Method and system for determining imaging parameter values

Abstract

This invention relates to a computer-implemented method for determining imaging parameter values ​​for controlling a medical technology device during the detection of a first image dataset. Here, the first image dataset is configured for transmission from the medical technology device to a remotely configured device. The method includes the steps of receiving (REC-1) and / or determining (DET-1) transmission parameter values. Here, the transmission parameter values ​​include information about what image information is relevant to the first image dataset to be transmitted. The method further includes the step of determining (DET-2) the imaging parameter values ​​based on the transmission parameter values. The method also includes the step of providing (PROV-1) the imaging parameter values.

Description

Technical Field

[0001] This invention relates to a computer-implemented method for determining imaging parameter values ​​for controlling a medical technology device during the detection of a first image dataset. The invention also relates to a determination system, a computer program product, and a computer-readable storage medium. Background Technology

[0002] It is known to use medical imaging, for example, to monitor and / or control medical interventions at a subject under examination. For this purpose, medical technology devices are typically used to examine image datasets of the subject. Typically, an image dataset includes at least one medical image. Generally, an image dataset includes a time series of individual medical images of the subject. In other words, an image dataset may include video sequences. The medical technology device can be controlled, particularly by means of imaging parameters, during the examination of the image dataset. It is known to transmit image datasets to remotely set devices. For example, a doctor, operator, or therapist can use this method to monitor and / or perform medical interventions in a different room, building, city, or country than the subject under examination. Thus, the doctor can monitor or perform medical interventions at a remotely set device.

[0003] Therefore, it is necessary for the image dataset to be transmitted to a remotely set device with little or no time delay. Transmission is particularly limited by the data transmission rate. Typically, the image dataset is compressed so that transmission can be performed with as little time delay as possible. Alternatively, time delay can be accepted. When compressing an image dataset, image information typically included in the dataset is lost. In other words, more image information is typically detected compared to the image information provided to the physician at the remotely set device. This is particularly problematic when the image dataset is detected using X-ray radiation. In other words, the subject may be exposed to X-ray radiation during the detection of the image dataset. Here, a radiation dose or intensity is applied to the subject. This dose should be kept as low as possible. More image information in the image dataset is generally associated with a higher dose to the subject. Furthermore, more image information is generally associated with higher time and / or cost in detecting and / or transmitting the image dataset. For these reasons, the image dataset should only include the amount of image information that can also be transmitted to the remotely set device, or only include image information that can also be transmitted to the remotely set device. The actual transmitted image information can also be described as the relevant image information.

[0004] Furthermore, image datasets are typically collected during the positioning of medical technology devices for monitoring purposes. During positioning, the medical technology device moves relative to the object being examined. It is necessary to provide the physician with an overview of the current positioning of the medical technology device relative to the object. It is known that imaging parameter values ​​already appropriate for the medical intervention are maintained during the positioning of the medical technology device. However, it is often feasible to reduce the quality of the image dataset during the positioning of the medical technology device compared to the quality of the image dataset during monitoring and / or performing the medical intervention. In other words, the image dataset during positioning may include less image information compared to the image dataset during monitoring and / or performing the medical intervention. In other words, it is generally unnecessary for the image dataset during positioning to include as much image information as the image dataset during monitoring and / or performing the medical intervention. Specifically, the image dataset during positioning includes less relevant image information compared to the image dataset during monitoring and / or performing the medical intervention. Therefore, if X-ray radiation is used to collect the image dataset, the dose applied to the object during positioning can be reduced. Alternatively or additionally, the time and / or cost of collecting the image dataset for positioning can be reduced. Summary of the Invention

[0005] Therefore, the object of the present invention is to provide a method that can adapt imaging parameter values ​​so that only image information relevant to the physician is detected.

[0006] The objective is achieved by a method for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset, an apparatus for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset, a computer program product, and a computer-readable storage medium, according to embodiments. Advantageous improvements are detailed in the following description.

[0007] In the following description, the solution to the claimed objective according to the invention is described not only with reference to the claimed device but also with reference to the claimed method. The features, advantages, or alternative embodiments mentioned herein can also be adapted to other claimed subjects, and vice versa. In other words, physical embodiments (e.g., for the device) can also be improved with features described or claimed in conjunction with the method. The corresponding functional features of the method are here constituted by corresponding physical modules.

[0008] This invention relates to a computer-implemented method for determining imaging parameter values ​​for controlling a medical technology device during the detection of a first image dataset. Here, the first image dataset is configured for transmission from the medical technology device to a remotely configured device. The method includes the steps of receiving and / or determining transmission parameter values. The transmission parameter values ​​include information about what type of image information or what scale of image information is associated with the first image dataset to be transmitted. The method further includes the step of determining imaging parameter values ​​based on the transmission parameter values. The method also includes the step of providing the imaging parameter values.

[0009] Medical technology devices can be, in particular, imaging devices. In other words, medical technology devices can constitute a set of devices for detecting medical images or image datasets. Medical technology devices can be, in particular, angiography systems, C-arm systems, computed tomography systems, magnetic resonance imaging (MRI) systems, ultrasound systems, and / or optical coherence tomography (OCT) systems.

[0010] Medical technology equipment can be controlled, in particular, through imaging parameter values. In other words, the medical technology equipment can be controlled by imaging parameter values ​​when detecting a first image dataset. Here, the imaging parameter values ​​can be preset to values ​​used for detecting the first image dataset. For example, the imaging parameter values ​​can be preset to the X-ray tube voltage, X-ray tube current, exposure time or acquisition time, image acquisition frequency, and / or detector merging, etc., when detecting the first image dataset. In particular, the image information included in the first image dataset can be controlled through imaging parameter values. In other words, the image information included in the first image dataset can be related to the imaging parameter values.

[0011] The first image dataset includes at least one medical image. Here, the medical image includes records using medical technology equipment. The medical image may include, for example, X-ray images, tomographic images and / or three-dimensional images and / or subtraction images and / or summed images, etc. The medical image may in particular be a pixelated image. A pixelated image includes multiple pixels arranged in a pixel matrix. Here, each pixel is associated with an image value. Alternatively, the medical image may be a voxelized image. A voxelized image includes multiple voxels arranged in a voxel matrix. Each voxel is associated with an image value. The first image dataset may in particular include a time series of medical images. In other words, the first image dataset may include a video sequence of medical images. The time interval between individual medical images may in particular be preset by image acquisition frequency.

[0012] The first image dataset can be configured specifically for transmission to a remotely set device. Through transmission, the first image dataset also becomes available on the remotely set device. The first image dataset can also be configured specifically for display on the remotely set device.

[0013] Besides data transmission connections, the remotely set device can be particularly independent of the medical technology device. In other words, the remotely set device is unrelated to the medical technology device. The remotely set device can particularly be spatially separated from the medical technology device. The remotely set device can particularly be set in the same room as the medical technology device. Alternatively, the remotely set device can be set in a different room and / or a different building and / or a different city and / or a different country. The remotely set device is particularly configured for data transmission with the medical technology device. The first image dataset can particularly be transmitted from the medical technology device to the remotely set device via data transmission. The first image dataset can particularly be transmitted via a local area network (LAN) or via a wireless local area network (WLAN) and / or via a mobile radio network. The remotely set device can particularly be configured for displaying the first image dataset or at least one medical image included in the first image dataset by means of a display unit. Here, the display unit can include a display or a monitor. The monitor can be a field emission display (FED), liquid crystal display (LCD), thin film transistor display (TFT-LCD), cathode ray tube display (CRT), plasma display, organic light-emitting diode (OLED), and / or surface conduction electron emission display (SED). The first image dataset is particularly useful for displaying to the physician. Here, the physician can perform and / or observe or monitor medical interventions based on the first image dataset.

[0014] In the method steps of receiving and / or determining transmission parameter values, the transmission parameter values ​​may be received particularly by means of an interface and / or determined by means of a computing unit. The transmission parameter values ​​here include information about what image information is relevant to the first image dataset to be transmitted. In particular, the transmission parameter values ​​may describe what image information of the first image dataset can actually be transmitted. In other words, the transmission parameter values ​​may include limits or restrictions on the transmission of the first image dataset. Specifically, the image information that can actually be transmitted may correspond to relevant image information or limit the relevant image information. Alternatively or additionally, the transmission parameter values ​​may describe what image information is actually needed, for example, what image information is actually needed based on the progress of a medical intervention. In other words, the transmission parameter values ​​may describe what image information is actually relevant to the physician performing and / or monitoring a medical intervention based on the progress of the intervention or the corresponding method steps. For example, to locate a medical technology device, a first image dataset with less image information is needed compared to performing image-controlled surgery or interventional procedures. Therefore, the first image dataset used for locating a medical technology device includes less relevant image information compared to the first image dataset used by the physician to perform surgery. Here, the transmission parameters may include, for example, information about the purpose or association in which the first image dataset is needed or used.

[0015] In the method steps of receiving transmission parameter values, the transmission parameter values ​​can be received from a database and / or manually entered. Multiple transmission parameter values ​​can be stored in the database for different purposes or situations. Alternatively or additionally, doctors or other medical personnel can manually provide transmission parameter values. In particular, transmission parameter values ​​can be manually provided using an input unit.

[0016] In the method steps for determining transmission parameter values, the transmission parameter values ​​can be determined based on the current situation and / or based on the current technical conditions.

[0017] In the method steps for determining imaging parameter values, the imaging parameter values ​​are determined based on transmission parameter values. Specifically, the imaging parameter values ​​are determined using a computing unit. The imaging parameter values ​​are determined such that the first image dataset detected based on the imaging parameter values ​​includes relevant image information. The imaging parameter values ​​are also determined such that the first image dataset detected based on the imaging parameter values ​​does not include more image information than the relevant image information. The imaging parameter values ​​can be determined such that lossy compression of the first image dataset is not required before transmission. Lossy compression could be, for example, the merging of medical images included in the first image dataset. Specifically, the image information lost due to merging can be subsequently avoided. The imaging parameter values ​​can be determined such that the first image dataset does not include image information or medical images or image fragments that are not of interest to the physician.

[0018] In the method steps of providing imaging parameter values, the imaging parameter values ​​are provided particularly by means of an interface. In particular, the imaging parameter values ​​are provided for controlling medical technology equipment. In other words, imaging parameter values ​​are provided to the medical technology equipment. Specifically, a first image dataset can be detected based on the imaging parameter values. In other words, the medical technology equipment can be controlled by means of the imaging parameter values ​​when detecting the first image dataset.

[0019] The methods for receiving and / or determining transmission parameter values, determining imaging parameter values, and / or providing imaging parameter values ​​can be performed, in particular, by means of an interface or computing unit of a medical technology device. Alternatively or additionally, the methods for receiving and / or determining transmission parameter values, determining imaging parameter values, and / or providing imaging parameter values ​​can be performed by means of an interface or computing unit of a remotely configured device.

[0020] The inventors have recognized that the applied dose, time, and / or cost can be reduced by flexibly adapting imaging parameter values. In particular, the inventors have recognized that imaging parameter values ​​can be adapted for this purpose based on transmissible image information and / or image information of interest to the physician. In other words, imaging parameter values ​​can be adapted based on relevant image information. Specifically, transmissible parameter values ​​can be determined based on the current situation before detecting the first image dataset. Here, transmissible parameter values ​​include constraints on the relevant image information. In other words, transmissible parameter values ​​include values ​​that constrain the image information of the first image dataset to the relevant image information. The inventors have recognized that imaging parameter values ​​can be determined based on transmissible parameter values.

[0021] According to one aspect of the invention, imaging parameter values ​​are determined such that the quality of the first image dataset is limited by the associated image information.

[0022] The quality of the first image dataset is quantified, for example, by spatial and / or temporal resolution and / or by signal-to-noise ratio. The image information of the first image dataset is particularly relevant to its quality. For example, spatial resolution determines the minimum structure that can be displayed in the first image dataset. In other words, spatial resolution determines the minimum structure, information about which is included in the image information.

[0023] Therefore, imaging parameter values ​​are determined such that the image dataset includes the maximum amount of relevant image information. The imaging parameter values ​​specifically presuppose the quality of the first image dataset. The imaging parameter values ​​may specifically include image acquisition frequency and / or merging and / or exposure time and / or dose or recording dose. The image acquisition frequency may, for example, presuppose a temporal resolution. Alternatively or additionally, merging or exposure time or dose may presuppose a spatial resolution. For example, imaging parameter values ​​are determined such that the spatial and / or temporal resolution is no greater than the transmittable resolution. In other words, the first image dataset should not include more medical images or medical images should not include more pixels or voxels than are medically meaningfully transmittable. Medically meaningful representation should minimize the time delay caused by transmission. In particular, the first image dataset should be transmitted in real time. A time delay of up to 500 ms is particularly tolerable and medically meaningful. If the first image dataset includes a sequence of medical images, the time required to transmit the medical images should not be greater than the time interval between detecting two successive medical images in that sequence. Alternatively or additionally, imaging parameter values ​​are determined such that the first image dataset does not include more image information than the image information of interest or relevance to the physician. The quality of the first image dataset should, in particular, not be better than the quality required by the physician. The required quality here corresponds to the necessary or relevant image information. Therefore, the relevant image information serves as a constraint on the quality of the first image dataset, which is preset by imaging parameters.

[0024] The inventors have recognized that the quality of the first image dataset can be limited by relevant image information. Additional image information obtained through better quality is irrelevant and causes unnecessary radiation load and / or cost and / or time loss. The inventors have also recognized that the quality of the first image dataset is preset by imaging parameter values.

[0025] According to another aspect of the invention, the transmission parameter value includes a first data transmission rate.

[0026] The first data transfer rate specifically describes the amount or quantity of data that can be transferred within a specific time interval. The terms "data transfer rate" and "data rate" are commonly used synonymously with the term "data transfer rate." Data transfer rates are typically indicated in bits per second (Bit / s).

[0027] The first data transmission rate specifically indicates the data transmission rate at the moment when the first image dataset should be transmitted. The first data transmission rate specifically indicates the average data transmission rate over the time interval during which the first image dataset should be transmitted. The first data transmission rate therefore indicates the limitation on the actual image information that can be transmitted within the time interval. Here, the actual image information that can be transmitted limits the relevant image information. Image information that cannot be transmitted is irrelevant.

[0028] If the first image dataset is transmitted via a 5G mobile radio network, the first data transmission rate can correspond to the reserved data transmission rate.

[0029] In particular, the imaging parameter values ​​can be determined based on the first transmission rate. Thus, the imaging parameter values ​​can be determined such that the first image dataset includes the amount of data that can be transmitted within a predetermined time period, for example, 500 ms. If the first image dataset includes a time series of medical images, the image acquisition frequency included by the imaging parameter values ​​can be selected such that the duration of transmitting the sequence of medical images corresponds at most to the time interval during the detection of the medical images. In other words, the image acquisition frequency can be selected such that "backflow" can be avoided when transmitting the time series of medical images based on the data transmission rate.

[0030] The inventors have recognized that the relevant image information is limited by the image information that can actually be transmitted within a specific time interval. The inventors have also recognized that the image information that can actually be transmitted is limited by a first data transmission rate. The inventors have recognized that imaging parameter values ​​can be determined based on the first data transmission rate, such that the first image dataset includes the amount of data that can be transmitted within a predetermined time interval. The inventors have recognized that in this way, it is possible to prevent the first image dataset from being lossily compressed for transmission and the image information from being partially not transmitted. This untransmitted image information does not need to be detected and causes unnecessary dose or radiation load, time loss, and / or cost.

[0031] According to another aspect of the present invention, the method steps for determining transmission parameter values ​​further include: a method step for determining a first data transmission rate for the time of transmitting the first image dataset.

[0032] The timing of transmitting the first image dataset is particularly important in relation to the timing when the first image dataset should be transmitted to a remotely configured device. The timing of transmitting the first image dataset can also correspond to a time period. Especially if the first image dataset comprises a time series of medical images, the first image dataset can be transmitted over a time period. For example, this time period can correspond to the duration of the sequence.

[0033] In the method steps of determining the first data transmission rate, the first data transmission rate for the time of transmitting the first image dataset can be invoked. This is especially true if the data transmission rate is immutable in time. It is also true if the data transmission rate for the time of transmitting the first image dataset is known, and in particular, reserved. The data transmission rate can be reserved for transmissions via a 5G mobile radio network and is known for the time of transmission.

[0034] Alternatively or additionally, the first data transmission rate may be estimated based on empirical values ​​in the method steps for determining the first data transmission rate. Empirical values ​​may, for example, be knowledge about typical data transmission rates at a specific time and / or on a specific workday.

[0035] Alternatively or additionally, the first data transmission rate can be determined based on a data transmission rate determined before the time of transmitting the first image dataset. The data transmission rate before the time of transmitting the first image dataset can be measured here. The first data transmission rate can be derived from said data transmission rate. For example, the first data transmission rate can correspond to the data transmission rate before the time of transmitting the first image dataset. Alternatively, the first data transmission rate can be assumed to be a fixed percentage of the data transmission rate before the time of transmitting the first image dataset. In particular, it can be assumed that the first data transmission rate is not less than said percentage. The percentage can, for example, include 90%, 80%, or 70%.

[0036] The inventors have recognized that the first data transmission rate at the moment of transmitting the first image dataset is limited only by the transmission of the first image dataset. In particular, the imaging parameters can precisely determine the first data transmission rate available at the moment of transmitting the image dataset, regardless of fluctuations over time before or after transmission. In other words, if the first data transmission rate at the moment of transmitting the first image dataset is known, fluctuations in the data transmission rate over time can be ignored.

[0037] According to another aspect of the invention, in the method step of determining a first data transmission rate, the first data transmission rate is predicted based on a plurality of second data transmission rates. Here, the plurality of second data transmission rates are determined before the moment of transmitting the first image dataset.

[0038] The variance of the data transmission rate over time can be determined based on multiple second data transmission rates. Thus, the first data transmission rate can specifically correspond to the median or average of the multiple second data transmission rates.

[0039] In particular, multiple second data transmission rates can be determined or detected irregularly or at random times before the transmission of the first image dataset.

[0040] Alternatively, the plurality of second data transmission rates may comprise a time series of the second transmission rates. In particular, two of the plurality of second data transmission rates can be detected or determined separately at moments having defined time intervals relative to each other. The defined time intervals may, for example, include one hour, 30 minutes, 15 minutes, 10 minutes, 5 minutes, 1 minute, and 30 seconds. Thus, the plurality of second data transmission rates describes the temporal variation of the data transmission rates. In the method step of determining the first data transmission rate, the first data transmission rate can be determined, in particular, based on the said temporal variation. In particular, the first data transmission rate can be extrapolated based on the temporal variation. In other words, the prediction can be based on extrapolation.

[0041] The inventors have realized that the first data transmission rate at the moment of transmitting the first image dataset can be derived from multiple second data transmission rates. In other words, the inventors have realized that the first data transmission rate can be derived from typical data transmission rates prior to the moment of transmission.

[0042] According to another aspect of the invention, a first data transmission rate is predicted by applying a first trained function to a plurality of second data transmission rates.

[0043] In other words, in the method steps of determining the first data transmission rate, the first data transmission rate is predicted or determined by applying a first trained function to multiple second data transmission rates.

[0044] Generally, a trained function mimics cognitive functions that connect humans with their thoughts. In particular, through training based on training data, a trained function can adapt to new situations and recognize and extrapolate patterns.

[0045] Generally, the parameters of the function being trained can be adapted through training. For this purpose, supervised training, semi-supervised training, unsupervised training, reinforcement learning, and / or active learning can be used. Furthermore, representation learning (or feature learning) can be used. In particular, the parameters of the function being trained can be iteratively adapted through multiple training steps.

[0046] The trained function may in particular include neural networks, support vector machines, random trees or decision trees, and / or Bayesian networks, and / or the trained function may be based on k-means clustering, Q-learning, genetic algorithms, and / or association rules. The trained function may in particular include a combination of multiple unrelated decision trees or an ensemble of decision trees (random forest). The trained function may be determined, in particular, by means of XGBoosting (eXtreme GradientBoosting). The neural network may in particular be a deep neural network, a convolutional neural network, or a convolutional deep neural network. Furthermore, the neural network may be an adversarial network, a deep adversarial network, and / or a generative adversarial network. The neural network may in particular be a recurrent neural network. Recurrent neural networks can be networks with long short-term memory (LSTM), particularly gated recurrent units (GRUs). The trained function can include combinations of the above schemes. The schemes for the trained function described herein are particularly referred to as the network architecture of the trained function.

[0047] A first trained function can be trained using training input data and training output data. The training input data may include multiple second training data transmission rates. These multiple second training data transmission rates may have been determined or measured over a time period prior to a past moment. The training output data includes the first training data transmission rate that has been determined or measured at a past moment. To train the first trained function, the first trained function is applied to the training input data, producing a result. The result is compared with the training output data. The first trained function is adapted such that the result matches the training output data as well as possible. Training can be performed using multiple training input data and training output data.

[0048] The inventors have realized that a first data transmission rate can be determined based on multiple second data transmission rates by applying a first trained function. The inventors have also realized that a first data transmission rate can be predicted using the first trained function.

[0049] According to another aspect of the invention, the first image dataset includes at least one medical image of the object being examined. Here, the transmission parameter values ​​include information about the movement of the object being examined and / or instruments located within the object relative to the medical technology equipment.

[0050] The object of examination can be, in particular, a person or a patient. Alternatively, the object of examination can be an animal or an object. The object of examination can be, in particular, a part of a person and / or an animal and / or an object, such as an organ, limb, etc. The instrument inside the object of examination can be, in particular, a catheter or an endoscope. Alternatively, the instrument inside the object of examination can be a surgical instrument, such as a scalpel, a suction device, and / or a swab, etc.

[0051] At least one medical image may be, for example, an X-ray image, an ultrasound image, and / or a tomographic image derived from magnetic resonance imaging (MRI), computed tomography (CT), and / or angiography, etc. Alternatively, the medical image may include a three-dimensional display of the object being examined and / or the instrument. The medical image may be configured in particular as described above. At least a portion of the image values ​​of the medical image may display the object being examined. Alternatively or additionally, at least a portion of the image values ​​may display the instrument. In other words, the medical image comprises an image of the object being examined and / or the instrument.

[0052] The object to be examined can be positioned, in particular, within a medical technology device. Here, the object may be positioned, for example, on a bed or examination table within the medical technology device. The object is positioned within the medical technology device such that a first image dataset detected using the medical technology device includes an image of at least a portion of the object being examined.

[0053] The movement of the object and / or instrument relative to the medical technology equipment can be specifically referred to as relative motion. Here, the medical technology equipment and / or the object and / or instrument can move. This motion can be visible in a first image dataset or in a medical image. In other words, the relative motion of the object and / or instrument relative to the medical technology equipment can be displayed or imaged in a medical image. Alternatively or additionally, relative motion can be displayed or imaged in multiple medical images. Multiple medical images can be included, in particular, by a first image dataset. Here, multiple medical images can form a sequence of medical images.

[0054] The relative motion of the object being examined can be caused by the movement of the object itself. Alternatively or additionally, the relative motion of the object may be caused by the movement of the medical device relative to the object. For example, when imaging with a C-arm system, the relative motion of the object may be caused by the movement of the object itself, by the movement of the C-arm, and / or by the movement of the examination table. In the following text, the movement of the examination table will also be referred to as the movement of the medical device.

[0055] The relative motion of instruments may be caused by the movement of the instrument itself, the movement of the object being examined, or the movement of medical technology equipment.

[0056] The inventors have recognized that the relative motion of the objects under inspection can limit relevant image information under certain circumstances. The inventors have also recognized that this limitation can be provided by means of transmission parameter values ​​and taken into account when inspecting a first image dataset.

[0057] According to another aspect of the invention, information regarding the relative motion of the object being examined and / or the instrument in the medical image includes information about the type of relative motion and / or information about the speed of the relative motion.

[0058] Information regarding the type of relative motion specifically indicates whether the object being examined, the instrument itself, or the medical device is moving. In other words, information regarding the type of relative motion indicates how the relative motion is caused. In other words, information regarding the type of relative motion indicates which object causes the movement of the object being examined and / or the instrument relative to the medical device. Here, the object can include the object being examined, the instrument, and / or the medical device. If multiple objects within the object are moving, or if multiple objects within the object cause relative motion, then information regarding the type of relative motion can indicate all objects causing the relative motion. Relative motion caused by multiple objects can be termed superimposed motion.

[0059] Information regarding the speed of relative motion indicates how fast the object being examined and / or the instrument is moving relative to the medical technology equipment. In particular, information regarding the speed of relative motion may also indicate how fast the object causing the motion is actually moving. In the case of superimposed motion, the individual speed of each object causing the relative motion together can be included from the information regarding the relative motion of the object being examined and / or the instrument. Alternatively or additionally, information regarding the speed of relative motion may include the speed of superimposed motion.

[0060] Alternatively or additionally, information regarding the relative motion of the object and / or instrument being inspected may include information about the direction of the relative motion and / or the duration of the relative motion.

[0061] Information regarding the direction of relative motion may include the direction of motion of the object being examined and / or the instrument relative to the medical technology device. Alternatively or additionally, in the case of superimposed motion, information regarding the direction of relative motion may include information regarding the respective direction of motion of each object that together causes the superimposed motion. In the case of superimposed motion, information regarding the direction of relative motion may particularly include information regarding the direction of the superimposed motion.

[0062] Information regarding the duration of relative motion may include information about how long the object being examined and / or the instrument has moved relative to the medical technology device. Alternatively or additionally, information regarding the duration of relative motion may include information about the predicted duration of the movement of the object being examined and / or the instrument relative to the medical technology device. Alternatively or additionally, in the case of superimposed motion, information regarding the duration of relative motion may include information about the duration of the respective motion of each object that together causes the superimposed motion.

[0063] The inventors have recognized that relevant image information can be correlated with or limited by the type and / or speed of relative motion. Motion of medical technology equipment, in particular, can cause less image information to be relevant, since the image information is used only to position the medical technology equipment relative to the object being examined. Motion of instruments, on the other hand, can indicate that as much image information as possible is relevant, since the instruments are used, for example, to perform medical interventions or surgeries, and thus the image dataset should be as highly resolved as possible in time and / or space.

[0064] According to another aspect of the invention, the method steps for determining transmission parameter values ​​further include a method step for determining information about the relative motion of the object being inspected and / or the instrument.

[0065] In the methodological steps for determining information about the relative motion of the object and / or instrument being inspected, information about the type of relative motion and / or the speed of the relative motion can be determined in particular. Alternatively or additionally, information about the direction of the relative motion and / or the duration of the relative motion can be determined.

[0066] Information regarding relative motion can be determined, in particular, based on image analysis of image datasets detected prior to the first image dataset. Here, the image dataset can be analyzed using image analysis of the relative motion of the object and / or instrument being inspected. Based on this image analysis, information regarding relative motion can be predicted for the time when the first image dataset was detected.

[0067] Alternatively or additionally, information about relative motion can be received during the method steps of determining information about relative motion. In particular, information about the motion of the medical technology equipment and / or instruments can be provided by medical technology equipment and / or by instruments and / or by personnel for receiving. The personnel can be present at the medical technology equipment site. The personnel can, in particular, observe the motion of the object being examined and / or the instrument and / or the medical technology equipment. The personnel can, in particular, identify which object or objects are moving. The personnel can provide information about relative motion. Alternatively, information about relative motion can be derived from the information about the motion of the object being examined and / or the medical technology equipment and / or instruments provided by the personnel. Alternatively or additionally, which object or objects are causing the relative motion can be derived from the information provided by the medical technology equipment and / or from the information provided by the instruments. In particular, information about the relative motion of the object being examined and / or the instruments can be derived from the information provided by the medical technology equipment and / or by the instruments.

[0068] The inventors have recognized that information about the relative motion of the object being inspected and / or the instrument can be deduced from human observation and / or from the information provided. In particular, information about relative motion can be determined.

[0069] According to another aspect of the invention, the method steps for determining information regarding the relative motion of the object and / or instrument being examined include determining information regarding the motion of the medical technology device with respect to the moment of detecting a first image dataset. Here, the information regarding the relative motion of the object and / or instrument is related to the motion of the medical technology device.

[0070] In the method steps for determining the motion of a medical device, it is determined whether and how the medical device is moving at the moment of detecting the first image dataset. Information regarding the motion of the medical device may specifically include information about whether the medical device is moving. Alternatively or additionally, the information regarding the motion of the medical device may include information about the speed and / or direction and / or duration of the motion. In particular, the information about the motion of the medical device may be provided by the medical device itself. Alternatively or additionally, information about the motion of the medical device may be observed by a person. Information about the motion of the medical device may be received here.

[0071] Information regarding the relative motion of the object being examined and / or the instrument relates here to information regarding the motion of the medical technology equipment. Information regarding the relative motion of the object being examined and / or the instrument may in particular include information regarding the motion of the medical technology equipment. Especially if the object being examined or the instrument itself is not moving, the relative motion of the object being examined and / or the instrument may correspond to the motion of the medical technology equipment. Information regarding the type of motion may here in particular include whether the medical technology equipment is moving.

[0072] The inventors have recognized that information about the movement of a medical device is relevant in order to determine relevant image information. In other words, relevant image information is related to the movement of the medical device. The inventors have also recognized that if the medical device is moving, less image information is relevant. In particular, spatial and / or temporal resolution can be reduced. Furthermore, the inventors have recognized that, by means of information about the movement of the medical device, a distinction can be made between the movement of the examination object and / or instrument and the movement of the medical device, based on information about the relative movement of the examination object and / or instrument. In other words, it can be distinguished whether the medical device or the examination object or instrument causes relative movement. The inventors have recognized that this is important for determining what image information is relevant.

[0073] According to another aspect of the invention, information regarding the movement of a medical technology device is determined based on at least one device parameter value of the medical technology device and / or on measurements from at least one sensor located at the medical technology device.

[0074] In other words, information about the movement of a medical technology device can be derived from at least one device parameter value and / or sensor measurements.

[0075] At least one device parameter value can constitute a control for the movement of a medical technology device. The device parameter value may in particular include the speed of the movement of the medical technology device and / or the direction of the movement of the medical technology device and / or the path traversed by the medical technology device during movement. The device parameter value can be preset according to the medical intervention or the progress of the medical intervention. Alternatively or additionally, the device parameter value can be preset manually, for example by a physician or other personnel. In particular, information about the movement of the medical technology device can be derived from the device parameter value.

[0076] Sensors are configured to detect or measure the motion of a medical device. Here, the sensor is positioned at the medical device such that it can detect or measure the motion of the medical device. The sensor may in particular include motion sensors and / or accelerometers. Motion sensors are particularly configured to detect the motion of the medical device. Motion sensors can specifically detect whether the medical device is moving and / or at what speed the medical device is moving. Accelerometers can specifically detect the acceleration of the medical device during its movement. From this, it can be deduced whether the medical device is moving and / or at what speed the medical device is moving. Alternatively or additionally, the direction in which the medical device is moving can be deduced from the measurements of the accelerometer. In particular, information about the motion of the medical device can be deduced from the motion detected by the sensors.

[0077] The inventors have realized that the movement of a medical device can be measured using at least one sensor. The inventors have also realized that, alternatively or additionally, information about the movement of the medical device can be derived from device parameter values. In other words, the inventors have realized that the medical device itself can provide information about its movement.

[0078] According to another aspect of the invention, in the method step of determining information about the movement of a medical technology device, information about the movement of the medical technology device is derived from an inspection protocol.

[0079] The inspection protocol can describe the medical intervention. Specifically, the inspection protocol can describe when and / or how the first image dataset should be examined during the medical intervention. Specifically, the inspection protocol can describe at what moment, what position the medical device should occupy relative to the examined object during the medical intervention. Specifically, the inspection protocol can indicate at what moment, the medical device should occupy the corresponding position during the medical intervention. The inspection protocol can indicate at what speed the medical device should move, especially at the moment of examining the first image dataset. Specifically, the inspection protocol can preset at least one device parameter value. Specifically, the inspection protocol can preset the movement of the medical device during the medical intervention. Therefore, the movement of the medical device at the moment of examining the first image dataset can be predicted according to the inspection protocol.

[0080] In particular, information about the movement of medical technology equipment can be derived from or determined based on the inspection protocol.

[0081] The inventors have realized that information about the movement of a medical device can be determined based on an inspection protocol. The inventors have realized that the inspection protocol presupposes the movement of the medical device during a medical intervention. The inventors have realized that the movement of the medical device at a given moment in the examination of a first image dataset can be predicted based on the inspection protocol.

[0082] According to another aspect of the invention, in the method step of determining information about the relative motion of the object and / or instrument being inspected, the information about the relative motion of the object and / or instrument being inspected is determined by means of edge analysis in at least one first image dataset.

[0083] Edge analysis is specifically applied to at least one medical image included in a first image dataset. Edge analysis specifically describes the shape of the image. In particular, the blurring of edges in the first image dataset can be determined by edge analysis. The direction of relative motion can be derived from the blurring. Here, edges oriented perpendicular to the direction are more blurred than edges oriented parallel to the direction. Furthermore, the speed of relative motion can be derived from the width or intensity of the blurring. The stronger the blurring of the edges, the faster the relative motion at the moment of detection of the first image dataset or the medical image.

[0084] In the case of pixel data, edge analysis may include, for example, one of the following operators: Sobel operator, Scharr operator, Laplacian filter or Laplacian operator, Prewitt operator, Roberts operator, Kirsch operator, Canny algorithm, Marr-Hildreth operator or Laplacian of Gaussian operator (LoG) or Sombrero filter, contrast enhancer, active contour, extreme range filter. The first image dataset or the first medical image may in particular include multiple pixels in a pixel matrix. Here, each pixel may be associated with an image value, and a corresponding operator is applied to said image value. Thus, it is possible to analyze, in particular, how many pixels each edge includes in the direction perpendicular to the edge. From this, the intensity and / or direction of the blur can be determined.

[0085] In particular, edge analysis can be applied to a first medical image included in a first image dataset. In this way, information about relative motion can be determined. Relative motion is included by transmission parameter values. Imaging parameter values ​​can be determined based on this for detecting subsequent medical images included in the first image dataset. The image acquisition frequency can be determined based on the information about relative motion. Here, if motion is identified in the first medical image according to edge analysis, the time interval between detecting the first medical image and a second medical image included in the first image dataset can be increased. Here, the first image dataset can include a time series of medical images. In other words, information about the relative motion at the time of detecting the medical images in that sequence can be predicted or determined based on edge analysis in previously detected medical images of that sequence.

[0086] The inventors have recognized that information about the relative motion of the object being inspected and / or the instrument can be directly derived from the first image dataset. In particular, edge analysis can be applied here as a method of image analysis.

[0087] According to another aspect of the invention, information regarding the relative motion of the object being inspected and / or the instrument is determined based on at least one second image dataset.

[0088] The second image dataset is particularly similar in composition to the first image dataset. Here, the first and second image datasets are detected at different times. Specifically, the second image dataset is detected before the first image dataset. Here, the second image dataset includes medical images of the object and / or instrument being examined. Information about relative motion can be derived, in particular, from the comparison between the first and second image datasets. The object and / or instrument being examined can move between the medical images included in the image datasets. Information about relative motion can be derived, in particular, from said movement. Specifically, the velocity of the relative motion can be derived from the movement and time interval between the detection of the two image datasets. Specifically, the direction of the relative motion can be derived from the movement, taking into account the order in which the two image datasets are detected.

[0089] In an alternative implementation, the first image dataset may include a first medical image and a second medical image. Here, in the above description regarding the determination of relative motion, the first medical image may correspond to the first image dataset and the second medical image may correspond to the second image dataset. Therefore, the relative motion of the object being examined and / or the instrument can also be derived, in particular, based on the first and second medical images. In other words, information regarding relative motion can be based on the first and second medical images.

[0090] Alternatively, the relative motion at the time of detecting the second image dataset can be determined based on edge analysis of the second image dataset as described above. Based on this, the relative motion at the time of detecting the first image dataset can be determined, derived, or predicted. In particular, for example, a linear motion between the times of detecting the second and first image datasets can be assumed.

[0091] The inventors have recognized that image analysis of a first image dataset and / or a second image dataset can be used to determine and / or predict the movement of the object to be inspected and / or the instrument between the two image datasets. Furthermore, the inventors have recognized that information about the relative motion of the object to be inspected and / or the instrument can be derived from said movement.

[0092] According to another aspect of the invention, in the method step of determining information about the relative motion of the object and / or instrument being inspected, information about the relative motion of the object and / or instrument being inspected is predicted by applying a second trained function to at least one second image dataset.

[0093] In particular, information about the relative motion of the object being inspected and / or the instrument at the moment the first image dataset is detected can be predicted by applying a second trained function to multiple second image datasets. Here, multiple second image datasets are detected before the first image dataset.

[0094] The second trained function can be constructed in a manner particularly similar to the first trained function described above. Training the second trained function can be constructed in a manner particularly similar to training the first trained function. Here, the first trained function and the second trained function are distinguished, particularly in terms of training input data and training output data. The training input data for the second trained function can particularly include at least one second training image dataset. The training input data can particularly include multiple second training image datasets. Thus, the training output data can include training information from the first training image dataset regarding the relative motion of the object and / or instrument being inspected. Here, at least one second training image dataset is detected before the first training image dataset. The training information regarding relative motion can particularly have been manually determined based on the first training image dataset.

[0095] The inventors have recognized that information about the relative motion of the object and / or instrument being inspected can be determined by applying a second trained function to at least one pre-detected second image dataset. In particular, information about the relative motion at the moment of detection of the first image dataset can be predicted.

[0096] According to another aspect of the invention, the imaging parameter values ​​include at least one value for the following parameters: dose, image acquisition frequency, and merging.

[0097] Dose indication: How much dose is applied to the object being examined when detecting the first image dataset. A smaller dose can result in lower spatial resolution. A decrease in dose particularly causes a reduction in the signal-to-noise ratio in the first image dataset. Specifically, it indicates the dose relative to X-ray radiation. The dose can be indicated, in particular, in Gray [Gy] or Sievert [Sv]. The dose can also be related to the acquisition time, the X-ray voltage or tube voltage and / or X-ray current or tube current used to generate the X-ray radiation.

[0098] Image acquisition frequency indicates the time interval between detecting two medical images. Here, the first medical image may be included in a first image dataset and the second medical image may be included in a second image dataset. The first and second medical images are detected immediately afterward. In this case, the image acquisition frequency can also describe, indicate, or include the time interval between the two image datasets. Alternatively, the first and second medical images may be included in the first image dataset. Here, the first and second medical images are detected sequentially. Thus, the image acquisition frequency describes the time interval of the medical images in the first image dataset. The image acquisition frequency particularly indicates temporal resolution. The image acquisition frequency can particularly be indicated in records per second (Aufnahmen).

[0099] Merging specifically indicates the spatial resolution of the detector. The detector can, in particular, constitute a first image dataset for detection. The detector can be an X-ray detector for detecting X-ray radiation. The detector can be, for example, a scintillation detector or a semiconductor detector. The detector can, in particular, be a pixelated detector. In other words, the detector can include a pixel matrix. Here, the detector can detect one image value for each pixel. The image value is displayed in the medical image or in the first image dataset. Merging indicates how many pixels form the merged pixel. In the case of merged pixels, multiple pixels are combined into one large pixel. This reduces the spatial resolution. Furthermore, merging reduces the amount of data included in the corresponding image dataset or medical image. Merging can improve the sensitivity of the detector. With higher sensitivity, a smaller dose is particularly needed to detect the medical image or the first image dataset. The medical image or the first image dataset here includes the merged pixels. In other words, the medical image or the first image dataset includes one image value for each merged pixel. The image value of the merged pixel can correspond to the sum, average, or median of the detected image values ​​of the pixels included in the merged pixel dataset. For example, you can choose to merge 1x1, 2x2, 4x4, or 16x16.

[0100] The inventors have recognized that the imaging parameter values ​​are adapted to suit the spatial and / or temporal resolution of the first image dataset, such that the first image dataset contains only relevant image information. In particular, the radiation load or the applied dose can be varied or adapted by adapting the imaging parameter values.

[0101] According to an optional aspect of the invention, the method further includes a method step of detecting a first image dataset based on imaging parameter values. The method also includes a method step of transmitting the first imaging data set from a medical device to a remotely configured device.

[0102] In the method steps for detecting the first image dataset, a medical device is used to detect the first image dataset. Here, the medical device is controlled by imaging parameter values. In other words, the imaging parameter values ​​are at least partially preset to determine how the medical device will be used to detect the first image dataset.

[0103] In the method step of transmitting the first image dataset, the first image dataset is transmitted from the medical device to a remotely configured device. Specifically, the first image dataset is transmitted as described above. In particular, the first image dataset can be transmitted via a LAN, WLAN, or mobile radio network.

[0104] The inventors have realized that if the first image dataset has been detected based on the transmission parameters, the first image dataset can be transmitted directly without lossy compression.

[0105] According to another optional aspect of the invention, the first image dataset includes a time series of medical images, the time series comprising a first medical image and a second medical image. Here, the second image is detected before the first image. Here, the image information of the transmitted first medical image is based on the image information of the transmitted second medical image.

[0106] This time series includes at least a first medical image and a second medical image. In particular, it may include more than two medical images. The medical images are detected at time intervals relative to each other. The medical images in this sequence are thus classified temporally. In other words, the medical images are presented sequentially. Here, the second medical image is set temporally before the first medical image. When transmitting the first medical image, only a portion of the first medical image is transmitted, including image information different from that included in the first medical image. Then, on a remotely set device, the image information of the transmitted first medical image can be combined with the known image information of the second medical image to display the total image information of the first medical image.

[0107] Alternatively, the first medical image may be included in a first image dataset and the second medical image may be included in a second image dataset. In particular, the image information may be transmitted as described above.

[0108] The inventors have realized that the amount of data transmitted can be minimized in this way. The inventors have realized that the transmission can be accelerated in this way. The inventors have realized that this method requires a lower data transmission rate and thus can save costs.

[0109] According to another optional aspect of the invention, the medical device is based on imaging using X-ray radiation. Here, a collimator is used according to transmission parameter values.

[0110] In this context, medical technology equipment can be, in particular, an X-ray system, angiography system, or C-arm system. Medical technology equipment here includes an X-ray tube and an X-ray detector. The X-ray tube and X-ray detector are aligned such that the X-ray detector detects the X-ray radiation emitted by the X-ray tube. To detect an image dataset of the subject being examined, the subject is positioned between the X-ray tube and the X-ray detector such that the subject is penetrated by the X-ray radiation. Here, the X-ray radiation penetrating the subject should be as much as possible so that the detected image dataset includes relevant image information. A collimator can be positioned upstream of the X-ray tube. The collimator is configured such that it absorbs X-ray radiation that no longer penetrates the subject. The collimator can be made of lead, in particular. The collimator can be wedge-shaped, especially in the radiation direction. In other words, the collimator can be thicker on one side than on the other side in the radiation direction. If the transmission parameters include information about relative motion, the collimator can be used according to said motion. In particular, if information about the direction of relative motion is included, a wedge-shaped collimator can be used such that the thicker portion of the collimator points against the direction of relative motion.

[0111] The inventors have recognized that it can be assumed that image information detected from a region of the object being examined that has been moved away from the first image dataset by means of relative motion is irrelevant. To apply the smallest possible dose, said region can be shielded from X-ray radiation by a collimator.

[0112] The present invention also includes a determination system for determining imaging parameter values ​​for controlling a medical technology device during the detection of a first image dataset. Here, the first image dataset is designed for transmission from the medical technology device to a remotely configured device. The determination system includes an interface and a computing unit. Here, the interface and / or the computing unit are configured to receive and / or determine transmission parameter values. Here, the transmission parameter values ​​include information about what image information is relevant to the first image dataset to be transmitted. Here, the computing unit is also configured to determine imaging parameter values ​​based on the transmission parameter values. Here, the interface is also configured to provide the imaging parameter values.

[0113] This determining system can in particular be configured to perform the aforementioned method and aspects for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset. The determining system is configured to perform the method and aspects in such a way that an interface and a computing unit are configured to perform the corresponding method steps.

[0114] This invention also relates to a computer program product having a computer program and a computer-readable medium. The primarily software-based implementation has the advantage that a currently used system can be easily upgraded via software to operate in the described manner. In addition to the computer program, this computer program product may, if necessary, include additional components such as documentation and / or additional parts, as well as hardware components, such as hardware keys (don's keys, etc.) for using the software.

[0115] The present invention also relates in particular to a computer program product having a computer program that can be directly loaded into the memory of a determining system, the computer program having program segments so that, when executed by the determining system, all steps and aspects of the method described above for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset are performed.

[0116] The present invention particularly relates to a computer-readable storage medium having stored thereon a program segment that is readable and executable by a determining system, so that when the program segment is executed by the determining system, all steps and aspects thereof of the method described above for determining the values ​​of imaging parameters for controlling a medical technology device when detecting a first image dataset are performed. Attached Figure Description

[0117] The above-described features, characteristics, and advantages of the present invention become clearer and more readily understood in conjunction with the following accompanying drawings and description. The drawings and description are not intended to limit the invention or its embodiments in any way. In different drawings, the same parts are given corresponding reference numerals. The drawings are generally not to scale.

[0118] The attached diagram shows:

[0119] Figure 1 A first embodiment of a method for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset is shown.

[0120] Figure 2 A second embodiment of a method for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset is shown.

[0121] Figure 3 A third embodiment of a method for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset is shown.

[0122] Figure 4 A fourth embodiment of a method for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset is shown.

[0123] Figure 5 An embodiment of method steps for determining information about the relative motion of an object under inspection and / or an instrument located within the object under inspection is shown.

[0124] Figure 6 A system for determining imaging parameter values ​​for controlling a medical technology device during the detection of a first image dataset is shown.

[0125] Figure 7 The training system is shown for providing the first or second trained function. Detailed Implementation

[0126] Figure 1 A first embodiment of a method for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset is shown.

[0127] The medical technology device is a medical technology device for performing imaging. In other words, the medical technology device constitutes a device for detecting an image dataset. Here, the medical technology device is particularly configured to detect a first image dataset. In the embodiment described, the medical technology device is an angiography system. Alternatively, the medical technology device may be a C-arm system, a computed tomography system, a magnetic resonance tomography system, an ultrasound system, and / or an optical coherence tomography system, etc.

[0128] Medical technology devices, in particular, can be controlled by imaging parameter values. These imaging parameter values ​​define how the medical technology device should detect a first image dataset. For example, imaging parameter values ​​can be preset for dosage and / or exposure time and / or image acquisition frequency and / or merging, etc., when detecting the first image dataset.

[0129] The first image dataset may in particular include images of the object under examination. In the described embodiment, the first image dataset includes at least one medical image. The medical image is, in this case, the object under examination. The first image dataset may in particular include time series of medical images. Alternatively, the first image dataset itself may be medical images.

[0130] Medical images can be pixelated or voxelized images. In other words, a medical image can comprise a pixel matrix or a voxel matrix. Here, a pixel matrix or voxel matrix comprises multiple pixels or voxels, each pixel or voxel associated with an image value. The image value is an image of the object being examined.

[0131] The object of examination here is at least a part of a person, especially a patient. A part of a patient may be, for example, an organ or a limb. Alternatively, the object of examination may include an animal or a part of an animal or an object.

[0132] The first image dataset constitutes a device for transmission to a remotely configured device. Through transmission, the first image dataset becomes available on the remotely configured device. The remotely configured device is here remotely configured relative to the medical technology device. The remotely configured device can be located in the same room as the medical technology device, spaced apart from it. Alternatively, the remotely configured device can be located in a different room, a different building, a different city, or a different country. The first image dataset can be transmitted from the medical device to the remotely configured device via a network, such as via a LAN, WLAN, or mobile radio network. The first medical dataset should be transmitted within a medically meaningful time interval. A medically meaningful time interval may, for example, include 500 ms or 1 s. If the first image dataset comprises a time series of medical images, the medically meaningful time interval can correspond at most to the time interval between two medical images in that sequence.

[0133] At a remotely configured device, a physician can monitor and / or perform medical interventions at the examination site based on a first image dataset. For this purpose, the first image dataset is displayed to the physician using a display unit. If the first image dataset includes more than one medical image, the medical images are displayed to the physician in a video sequence. In this context, a medically meaningful time interval indicates a time interval in which the medical intervention does not delay or interrupt the procedure.

[0134] In the method steps of receiving REC-1 and / or determining DET-1 transmission parameter values, the transmission parameters are received in particular by means of the interface SYS.IF and / or determined by means of the computing unit SYS.CU. The transmission parameter values ​​here include information about what image information is relevant to the first image dataset to be transmitted.

[0135] Here, the image information that can actually be transmitted via the network is particularly relevant. Image information that cannot be transmitted due to lossy compression is irrelevant to the first image dataset. Image information that is not transmitted may not be provided at a remotely configured device, especially not displayed to the doctor. Therefore, such image information is irrelevant to the doctor. Thus, the description of the actually transmittable image information limits the relevant image information.

[0136] Alternatively or additionally, different image information is relevant depending on the progress of the medical intervention. For example, a physician can locate a medical device relative to the object being examined based on a first image dataset. Here, good spatial resolution and / or good temporal resolution of the first image dataset is not required. Spatial resolution is defined here, in particular, by the number of pixels or voxels in the first medical image dataset. Furthermore, spatial resolution can be related to the signal-to-noise ratio. Temporal resolution, in the case of a sequence of medical images, is related to the time interval (image acquisition frequency) between detecting two successive medical images. For localization, interpolated medical images between the actually detected medical images after transmission can be determined and provided or displayed to the physician. When performing a medical intervention, relatively higher spatial and / or temporal resolution is relevant to the physician. Therefore, more image information is relevant here compared to localization. In other words, limitations on relevant image information can be preset for the image information of interest to the physician.

[0137] In the method steps for receiving REC-1 transmission parameter values, the transmission parameter values ​​can be received, for example, from a database. Alternatively, the physician can provide the transmission parameter values. These transmission parameter values ​​can be based on the progress of the medical intervention.

[0138] In the method steps for determining DET-1 transmission parameter values, the transmission parameter values ​​can be determined, for example, based on the progress of medical intervention. Alternatively or additionally, the transmission parameter values ​​can be determined based, for example, on the current technical conditions of the network.

[0139] In the method steps for determining DET-2 imaging parameter values, the imaging parameter values ​​are determined based on the transmission parameter values. Based on these imaging parameter values, a first image dataset can be detected using medical equipment.

[0140] The imaging parameter values ​​are determined such that the first image dataset includes the most relevant image information. In other words, the imaging parameter values ​​are determined such that the first image dataset includes only the relevant image information as limited by the transmission parameter values. In other words, the imaging parameter values ​​are determined such that the quality of the first image dataset is limited by the relevant image information. Here, the quality determines what image information the first image dataset includes. This quality, for example, relates to spatial resolution and / or temporal resolution and / or signal-to-noise ratio. For example, related to spatial resolution and signal-to-noise ratio is: what object structures are included by the image information in the first image dataset. Related to temporal resolution is: at what time scale can changes be observed in the first image dataset. That is, related to temporal resolution is: what temporal changes are included by the image information.

[0141] Therefore, the imaging parameter values ​​are determined such that "unnecessary" image information is not detected, which is either not transmittable or not of interest to the physician. Thus, only relevant image information in the first image dataset is detected based on the imaging parameter values.

[0142] In the method steps of providing PROV-1 imaging parameter values, the imaging parameter values ​​are provided in particular by means of the interface SYS.IF. Specifically, the imaging parameter values ​​are provided for use by the medical device. In other words, the imaging parameter values ​​are provided so that the medical device can be controlled using the imaging parameter values ​​when detecting the first image dataset.

[0143] Figure 2 A second embodiment of a method for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset is shown.

[0144] The steps for determining the DET-1 transmission parameter values, determining the DET-2 imaging parameter values, and providing PROV-1 can be found here according to the relevant information. Figure 1 The description is composed of.

[0145] In an alternative implementation, the embodiments shown herein may further include Figure 1 The method steps for receiving REC-1 transmission parameter values ​​are described in the document. The received transmission parameter values ​​may, in particular, relate to transmission parameters different from those of a specific transmission parameter value. For example, the received transmission parameter values ​​may relate to transmission parameters concerning the network, and a specific transmission parameter value may relate to transmission parameters concerning the progress of a medical intervention.

[0146] The transmission parameter value may in particular include a first data transmission rate. The first data transmission rate indicates the amount of data that can be transmitted via the network in a specific time interval. The first data transmission rate specifically indicates what amount of data can be transmitted at the moment the first image dataset is transmitted via the network. The transmission time may in particular include a time interval. The first data transmission rate is indicated herein in bits per second (Bit / s). The first image dataset herein includes the amount of data to be transmitted. The amount of data herein is particularly related to the number of pixels or voxels included in the first image dataset or medical image. When transmitting the first image dataset via a 5G mobile radio network, the first data transmission rate may be reserved. Then, in particular, the reserved first data transmission rate may be received as a transmission parameter value in the method step of receiving the REC-1 transmission parameter value.

[0147] Alternatively, the method steps for determining the DET-1 transmission parameter values ​​may include the method steps for determining the first data transmission rate of DET-3 for the time of transmitting the first image dataset.

[0148] Here, the first data transmission rate can be determined based on the experience of the personnel. For example, the personnel may know the typical data transmission rate for different times and / or workdays and apply this to the time of transmitting the first image dataset.

[0149] Alternatively, the first data transmission rate can be predicted based on multiple second data transmission rates. Here, multiple second data transmission rates are determined before the time of transmitting the first image dataset.

[0150] Multiple second data transmission rates can particularly depict the temporal variation of the network's data transmission rate. Based on this temporal variation, the first data transmission rate can be determined by extrapolation.

[0151] Alternatively, the first data transmission rate can be predicted by applying the first trained function to multiple second data transmission rates. The first trained function can here be constructed as a long short-term memory (LSM). (Abbreviation: LSTM) network. The first trained function is trained to predict the future data transmission rate based on the temporal variation of past data transmission rates. This can be achieved by training the first trained function using measured data transmission rates from the past.

[0152] Figure 3 A third embodiment of a method for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset is shown.

[0153] The steps for determining the DET-1 transmission parameter values, determining the DET-2 imaging parameter values, and providing PROV-1 can be found here according to the relevant information. Figure 1 The description is composed of.

[0154] In an alternative implementation, the embodiments shown herein may further include Figure 1 The method steps for receiving REC-1 transmission parameter values ​​are described in the document. The received transmission parameter values ​​may, in particular, relate to transmission parameters different from those of a specific transmission parameter value. For example, the received transmission parameter values ​​may relate to transmission parameters concerning the network, and a specific transmission parameter value may relate to transmission parameters concerning the progress of a medical intervention.

[0155] The transmitted parameter values ​​here include information about the movement of the object being examined and / or the instruments within the object being examined relative to the medical technology equipment. In alternative embodiments to the described embodiment, information about relative movement may be received, for example, by the medical technology equipment and / or by a person.

[0156] The instrument may be, in particular, a catheter or endoscope. Alternatively or additionally, the instrument may be a scalpel, swab, syringe, or other surgical instrument. The instrument may be configured to perform a medical intervention.

[0157] When the objects being examined are in relative motion, not only the objects themselves and / or the medical equipment can move. When the instruments are in relative motion, the instruments themselves, the objects being examined, and / or the medical equipment can move. Relative motion can be superimposed motion. In the case of superimposed motion, more than one object moves relative to each other. Here, the moving objects can include the objects being examined and / or the instruments and / or the medical equipment.

[0158] Information regarding the relative motion of the object being inspected and / or the instrument may include, in particular, information about the type of relative motion and / or the speed of the relative motion.

[0159] Information about the type of relative motion indicates which object causes the relative motion. In other words, information about the type of relative motion indicates which object actually moves. In the case of superimposed motion, information about the type of relative motion indicates all objects that actually move.

[0160] Information about velocity includes, in particular, the velocity of relative motion. Alternatively or additionally, information about velocity may include the velocity of each object causing the relative motion.

[0161] Alternatively or additionally, information about relative motion may include information about the direction of relative motion and / or the duration of relative motion. Here, the information may also relate, in general, to the relative motion and / or to the actual motion of the individual induced objects.

[0162] The method steps for determining DET-1 transmission parameter values ​​include the method steps for determining DET-4 information regarding the relative motion of the object being inspected and / or the instrument.

[0163] Information about relative motion can be determined, particularly by edge analysis in at least one image dataset. Edge analysis can be performed on at least one medical image included in a first image dataset. Edge analysis can be performed on pixelated or voxelized medical images, particularly by applying the following operators: Sobel operator, Scharr operator, Laplacian filter or Laplacian operator, Prewitt operator, Roberts operator, Kirsch operator, Canny algorithm, Marr-Hildreth operator or Gaussian Laplacian operator (LoG) or wide-brimmed cap filter, contrast enhancer, active contour, extreme range filter. Here, for example, the velocity of relative motion can be determined from the width of the edge or the blurring of the edge. The direction of the relative velocity can be determined based on which edges are blurred. If the first image dataset includes a sequence of medical images, information about the relative motion at the time of detection of the first medical image is determined based on edge analysis of the second medical image. Here, the first medical image is determined temporally after the second medical image. In other words, information about the relative motion at the time of detection of the first medical image can be predicted.

[0164] Alternatively or additionally, information regarding the relative motion of the object and / or instrument under inspection in DET-4 can be determined based on at least one second image dataset. Here, at least one second image dataset is detected temporally prior to the first image dataset. For at least one second image dataset, information regarding relative motion can be determined. Based on this, information regarding relative motion can be derived for the first image dataset. In other words, information regarding relative motion for the first image dataset can be predicted based on information regarding relative motion for at least one second image dataset. For example, linear motion can be assumed, and it can be assumed that information regarding relative motion does not change between detecting the second image dataset and detecting the first image dataset. Alternatively, as described above for the first and second medical images, information regarding relative motion with respect to the time of detecting the first image dataset can be determined based on edge analysis of the second image dataset.

[0165] Alternatively, information about the relative motion of the object and / or instrument being inspected can be determined by applying a second trained function to at least one second image dataset. The second trained function can be similar to the first trained function based on information about... Figure 2 The description is structured as follows. Specifically, the second trained function can be applied to multiple second image datasets. Here, multiple second image datasets are detected before the detection of the first image dataset. The second trained function can be trained based on past image datasets. Here, it can be manually based on observed and annotated image datasets.

[0166] Figure 4A fourth embodiment of a method for determining imaging parameter values ​​for controlling a medical technology device when detecting a first image dataset is shown.

[0167] The steps for determining the DET-1 transmission parameter values, determining the DET-2 imaging parameter values, and providing PROV-1 can be found here according to the relevant information. Figure 1 The description is composed of.

[0168] In an alternative implementation, the embodiments shown herein may further include Figure 1 The method steps for receiving REC-1 transmission parameter values ​​are described in the document. The received transmission parameter values ​​may, in particular, relate to transmission parameters different from those of a specific transmission parameter value. For example, the received transmission parameter values ​​may relate to transmission parameters concerning the network, and a specific transmission parameter value may relate to transmission parameters concerning the progress of a medical intervention.

[0169] The method for determining the first data transmission rate of DET-3 is similar to that for... Figure 2 The description constitutes the process. The steps for determining the relative motion of the DET-4 inspection object and / or instrument are similar to those regarding... Figure 3 The description is composed of.

[0170] The method steps of determining the first data transmission rate of DET-3 and determining the relative motion of DET-4 can be performed simultaneously or in any order. Therefore, the transmission parameter values ​​determined in the method step of determining the transmission parameter values ​​of DET-1 can include more than one parameter value. Such parameter values ​​can be the first data transmission rate or information about the relative motion.

[0171] Figure 5 An embodiment of a method for determining information about the relative motion of DET-4 with respect to the object under inspection and / or the instruments within the object under inspection is shown.

[0172] The embodiments described below are particularly compatible with those based on Figure 3 and Figure 4 Examples of combinations thereof. Information regarding relative motion is provided herein as in the section on... Figure 3 It is constructed as described in the description.

[0173] The method steps for determining DET-4 information regarding relative motion include the method steps for determining DET-5 information regarding the motion of the medical technology device at the moment of detecting the first image dataset. Here, information regarding the relative motion of the object being examined and / or the instrument is related to the motion of the medical technology device. This information regarding relative motion can include information about the motion of the medical technology device. Additionally, the information regarding relative motion can include information derived from the information about the motion of the medical technology device. For example, the relative motion of the object being examined can be determined based on edge analysis. Combined with the information about the motion of the medical technology device, it is possible to deduce which type of motion (the motion of the object being examined and / or the motion of the medical technology device) and how fast the object causing the relative motion is moving.

[0174] Information about the movement of a medical technology device can be determined using DET-5 based on at least one device parameter value of the medical technology device and / or based on measurements from at least one sensor located at the medical technology device.

[0175] At least one device parameter value constitutes a means for controlling a medical technology device. Specifically, the device parameter value constitutes a means for controlling the movement of the medical technology device. The device parameter value can, for example, preset a speed, direction, and / or target position for the movement of the medical technology device. The movement of the medical technology device can satisfy the preset device parameter value. Based on the device parameter value, the speed and / or direction and / or duration of the movement of the medical technology device can be derived or determined. Specifically, it can be determined whether the medical technology device is moving based on the device parameter value. Therefore, information about the type of relative motion can be determined based on the device parameter value. Specifically, the device parameters can be received by the medical device.

[0176] At least one sensor can be configured as a motion sensor and / or an accelerometer. The motion sensor can be configured to detect the motion of a medical device, particularly the speed of the motion. The accelerometer can be configured to detect the motion of the medical device, particularly the direction and / or speed of the motion. The motion sensor and / or accelerometer can be configured to detect whether the medical device is moving. As described above for device parameter values, information about relative motion can be derived or determined based on the measured values. In particular, the measured values ​​can be received.

[0177] In particular, information about the motion of a medical technology device at the moment of detecting the first image dataset can be predicted based on device parameter values ​​and / or measurements determined or received before the moment of detecting the first image dataset.

[0178] Alternatively or additionally, information regarding the movement of a medical technology device may be derived from the inspection protocol in the method steps for determining information regarding the movement of a medical technology device in DET-5.

[0179] The inspection protocol may, in particular, pre-define or include the procedures for the medical intervention. The inspection protocol may indicate when to examine the first image dataset. Alternatively or additionally, the inspection protocol may indicate at what time and how the medical device should move or how it should be positioned. At least one device parameter value may, in particular, be pre-define or included in the inspection protocol. Based on the inspection protocol, information regarding the movement of the medical device at the time of examining the first image dataset can be predicted. As described above, information regarding relative motion can be derived or determined based on information about the movement of the medical device.

[0180] Figure 6 The diagram illustrates a determination system SYS for determining imaging parameter values ​​used to control a medical technology device during the detection of a first image dataset. Figure 7 The training system TSYS is shown for providing a first trained function or a second trained function.

[0181] The illustrated determination system SYS is configured to perform a method according to the invention for determining imaging parameter values ​​for controlling a medical device when detecting a first image dataset. The illustrated training system TSYS is configured to perform a method according to the invention for providing a first trained function or a second trained function. The determination system SYS includes an interface SYS.IF, a computation unit SYS.CU, and a storage unit SYS.MU. The training system TSYS includes a training interface TSYS.IF, a training computation unit TSYS.CU, and a training storage unit TSYS.MU.

[0182] The determination system SYS and / or training system TSYS can be, in particular, a computer, a microcontroller, or an integrated circuit (IC). Alternatively, the determination system SYS and / or training system TSYS can be a real or virtual computer network (the technical name for a real computer network is "Cluster," and the technical name for a virtual computer network is "Cloud"). The determination system SYS and / or training system TSYS can be configured as a virtual system that is executed on a computer or a real or virtual computer network (the technical name is "virtualization").

[0183] The interfaces SYS.IF and / or TSYS.IF can be hardware or software interfaces (e.g., PCI bus, USB, or FireWire). The compute unit SYS.CU and / or the training compute unit TSYS.CU can include hardware and / or software components, such as a microprocessor or a so-called FPGA (Field Programmable Gate Array). The storage unit SYS.MU and / or the training storage unit TSYS.MU can be configured as non-permanent working memory (RAM) or permanent mass storage (hard drive, USB flash drive, SD card, solid-state drive (SSD)).

[0184] The interface SYS.IF and / or the training-interface TSYS.IF may in particular include multiple sub-interfaces, which perform different method steps according to the method according to the invention. In other words, the interface SYS.IF and / or the training-interface TSYS.IF may be configured as multiple interfaces SYS.IF and / or training-interface TSYS.IF. The computing unit SYS.CU and / or the training-computing unit TSYS.CU may in particular include multiple sub-computing units, which perform different method steps according to the method according to the invention. In other words, the computing unit SYS.CU and / or the training-computing unit TSYS.CU may be configured as multiple computing units SYS.CU and / or training-computing unit TSYS.CU.

[0185] Even if not explicitly stated, various embodiments, sub-aspects, or features of embodiments may be combined or interchanged with each other in a meaningful and illustrative sense without departing from the scope of the invention. Where applicable, the advantages of the invention described with reference to the embodiments also apply to other embodiments without explicit mention.

Claims

1. A computer-implemented method for determining imaging parameter values ​​for controlling a medical technology device during the detection of a first image dataset. The first image dataset is configured for transmission from the medical technology device to a remotely configured device. The method includes the following steps: - Receive and / or determine transmission parameter values, The transmission parameter values ​​include information about what image information is relevant to the first image dataset to be transmitted. - Determine the imaging parameter value based on the transmission parameter value. - Provide the imaging parameter values, wherein the transmission parameter values ​​include a first data transmission rate, and The method steps for determining the transmission parameter values ​​further include the following method steps: The first data transmission rate for the moment of transmitting the first image dataset is determined, and the first data transmission rate is predicted by applying a first trained function to a plurality of second data transmission rates.

2. The method according to claim 1, The imaging parameter values ​​are determined such that the quality of the first image dataset is limited by the associated image information.

3. The method according to claim 1, In the method step of determining the first data transmission rate, the first data transmission rate is predicted based on multiple second data transmission rates. The plurality of second data transmission rates are determined before the moment the first image dataset is transmitted.

4. The method according to any one of claims 1 to 3, The first image dataset includes at least one medical image of the object being examined. The transmission parameter values ​​include information about the movement of the object being examined and / or the instruments within the object being examined relative to the medical technology equipment.

5. The method according to claim 4, The information regarding the relative motion of the object being examined and / or the instrument in the medical image includes information about the type of relative motion and / or information about the speed of the relative motion.

6. The method according to claim 4, The method for determining the transmission parameter value further includes the following steps: - Determine information regarding the relative motion of the object being inspected and / or the instrument.

7. The method according to claim 6, The method steps for determining information regarding the relative motion of the object under inspection and / or the instrument include the following steps: - Determine information about the motion of the medical technology device at the moment of detecting the first image dataset. Information regarding the relative motion of the object being examined and / or the instrument is related to the motion of the medical technology equipment.

8. The method according to claim 7, The information regarding the movement of the medical technology device is determined based on at least one device parameter value of the medical technology device and / or on measurements from at least one sensor located on the medical technology device.

9. The method according to claim 7, In the method step of determining information about the movement of the medical technology device, information about the movement of the medical technology device is derived from an inspection protocol.

10. The method according to claim 6, In the method step of determining information about the relative motion of the object being inspected and / or the instrument, the information about the relative motion of the object being inspected and / or the instrument is determined by means of edge analysis in the at least one first image dataset.

11. The method according to claim 6, The information regarding the relative motion of the object under inspection and / or the instrument is determined based on at least one second image dataset.

12. The method according to claim 11, In the method step of determining information about the relative motion of the object being inspected and / or the instrument, information about the relative motion of the object being inspected and / or the instrument is predicted by applying a second trained function to the at least one second image dataset.

13. The method according to any one of claims 1 to 3, The imaging parameter values ​​include at least one value for the following parameters: dose, image acquisition frequency, and merging.

14. A determination system for determining imaging parameter values ​​for controlling a medical technology device during the detection of a first image dataset, The first image dataset is configured for transmission from the medical technology device to a remotely configured device. The determination system includes an interface and a computing unit. The interface and / or the computing unit constitute a means for receiving and / or determining transmission parameters. The transmission parameter values ​​include information about what image information is relevant to the first image dataset to be transmitted. The computing unit is further configured to determine the imaging parameter value based on the transmission parameter value. The interface also serves to provide the imaging parameter values. The transmission parameter values ​​include a first data transmission rate, and The interface and / or the computing unit constitute a method for determining the first data transmission rate for the moment of transmitting the first image dataset, the first data transmission rate being predicted by applying a first trained function to a plurality of second data transmission rates.

15. A computer program product having a computer program that can be directly loaded into the memory of a determined system, the computer program having program segments that, when executed by the determined system, perform all the steps of the method according to any one of claims 1 to 13.

16. A computer-readable storage medium having stored thereon a program segment readable and executable by a determining system, such that when the program segment is executed by the determining system, all steps of the method according to any one of claims 1 to 13 are performed.

Images