Automatic configuration of medical systems

Abstract

A medical system (100, 400) is disclosed herein, comprising a memory (110) storing machine-executable instructions (112) and a network graph (114, 114'). The network graph comprises network nodes (300) and network edges (302). At least a portion of the network nodes comprises node-specific object metadata. The network edges comprise configuration metadata, the configuration metadata comprising routing data for establishing a communication channel for transmitting data between two network nodes of the network graph using a communication device. Execution of the machine-executable instructions causes a computing system (104) to: create (200) a new node (116) in the network graph comprising new object metadata (118); search (202) for a closest matching network node (122) selected from the network nodes; and construct (204) a new network edge (126) for the new node in the network graph by copying at least a portion of the network edge of the closest matching network node.

Description

Technical Field

[0001] The present invention relates to medical systems, and in particular to automated configuration of communication systems integrated into medical devices. Background Art

[0002] Various types of medical systems, such as medical imaging systems, require extensive training and experience to operate.Recently, telecommunication developments have begun to enable telemedicine and remote control of medical equipment and medical imaging equipment. Summary of the Invention

[0003] The present invention provides a medical system, a computer program and a method according to the independent claims. Embodiments are given in the dependent claims.

[0004] In one aspect, the present invention provides a medical system comprising a memory storing machine-executable instructions and a network graph. The network graph comprises network nodes and network edges linking the network nodes together. At least some of the network nodes comprise node-specific object metadata. The network edges comprise configuration metadata. The configuration metadata comprises routing data configured to establish a communication channel for transmitting data between two network nodes of the network graph using a communication device. The medical system further comprises a computing system.

[0005] Execution of the machine-executable instructions causes the computing system to create a new node in the network graph that includes new object metadata. Execution of the machine-executable instructions further causes the computing system to search for a closest matching network node selected from the network nodes. The closest matching network node is selected by comparing the new object metadata with node-specific object metadata of different nodes comprising the network graph. Execution of the machine-executable instructions further causes the computing system to construct a new network edge in the network graph for the new node. The new network edge comprises a copy of at least a portion of the network edge of the closest matching network node.

[0006] In another aspect, the present invention provides a method for operating a medical system. The medical system includes a memory storing machine-executable instructions and a network graph. The network graph includes network nodes and network edges linking the network nodes. At least some of the network nodes include node-specific object metadata. The network edges include configuration metadata. The configuration metadata includes routing data configured to establish a communication channel for transmitting data between two network nodes of the network graph using a communication device.

[0007] The method includes creating a new node in a network graph that includes metadata for a new object. The method also includes searching for a closest matching network node selected from the network nodes. The closest matching network node is selected by comparing the new object metadata with the node-specific metadata. The method also includes constructing a new network edge in the network graph for the new node. The new network edge includes a copy of at least a portion of the network edge of the closest matching network node.

[0008] In another aspect, the present invention provides a computer program comprising machine-executable instructions for execution by a computing system. Execution of the machine-executable instructions causes the computing system to create a new node in a network graph comprising new object metadata. The network graph comprises network nodes and network edges linking the network nodes. At least some of the network nodes comprise node-specific object metadata. The network edges comprise configuration metadata. The configuration metadata comprises routing data configured to establish a communication channel for transmitting data between two network nodes of the network graph using a communication device.

[0009] Execution of the machine-executable instructions further causes the computing system to search for a closest matching network node selected from the network nodes. The closest matching network node is selected by comparing new object metadata with new node-specific object metadata. Execution of the machine-executable instructions further causes the computing system to construct a new network edge in the network graph for the new node. The new network edge comprises a copy of at least a portion of the network edge of the closest matching network node. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Preferred embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0011] Figure 1 An example of a medical system is shown.

[0012] Figure 2 Shows instructions for use Figure 1 Flowchart of a method of a medical system.

[0013] Figure 3 Several examples of network graphs are shown.

[0014] Figure 4 Another example of a medical system is shown.

[0015] Figure 5 Shows instructions for use Figure 4 Flowchart of a method of a medical system.

[0016] Reference Signs List

[0017] 100 Medical Systems

[0018] 102 Computer

[0019] 104 Computing System

[0020] 106 hardware interfaces

[0021] 108 Communication equipment (network interface)

[0022] 110 memory

[0023] 112 machine-executable instructions

[0024] 114 Network Graphics

[0025] 114' Modified network graphics

[0026] 116 new nodes

[0027] 118 New Object Metadata

[0028] 120 comparison modules

[0029] 122 closest matching network nodes

[0030] 124 network edges closest to matching network nodes

[0031] 126 New Network Edge

[0032] 200 creating a new node in the network graph including metadata of the new object;

[0033] 202 searching for the closest matching network node selected from the network nodes;

[0034] 204 constructing a new network edge for the new node in the network graph;

[0035] 300 network nodes

[0036] 302 Network Edge

[0037] 400 Medical Systems

[0038] 402 Magnetic Resonance Imaging System (Medical Imaging System)

[0039] 404 magnet

[0040] 406 magnet bore

[0041] 408 Imaging Area

[0042] 409 field of view

[0043] 410 magnetic field gradient coil

[0044] 412 magnetic field gradient coil power supply

[0045] 414 RF coil

[0046] 416 transceiver

[0047] 418 Object

[0048] 420 object supports

[0049] 430 Preliminary Pulse Sequence Command

[0050] 432 preliminary k-space data (preliminary medical imaging data)

[0051] 434 reconnaissance image

[0052] 436 Image Classification Neural Network

[0053] 438 image classification

[0054] 442 Selected pulse sequence command

[0055] 444 Remote Entity

[0056] 446 Other Remote Entities

[0057] 448 Configuration Modification Command

[0058] 450 clinical k-space data (medical imaging data)

[0059] 452 Clinical MRI

[0060] 500 controls the medical imaging system to acquire preliminary medical imaging data using preliminary configuration data

[0061] 502 Receiving the image classification in response to inputting the preliminary medical imaging data into the image classification neural network

[0062] 504 Select a selected network edge from one of the new network edges using the image classification

[0063] 506 Establishing a communication channel between the communication device and the remote entity using the routing data of the new network edge

[0064] 508 Sending the image classification to the remote entity via the communication channel

[0065] 510 Receive a configuration modification command from the remote entity via the communication channel

[0066] 512 Using the configuration modification command to modify the acquisition of the medical imaging data

[0067] 514 Control the medical imaging system to use the device configuration data of the selected network edge to acquire the medical imaging data DETAILED DESCRIPTION

[0068] Elements with the same number in these figures are equivalent elements or perform the same function. If the function is equivalent, elements that have been discussed previously will not necessarily be discussed in the following figures.

[0069] Examples may be beneficial because they may provide a means for automatically replicating routing data configured to establish a communication channel. Routing data may conform to various examples. For example, routing data may include data such as a telephone number, an email address, an IP address, or other data that may be used to connect two communication devices together.

[0070] In different examples, different criteria can be used to determine the closest matching network node. In some examples, the closest matching network node has the greatest match with its node-specific object metadata and the new object metadata of the new node. For example, the closest match can be determined by providing a score calculated by how many fields of the node-specific object metadata for a particular node match the new object metadata. A modification of this would be to have a weighted score with a weighting factor that emphasizes specific fields that have a closer match. The closest matching network node can also be generated as the average of multiple closely matching nodes using, for example, a k-nearest neighbor search method.

[0071] In one example, the closest matching network node is identified by providing an identifier, classification, or specific metadata associated with the closest matching network. The closest matching network node is then found by searching for the identifier, classification, or specific metadata among the existing network nodes in the network graph. For example, a new member (new node) may simply indicate a predecessor in the organization represented by the network graph. Thus, some examples are: "I am a new service engineer, please give me all the contacts that my fellow service engineers also have," or "I am replacing Mr. X, please give me all the contacts that Mr. X once had." The current text highly emphasizes automatically finding the closest match.

[0072] In some examples, new object metadata may be populated by job role or position, and the closest matching network node may be searched as described in the above paragraph.

[0073] In some examples, the closest matching network node has edges connected to other parts of the network graph. The new network edges are simply duplicates or copies of these connections to the new node, replacing the closest matching network node.

[0074] In another example, the medical system includes a medical imaging system configured to acquire medical imaging data. The medical system includes a communication device. The subject-specific metadata includes image classification. The configuration metadata includes device configuration data suitable for controlling the medical imaging system to acquire the medical imaging data. Execution of the machine-executable instructions further causes the computing system to select a selected network edge from one of the network edges using the image classification. The new node represents the communication device of the medical imaging system. The selected network edge is connected to an additional network edge representing a remote entity.

[0075] For example, an image classification can be an identification of a specific type of anatomical structure or abnormal anatomical structure. A network edge can include an identifier. When the identifier of a network edge matches the image classification, the network edge is selected. This can be used, for example, to automatically configure a medical imaging system to image the anatomical structure specified by the identifier. In some examples, the medical system can display the image classification on a display for review and / or correction before searching for the closest matching network node.

[0076] Execution of the machine-executable instructions further causes the computing system to establish a communication channel between the communication device and the remote entity using the routing data for the new network edge. The medical system is configured such that the communication channel enables the remote entity to control at least part of the medical imaging system. Execution of the machine-executable instructions further causes the computing system to control the medical imaging system to acquire medical imaging data using the device configuration data for the selected network edge. This example can be beneficial because it can provide a means for both establishing a remote communication link and controlling the medical system.

[0077] In another example, execution of the machine-executable instructions further causes the computing system to transmit the image classification to the remote entity via the communication channel. Execution of the machine-executable instructions further causes the computing system to receive a configuration modification command from the remote entity via the communication channel. Execution of the machine-executable instructions further causes the computing system to modify the acquisition of medical imaging data using the configuration modification command. This example can be beneficial because it can provide a means for automating or remotely controlling a medical system.

[0078] In another example, execution of the machine-executable instructions causes the computing system to use the preliminary configuration data to control the medical imaging system to acquire preliminary medical imaging data. Execution of the machine-executable instructions also causes the computing system to receive an image classification in response to inputting the preliminary medical imaging data into an image classification neural network. The neural network can be programmed to perform image classification. For example, the image classification neural network can be a convolutional neural network or, in some examples, a U-Net neural network. The neural network can be trained using training images labeled with the correct image classification.

[0079] In another example, the medical imaging system is a magnetic resonance imaging system.

[0080] In another example, the medical imaging system is an ultrasound imaging system.

[0081] In another example, the medical imaging system is an X-ray system.

[0082] In another example, the medical imaging system is a fluoroscope.

[0083] In another example, the medical imaging system is a positron emission tomography system.

[0084] In another example, the medical imaging system is a single photon emission tomography system.

[0085] In another example, the medical imaging system is a computed tomography system.

[0086] In another example, the medical imaging system is a telemedicine system.

[0087] In another example, the medical imaging system is a hospital information system.

[0088] In another example, the medical imaging system is a medical diagnostic machine.A medical diagnostic machine as used herein is a laboratory instrument used to perform medical tests or assays.

[0089] In another example, configuration metadata includes work processes and workflows.

[0090] In another example, the configuration metadata includes a flag indicating whether the configuration metadata is private or non-private. Execution of the machine-executable instructions further causes the computing system to exclude the network edge closest to the matching network node from being copied when the flag is set to private. This example can be beneficial because it can provide greater security because confidential or private data is protected from leakage and cannot be copied when a new node is added.

[0091] In another example, the object-specific metadata includes job role.

[0092] In another example, the object-specific metadata includes functional requirements satisfied by the object.

[0093] In another example, execution of the machine-executable instructions causes the computing system to receive a selection to deregister a network node. The deregistered network node is one of the network nodes. Execution of the machine-executable instructions causes the computing system to: for network nodes connected to the deregistered network node via network edges, send a pre-recorded message via the communication channel to network nodes connected to the deregistered network node. Execution of the machine-executable instructions also causes the computing system to remove the deregistered network node and the network edges connected to the deregistered network node from the network graph. This example can be beneficial because it can provide an automated means for editing and removing nodes representing specific objects. Using the routing data, other users or objects in the database can be alerted that a specific user is no longer part of the network graph.

[0094] In another example, the configuration metadata includes usage frequency data. Network edges matching network nodes whose usage data falls below a predetermined usage threshold are excluded from replacement. This can be beneficial because it may represent sparsely used data that is not wasted when transferred and then stored in another location.

[0095] In another example, execution of the machine-executable instructions further causes the computing system to receive a plurality of network nodes including the created object metadata. Execution of the machine-executable instructions further causes the computing system to iteratively add the plurality of network nodes to a new network graph. Iteratively adding the plurality of network nodes includes adding one of the plurality of network nodes to the new network graph and copying network edges of the network graph to the new network graph by matching the created object metadata with the object metadata of the network graph. This example can be beneficial because it can provide a means of taking the structure of a previous network graph and then using it to construct a new network graph. For example, this can be useful in the case of a network graph for a hospital, such as when creating a new network graph for a new hospital or a new location.

[0096] Figure 1 An example of a medical system 100 is shown. The medical system 100 is shown as including a computer 102 having a computing system 104. The computing system 104 is in communication with an optional hardware interface 106. The computing system 104 is further in communication with a communication device 108. For example, the communication device 108 may be a network interface.

[0097] Computer 102 is also shown as including memory 110 that is also in communication with computing system 104. Memory 110 is intended to represent various types of memory accessible by computing system 104.

[0098] Memory 110 is shown as including machine-executable instructions 112 that enable computing system 104 to control other components of medical system 100 and perform various data processing and numerical tasks. Memory 110 is also shown as containing a network graph 114. The network graph includes network nodes and network edges that link the network nodes together. At least some of the network nodes include node-specific object metadata. The network edges include configuration metadata. The configuration metadata includes routing data configured to establish a communication channel for transmitting data between two network nodes of the network graph using communication device 108.

[0099] Memory 110 is also shown as containing a new node 116 to be added to network graph 114. New node 116 includes new object metadata 118. Memory 110 is shown as including a comparison module 120 that is capable of comparing new object metadata 118 with the node-specific object metadata of the rest of network graph 114. For example, this may find the node with the closest value for its metadata, or it may be a closer weighted average thereof.

[0100] Memory 110 is also shown as containing the closest matching network node 122 selected from the network nodes of network graph 114. Memory 110 is then shown as also containing the network edge of the closest matching network node 124. Memory 110 is then shown as containing new network edges 126, which are copies of the network edges of the closest matching network node 124. In some cases, some network edges 124 may be omitted. For example, there may be a flag indicating that the network connection is personal, or there may be other data that excludes certain network edges 124. New node 116 is then added to network graph 114 by connecting it using new network edge 126. New network edge 126 originates from or connects to the new node 116 and any node to which the closest matching network node 122 is connected.

[0101] Figure 2 Shows the instructions for operation Figure 1 1. A flowchart of a method of a medical system 100 is shown. In step 200, a new node 116 is created. The new node includes new object metadata 118. In step 202, a search is performed for a closest matching network node 122 within the network nodes of the network graph 114. In step 204, new network edges 126 for the new node 116 are constructed in the network graph by copying at least some of the network edges 124 of the closest matching network node 122.

[0102] When a new user is added to a communication system, they may be presented with a blank contact list or a contact list pre-populated with people within their company. This is often not useful for new users and does not provide context for their contacts. As an example, if a user needs to find technical support for a specific device or process, searching their contact list may produce many results, and it may be difficult to know who they should talk to. This often drives the user to search other Internet / intranet sites to first find additional background information or ask for recommendations from other employees to identify the best contact to help them. Examples can apply network theory at a company-wide or even inter-company level to determine what and why certain people have each other in their contact lists, and then automatically generate a contact list when a new person enters the network.

[0103] Network theory is a version of graph theory in which the nodes and edges of a graph have data associated with them. It has been used for some time in the analysis of social networks and is used to understand the relationships between social entities within a network. In the case of a contact list system, each person can be considered a node in the graph, and an edge between nodes is two people who have one or the other in their contact list.

[0104] When new people join an organization, they are typically faced with a blank email address book and little information about who the best contacts are for their role within the company. The present invention aims to automatically and adaptively construct a network of connections between people.

[0105] Some examples may provide one or more of the following features:

[0106] A system capable of representing people as nodes within a network graph.

[0107] o Each node has associated with it a set of metadata that represents the person in terms of job role and specific functional requirements.

[0108] o An edge linking nodes indicates the presence of an entry in one or both nodes associated with the contact list.

[0109] oMetadata associated with the edge, which indicates why the edge exists.

[0110] A method that analyzes a graph and automatically creates / suggests new edges between existing nodes.

[0111] A method for automatically creating edges when new nodes are added to the network without pre-existing edges.

[0112] A method to create a new network with a completely new set of nodes and use the existing network metadata to create edges.

[0113] As a specific example, a network graph may be constructed, wherein each node of the graph represents an individual person (or functional person) with associated metadata, such as:

[0114] Class::node{

[0115] uid:<unique reference number>

[0116] name:<Name of the person>

[0117] job_family:<broad job category>

[0118] job_role:<more specific job role>

[0119] keywords:<comma-separated list of keywords for person>

[0120] …

[0121] }

[0122] Each edge of the graph represents a link between two people,forexample:

[0123] Class::edge{

[0124] uid:<unique reference number>

[0125] nodeA:<uid of first node>

[0126] nodeB:<uid of second node>

[0127] connectionDirection:<direction of edge,AB,BA,ABA>

[0128] …

[0129] }

[0130] When a new person (node) is entered into the graph, metadata describing that person is created. By searching the graph for existing people with similar metadata to the new person, their existing connections can be used to create or suggest connections for the new person.

[0131] Figure 3 Two different views of a network graph 114 are shown. Network graph 114 shows an example of an original network graph 114. Network graph 114' is network graph 114 after a new node 116 has been added. Network graph 114 is shown as including a system of network nodes 300 and network edges 302 connecting network nodes 300. Some of the network edges 302 indicate bidirectional connections, and some of the network edges indicate directed or unidirectional connections.

[0132] In the modified network graph 114, a new node 116 and a closest matching network node 122 are identified. The closest matching network node 122 is connected to a network edge 124 of the closest matching network node 122. The closest matching network node 122 has four network edges 124. The new network edge 126 is constructed by taking only two of the network edges and then connecting the new node 116 using these same connections 126. Various metadata or flags can be set in the metadata of the network edges 124 to enable their selection or deselection.

[0133] In another example, the system is extended so that metadata associated with all nodes and edges is sufficiently anonymized so that a new network can be created with only available node information, and edge information from the first network will be used to construct edges of the second network.

[0134] In another example, additional metadata may be added to edge connections that specifies whether the edge was created manually or automatically, where manually created edges are given different importance functions that will affect their likelihood of being suggested when adding new nodes.

[0135] In another example, the system is extended to be able to track the usage frequency of each edge connection via email, voice call, text messaging service, etc., so that the edge metadata can be extended with a weighted function of the usage frequency.

[0136] In another example, the system enables edge connections and node metadata to also include information related to a business process or workflow. For example, the process may be from role A to role B to role C, and the system may be configured to limit the number of new pre-populated contacts to a maximum number based on the new functionality, so that, for example, a new person in role A will cause up to 50 people in role B to be pre-populated, but only the first 10 people in role C. Meanwhile, a new person in role B will cause more people in role C to be pre-populated.

[0137] In another example, the system is extended to enable users to manually specify additional metadata for edge connections, such as if the connection is private and should not be automatically suggested for others; or has high relevance even if it is not used frequently, so it should have a higher importance weight.

[0138] In another example, metadata about previous job roles (internal and external to the network) can be added to suggest contacts who may be useful not because of their current job role but because of previous knowledge and experience.

[0139] In another example, the system may be extended to work across a network, where a network may represent a single organization to allow connections between organizations, which may be particularly useful for job roles such as technical support personnel.

[0140] In another example, the system can be extended so that if a person leaves the organization and is replaced by a new person, all connections from the first person can be automatically notified about the change in node personnel and the connection information is updated. This can also be applied when people change job roles within the organization.

[0141] In another example based on the second embodiment, analysis of many networks representing different organizations can allow organizations to be clustered by type. When creating a new network, the organization can be characterized and only metadata of other organizations of similar types will be used to generate the new network.

[0142] Figure 4 Another example of a medical system 400 is shown. This is intended to represent a telemedicine system or a remotely controlled medical imaging system. In this example, the example is explained in the context of a magnetic resonance imaging system 402. However, other types of medical imaging systems, such as computed tomography, ultrasound, or other types of medical imaging systems, may be substituted.

[0143] Magnetic resonance imaging system 402 includes magnet 404. Magnet 404 is a superconducting cylindrical magnet with a bore 406 extending therethrough. Split cylindrical magnets and so-called open magnets can also be used. Split cylindrical magnets are similar to standard cylindrical magnets, except that the cryostat has been divided into two sections to allow access to the isoplane of the magnet; such magnets can be used, for example, in conjunction with charged particle beam therapy. Open magnets have two magnet sections, one above the other, with a space large enough to accommodate the subject. The arrangement of the two section areas is similar to that of Helmholtz coils. Open magnets are popular because they place fewer restrictions on the subject. Inside the cryostat of the cylindrical magnet, there is a collection of superconducting coils.

[0144] Within the bore 406 of the cylindrical magnet 404 lies an imaging zone 408, where the magnetic field is sufficiently strong and uniform to perform magnetic resonance imaging. A field of view 409 is shown within the imaging zone 408. K-space data is acquired for the field of view 409. A region of interest may be the same as the field of view 409, or it may be a subvolume of the field of view 409. An object 418 is shown supported by an object support 420, such that at least a portion of the object 418 is within the imaging zone 408 and the field of view 409.

[0145] Also present within the bore 406 of the magnet is a set of magnetic field gradient coils 410 for acquiring measured k-space data to spatially encode the magnetic spins within the imaging region 408 of the magnet 404. The magnetic field gradient coils 410 are connected to a magnetic field gradient coil power supply 412. The magnetic field gradient coils 410 are intended to be representative. Typically, the magnetic field gradient coils 410 include three separate sets of coils for spatial encoding in three orthogonal spatial directions. The magnetic field gradient power supply provides current to the magnetic field gradient coils. The current provided to the magnetic field gradient coils 410 is controlled as a function of time and can be ramped or pulsed.

[0146] Adjacent to the imaging zone 408 is a radio frequency coil 414, which is used to manipulate the orientation of magnetic spins within the imaging zone 408 and to receive radio transmissions from spins also within the imaging zone 408. An radio frequency antenna may include multiple coil elements. An radio frequency antenna may also be referred to as a channel or antenna. The radio frequency coil 414 is connected to a radio frequency transceiver 416. The radio frequency coil 414 and radio frequency transceiver 416 may be replaced by separate transmit and receive coils, and separate transmitters and receivers. It should be understood that the radio frequency coil 414 and radio frequency transceiver 416 are representative. The radio frequency coil 414 is also intended to represent a dedicated transmit antenna and a dedicated receive antenna. Similarly, the transceiver 416 may also represent a separate transmitter and receiver. The radio frequency coil 414 may also have multiple transmit / receive elements, and the radio frequency transceiver 416 may have multiple transmit / receive channels. The transceiver 416 and gradient controller 412 are shown connected to the hardware interface 106 of the computer system 102.

[0147] In this example, the added node 116 represents a clinical procedure or new object being added to the medical database. New object metadata 118 is matched to the closest matching network node 122 for automatic selection of controls for the magnetic resonance imaging system. This metadata may include things like the desired procedure type and metadata describing the object 418. For example, some magnetic resonance imaging protocols are affected by the size and weight of the object 418.

[0148] In some examples, preliminary pulse sequence commands 430 are contained within memory 110. The computing system 104 uses these to acquire preliminary k-space data 432. This can be, for example, scout or survey k-space data. Memory 110 is then further shown as containing a scout or survey image 434 that has been reconstructed from the preliminary k-space data 432. Memory 110 is optionally shown as containing an image classifier neural network 436. This can be, for example, a convolutional neural network used or trained to classify the scout image 434. Memory 110 is also shown as containing an image classification 438 received in response to inputting the scout image 434 into the image classifier neural network 436. This can be used, for example, to identify various types of pathologies or locations that have been imaged within the subject 418. In any case, this image classification 438 is used to select one of the new network edges 126.

[0149] When a new network edge 126 is selected, this is equivalent to selecting a remote entity 444 to connect to and selecting a pulse sequence command 442. This then selects a specific pulse sequence command for acquiring additional data and automatically connects to the remote entity 444. The medical system 400 is shown as including the remote entity 444 and several other remote entities 446. The remote entity 444 can be a connection to a physician, such as for telemedicine, or it can be an automated control system, such as a robotic computer program for remotely and automatically controlling the magnetic resonance imaging system 402. The memory 110 is also shown as containing configuration modification commands 448 received from the remote entity 444. These are used to modify the selected pulse sequence command 442. After modification, the selected pulse sequence command 442 is used to acquire clinical k-space data 450. The computing system 104 uses the selected pulse sequence command 442 to control the operation of the magnetic resonance imaging system to acquire clinical k-space data. The clinical k-space data 450 is then used to reconstruct a clinical magnetic resonance image 452.

[0150] Figure 5 Shows instructions for use Figure 4 Flowchart of a method of a medical system 400. Some of the steps in the flowchart may be performed in a different order. For example, steps 500 and 502 may be performed before steps 200, 202, and 204. In other examples, steps 200, 202, and 204 may be performed before steps 500 and 502.

[0151] In step 500, a medical imaging system or magnetic resonance imaging system 402 is controlled to acquire preliminary medical imaging data, in this case preliminary k-space data 432, using preliminary pulse sequence commands 430. The preliminary pulse sequence commands 430 are preliminary configuration data. In step 502, an image classification 432 is received in response to inputting a scout image 434 into an image classification neural network. Next, as Figure 2 As shown, steps 200, 202, and 204 are performed. In this example, the new node represents the object 418 and any metadata describing the object or the selection of a specific magnetic resonance imaging protocol type. The selection of the closest matching neural network node 122 and its network edge 124 represents the magnetic resonance imaging protocol that can be used to image the object 418.

[0152] In step 504, the image classification 438 is used to select a selected network edge from the new network edges 126. This provides the selected pulse sequence command 442 and establishes a communication link or data, which is used to establish a communication link with the remote entity 444. In step 508, the image classification 438 is sent to the remote entity 444. In step 510, a configuration modification command 448 is received from the remote entity 444. In step 512, the configuration modification command is used to modify the acquisition of medical imaging data. In this particular example, this is accomplished by modifying the selected pulse sequence command 442 using the configuration modification command 448. Then, in step 514, the device configuration of the selected network edge, in this example the modified selected pulse sequence command 442, is used to control the medical imaging system, or in this case the magnetic resonance imaging system 402.

[0153] It should be understood that one or more of the foregoing examples may be combined as long as the combined examples are not mutually exclusive.

[0154] As will be appreciated by those skilled in the art, aspects of the present invention may be embodied as devices, methods, or computer program products. Thus, aspects of the present invention may take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, microcode, etc.), or embodiments combining software and hardware aspects, which may be collectively referred to herein as "circuits," "modules," or "systems." Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer-readable media having computer-executable code embodied thereon.

[0155] Any combination of one or more computer-readable media can be utilized. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. As used herein, "computer-readable storage medium" encompasses any tangible storage medium that can store instructions that can be executed by a processor or computing system of a computing device. A computer-readable storage medium can be referred to as a computer-readable non-transitory storage medium. A computer-readable storage medium can also be referred to as a tangible computer-readable medium. In some embodiments, a computer-readable storage medium can also be capable of storing data that can be accessed by a computing system of a computing device. Examples of computer-readable storage media include, but are not limited to, floppy disks, magnetic hard disk drives, solid-state drives, flash memory, USB thumb drives, random access memory (RAM), read-only memory (ROM), optical disks, magneto-optical disks, and register files of computing systems. Examples of optical disks include compact disks (CDs) and digital versatile disks (DVDs), such as CD-ROMs, CD-RWs, CD-Rs, DVD-ROMs, DVD-RWs, or DVD-Rs. The term computer-readable storage medium also refers to various types of recording media that can be accessed by a computing device via a network or communication link. For example, data can be retrieved via a modem, via the Internet, or via a local area network. Computer executable code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0156] A computer-readable signal medium may include a propagated data signal having computer-executable code embodied therein (e.g., in baseband or as part of a carrier wave). Such a propagated signal may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium that is not a computer-readable storage medium and that can convey, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0157] "Computer memory" or "memory" is an example of a computer-readable storage medium. Computer memory is any memory directly accessible to a computing system. "Computer storage" or "storage" is another example of a computer-readable storage medium. Computer storage is any non-volatile computer-readable storage medium. In some embodiments, computer storage may also be computer memory, and vice versa.

[0158] As used herein, a “computing system” encompasses an electronic component capable of executing a program or machine-executable instructions or computer-executable code. References to computing systems including examples of a “computing system” should be interpreted as potentially including more than one computing system or processing core. For example, a computing system may be a multi-core processor. A computing system may also refer to a collection of computing systems within a single computer system or distributed across multiple computer systems. The term computing system should also be interpreted as potentially referring to a collection or network of computing devices, each of which includes a processor or computing system. Machine-executable code or instructions may be executed by multiple computing systems or processors, which may be within the same computing device or even distributed across multiple computing devices.

[0159] Machine executable instructions or computer executable code can include instructions or programs that cause a processor or other computing system to perform an aspect of the present invention. The computer executable code for performing the operations of various aspects of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Java, Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as "C" programming language or similar programming languages), and compiled into machine executable instructions. In some cases, the computer executable code can be in the form of a high-level language or in precompiled form and used in conjunction with an interpreter that generates machine executable instructions on the fly. In other cases, the machine executable instructions or computer executable code can be in the form of programming for a programmable logic gate array.

[0160] The computer-executable code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0161] Aspects of the present invention are described with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box or a portion of a box of a flow chart, diagram and / or block diagram can be implemented by computer program instructions in the form of computer executable code when applicable. It should also be understood that, when not mutually exclusive, the combination of boxes in different flow charts, diagrams and / or block diagrams can be combined. These computer program instructions can be provided to a computing system of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, so that the instructions executed via the computing system of a computer or other programmable data processing device create a device for implementing the function / action specified in one or more boxes of a flow chart and / or block diagram.

[0162] These machine-executable instructions or computer program instructions may also be stored in a computer-readable medium, which may direct a computer, other programmable data processing apparatus or other device to operate in a specific manner so that the instructions stored in the computer-readable medium produce an article of manufacture including instructions for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0163] Machine-executable instructions or computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide a process for implementing the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0164] As used herein, a "user interface" is an interface that allows a user or operator to interact with a computer or computer system. A "user interface" may also be referred to as a "human interface device." A user interface can provide information or data to an operator and / or receive information or data from an operator. A user interface can enable input from an operator to be received by the computer and can provide output from the computer to the user. In other words, a user interface can allow an operator to control or manipulate the computer, and the interface can allow the computer to indicate the effects of the operator's controls or manipulations. Displaying data or information on a display or graphical user interface is an example of providing information to an operator. Receiving data via a keyboard, mouse, trackball, touchpad, pointing stick, graphics tablet, joystick, game controller, webcam, headset, pedal, wired gloves, remote control, and accelerometer are all examples of user interface components that can receive information or data from an operator.

[0165] As used herein, a "hardware interface" includes an interface that enables a computing system of a computer system to interact with and / or control an external computing device and / or apparatus. A hardware interface can allow a computing system to send control signals or instructions to an external computing device and / or apparatus. A hardware interface can also enable a computing system to exchange data with an external computing device and / or apparatus. Examples of hardware interfaces include, but are not limited to, a universal serial bus, an IEEE 1394 port, a parallel port, an IEEE 1284 port, a serial port, an RS-232 port, an IEEE-488 port, a Bluetooth connection, a wireless LAN connection, a TCP / IP connection, an Ethernet connection, a control voltage interface, a MIDI interface, an analog input interface, and a digital input interface.

[0166] As used herein, a "display" or "display device" encompasses an output device or user interface suitable for displaying images or data. A display may output visual, audio, and / or tactile data. Examples of displays include, but are not limited to, computer monitors, television screens, touch screens, tactile electronic displays, Braille screens, cathode ray tubes (CRTs), memory tubes, bi-stable displays, electronic paper, vectorscopes, flat panel displays, vacuum fluorescent displays (VFs), light emitting diode (LED) displays, electroluminescent displays (ELDs), plasma display panels (PDPs), liquid crystal displays (LCDs), organic light emitting diode displays (OLEDs), projectors, and head-mounted displays.

[0167] Medical imaging data is defined herein as recorded measurements taken by a medical imaging system (such as a tomographic medical imaging system) that describe the internal anatomical structure of an object. Medical imaging data can be reconstructed into a medical image. A medical image is defined herein as a reconstructed two-dimensional or three-dimensional visualization of the anatomical data contained within the medical imaging data. This visualization can be performed using a computer.

[0168] K-space data is defined herein as recorded measurements of radio frequency signals emitted by atomic spins using an antenna of a magnetic resonance apparatus during a magnetic resonance imaging scan.Magnetic resonance data is an example of medical image data.

[0169] A magnetic resonance imaging (MRI) image or MR image is defined herein as a reconstructed two- or three-dimensional visualization of anatomical data contained within magnetic resonance imaging data.The visualization may be performed using a computer.

[0170] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments.

[0171] Other variations to the disclosed embodiments may be understood and implemented by those skilled in the art in practicing the claimed invention by studying the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may perform the functions of several items recited in a claim. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. The computer program may be stored / distributed on a suitable medium, such as an optical storage medium or solid-state medium provided together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. A medical system (100, 400), comprising: a memory (110) storing machine-executable instructions (112) and a network graph (114, 114'), wherein the network graph includes network nodes (300) and network edges (302) linking the network nodes, wherein at least some of the network nodes include node-specific object metadata, wherein the network edges include configuration metadata, wherein the configuration metadata includes routing data configured to establish a communication channel for transmitting data between two network nodes of the network graph using a communication device; A computing system (104), wherein execution of the machine-executable instructions causes the computing system to: creating (200) a new node (116) in the network graph including new object metadata (118); searching (202) for a closest matching network node (122) selected from the network nodes, wherein the closest matching network node is selected by comparing the new object metadata with the node-specific object metadata, A new network edge (126) for the new node is constructed (204) in the network graph, wherein the new network edge comprises a copy of at least a portion of the network edge of the closest matching network node.

2. The medical system according to claim 1, wherein The medical system comprises a medical imaging system (402) configured to acquire medical imaging data (450), wherein the medical system comprises the communication device, wherein the node-specific object metadata comprises an image classification (438), wherein the configuration metadata comprises device configuration data adapted to control the medical imaging system to acquire the medical imaging data, wherein execution of the machine-executable instructions further causes the computing system to: selecting (504) a selected network edge from one of the new network edges using the image classification, wherein the new node represents the communication device of the medical imaging system, wherein the selected network edge is connected to an additional network edge representing a remote entity (444); establishing (506) the communication channel between the communication device and the remote entity using the routing data of the new network edge, wherein the medical system is configured such that the communication channel enables at least partial control of the medical imaging system by the remote entity; The medical imaging system is controlled (514) to acquire the medical imaging data using the device configuration data of the selected network edge.

3. The medical system according to claim 2, wherein: Execution of the machine-executable instructions further causes the computing system to: sending (508) the image classification to the remote entity via the communication channel; receiving (510) a configuration modification command (448) from the remote entity via the communication channel; as well as The acquisition of the medical imaging data is modified (512) using the configuration modification command.

4. The medical system according to claim 2 or 3, wherein: Execution of the machine-executable instructions further causes the computing system to: controlling (500) the medical imaging system to acquire preliminary medical imaging data (432) using the preliminary configuration data; The image classification is received (502) in response to inputting the preliminary medical imaging data into an image classification neural network (436).

5. The medical system according to claim 2 or 3, wherein: The medical imaging system is any one of the following: a magnetic resonance imaging system (402), an ultrasound imaging system, an X-ray system, a fluoroscope, a positron emission tomography system, a single photon emission tomography system, and a computed tomography system.

6. The medical system according to any one of the preceding claims 1 to 3, wherein: The medical system is a telemedicine system.

7. The medical system according to any one of claims 1 to 3, wherein: The medical system is a hospital information system or a medical diagnosis machine.

8. The medical system according to any one of the preceding claims 1 to 3, wherein: The configuration metadata includes any one of the following: work process and workflow.

9. The medical system according to any one of the preceding claims 1 to 3, wherein: The configuration metadata includes a flag indicating whether the configuration metadata is private or non-private, wherein execution of the machine-executable instructions further causes the computing system to exclude a network edge of the closest matching network node from replication if the network edge has a private indication.

10. The medical system according to any one of the preceding claims 1 to 3, wherein The new object metadata includes job role and / or functional requirements.

11. The medical system according to any one of the preceding claims 1 to 3, wherein: Execution of the machine-executable instructions further causes the computing system to: receiving a selection of a cancel network node, wherein the cancel network node is one of the network nodes; For a network node connected to the canceled network node via a network edge, sending a pre-recorded message to the network node connected to the canceled network node via the communication channel; and The cancelled network node and the network edges connected to the cancelled network node are removed from the network graph.

12. The medical system according to any one of the preceding claims 1 to 3, wherein: The configuration metadata includes usage frequency data, wherein the network edges of the matching network nodes having usage frequency data below a predetermined usage threshold are excluded from replication.

13. The medical system according to any one of the preceding claims 1 to 3, wherein: Execution of the machine-executable instructions further causes the computing system to: receiving a plurality of network nodes including metadata of the created object; Iteratively adding the plurality of network nodes to a new network graph, wherein iteratively adding the plurality of network nodes comprises: adding one of the plurality of network nodes to the new network graph, The network edges of the network graph are copied to the new network graph by matching the created object metadata with the object metadata of the network graph.

14. A method of operating a medical system (100, 400), wherein: The medical system includes a memory (110) storing machine-executable instructions (112) and a network graph (114, 114'), wherein the network graph includes network nodes (300) and network edges (302) linking the network nodes, wherein at least some of the network nodes include node-specific object metadata, wherein the network edges include configuration metadata, wherein the configuration metadata includes routing data configured to establish a communication channel for transmitting data between two network nodes of the network graph using a communication device, wherein the method includes: creating (200) a new node (116) in the network graph including new object metadata (118); searching (202) for a closest matching network node (122) selected from the network nodes, wherein the closest matching network node is selected by comparing the new object metadata with the node-specific object metadata, A new network edge (126) for the new node is constructed (204) in the network graph, wherein the new network edge comprises a copy of at least a portion of the network edge of the closest matching network node.

15. A computer program comprising machine-executable instructions (112) for execution by a computing system (104), wherein Execution of the machine-executable instructions causes the computing system to: creating (200) a new node (116) including new object metadata (118) in a network graph, wherein the network graph includes network nodes (300) and network edges (302) linking the network nodes, wherein at least some of the network nodes include node-specific object metadata, wherein the network edges include configuration metadata, wherein the configuration metadata includes routing data configured to establish a communication channel for transmitting data between two network nodes of the network graph using a communication device; searching (202) for a closest matching network node (122) selected from the network nodes, wherein the closest matching network node is selected by comparing the new object metadata with the node-specific object metadata, A new network edge (126) for the new node is constructed (204) in the network graph, wherein the new network edge comprises a copy of at least a portion of the network edge of the closest matching network node.

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