CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. national phase application of International Application No. PCT/EP2019/059872 filed on Apr. 17, 2019, which claims the benefit of EP Application Serial No. 18168248.5 filed on Apr. 19, 2018 and is incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to magnetic resonance imaging, in particular to reduction of artifacts during magnetic resonance imaging.

BACKGROUND OF THE INVENTION

A large static magnetic field is used by Magnetic Resonance Imaging (MRI) scanners to align the nuclear spins of atoms as part of the procedure for producing images within the body of a subject. This large static magnetic field is referred to as the BO field or the main magnetic field. Various quantities or properties of the subject can be measured spatially using MRI. However, the acquisition of a magnetic resonance imaging data is not instantaneous and voluntary and/or involuntary subject motion can cause artifacts or anomalies with the magnetic resonance image. Additionally, incorrectly configuring the magnetic resonance imaging system or the presence of metallic implants with the subject can also cause anomalies within the magnetic resonance image. Among the most common problems are motion artifacts caused by bulk subject movement, cardiac motion, or blood flow, aliasing artifacts due to improper scan planning, signal voids due to magnetic materials in the patient, geometric distortion caused by field inhomogeneity, and chemical shift artifacts

United States patent application publication US 2011/0110572 A1 discloses a system that dynamically improves quality of medical images using at least one processing device including an image analyzer, a correction processor and a message generator. The image analyzer automatically parses and analyzes data representing an image of a particular anatomical feature of a patient acquired by a medical image acquisition device to identify defects in the image by examining the data representing the image for predetermined patterns associated with image defects. The correction processor uses a predetermined information map associating image defects with corresponding corrective image acquisition parameters to determine corrected image acquisition parameters for use in reacquiring an image using the image acquisition device in response to an identified defect. The message generator generates a message for presentation to a user indicating an identified defect and suggesting use of the corrected image acquisition parameters for reacquiring an image.

SUMMARY OF THE INVENTION

The invention provides for a magnetic resonance imaging system, a method, and a computer program product in the independent claims. Embodiments are given in the dependent claims.

Embodiments of the invention provide for a means of reducing artifacts in magnetic resonance images by using a MRI artifact detection module that comprises at least one neural network. The MRI artifact detection module identifies artifacts in a magnetic resonance image and provides a list of suggested pulse sequence command changes. That is, the magnetic resonance image with its metal data is input the MRI artifact detection module. The MRI artifact detection module in configured in software to recognise and identify image artifacts in the magnetic resonance image. The MRI artifact detection module's trained neural network functions as an artifact classifier outputting an artifact classification of the input. magnetic resonance image the list of suggested pulse sequence command changes. One of the list of suggested pulse sequence command changes may be used to modify the pulse sequence commands. The modified pulse sequence commands can for example be used to reacquire the magnetic resonance image. When a series of magnetic resonance images are being acquired, the modified pulse sequence commands can also be used to correct and improve the quality of subsequently acquired magnetic resonance image.

The MRI artifact detection module may be incorporated in the software architecture of the magnetic resonance image system, that is the MRI artifact detection module is incorporate in the computer system that controls the magnetic resonance imaging system. Alternatively, the MRI artifact detection module, as well as the reconstruction module may be configured in software that may be located remotely and is accessible by the magnetic resonance imaging system. Thus, the functions of the MRI artefact detection module may be performed remotely. This remote performance may be done at a central server accommodating several magnetic resonance imaging systems within a healthcare institution. Even, the remote performance of the MRI artifact detection module may take place on-line from The Cloud. Also image reconstruction may be done remotely, e.g. from The Cloud. The at least one neural network and other neural networks referred to herein may in some cases be Convolutional Neural Networks. The training of neural networks referred to herein may in some cases be performed using an optimizing algorithm. In some examples, the arrangement for automatic artefact detection therefore makes use of a convolutional neural network (CNN) and deep learning. The CNN may possibly be trained from artefact-corrupted magnetic resonance images with corresponding labels that mention the type of artefact and a likely MR technique to avoid this artefact. This training may possibly be done from labelled images that are collected on the fly by regularly enquiring with experienced radiologist when an artefact is noted in the magnetic resonance image if the alternative MR technique has solved the issue. This training build-up may run in the background of regular imaging protocols. Training data may also be acquired from the system log-files by noting repeated scans and analyzing that situation for artefact type and how that was resolved. In some implementations the CNN may provide a probability score (or an image improvement likelihood score) for the alternative MR technique to be successful. That is, the image improvement likelihood score associated with a pulse sequence command change represents the probability that the change of the pulse sequence will reduce the artifact level of the magnetic resonance image reconstructed from magnetic resonance signals acquired with the changed pulse sequence. The CNN may also analyze images in a series and provide a recommendation if and how to continue the acquisition of a series of images after corruption have been identified in the first (fewer) images in the series. Some embodiments may therefore improve the imaging workflow in that artefacts are timely identified and properly corrected for. This may help avoid or reduce the need for extensive re-scanning of the subject.

In one aspect the invention provides for a magnetic resonance imaging system configured for acquiring magnetic resonance imaging data from a subject. The magnetic resonance imaging data may be acquired from an imaging zone generated by a magnet of the magnetic resonance imaging system. The magnetic resonance imaging system comprises a memory for storing machine-executable instructions and pulse sequence commands. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire the magnetic resonance imaging data according to a magnetic resonance imaging protocol. Pulse sequence commands as used herein encompass commands or data which may be compiled or constructed into such commands that are used to control the operation and function of the magnetic resonance imaging system. Sometimes pulse sequence commands take the form of a timing diagram which indicates what operations are performed by which component of the magnetic resonance imaging system.

The magnetic resonance imaging system further comprises a processor configured for controlling the magnetic resonance imaging system. Execution of the machine-executable instructions causes the processor to control the magnetic resonance imaging system to acquire the magnetic resonance imaging data using the pulse sequence commands. Execution of the machine-executable instructions further causes the processor to reconstruct a magnetic resonance image using the magnetic resonance imaging data.

Execution of the machine-executable instructions further causes the processor to receive a list of suggested pulse sequence command changes by inputting the magnetic resonance image and image metadata into an MRI artifact detection module. The MRI artifact detection module comprises at least one neural network. The image metadata comprises pulse sequence parameters selected from the pulse sequence commands. Each of the suggested pulse sequence command changes comprises an image improvement likelihood score. The image improvement likelihood score may in some instances be a probability or other number which indicates the likelihood that a particular suggested pulse sequence command change will improve the quality or reduce anomalies in the magnetic resonance image. Execution of the machine-executable instructions further causes the processor to receive a selection of a chosen pulse sequence command change from the list of suggested pulse sequence command changes. Execution of the machine-executable instructions further causes the processor to modify the pulse sequence commands using the chosen pulse sequence command change.

In one embodiment the at least one neural network is at least one convolution neural network.

In some examples the modification of the pulse sequence commands could be minor such as changing a few parameters such as a repetition time or the modification of a flip angle. In other examples the modification of the pulse sequence commands could be major such as replacing the imaging protocol with a different imaging protocol.

The pulse sequence parameters are data which is descriptive of the magnetic resonance imaging protocol being used for imaging. The pulse sequence parameters may include such information as the region of interest, the type of anatomy being imaged, the type of pulse sequence being used, and also individual parameters used within a pulse sequence.

In some examples the receiving of the selection of the chosen pulse sequence command changes from the list of suggested pulse sequence command changes may be performed automatically or may be received manually from a user of the magnetic resonance imaging system. For example, the machine-executable instructions could be programmed to select the chosen pulse sequence command change using a predetermined criterion and the image improvement likelihood score.

In another embodiment execution of the machine-executable instructions further cause the processor to display a review prompt and the magnetic resonance image on the user interface. The review prompt displays an artifact type listing. Execution of the machine-executable instructions further cause the processor to receive a selection from the artifact type listing. Execution of the machine-executable instructions further cause the processor to train the parameters of the at least one neural network using the selection from the artifact type listing. This embodiment may be beneficial because it may provide for automated training of the MRI artifact detection module as it is being used.

In another embodiment the at least one neural network is trained using deep learning. Other references herein to the training of neural networks may be performed using an optimizing algorithm.

In another embodiment the at least one neural network is trained using deep learning. Other references herein to the training of neural networks may be performed using deep learning.

In another embodiment execution of the machine-executable instructions further causes the processor to provide a localization marking tool on the review prompt for the magnetic resonance image. The localization marking tool may for example be an object on the user interface which is used to indicate at least a portion of the location in the image of where the artifact is. For example, the localization marking tool may comprise a tool for indicating a segmentation of the image, a bounding box, or a central location of the artifact. Execution of the machine-executable instructions further cause the processor to receive a localization selection indicating at least a portion of the magnetic resonance image. The MRI artifact detection module is further configured for outputting an artifact location indicator. Execution of the machine-executable instructions further cause the processor to train the at least one neural network to generate the artifact location indicator using the localization selection.

Execution of the machine-executable instructions further cause the processor to train the at least one neural network to generate the artifact location indicator using the localization selection. Execution of the machine-executable instructions further cause the processor to receive the artifact location indicator from the MRI artifact detection module in response to the magnetic resonance image and the image metadata. Execution of the machine-executable instructions further cause the processor to display the magnetic resonance image on the warning prompt. Execution of the machine-executable instructions further cause the processor to display the artifact location indicator superimposed on the magnetic resonance image on the warning prompt. This embodiment may be beneficial because the addition of the artifact location indicator may improve the ability of an operator to identify an artifact in the magnetic resonance image.

In another embodiment execution of the machine-executable instructions further causes the processor to record manual changes in the pulse sequence commands if acquisition of the magnetic resonance imaging data is manually repeated. Execution of the machine-executable instructions further cause the processor to receive an image quality rating descriptive of a repeated magnetic resonance image reconstructed from the manually repeated magnetic resonance imaging data. Execution of the machine-executable instructions further causes the processor to train the at least one neural network using the manual changes in the pulse sequence commands if the image quality rating is above a predetermined threshold. This embodiment may be beneficial because as an operator manually reconfigures the magnetic resonance imaging system to improve image quality the system will learn how to do this automatically.

In another embodiment the memory stores a log file which logs execution of the previous magnetic resonance imaging protocols. Execution of the machine-executable instructions further cause the processor to search the log file for magnetic resonance imaging protocols labeled as failed. Execution of the machine-executable instructions further causes the processor to train the at least one neural network using images from the failed magnetic resonance imaging protocols and/or magnetic resonance data extracted from the magnetic resonance imaging protocols labeled as failed. In some examples a review prompt as disclosed above may also be displayed as part of the training process. This embodiment may be beneficial because it may provide a means for retrospectively looking at magnetic resonance data acquired by the magnetic resonance imaging system and using it to further train the at least one neural network.

In another embodiment execution of the machine-executable instructions further causes the processor to receive subject data descriptive of the subject. The training is supplemented using the subject data. The image metadata comprises the subject data. The subject data may for example be descriptive of a physical condition of the subject. The subject data may include such information as the age, size, the medical status, the possible presence of implants within the subject, as well as the image quality ratings of previous exams of the same patient. This embodiment may be beneficial because it may provide for a means of improving the identification of artifacts in magnetic resonance images.

In another embodiment execution of the machine-executable instructions further causes the processor to receive system data descriptive of a state and/or configuration of the magnetic resonance imaging system. The training is supplemented using the system data. The image metadata comprises the system data. This embodiment may be beneficial because certain configurations of the magnetic resonance imaging system may increase or reduce the probability of obtaining a magnetic resonance image within an image artifact.

In another embodiment the at least one neural network is a single neural network. This embodiment may be beneficial because the entire MRI artifact detection module is implemented as a single neural network. This may be implemented in several different ways, for example the image metadata might be clustered into a few distinct classes and then the network may be trained for each of these classes.

In another embodiment the single neural network comprises at least one fully connected layer. The image metadata is input into the at least one fully connected layer. This embodiment may be beneficial because it provides for an efficient means of integrating the input of the image metadata into the single neural network.

In another embodiment the MRI artifact detection module comprises an artifact classifier. The artifact classifier is a first neural network. The at least one neural network comprises the first neural network. The first neural network is configured to output an artifact classification of the magnetic resonance image in response to inputting the magnetic resonance image. The MRI artifact detection module further comprises a pulse sequence modification module configured to output the list of suggested pulse sequence command changes in response to the artifact classification and the pulse sequence parameters selected from the pulse sequence commands. This embodiment may be beneficial because the neural network may be used to effectively identify the artifact and then other means may be used to provide the list of suggested pulse sequence command changes.

The pulse sequence modification module may for example be any one of the following: a second neural network, an expert system, and a statistical learning module. This embodiment may be beneficial because it may provide for various means of providing the list of suggested pulse sequence command changes.

In another embodiment the memory comprises an MRI artifact detection module database comprising multiple MRI artifact detection modules. The multiple MRI artifact detection modules comprise the MRI artifact detection module. Execution of the machine-executable instructions further causes the processor to select the MRI artifact detection module using the magnetic resonance imaging protocol. This embodiment may be beneficial because there may be an MRI artifact detection module that has been trained for each of a particular class of magnetic resonance imaging protocols. The magnetic resonance imaging protocols may be divided in different ways. For example, the type of pulse sequence may be used to differentiate how the MRI artifact detection module is selected. In other examples the particular anatomy being imaged may be used as a selection. For example, there may be one MRI artifact detection module that is used when imaging knees and another when the brain is imaged. This may be beneficial because the characteristics of the images may be different.

In another embodiment execution of the machine-executable instructions further causes the processor to reacquire the magnetic resonance imaging data after modifying the pulse sequence commands using the chosen pulse sequence command change. Execution of the machine-executable instructions further cause the processor to reconstruct the magnetic resonance image using the reacquired magnetic resonance imaging data. This embodiment may be beneficial because it may provide for a means of providing improved magnetic resonance images.

In another embodiment the pulse sequence commands are configured for acquiring a series of magnetic resonance images. Execution of the machine-executable instructions further causes the processor to resume acquisition of the series of magnetic resonance images after modifying the pulse sequence commands using the chosen pulse sequence command change. In this embodiment the quality of a series of magnetic resonance images is improved by modifying them with the chosen pulse sequence command change.

In another embodiment the selection criteria is received from a selection module that is configured to select the chosen pulse sequence commands using a predetermined criteria applied to the image improvement likelihood score. That is, e.g. the selection of the chosen pulse sequence command change is made on the basis of the image improvement likelihood score of the pulse sequence command change. In other words, the pulse sequence command may be changed to a changed pulse sequence command that has a higher, or even the highest image improvement likelihood score. Thus, the changed pulse sequence is highly likely to lead to reduction of the artifact level of the magnetic resonance image. This embodiment may be beneficial because it may provide for an automated improvement of magnetic resonance images. In another embodiment execution of the machine-executable instructions further cause the processor to display a warning prompt on the user interface. The warning prompt may be displayed if at least one image improvement likelihood score is above a predetermined threshold. The warning prompt displays a list of the suggested pulse sequence command changes. The selection of the chosen pulse sequence command changes is received from the user interface. This embodiment may be beneficial because it may provide for a means for an operator to manually improve the magnetic resonance images.

The display of the list of suggested pulse sequence command changes may be done in different ways in different examples. In some cases, the list of suggested pulse sequence command changes are ranked using the image improvement likelihood score. In some examples the image improvement likelihood score is displayed along with each of the elements of the list of the suggested pulse sequence command changes. This may provide a means to facilitate the selection of the preferred chosen pulse sequence command change.

In another aspect, the invention provides for a computer program product comprising machine executable instructions for execution by a processor. Execution of the machine executable instructions causes the processor to receive a magnetic resonance image. Execution of the machine executable instructions further causes the processor to receive image metadata comprising pulse sequence parameters selected from pulse sequence commands. Execution of the machine executable instructions further causes the processor receive a list of suggested pulse sequence command changes by inputting the magnetic resonance image and image metadata into an MRI artifact detection module. The MRI artifact detection module comprises at least one neural network. Each of the suggested pulse sequence command changes comprises an image improvement likelihood score. The advantages of this embodiment have been previously discussed. This embodiment could for example be used as add on software or a plugin for machine executable instructions that control a magnetic resonance imaging system.

In another embodiment, execution of the machine-executable instructions further causes the processor to receive a selection of a chosen pulse sequence command change from the list of suggested pulse sequence command changes. Execution of the machine-executable instructions further causes the processor to modify the pulse sequence commands using the chosen pulse sequence command change.

In another aspect the invention provides for a computer program product comprising machine-executable instructions for execution by a processor controlling a magnetic resonance imaging system that is configured for acquiring magnetic resonance imaging data from a subject within an imaging zone. Execution of the machine-executable instructions causes the processor to control the magnetic resonance imaging system to acquire the magnetic resonance imaging data using pulse sequence commands. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire the magnetic resonance imaging data according to a magnetic resonance imaging protocol.

Execution of the machine-executable instructions further causes the processor to reconstruct a magnetic resonance image using the magnetic resonance imaging data. Execution of the machine-executable instructions further causes the processor to receive a list of suggested pulse sequence command changes by inputting the magnetic resonance image and image metadata into a magnetic resonance imaging artifact detection module. The MRI artifact detection module comprises at least one neural network. The image metadata comprises pulse sequence parameters selected from the pulse sequence commands. Each of the suggested pulse sequence command changes comprises an image improvement likelihood score. Execution of the machine-executable instructions further causes the processor to receive a selection of a chosen pulse sequence command change from the list of suggested pulse sequence command changes. Execution of the machine-executable instructions further causes the processor to modify the pulse sequence commands using the chosen pulse sequence command change. The advantages of this embodiment have been previously discussed.

In another aspect the invention provides for a method of operating a magnetic resonance imaging system configured for acquiring magnetic resonance imaging data from a subject. The subject is within the imaging zone. The method comprises controlling the magnetic resonance imaging system to acquire the magnetic resonance imaging data using the pulse sequence commands. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire the magnetic resonance imaging data according to a magnetic resonance imaging protocol. The method further comprises reconstructing a magnetic resonance image using the magnetic resonance imaging data.

The method further comprises receiving a list of suggested pulse sequence command changes by inputting the magnetic resonance image and image metadata into an MRI artifact detection module. The MRI artifact detection module comprises at least one neural network. The image metadata comprises pulse sequence parameters selected from the pulse sequence commands. Each of the suggested pulse sequence command changes comprises an image improvement likelihood score. The method further comprises receiving a selection of a chosen pulse sequence command change from the list of suggested pulse sequence command changes. The method further comprises modifying the pulse sequence commands using the chosen pulse sequence command change. The advantages of this embodiment have been previously discussed.

It is understood that one or more of the aforementioned embodiments of the invention may be combined as long as the combined embodiments 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. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” 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.

Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A ‘computer-readable storage medium’ as used herein encompasses any tangible storage medium which may store instructions which are executable by a processor 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 processor 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 processor. 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, a 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.

A computer readable signal medium may include a propagated data signal with computer executable code embodied therein, for example, 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, electro-magnetic, 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 communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

‘Computer memory’ or ‘memory’ is an example of a computer-readable storage medium. Computer memory is any memory which is directly accessible to a processor. ‘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 ‘processor’ 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 computing device comprising “a processor” should be interpreted as possibly containing more than one processor or processing core. The processor may for instance be a multi-core processor. A processor may also refer to a collection of processors within a single computer system or distributed amongst multiple computer systems. The term computing device should also be interpreted to possibly refer to a collection or network of computing devices each comprising a processor or processors. The computer executable code may be executed by multiple processors that may be within the same computing device or which may even be distributed across multiple computing devices.

Computer executable code may comprise machine executable instructions or a program which causes a processor 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.

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 scenario, 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 the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

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 under stood 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 processor 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 processor 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 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 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 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 processor of a computer system to interact with and/or control an external computing device and/or apparatus. A hardware interface may allow a processor to send control signals or instructions to an external computing device and/or apparatus. A hardware interface may also enable a processor 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 1394 port, parallel port, IEEE 1284 port, serial port, RS-232 port, IEEE-488 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.

Magnetic Resonance Imaging (MRI) 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. A Magnetic Resonance 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, for example, be performed using a computer.

