for example.

In step V, an identification unit6is provided that has been produced, for example, according to a method shown inFIG. 1 or 2. The artifact object AO shown schematically in the identification unit6is intended to make clear that the identification unit6has been trained to identify this pattern as an image artifact BA in image acquisitions B. The image artifact BA and the artifact object AO are a ripple pattern in this example, but this is merely representative of all the other possible types of artifact.

In step VI, an image acquisition B from a medical imaging system1is provided. In this case, the image acquisition B is a tomogram through a human skull. In the upper part of the image acquisition B can be seen a ripple pattern, which represents an image artifact BA, which has not yet been identified automatically.

In step VII, the image acquisition B is inspected for image artifacts BA by the identification unit6. The ripple pattern is identified in this process as an image artifact BA, because it has the same structure as the artifact object AO depicted in the identification unit6in step V.

In step VIII, the ascertained image artifact BA is labeled automatically, which is represented by a dashed circle.

Up until this step, an example method for purely identifying image artifacts was presented. With the automatic identification of image artifacts BA, however, it is also possible to introduce, in addition, or as an alternative, to the automatic labeling, elimination of this image artifact BA and/or automatic control of a medical imaging system1, as the further method steps illustrate.

Below, the ascertained image artifacts BA are eliminated or at least reduced by processing the image acquisition B. This is done here by providing a first base-element library BEB1and a second base-element library BEB2, as were presented in more detail above in the context ofFIG. 2. For reasons of clarity, the fine details of the two base-element libraries BEB1, BEBS have not been shown. The symbol for the ripple pattern in the top library and the symbol for the segment of the CT image in the bottom library are meant to indicate, however, that they comprise base elements BE in the form of artifact objects AO (first base-element library BEB1) and non-artifact objects NO (second base-element library BEB2).

In step IX, at least one group comprising two combination images KB is produced by combinations of the base elements BE of the two provided base-element libraries BEB1, BEB2.

In this process, a combination image KB composed of image artifacts BA is generated, and preferably also a complementary combination image KB containing reduced artifacts is generated. The two combination images are shown one below the other inFIG. 4to improve understanding. In practice, the combination images can exist in a combination-image group. For instance, the combination image composed of image artifacts and the combination image containing reduced artifacts can exist as different layers in the combination-image group.

Theoretically, the previously described step IX can be omitted for the subsequent step X (itself also optional). For the control performed in step X, in principle an image artifact merely needs to be identified.

First, a control data library SB is provided, in which are held control datasets SD for a medical imaging system1. These control datasets SD are computationally linked to artifact objects AO, so that a control dataset can be selected on the basis of an identified image artifact.

In step X, a control dataset SD is selected according to at least one identified image artifact BA in an examined image acquisition B.

In step XI, this selected control dataset SD is used for controlling the medical imaging system1for the re-acquisition of at least the examined image acquisition B. This new image acquisition can thereby be acquired using new parameters, which are selected specifically for eliminating the recognized artifact in the acquisition. The end result of this is a stack of artifact-reduced images.

FIG. 5shows highly schematically a computed tomography system1having a control device10for performing identification of image artifacts, and preferably also for performing a learning process in accordance with a method according to an embodiment of the invention.

The computed tomography (CT) system1comprises, as is standard practice, a scanner2having a gantry, in which an X-ray source3rotates, which sends radiation through a patient P, who is moved by a couch5into a measuring chamber of the gantry so that the radiation arrives at a detector4situated opposite the X-ray source3. It is mentioned explicitly that the example embodiment shown inFIG. 5is only one example of a CT system, and the invention can also be used on any other CT systems.

Likewise, for the control device10, only those components are shown that are essential to explaining the invention or aid understanding. Such CT systems and associated control devices generally are known to a person skilled in the art and therefore need not be explained in detail.

Similarly, the invention can also be used on any other medical imaging systems.

A core component of the control device10is here a processor11, on which various components are implemented in the form of software modules. The control device10also comprises a terminal interface14, connected to which is a terminal20, via which an operator can operate the control device10and hence the computed tomography system1. A further interface15is a network interface for connecting to a data bus21, in order to establish thereby a connection to an RIS or PACS (RIS: radiology information system; PACS: picture archiving and communication system). For instance, image data from image acquisitions can be forwarded via this bus21.

The scanner2can be controlled by the control device10via a control interface13, i.e. it is possible to control, for instance, the rotation speed of the gantry, the movement of the patient couch5and the X-ray source3itself. The raw data RD is read out from the detector4via an acquisition interface12.

The control device10also comprises a storage unit16, in which is held a control data library SB containing control datasets SD. The control datasets SD are in this case computationally linked to artifact objects AO.

One component on the processor11is an image data reconstruction unit18, which can be used to reconstruct from the raw data RD received via the data acquisition interface12, the desired image data B of the image acquisitions B. This image data reconstruction unit18passes the reconstructed image data B of an image acquisition B to an identification unit6, in which first, in accordance with a method according to an embodiment of the invention, image artifacts BA are identified in the image acquisition B and are processed as artifact objects by the identification unit6(see e.g.FIG. 4).

In the event that an image artifact BA has been found and an artifact object AO has been created, a control dataset SD is selected from the control data library (SB), which is provided by the storage unit16, on the basis of at least one identified image artifact BA or its artifact object BO, and the selected control dataset SD is used to produce a new image acquisition B, which preferably likewise is inspected for artifacts. A set of artifact-free or at least artifact-reduced image acquisitions B can thereby be produced automatically.

Finally it should be reiterated that the method described in detail above and the presented apparatuses are merely example embodiments, which can be modified by a person skilled in the art in many ways without departing from the scope of the invention. In addition, the use of the indefinite article “a” or “an” does not rule out the possibility of there also being more than one of the features concerned. Likewise, the terms “unit” and “module” do not exclude the possibility that the components in question consist of a plurality of interacting sub-components, which may also be spatially distributed if applicable.