Individual determination of breast compression in mammography with artificial intelligence

A method is for determining a patient-adjusted breast compression in mammography. In an embodiment of the method, input data including individual, person-related data of a female patient, is determined. Furthermore, an adjusted individual compression point is determined by applying a function, trained by an algorithm based on machine learning, to the input data. The adjusted individual compression point is generated as the output data. Other embodiments include a method for providing a trained function; a breast compression determining device; a training device; and a mammography system.

PRIORITY STATEMENT

The present application hereby claims priority under 35 U.S.C. § 119 to German patent application number DE 102020212205.9 filed Sep. 28, 2020, the entire contents of which are hereby incorporated herein by reference.

FIELD

Example embodiments of the invention generally relate to a method for determining a patient-adjusted breast compression in mammography, to a method for providing a trained function, to a breast compression determining device, to a training device and to a mammography system.

BACKGROUND

Mammography continues to play an important role in the early detection of breast carcinoma. In conventional mammography, an X-ray image is created of the female breast. The X-ray images are viewed at a special mammography diagnostic workstation, which comprises one or two gray scale monitors, with which the X-ray images are visually depicted.FIG.1shows an arrangement for two-dimensional mammography. To be able to identify lesions on the images, the breast tissue to be depicted has to be compressed. However, sub-optimum parameters are often conventionally chosen for breast compression. Conventional examination equipment often specifies only the parameters “compression force” and the “currently compressed breast thickness” for the user. Even more parameters have to be considered for a patient-adjusted compression, however, such as the tissue density, in other words the elastic properties of the breast, and the area of the breast. Since these additional parameters are not known in conventional procedures only an under-determined system of equations can conventionally be constructed for calculation of an optimum compression of the breast, so a clear solution cannot be found. However, a sub-optimum breast compression often leads to a sub-optimal image quality or is associated with pain for the female patient being examined.

U.S. Pat. No. 5,335,257 A describes a method for the adjustment of the breast compression, with which a value for the compression is determined. However, in this case only the change over time in the compression force and thickness is considered and there is a reaction if the ratio of the change in the compression force and the change in the compression thickness exceeds a preset value.

EP 2 536 336 B1 describes a compression paddle with a plurality of spatially resolved capacitive pressure sensors, which measure the pressure really applied to the breast and make it available for a compression regulation.

DE 10 2018 200 108 A1 describes a method for the positioning of an examination object in respect of an X-ray machine in which image acquisitions from a camera are used to correctly position the breast and to estimate the size of the breast area.

SUMMARY

At least one embodiment of the present invention is directed to developing a better-adjusted breast compression for a mammography method with a good image quality and adequate comfort for the female patient.

Embodiments of the application are directed to a method for determination of a patient-adjusted breast compression in mammography, a method for providing a trained function, a breast compression determining device, a training device and a mammography system.

In at least one embodiment of the inventive method for determining a patient-adjusted breast compression in mammography, input data is determined, which comprises individual, person-related data of a female patient. Furthermore, an adjusted individual compression point is determined by applying a function, which was trained by an algorithm based on machine learning, to the input data, wherein the adjusted individual compression point is generated as the output data. The “adjusted individual” compression point should be taken to mean a compression point at which, on the one hand, predetermined demands for a minimum image quality are met and, on the other hand, a predetermined minimum level of patient comfort, which can depend on individual properties of a patient's breast and the subjective perception of a female patient, is achieved. The two demands can be individually established depending on the intended application.

In the inventive method of at least one embodiment, for providing a trained function, which can be used for at least one embodiment of the inventive method for determining a patient-adjusted breast compression in mammography, input training data is received, which comprises individual, person-related data of persons in a training database. Output training data, which is assigned to the input training data, is also received, wherein the output training data comprises an individually adjusted candidate compression point. Furthermore, a function is trained by an algorithm based on machine learning based upon the input data and the output data. Advantageously, no complex modeling approach has to be laboriously constructed for determination of the function, instead the function is automatically generated based upon the existing database of training data. It is in particular with a large number of different parameters that should be taken into account that such a procedure is superior to the purely model-based approach.

The inventive breast compression determining device of at least one embodiment has an input data determining unit for determining input data, which comprises individual, person-related data of a female patient. The inventive breast compression determining device also comprises a compression point determining unit for determining an adjusted individual compression point by applying a function, which was trained by an algorithm based on machine learning, to the input data, wherein the adjusted individual compression point is generated as the output data. In addition, the inventive breast compression determining device comprises a second interface for outputting the determined adjusted individual compression point. The inventive breast compression determining device shares the advantages of the inventive method for determining the breast compression of a female patient in mammography.

The inventive training device of at least one embodiment, which can be used to implement the training of the function based on machine learning used in the breast compression determining device of at least one embodiment has a first training interface for receiving input training data, which comprises individual, person-related data of persons in a training database. The inventive training device of at least one embodiment also comprises a second training interface for receiving output training data, which is assigned to the input training data, wherein the output training data comprises an individually adjusted candidate compression point. A training unit for training a function based on the training input data and the training output data, and an output interface for outputting the trained function is also part of the inventive training device of at least one embodiment. The inventive training device of at least one embodiment shares the advantages of the inventive training method of at least one embodiment.

The inventive mammography system of at least one embodiment features the inventive breast compression determining device of at least one embodiment and the inventive training device of at least one embodiment. The inventive mammography system of at least one embodiment combines the advantages of the breast compression determining device and the inventive training device of at least one embodiment.

The fundamental components of the inventive breast compression determining device of at least one embodiment and the inventive training device of at least one embodiment can be designed for the most part in the form of software components. This applies, in particular, to parts of the input data determining unit and the compression point determining unit of the breast compression determining device and the training unit of the training device.

An implementation largely in terms of software has the advantage that even previously used mammography devices can be easily adapted, possibly by retrofitting required hardware, by way of a software update in order to work inventively. In this regard the object is also achieved by a corresponding computer program product with a computer program, which can be loaded directly into a storage device of a mammography device and comprises program segments in order to carry out all steps of the inventive method of at least one embodiment when the computer program is run in the mammography device.

The inventive method of at least one embodiment for providing a trained function, comprises:receiving input training data, including individual, person-related data of persons in a training database;receiving output training data, assigned to the input training data, the output training data including an adjusted individual candidate compression point; andtraining a function by way of an algorithm based on machine learning based upon the input training data and the output training data.

The inventive training device breast compression determining device of at least one embodiment, comprises:an input data determining unit to determine input data, including individual, person-related data of a female patient;a compression point determining unit to determine an adjusted individual compression point by applying a function, trained by an algorithm based on machine learning, to the input data; anda second interface to output the determined adjusted individual compression point determined.

The inventive training device for a breast compression determining device of at least one embodiment, comprises:a first training interface to receive input training data including individual, person-related data of persons in a training database;a second training interface to receive output training data, assigned to the input training data, wherein the output training data includes an adjusted individual candidate compression point;a training unit to train a function based on the training input data and the training output data; andan output interface to output the trained function.

The inventive mammography system of at least one embodiment, comprises:the breast compression determining device of an embodiment; anda training device for the breast compression determining device, comprising:a first training interface to receive input training data including individual, person-related data of persons in a training database;a second training interface to receive output training data, assigned to the input training data, wherein the output training data includes an adjusted individual candidate compression point;a training unit to train the function based on the training input data and the training output data; andan output interface to output the trained function.

At least one embodiment is directed to an inventive non-transitory computer program product storing a computer program, directly storable to a storage device of a data processing device, including program segments to carry out the method of an embodiment when the computer program is run in the data processing device.

At least one embodiment is directed to an inventive non-transitory computer-readable medium, storing program segments, readable and runnable by an arithmetic unit, to carry out the method of an embodiment when the program segments are run by the arithmetic unit.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

In at least one embodiment of the inventive method for determining a patient-adjusted breast compression in mammography, input data is determined, which comprises individual, person-related data of a female patient. Furthermore, an adjusted individual compression point is determined by applying a function, which was trained by an algorithm based on machine learning, to the input data, wherein the adjusted individual compression point is generated as the output data. The “adjusted individual” compression point should be taken to mean a compression point at which, on the one hand, predetermined demands for a minimum image quality are met and, on the other hand, a predetermined minimum level of patient comfort, which can depend on individual properties of a patient's breast and the subjective perception of a female patient, is achieved. The two demands can be individually established depending on the intended application.

Preferably, an optimum compression point is determined as the adjusted individual compression point for a female patient. An optimum compression point of this kind can, comprise, for example an optimum image quality with a predetermined minimum level of comfort that is still acceptable to the patient. Conversely, it can also comprise an optimum level of comfort with a predetermined minimum level of the quality of the image representation. Moreover, an optimum compression point of this kind can also be established such that an optimization of the two parameters takes place as a function of a previously established weighting of the parameters or of a previously established parameter value interval in which the two parameters should lie.

Advantageously, determining the individually adjusted compression point includes individual data of a person and a functional correlation between this individual data and the adjusted individual compression point. A function based on artificial intelligence can also take into account influencing variables that are difficult to model, such as the subjective pain perception of a person, in order to reach a compromise between a reasonable level of comfort for the patient and an adequate image quality.

In the inventive method of at least one embodiment, for providing a trained function, which can be used for at least one embodiment of the inventive method for determining a patient-adjusted breast compression in mammography, input training data is received, which comprises individual, person-related data of persons in a training database. Output training data, which is assigned to the input training data, is also received, wherein the output training data comprises an individually adjusted candidate compression point. Furthermore, a function is trained by an algorithm based on machine learning based upon the input data and the output data. Advantageously, no complex modeling approach has to be laboriously constructed for determination of the function, instead the function is automatically generated based upon the existing database of training data. It is in particular with a large number of different parameters that should be taken into account that such a procedure is superior to the purely model-based approach.

The algorithm based on machine learning is preferably based on an artificial neural feedforward network. The advantage of such a “feedforward network” is that it can map any complex mathematical correlations, and this is described, for example, in Bishop: Neural Networks for Pattern Recognition, 1995, the entire contents of which are hereby incorporated herein by reference.

In general there are three types of neural network:convolutional networks: convolutional operators use local correlations, so the number of weights is reduced compared to “feedforward networks”,recurrent networks: feedback allows the acquisition of time-related correlations in sequential data,“feedforward networks”: all input variables are connected to all nodes in the next intermediate layer. These networks are primarily used when time- and location-related correlations of the input variables do not exist or are unknown, as is the case with the scenario forming the basis of at least one embodiment the invention.

The inventive breast compression determining device of at least one embodiment has an input data determining unit for determining input data, which comprises individual, person-related data of a female patient. The inventive breast compression determining device also comprises a compression point determining unit for determining an adjusted individual compression point by applying a function, which was trained by an algorithm based on machine learning, to the input data, wherein the adjusted individual compression point is generated as the output data. In addition, the inventive breast compression determining device comprises a second interface for outputting the determined adjusted individual compression point. The inventive breast compression determining device shares the advantages of the inventive method for determining the breast compression of a female patient in mammography.

The inventive training device of at least one embodiment, which can be used to implement the training of the function based on machine learning used in the breast compression determining device of at least one embodiment has a first training interface for receiving input training data, which comprises individual, person-related data of persons in a training database. The inventive training device of at least one embodiment also comprises a second training interface for receiving output training data, which is assigned to the input training data, wherein the output training data comprises an individually adjusted candidate compression point. A training unit for training a function based on the training input data and the training output data, and an output interface for outputting the trained function is also part of the inventive training device of at least one embodiment. The inventive training device of at least one embodiment shares the advantages of the inventive training method of at least one embodiment.

The inventive mammography system of at least one embodiment features the inventive breast compression determining device of at least one embodiment and the inventive training device of at least one embodiment. The inventive mammography system of at least one embodiment combines the advantages of the breast compression determining device and the inventive training device of at least one embodiment.

The fundamental components of the inventive breast compression determining device of at least one embodiment and the inventive training device of at least one embodiment can be designed for the most part in the form of software components. This applies, in particular, to parts of the input data determining unit and the compression point determining unit of the breast compression determining device and the training unit of the training device.

Basically, these components can, however, also be implemented partly, in particular when especially fast calculations are needed, in the form of software-assisted hardware, for example FPGAs or the like. Similarly, the required interfaces can be designed as software interfaces, for example when it is only a matter of acceptance of data from other software components. However, they can also be designed as interfaces constructed in terms of hardware, which interfaces are actuated by suitable software.

An implementation largely in terms of software has the advantage that even previously used mammography devices can be easily adapted, possibly by retrofitting required hardware, by way of a software update in order to work inventively. In this regard the object is also achieved by a corresponding computer program product with a computer program, which can be loaded directly into a storage device of a mammography device and comprises program segments in order to carry out all steps of the inventive method of at least one embodiment when the computer program is run in the mammography device.

Apart from the computer program, a computer program product of this kind can optionally comprise additional elements, such as documentation and/or additional components, also hardware components, such as hardware keys (dongles, etc.) in order to use the software.

The methods can be reproducibly carried out, and so as to be less prone to errors, on different mammography devices by way of a software implementation.

A computer-readable medium, for example a memory stick, a hard drive or another transportable or permanently installed data carrier, on which the program segments of the computer program, which can be read and run by a data processing device, for example an arithmetic unit, are stored, can serve for transportation to the storage device of the mammography device and/or for storage on the mammography device. For this the arithmetic unit can have, for example, one or more cooperating microprocessors or the like.

The claims and the subsequent description each contain particularly advantageous embodiments and developments of the invention. In particular the claims of one category can also be developed analogously to the dependent claims of a different category. In addition, the various features of different example embodiments and claims can also be combined to form new example embodiments within the context of the invention.

The inventive method of at least one embodiment for determining a patient-adjusted breast compression in mammography can be designed as a multi-stage sequential method. Determining input data also comprises experimental determining of an individual candidate compression point, which comprises a value for a compression force and a compression thickness of a breast of an individual female patient. In this variant, determining an adjusted individual compression point features generating or determining a deviation from the individual candidate compression point as the output data. The adjusted compression point is then determined based upon the individual candidate compression point and the determined deviation. A candidate compression point is determined for generating the input data therefore, and this comprises a value for a compression force and a compression thickness of a breast of an individual patient. The candidate compression point is determined experimentally. A compression force is exerted on the breast of an individual female patient and a change in the thickness is determined as a function of the change in the compression force. The candidate compression point is reached when the gradient of thickness relative to the compression force falls below a predetermined threshold value. The candidate compression point comprises a value for a thickness of the breast of an individual female patient and a value, assigned to this thickness, of a force or compression force exerted on this breast.

The determined candidate compression point is then checked for whether it is suitable or even optimum for an individual female patient. This does not have to be the case since, for example, the patient's comfort is not also directly included in the experimental determination of the candidate compression point. For this, further input data is received in addition to the candidate compression point, and this comprises individual, person-related data. A function, which was trained by an algorithm based on machine learning, is then applied to the input data and the candidate compression point, wherein a deviation from the candidate compression point is generated as the output data. The pain perception and the image quality of the test person achieved at a particular level of pain play a decisive role in the training of the algorithm or of the applied function. Firstly, the pain intensity should be as low as possible. Secondly, an adequate image quality should be achieved, however. The deviation has values for a deviation of the thickness of the compressed breast from the thickness value of the candidate compression point and the force value of the candidate compression point.

Finally, a corrected compression point is determined based upon the candidate compression point and the determined deviation. In the simplest case, the deviation and the candidate compression point are simply added vectorially in order to obtain the corrected compression point.

Advantageously, the determination of the corrected compression point includes individual data of a person and a functional correlation between this individual data and a deviation from a purely experimentally calculated candidate compression point. As already mentioned, a function based on artificial intelligence can also take into account influencing variables that are difficult to model, such as the subjective pain perception of a person, in order to reach a compromise between a reasonable level of comfort for a female patient and an adequate image quality. Advantageously, a further parameter, the candidate compression point, may be added to the input data in this multi-stage sequential variant, so the accuracy of determination of an individually adjusted compression point can be improved.

Preferably, the input data in the inventive method of an embodiment for determining the breast compression of a female patient in mammography comprises at least one of the following types of data:person-related, individual data relating to the general condition of the female patient,person-related, individual physical data based on previous mammography examinations,functional data of the mammography device,person-related, individual physical supplementary data from external sources.

The data relating to the general condition of the female patient comprises physiological data, which was also acquired without data from previous examinations with the relevant mammography device. For example, this personal, physiological data comprises the age of the female patient, their BMI (Body Mass Index), the number of children of the female patient, their bra size, information about a pre-existing condition in the region of the breast, an estimated breast density or a maximum tolerable pressure on the breasts.

When details on the estimated breast density are available, they are also already categorized in BI-RADS-ACR. This data can be found in the mammography image or an ultrasound image acquired in addition to the preceding mammography.

Information about pre-existing conditions in the region of the breasts can comprise, for example, information about scarring, which occurs with breast surgery. Painful changes to the breast can reduce the pressure tolerance of the female patient. The data can be used to estimate the breast density or the proportion of glands and fatty tissues based upon known statistical breast density distributions as a function of these values, in particular of the age, BMI, the breast size and the number of children. The information about pre-existing conditions can, as already mentioned, be used to estimate the extent of tolerable pressure.

The person-related, physical data based on previous examinations comprises at least one of the following types of data:the applied compression force,the compression thickness,the breast area on the detector,the BI-RADS-ACR-value.

The value of the applied compression force can be used in connection with the breast area on the detector for calculation of the pressure on the breast of the female patient used in the preceding acquisitions and can be used as a validation and rough guide value. If the newly determined values of the compression parameters compression force and compression thickness deviate greatly from the previously used values, a warning can be output to the medical staff. However, with small breast sizes a great change in the compression parameters is to be expected, so an exception has to be made here. The BI-RADS-ACR categories provide, on the one hand, information about the finding (BI-RADS category) and on the other hand, about the density of the breast or how pronounced the involution of the breast is (ACR category). Determination of the volumetric breast density enables a plausible estimation of the elastic properties of the breast and, together with the changes in the breast estimated by the intelligent algorithm in the time between current acquisition and a priori data, thus enables an optimized, patient-indicated compression or breast fixing.

Functional data of the mammography device comprises apparatus information, such as the absolute and time-resolved compression force and the absolute and time-resolved compression thickness. Until now this data alone has been used for determination of the compression point. The inclusion of the person-related, individual data relating to the general condition of the female patient and referred to as a complementary parameter, and the person-related, individual physical data based on previous mammography examinations allows a compression setting that is heavily adjusted to an individual patient.

The person-related, individual physical supplementary data from external sources comprises, for example, the breast size of the female patient based upon an estimation by a medically qualified person, an estimated BI-RADS-ACR value, which was determined by palpation of the breast by a medically qualified person, a breast size, which was determined by a body scanner, also referred to as a “full-body scanner”, and the patient's weight, which was determined by way of scales. This additional data can be used for verification of the other data or for the addition of new information as the basis for determination of a corrected compression point.

Different interpretation procedures can be applied to take account of the described four different types of data by the mapping function based on artificial intelligence. In a first procedure, at least some of the input data of different types of data, preferably all input data, is considered and weighted as if equal for this function, independently of the data source. With this approach there is, for example, a separate input of a neural network for each type of data. No analysis or interpretation prior to application of the AI algorithm is required with this variant.

Alternatively, different data can also be weighted according to its significance, so particular data has a greater influence on the AI algorithm. For example, types of data, for which only an incomplete database exists, can have a lower weighting, or data of different types of data can be allocated a shared value, which is then incorporated in the AI algorithm with an appropriately lower weight. This variant can be expedient, for example, when a complete data set does not exist for every female patient.

The correlations between different types of data can also be taken into account and data of different types of data can be combined according to a mapping rule in order to extract particularly relevant features for the AI-based function. For example, one single significant value respectively could be determined for each of the four different groups of data. For example, exactly one separate input respectively would then be allocated to each of the groups of data with application of an artificial neural network in order to generate the function. Advantageously, data processing is simplified by the AI-based function, so computing effort is reduced.

The choice of evaluation strategy can be achieved, for example, by way of a heuristic procedure, although it can also be achieved with modeling or by way of AI-based algorithms.

Particularly preferably, the input data determining unit of the inventive breast compression determining device has a candidate determining unit for determining a candidate compression point. The candidate compression point comprises a value for a compression force and a compression thickness of a breast of an individual patient, which can be determined by gradually increasing a compression force and determining a change in the thickness as a function of the change in the compression force.

The input data determining unit of the inventive breast compression determining device of at least one embodiment also comprises a first interface for receiving input data, which comprises individual, person-related data. In addition, the inventive breast compression determining device or, more precisely, the compression point determining unit, comprises a deviation determining unit for applying a function, which was trained by an algorithm based on machine learning, to the input data. A deviation from the candidate compression point is determined or generated as the output data in this case. A correction unit for determining a corrected or individually adjusted compression point based upon the candidate compression point and the determined deviation is also part of the inventive breast compression determining device or the compression point determining unit in this variant.

Advantageously, based upon a correction value determined based upon AI, the correction unit determines an adjusted compression point, which brings about improved patient comfort and/or improved image quality of a mammography image of an individual female patient.

FIG.1shows a schematic representation of a conventional mammography unit10. The mammography unit10comprises an X-ray tube1with a rotating anode1aand a cathode1b, between which a tube voltage of 25 to 35 kV is generated. An X-ray beam R, which passes through a filter1cand a diaphragm1dwith a light-beam localizer, is generated on a focal track. The X-ray beam R penetrates a compression paddle2and strikes the breast3to be examined. The X-ray beams are attenuated in the breast tissue. The attenuated X-ray beams then penetrate a supporting plate4, are conducted through an anti-scatter grid5, with which scatter radiation is removed, and finally strike a screen-film combination6.

The screen-film combination6guarantees the representation of the finest structures in the mamma. For quality assurance an automatic exposure8is used in diagnostic radiology, with which an adjustment to the thickness, the density and the tube voltage used is made. The function of the automatic exposure is to attain a mean optical density of 1.2 to 1.6, as far as possible independently of the breast thickness and the breast density. The automatic exposure collaborates with a measuring chamber7, which is arranged in the beam path below the cassette of the screen-film combination6. The dose behind the cassette in a representative area is measured with the measuring chamber7. Once the activation dose necessary for the chosen mean optical film density is reached, the automatic system cuts off the radiation. A generator9with a short switching time is controlled by the automatic exposure. The X-ray voltage is generated by the generator9. The breast3is pressed and compressed against the supporting plate4with the aid of the compression paddle2. The compression of the breast3improves the resolution by reducing the spacing of details remote from the screen in the mammary glands. The geometric blurring is also reduced thereby. Furthermore, the contrast is improved since primarily high-contrast, low-energy radiation is effective in the penetration of thinner layers of tissue. Furthermore, a clear dose reduction is achieved by the compression owing to the reduction in the breast thickness to be radiographed. The scattered radiation fraction is reduced by the compression, moreover, so the image contrast is improved. In addition, the compression allows the visualization of the smallest masses, since normal tissue may usually be spread apart by compression, in contrast to small malignant tumor masses. A further advantage is a reduction in the half-shade formation since, due to the reduced thickness of the breast3, the spacing between a possible tumor in the breast3and the exposure film6is reduced.

FIG.2shows a flowchart200which illustrates a method for determining a breast compression of a female patient in mammography according to one example embodiment of the invention. In step 2.I, firstly a candidate compression point K-KP is determined. The candidate compression point K-KP comprises a two-dimensional vector, which comprises a value for a compression force and a compression thickness of a breast of an individual patient. This candidate compression point K-KP is determined before X-ray imaging by gradually increasing a compression force and determining a change in the thickness as a function of the change in the compression force. The compression force is increased until the gradient of the compression thickness after the compression force falls below a predetermined minimum value. But this candidate compression point K-KP can now have an excessively high value of a compression force if the compressed breast is particularly soft and is already greatly compressed at the candidate compression point K-KP, therefore, so the female patient is already experiencing great pain. On the other hand, the candidate compression point K-KP can also have an insufficient value of a compression force if the compressed breast is very hard and the breast is compressed only very slightly at the candidate compression point K-KP, therefore, so an adequate image quality would not be attained during a subsequent mammogram. A calibration, in other words a determination of a deviation A of a corrected compression point KKP from the candidate compression point K-KP, is accordingly necessary following the mechanical adjustment of the compression point. The demand for a minimum image quality and a minimum level of patient comfort should be met at the corrected compression point KKP. The minimum level of patient comfort is then attained if a compression pressure is lower than a pressure at which a pain threshold of a female patient is exceeded. The compression point can then be adjusted accordingly based upon the calibration value or calibration vector A.

Additional input data ED is acquired for calibration in step 2.II, therefore, which data comprises individual, person-related data. In this simple example embodiment, this input data ED comprises person-related, individual data relating to the general condition of the female patient, such as the age, the BMI and the number of children, as the individual, person-related data.

The input data can additionally also comprise person-related, individual physical data based on previous mammography examinations, functional data of the mammography device and person-related, individual physical supplementary data from external sources.

In step 2.III, a function based on a neural network is applied to the input data ED. The function maps the input data ED onto output data AD. The output data AD specifies a deviation A, determined by the function, from the candidate compression point K-KP. The deviation A is a vector, which specifies a deviation of the compression force and the compression thickness from the candidate compression point K-KP.

Finally, in step 2.IV, a corrected compression point KKP is determined based upon the candidate compression point K-KP and the determined deviation A. The two vectors of the candidate compression point K-KP and the deviation are simply added together in this case.

FIG.3shows a flowchart300, which illustrates a training method, a method for providing a trained function for determination of a deviation from a candidate compression point K-K, therefore.

In step 3.I, firstly input training data ELD is received, for example from a database, which comprises individual, person-related data of persons in a training database. In the example embodiment illustrated inFIG.3, this individual, person-related data features the age AL, the BMI (Body-Mass-Index) and the number ZK of children of a large number of test persons.FIG.4represents, by way of example, just one such set of input data ELD for a person.

Furthermore, in step 3.II, what is known as cabled output training data ALD assigned to the input training data ELD is received from the database. The output training data ALD is in each case assigned to the input training data ELD. In other words, the individual data sets of the output training data ALD, which are to be assigned to different test persons, are in each case assigned to the individual input training data sets ELD, which are associated with the respective test persons. The output training data ALD in each case has a deviation A from a candidate compression point K-KP.

Finally, in step 3.III, an artificial neural network is trained based upon the input training data ELD and the output training data ALD.

FIG.4shows a schematic representation of a single training data set40of a single test person with an input training data vector ELD and an output training data vector ALD. The input training data vector ELD comprises the age AL, the BMI and the number ZK of children of a test person, the output data training data vector ALD comprises deviation values A from a previously determined candidate compression point K-KP of a test person. In the example embodiment shown inFIG.4, a test person is 50 years old, has a BMI von22and two children. The deviation A from a candidate compression point lies at an additional force K of +300 N and a reduced thickness D of −20 mm.

An artificial neural network may accordingly be trained based upon a large number of training data sets40of this kind in such a way that for any female patient to be examined, it determines a deviation A from a candidate compression point K-KP experimentally determined in advance.

FIG.5schematically represents a breast compression determining device50according to one example embodiment of the invention. The breast compression determining device50comprises a candidate determining unit51for determining a candidate compression point K-KP. The candidate compression point K-KP comprises a value for a compression force and a compression thickness of a breast of an individual patient. A candidate compression point K-KP of this kind can be experimentally determined by gradually increasing a compression force and determining a change in the thickness or the spacing between the supporting plate4and the compression paddle2(seeFIG.1) as a function of the change in the compression force. Furthermore, the breast compression determining device50also comprises a first interface52for receiving input data ED, which comprises individual, person-related data. A deviation determining unit53, which processes the input data ED by applying an artificial neural network to the input data ED, is also part of the breast compression determining device50. Output data AD, which specifies a deviation A from the candidate compression point K-KP, is generated by the application of the artificial neural network. In addition, the breast compression determining device50also comprises a correction unit54, which determines a corrected compression point KKP based upon the candidate compression point K-KP and the determined deviation A. Furthermore, the breast compression determining device50comprises a second interface55for outputting the determined corrected compression point KKP.

FIG.6schematically represents a training device60according to one example embodiment der invention. The training device60comprises a first training interface61afor receiving input training data ELD, which comprises individual, person-related data of persons in a training database (not shown), and a second training interface61bfor receiving output training data ALD, which is assigned to the input training data ELD, wherein the output training data ALD comprises a deviation A from a candidate compression point K-KP. The training device60also comprises a training unit62, which is adapted to train an artificial neural network based on the training input data ELD and the training output data ALD. Furthermore, the training device60also comprises an output interface63for outputting the generated artificial neural network KNN to a breast compression determining device50or the deviation determining unit53of the breast compression determining device50(seeFIG.5).

In conclusion, reference is made one again to the fact that the above-described methods and devices are merely preferred example embodiments of the invention and that the invention can be varied by a person skilled in the art without departing from the scope of the invention insofar as it is specified by the claims. For the sake of completeness, reference is also made to the fact that use of the indefinite article “a” or “an” does not preclude the relevant features from also being present several times. Similarly, the term “unit” does not preclude this from comprising a plurality of components, which can optionally also be spatially distributed.

Of course, the embodiments of the method according to the invention and the imaging apparatus according to the invention described here should be understood as being example. Therefore, individual embodiments may be expanded by features of other embodiments. In particular, the sequence of the method steps of the method according to the invention should be understood as being example. The individual steps can also be performed in a different order or overlap partially or completely in terms of time.