Patent Description:
Various types of medical systems such as medical imaging systems require great deals of training and experience to operate. Recently, telecommunication advances have begun to enable telemedicine and remote control of medical devices and medical imaging devices. <CIT> discloses an example of a method and a system configured to execute the method for intrusion detection to detect malicious insider threat activities within a network of multiple interconnected computerized user profiles, comprising the following method steps: training a Neural Network on multiple sets of user profile data for multiple user profiles and on multiple sets of activity data of the multiple user profiles of the network, such that the Neural Network is capable of predicting for future dates activities for multiple user profiles, and applying the trained Neural Network on the set of further user profile data of the further user profile, and predicting an activity of the further user profile based on the multiple sets of activity data by the trained Neural Network, and observing activity of the further user profile, and applying the trained Neural Network on the observed activity, and detecting malicious activity for the further user profile by the trained Neural Network, if the observed activity deviates from the predicted activity. <CIT> discloses an example of a system and method for generating data visualizations using semantic graphs. The method includes generating an enriched data layer based on a plurality of knowledge graphs, the plurality of knowledge graphs including a plurality of first nodes, the enriched data layer including a plurality of second nodes, wherein each of the plurality of second nodes is connected via an edge to at least one of the plurality of first nodes; and generating a data visualization based on the enriched data layer and a request for data, wherein the request for data indicates a type of data corresponding to at least one of the plurality of second nodes, wherein the data visualization is generated using data represented by at least one of the plurality of first nodes connected to the at least one of the plurality of second nodes.

The invention provides for a medical system, a computer program, and a method as defined in the independent claims. Embodiments are given in the dependent claims.

In one aspect the invention provides for a medical system that comprises a memory storing machine-executable instructions and network graph. The network graph comprises network nodes and network edges that link the network nodes together. At least a portion of the network nodes comprise node-specific subject metadata. The network edges comprise configuration metadata. The configuration metadata comprises routing data configured for establishing a communication channel for communicating data between two network nodes of the network graph using a communication device. The medical system further comprises a computational system.

Execution of the machine-executable instructions causes the computational system to create a new node comprising new subject metadata in the network graph. Execution of the machine-executable instructions further causes the computational 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 subject metadata to the node-specific subject metadata of the different nodes that make up the network graph. Execution of the machine-executable instructions further causes the computational system to construct new network edges for the new node in the network graph. The new network edges comprise a replication of at least a portion of the network edges of the closest matching network node.

In another aspect the invention provides for a method of operating a medical system. The medical system comprises a memory storing machine-executable instructions and a network graph. The network graph comprises network nodes and network edges that link the network nodes. At least a portion of the network nodes comprise node-specific subject metadata. The network edges comprise configuration metadata. The configuration metadata comprises routing data configured for establishing a communication channel for communicating data between two network nodes of the network graph using a communication device.

The method comprises creating a new node comprising new subject metadata in the network graph. The method further comprises searching for a closest matching network node selected from the network nodes. The closest matching network node is selected by comparing the new subject metadata to the node-specific metadata. The method further comprises constructing new network edges for the new node in the network graph. The new network edges comprise a replication of at least a portion of the network edges of the closest matching network node.

In another aspect, the invention provides for a computer program comprising machine-executable instructions for execution by a computational system. Execution of the machine-executable instructions causes the computational system to create a new node comprising new subject metadata in a network graph. The network graph comprises network nodes and network edges that link the network nodes. At least a portion of the network nodes comprise node-specific subject metadata. The network edges comprise configuration metadata. The configuration metadata comprises routing data configured for establishing a communication channel for communicating data between two network nodes of the network graph using a communication device.

Execution of the machine-executable instructions further causes the computational 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 subject metadata to the new node-specific subject metadata. Execution of the machine-executable instructions further causes the computational system to construct new network edges for the new node in the network graph. The new network edges comprise a replication of at least a portion of the network edges of the closest matching network node.

Like numbered elements in these Figures are either equivalent elements or perform the same function. Elements which have been discussed previously will not necessarily be discussed in later Figures if the function is equivalent.

Examples may be beneficial because it may provide for a means of automatically replicating routing data that is configured for establishing a communication channel. The routing data may conform in different examples. For example, the routing data may contain data such as a telephone number, an email address, an IP address, or other data which may be used to connect two communication devices together.

The closest matching network node may be determined using different criteria in different examples. In some examples the closest matching network nodes has the largest match with its node specific subject metadata and the new subject metadata of the new node. For example, the closest match could be determined by providing a score calculated by how many fields of the node specific subject metadata for a particular node matches the new subject metadata. A modification of this would be to have a weighted score which has weighting factors which emphasize particular fields having a closer match. The closest matching network node may also be generated as the average of a number of closely matching nodes using, for example, a k-nearest neighbors search approach.

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 nodes is then found by searching for the identifier, classification, or specific metadata in the existing network nodes of the network graph. For example, a new member of staff (the new node) could just indicate a predecessor in the organization represented by the network graph. So, something like; "I am a new service engineer, please give me all the contacts that my colleague service engineers have as well", or "I succeed Mr. X, please give me all the contacts that Mr. X used to have". The current text has a high emphasis on automatically finding the closest match.

In some examples, the new subject metadata could be populated by a job role or position and the closest matching network node could be searched for as described in the paragraph above.

In some examples, the closest matching network node has edges that connect to other portions of the network graph. The new network edges are simply a replication or copying of these connections with the new node in place of the closest matching network node.

In another example, the medical system comprises a medical imaging system that is configured for acquiring medical imaging data. The medical system comprises the communication device. The subject-specific metadata comprises an image classification. The configuration metadata comprises device configuration data adapted for controlling the medical imaging system to acquire the medical imaging data. Execution of the machine-executable instructions further causes the computational system to select a chosen 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 chosen network edge is connected to an additional network edge representing a remote entity.

The image classification could for example be an identification of a particular type of anatomical structure or abnormal anatomical structure being present. The network edges could contain identifiers. When the identifier of a network edge matches the image classification, that network edge is then selected. This may for example be useful for automatically configuring the medical imaging system to image the anatomical structure specified by the identifier. In some examples, the medical system may display the image classification on a display for approval and/or correction before searching for the closest matching network node.

Execution of the machine-executable instructions further causes the computational system to establish the communication channel between the communication device and the remote entity using the routing data of the new network edge. The medical system is configured such that the communication channel enables at least partial control of the medical imaging system by the remote entity. Execution of the machine-executable instructions further causes the computational system to control the medical imaging system to acquire the medical imaging data using the device configuration data of the chosen network edge. This example may be beneficial because it may provide a means of both establishing a telecommunications link and controlling the medical system.

In another example execution of the machine-executable instructions further causes the computational system to send the image classification to the remote entity via the communication channel. Execution of the machine-executable instructions further causes the computational system to receive configuration modification commands from the remote entry via the communication channel. Execution of the machine-executable instructions further causes the computational system to modify the acquisition of the medical imaging data using the configuration modification commands. This example may be beneficial because it may provide for a means of automated or remote control of the medical system.

In another example, execution of the machine-executable instructions further causes the computational system to control the medical imaging system to acquire preliminary medical imaging data using preliminary configuration data. Execution of the machine-executable instructions further causes the computational system to receive the image classification in response to inputting the preliminary medical imaging data into an image classification neural network. Neural networks may be programmed to perform a classification of an image. For example, the image classification neural network may be a convolutional neural network or in some examples a U-Net neural network. The neural network may be trained by having training images that are labeled with the correct image classification.

In another example the medical imaging system is a magnetic resonance imaging system.

In another example the medical imaging system is an ultrasound imaging system.

In another example the medical imaging system is an X-ray system.

In another example the medical imaging system is a fluoroscope.

In another example the medical imaging system is a positron emission tomography system.

In another example the medical imaging system is a single-photon emission tomography system.

In another example the medical imaging system is a computed tomography system.

In another example the medical imaging system is a telemedicine system.

In another example the medical imaging system is a hospital information system.

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.

In another example the configuration metadata comprises work processes and workflows.

In another example the configuration metadata comprises a flag indicating if the configuration metadata is private or non-private. The execution of the machine-executable instructions further causes the computational system to exclude network edges of the closest matching network node from replicated if the flag is set to private. This example may be beneficial because it may provide for higher security because the leak of confidential or private data is secured and is not allowed to be copied when a new node is added.

In another example the subject-specific metadata comprises a job role.

In another example the subject-specific metadata comprises functional requirements fulfilled by the subject.

In another example, execution of the machine-executable instructions further causes the computational system to receive a selection of a cancelled network node. The cancelled network node is one of the network nodes. Execution of the machine-executable instructions further causes the computational system to send a pre-recorded message to network nodes connected in the cancelled network node via the communication channel for network nodes connected to the cancelled network node via the network edges. Execution of the machine-executable instructions further causes the computational system to remove the cancelled network nodes and network edges connected to the cancelled network node from the network graph. This example may be beneficial because it may provide for an automated means of editing and removing nodes that represent a particular subject. Using the routing data other users or objects in the database may be warned that a particular user is no longer part of the network graph.

In another example the configuration metadata comprises usage frequency data. The network edges of the matching network node with usage data below a predetermined usage threshold are excluded from being replaced. This may be beneficial because this may represent sparsely used data will not be wasted when it is transferred and then stored in another location.

In another example, execution of the machine-executable instructions further causes the computational system to receive multiple network nodes comprising created subject metadata. Execution of the machine-executable instructions further causes the computational system to iteratively add the multiple network nodes to a new network graph. The iteratively adding of the multiple network nodes comprises adding one of the multiple network nodes to the new network graph and copying the network edges of the network graph to the new network graph by matching the created subject metadata to the subject metadata of the network graph. This example may be beneficial because it may provide a means of taking the structure of a previous network graph and then using it to construct a new network graph. This may for example be useful in situations where the network graph for a hospital is for example used to create a new network graph in a new hospital or new location.

<FIG> illustrates an example of a medical system <NUM>. The medical system <NUM> is shown as comprising a computer <NUM> that has a computational system <NUM>. The computational system <NUM> is in communication with an optional hardware interface <NUM>. The computational system <NUM> is in further communication with a communication device <NUM>. The communication device <NUM> could for example be a network interface.

The computer <NUM> is further shown as comprising a memory <NUM> that is also in communication with the computational system <NUM>. The memory <NUM> is intended to represent various types of memory which are accessible to the computational system <NUM>.

The memory <NUM> is shown as comprising machine-executable instructions <NUM> which enable the computational system <NUM> to control the other components of the medical system <NUM> as well as perform various data processing and numerical tasks. The memory <NUM> is shown as further containing a network graph <NUM>. The network graph comprises network nodes and network edges that link the network nodes together. At least a portion of the network nodes comprise the node-specific subject metadata. The network edges comprise configuration metadata. The configuration metadata comprises routing data configured for establishing a communication channel for communicating data between two network nodes of the network graph using the communication device <NUM>.

The memory <NUM> is further shown as containing a new node <NUM> to be added to the network graph <NUM>. The new node <NUM> comprises new subject metadata <NUM>. The memory <NUM> is shown as comprising a comparison module <NUM> that is able to compare the new subject metadata <NUM> with node-specific subject metadata of the rest of the network graph <NUM>. This may for example find the node with the closest values for its metadata or it may be a weighted average of which one is closer.

The memory <NUM> is further shown as containing a closest-matching network node <NUM> that was selected from the network nodes of the network graph <NUM>. The memory <NUM> is then shown as also containing the network edges of the closest-matching network node <NUM>. The memory <NUM> is then shown as containing new network edges <NUM> that were replications of the network edges of the closest-matching network node <NUM>. In some cases, some of the network edges <NUM> may be left out. For example, there may be a flag to indicate that a network connection is personal or there may be other data used to exclude certain of the network edges <NUM>. The new node <NUM> is then added to the network graph <NUM> by connecting the new node <NUM> using the new network edges <NUM>. The new network edges <NUM> originate or are connected to the new node <NUM> and whatever nodes the closest-matching network node <NUM> was connected to.

<FIG> shows a flowchart which illustrates a method of operating the medical system <NUM> of <FIG>. In step <NUM> a new node <NUM> is created. The new node comprises new subject metadata <NUM>. In step <NUM> the closest-matching network node <NUM> is searched for within the network nodes of the network graph <NUM>. In step <NUM> the new network edges <NUM> for the new node <NUM> are constructed in the network graph by replicating at least some of the network edges <NUM> of the closest-matching network node <NUM>.

When new users are added to a communication system, they may be presented with either an empty contact list or a contact list prefilled with persons within their company. Often times this is not useful to the new user and gives no context to their contacts. As an example, if the user needs to find technical support for a specific device or procedure searching their contact list may produce many results and it is difficult to know who they should speak with. This often drives the user to need to search on other inter-/intra-net sites to find additional contextual information first or ask for referrals from other employees to identify the best contact to assist them. Examples may apply network theory at a company-wide or even between-company level to determine what and why certain persons have each other in their contact lists, and to then automatically generate a contact list when a new person is entered into the network.

Network theory is a version of graph theory where both the nodes and the edges of the graph have data associated with them. It has been used for some time in the analysis of social networks, and to understand the relationships between social entities within a network. In the case of a contacts list system each person can be considered a node in the graph, and the edge between nodes is the existence of one or both persons having the other in their contact list.

When a new person joins an organization, they are often confronted by an empty email address book, with little to no information on who the best contacts are for their role within a company. The invention aims to automatically, and adaptively, construct a network of connections between people.

Some examples may provide one or more of the following features:.

As a concrete example, a network graph may be constructed where each node of the graph represents an individual person (or functional person) with associated metadata, for example:
Class::node{
uid: <unique reference number>
name: <Name of the person>
job_family: <broad job category>
job_role: <more specific job role>
keywords: <comma-separated list of keywords for person>.

Each edge of the graph represents a link between two people, for example:
Class::edge{
uid: <unique reference number>
nodeA: <uid of first node>
nodeB: <uid of second node>
connectionDirection: <direction of edge, AB, BA, ABA>.

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

<FIG> shows two different views of the network graph <NUM>. The network graph <NUM> illustrates an example of the original network graph <NUM>. The network graph <NUM>' is the network graph <NUM> after the new node <NUM> has been added. The network graph <NUM> is shown as comprising network nodes <NUM> and a system of network edges <NUM> that are used to connect the network nodes <NUM>. Some of the network edges <NUM> indicate a bidirectional connection and some of the network nodes indicate a directed connection or a one-way connection.

In the modified network graph <NUM> the new node <NUM> and the closest-matching network node <NUM> are identified. The closest-matching network node <NUM> is connected to the network edges <NUM> of the closest-matching network node <NUM>. The closest-matching network node <NUM> has four network edges <NUM>. The new network edges <NUM> were constructed by taking just two of those and then connecting the new node <NUM> using these same connections <NUM>. Various metadata or flags could be set in the metadata of the network edges <NUM> to enable their selection or deselection.

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

In another example additional metadata can be added to the edge connections that specifies if the edge was manually or automatically created, with manually created edges given a different importance function, which will affect the likelihood of their suggestion when adding a new node.

In a further example the system is extended to be able to track the frequency of use of each edge connection, via email, voice calling, text messaging service, etc. such that the edge metadata can be extended with a weighting function for the frequency of use.

In a further example the system such that the edge connection and node metadata also include information related to the business processes or workflows. For instance, that flow could be from role A to role B to role C, and the system could then be configured to limit the new prefilled contacts to maximum numbers based on the new function, such that for instance a new person in role A gets a maximum of <NUM> B persons prefilled, but only the top <NUM> C roles. Whereas a new person in role B would get much more people in role C prefilled.

In a further example the system is extended such that users are able to manually specify additional metadata for edge connections, such as if the connection is private, and should not be automatically suggested for others; or of high relevance even if infrequently used such that it should have a higher importance weighting.

In a further example meatadata on previous job roles, inside and outside of the network may be added to suggest contacts that may be useful not due to their current job role, but for previous knowledge and experience.

In a further example the system can be extended to work across networks where a network may represent a single organization to allow for connections between organizations, this can be especially useful for job roles such as technical support persons.

In a further example the system may be extended such that if a person leaves an organization and is replaced by a new person, then all connected nodes from the first person can be automatically notified of the change of person and the update of the connection information. This can also apply when the person changes job role within the organization.

In a further example based on the second embodiment, analysis of many networks representing different organizations may allow for clustering the organizations by type. When a new network is created the organization can be characterized and the metadata for only other organizations of a similar type will be used for the generation of the new network.

<FIG> illustrates a further example of a medical system <NUM>. It is intended to represent a telemedicine system or a remote-controlled medical imaging system. In this example the example is explained in the context of a magnetic resonance imaging system <NUM>. However, other types of medical imaging systems such as computed tomography or ultrasound or other types of medical imaging systems could also be substituted.

The magnetic resonance imaging system <NUM> comprises a magnet <NUM>. The magnet <NUM> is a superconducting cylindrical type magnet with a bore <NUM> through it. It is also possible to use both a split cylindrical magnet and a so-called open magnet. A split cylindrical magnet is similar to a standard cylindrical magnet, except that the cryostat has been split into two sections to allow access to the iso-plane of the magnet, such magnets may for instance be used in conjunction with charged particle beam therapy. An open magnet has two magnet sections, one above the other with a space in-between that is large enough to receive a subject. The arrangement of the two sections area is similar to that of a Helmholtz coil. Open magnets are popular because the subject is less confined. Inside the cryostat of the cylindrical magnet there is a collection of superconducting coils.

Within the bore <NUM> of the cylindrical magnet <NUM> there is an imaging zone <NUM> where the magnetic field is strong and uniform enough to perform magnetic resonance imaging. A field of view <NUM> is shown within the imaging zone <NUM>. The k-space data are acquired for the field of view <NUM>. The region of interest could be identical with the field of view <NUM> or it could be a sub volume of the field of view <NUM>. A subject <NUM> is shown as being supported by a subject support <NUM> such that at least a portion of the subject <NUM> is within the imaging zone <NUM> and the field of view <NUM>.

Within the bore <NUM> of the magnet there is also a set of magnetic field gradient coils <NUM> which is used for the acquisition of measured k-space data to spatially encode magnetic spins within the imaging zone <NUM> of the magnet <NUM>. The magnetic field gradient coils <NUM> are connected to a magnetic field gradient coil power supply <NUM>. The magnetic field gradient coils <NUM> are intended to be representative. Typically, magnetic field gradient coils <NUM> contain three separate sets of coils for spatial encoding in three orthogonal spatial directions. A magnetic field gradient power supply supplies current to the magnetic field gradient coils. The current supplied to the magnetic field gradient coils <NUM> is controlled as a function of time and may be ramped or pulsed.

Adjacent to the imaging zone <NUM> is a radio frequency coil <NUM> for manipulating the orientations of magnetic spins within the imaging zone <NUM> and for receiving radio transmissions from spins also within the imaging zone <NUM>. The radio frequency antenna may contain multiple coil elements. The radio frequency antenna may also be referred to as a channel or antenna. The radio frequency coil <NUM> is connected to a radio frequency transceiver <NUM>. The radio frequency coil <NUM> and radio frequency transceiver <NUM> may be replaced by separate transmit and receive coils and a separate transmitter and receiver. It is understood that the radio frequency coil <NUM> and the radio frequency transceiver <NUM> are representative. The radio frequency coil <NUM> is intended to also represent a dedicated transmit antenna and a dedicated receive antenna. Likewise, the transceiver <NUM> may also represent a separate transmitter and receiver. The radio frequency coil <NUM> may also have multiple receive/transmit elements and the radio frequency transceiver <NUM> may have multiple receive/transmit channels. The transceiver <NUM> and the gradient controller <NUM> are shown as being connected to the hardware interface <NUM> of the computer system <NUM>.

In this example, the node <NUM> that is added represents a clinical procedure or new subject being added to a medical database. The matching the new subject metadata <NUM> with the closest-matching network node <NUM> is used to select controls for the magnetic resonance imaging system automatically. This metadata could include such things as the type of procedure desired as well as the metadata descriptive of the subject <NUM>. For example, some magnetic resonance imaging protocols are affected by the size and weight of the subject <NUM>.

In some examples, preliminary pulse sequence commands <NUM> are contained within the memory <NUM>. The computational system <NUM> uses these to acquire preliminary k-space data <NUM>. This for example could be scout or survey k-space data. The memory <NUM> is then further shown as containing a scout or survey image <NUM> that has been reconstructed from the preliminary k-space data <NUM>. The memory <NUM> is optionally shown as containing an image classifier neural network <NUM>. This could for example be a convolutional neural network that is used to classify or trained to classify scout images <NUM>. The memory <NUM> is further shown as containing an image classification <NUM> that has been received in response to inputting the scout image <NUM> into the image classifier neural network <NUM>. This may for example be used to identify various types of pathologies or locations that have been imaged within the subject <NUM>. In any case, this image classification <NUM> is used to select one of the new network edges <NUM>.

When the new network edge <NUM> is selected, this is equivalent to selecting a remote entity <NUM> to connect to and selected pulse sequence commands <NUM>. This then selects the specific pulse sequence commands to use to acquire additional data as well as automatically connecting to the remote entity <NUM>. The medical system <NUM> is shown as comprising a remote entity <NUM> and several other remote entities <NUM>. The remote entity <NUM> could be a connection with a physician such as is used for telemedicine, or it may also be an automatic control system such as a robotic computer program which is used to control the magnetic resonance imaging system <NUM> remotely and automatically. The memory <NUM> is further shown as containing configuration modification commands <NUM> received from the remote entity <NUM>. These are used to modify the selected pulse sequence commands <NUM>. After modification the selected pulse sequence commands <NUM> are used to acquire clinical k-space data <NUM>. The selected pulse sequence commands <NUM> are used by the computational system <NUM> to control the operation of the magnetic resonance imaging system to acquire the clinical k-space data. The clinical k-space data <NUM> is then used to reconstruct the clinical magnetic resonance image <NUM>.

<FIG> shows a flowchart which illustrates a method of using the medical system <NUM> of <FIG>. Some of these steps in this flowchart can be performed in a different order. For example, steps <NUM> and <NUM> are performed before steps <NUM>, <NUM>, and <NUM>. In other examples steps <NUM>, <NUM>, and <NUM> are performed before steps <NUM> and <NUM>.

In step <NUM> the medical imaging system or magnetic resonance imaging system <NUM> is controlled to acquire the preliminary medical imaging data, which in this case is the preliminary k-space data <NUM>, using the preliminary pulse sequence commands <NUM>. The preliminary pulse sequence commands <NUM> are the preliminary configuration data. In step <NUM> the image classification <NUM> is received in response to inputting the scout image <NUM> into the image classification neural network. Next, steps <NUM>, <NUM>, and <NUM> are performed as was illustrated in <FIG>. In this example the new node represents the subject <NUM> and any metadata descriptive of the subject or a selection of a particular type of magnetic resonance imaging protocol. The selection of the closest-matching neural network node <NUM> and its network edges <NUM> represent magnetic resonance imaging protocols that are available to image the subject <NUM>.

In step <NUM> the image classification <NUM> is used to select a chosen network edge from the new network edges <NUM>. This provides the selected pulse sequence commands <NUM> and establishes a communication link or data for establishing the communication link with the remote entity <NUM>. In step <NUM>, the image classification <NUM> is sent to the remote entity <NUM>. In step <NUM> the configuration modification commands <NUM> are received from the remote entity <NUM>. In step <NUM> the acquisition of the medical imaging data is modified using the configuration modification commands. In this particular example this is done by modifying the selected pulse sequence commands <NUM> with the configuration modification commands <NUM>. Then, in step <NUM>, the medical imaging system, or in this case the magnetic resonance imaging system <NUM>, is controlled using the device configuration of the chosen network edge, which in this example is the modified selected pulse sequence commands <NUM>.

It is understood that one or more of the aforementioned examples may be combined as long as the combined examples are not mutually exclusive.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as an apparatus, method or computer program product. " Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer executable code embodied thereon.

A 'computer-readable storage medium' as used herein encompasses any tangible storage medium which may store instructions which are executable by a processor or computational system of a computing device. The computer-readable storage medium may be referred to as a computer-readable non-transitory storage medium. The computer-readable storage medium may also be referred to as a tangible computer readable medium. In some embodiments, a computer-readable storage medium may also be able to store data which is able to be accessed by the computational system of the computing device. Examples of computer-readable storage media include, but are not limited to: a floppy disk, a magnetic hard disk drive, a solid state hard disk, flash memory, a USB thumb drive, Random Access Memory (RAM), Read Only Memory (ROM), an optical disk, a magneto-optical disk, and the register file of the computational system. Examples of optical disks include Compact Disks (CD) and Digital Versatile Disks (DVD), for example CD-ROM, CD-RW, CD-R, DVD-ROM, DVD-RW, or DVD-R disks. The term computer readable-storage medium also refers to various types of recording media capable of being accessed by the computer device via a network or communication link. For example, data may be retrieved over a modem, over the internet, or over 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, wire line, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

'Computer memory' or 'memory' is an example of a computer-readable storage medium. Computer memory is any memory which is directly accessible to a computational system. 'Computer storage' or 'storage' is a further 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 or vice versa.

A 'computational system' as used herein encompasses an electronic component which is able to execute a program or machine executable instruction or computer executable code. References to the computational system comprising the example of "a computational system" should be interpreted as possibly containing more than one computational system or processing core. The computational system may for instance be a multi-core processor. A computational system may also refer to a collection of computational systems within a single computer system or distributed amongst multiple computer systems. The term computational system should also be interpreted to possibly refer to a collection or network of computing devices each comprising a processor or computational systems. The machine executable code or instructions may be executed by multiple computational systems or processors that may be within the same computing device or which may even be distributed across multiple computing devices.

Machine executable instructions or computer executable code may comprise instructions or a program which causes a processor or other computational system to perform an aspect of the present invention. Computer executable code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages and compiled into machine executable instructions. In some instances, the computer executable code may be in the form of a high-level language or in a pre-compiled form and be used in conjunction with an interpreter which generates the machine executable instructions on the fly. In other instances, the machine executable instructions or computer executable code may be in the form of programming for programmable logic gate arrays.

Aspects of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It is understood that each block or a portion of the blocks of the flowchart, illustrations, and/or block diagrams, can be implemented by computer program instructions in form of computer executable code when applicable. It is further understood that, when not mutually exclusive, combinations of blocks in different flowcharts, illustrations, and/or block diagrams may be combined. These computer program instructions may be provided to a computational system of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the computational system of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These machine executable instructions or computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The machine executable instructions or computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

A 'user interface' as used herein is an interface which 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 may provide information or data to the operator and/or receive information or data from the operator. A user interface may enable input from an operator to be received by the computer and may provide output to the user from the computer. In other words, the user interface may allow an operator to control or manipulate a computer and the interface may allow the computer to indicate the effects of the operator's control or manipulation. The display of data or information on a display or a graphical user interface is an example of providing information to an operator. The receiving of data through a keyboard, mouse, trackball, touchpad, pointing stick, graphics tablet, joystick, gamepad, webcam, headset, pedals, wired glove, remote control, and accelerometer are all examples of user interface components which enable the receiving of information or data from an operator.

A 'hardware interface' as used herein encompasses an interface which enables the computational system of a computer system to interact with and/or control an external computing device and/or apparatus. A hardware interface may allow a computational system to send control signals or instructions to an external computing device and/or apparatus. A hardware interface may also enable a computational system to exchange data with an external computing device and/or apparatus. Examples of a hardware interface include, but are not limited to: a universal serial bus, IEEE <NUM> port, parallel port, IEEE <NUM> port, serial port, RS-<NUM> port, IEEE-<NUM> port, Bluetooth connection, Wireless local area network connection, TCP/IP connection, Ethernet connection, control voltage interface, MIDI interface, analog input interface, and digital input interface.

A 'display' or 'display device' as used herein encompasses an output device or a user interface adapted for displaying images or data. A display may output visual, audio, and or tactile data. Examples of a display include, but are not limited to: a computer monitor, a television screen, a touch screen, tactile electronic display, Braille screen,.

Cathode ray tube (CRT), Storage tube, Bi-stable display, Electronic paper, Vector display, Flat panel display, Vacuum fluorescent display (VF), Light-emitting diode (LED) displays, Electroluminescent display (ELD), Plasma display panels (PDP), Liquid crystal display (LCD), Organic light-emitting diode displays (OLED), a projector, and Head-mounted display.

Medical imaging data is defined herein as being recorded measurements made by a medical imaging system, such as a tomographic medical imaging system, that descriptive of an internal anatomy of a subject. The medical imaging data may be reconstructed into a medical image. A medical image is defined herein as being the reconstructed two- or three-dimensional visualization of anatomic data contained within the medical imaging data. This visualization can be performed using a computer.

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

A Magnetic Resonance Imaging (MRI) image or MR image is defined herein as being the reconstructed two- or three-dimensional visualization of anatomic data contained within the magnetic resonance imaging data. This visualization can be performed using a computer.

Claim 1:
A medical system (<NUM>, <NUM>) comprising:
- a memory (<NUM>) storing machine executable instructions (<NUM>) and a network graph (<NUM>, <NUM>'), wherein the network graph comprises network nodes (<NUM>) and network edges (<NUM>) that link the network nodes, wherein at least a portion of the network nodes comprise node specific subject metadata, wherein the network edges comprise configuration metadata, wherein the configuration metadata comprises routing data configured for establishing a communication channel for communicating data between two network nodes of the network graph using a communication device;
- a computational system (<NUM>), wherein execution of the machine executable instructions causes the computational system to:
- create (<NUM>) a new node (<NUM>) comprising new subject metadata (<NUM>) in the network graph;
- search (<NUM>) for a closest matching network node (<NUM>) selected from the network nodes, wherein the closest matching network node is selected by comparing the new subject metadata to the node specific subject metadata,
- construct (<NUM>) new network edges (<NUM>) for the new node in the network graph, wherein the new network edges comprise a replication of at least a portion of the network edges of the closest matching network node.