WO2015140277A1

WO2015140277A1 - Control of magnetic resonance imaging acquisition using modeling

Info

Publication number: WO2015140277A1
Application number: PCT/EP2015/055859
Authority: WO
Inventors: Johannes Martinus Peeters; Erkki Tapani VAHALA
Original assignee: Koninklijke Philips N.V.
Priority date: 2014-03-21
Filing date: 2015-03-20
Publication date: 2015-09-24

Abstract

The invention provides for a medical instrument (100, 500) comprising a magnetic resonance imaging system (102). Execution of machine executable instructions causes a processor controlling the medical instrument to: receive (200) constraint data (142) for a set of examination parameters, execute (202) a magnetic resonance imaging system model (164) to determine a set of scan parameters (144) for pulse sequence data that satisfy the set of examination parameters, modify (204) the pulse sequence data using the scan parameters, acquire (206) the magnetic resonance data using the pulse sequence data, and reconstruct (208) a magnetic resonance image using the magnetic resonance data. The magnetic resonance imaging system model is configured to model the acquisition of magnetic resonance data from the subject. The pulse sequence data specifies a magnetic resonance imaging protocol for controlling the magnetic resonance imaging system to acquire the magnetic resonance data.

Description

Control of magnetic resonance imaging acquisition using modeling

TECHNICAL FIELD

The invention relates to magnetic resonance imaging systems, in particular to the adjustment of scan parameters for controlling the acquisition or generation of magnetic resonance data.

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 patient. This large static magnetic field is referred to as the BO field.

During an MRI scan, Radio Frequency (RF) pulses generated by a transmitter coil cause perturbations to the local magnetic field, and RF signals emitted by the nuclear spins are detected by a receiver coil. These RF signals are used to construct the MRI images. These coils can also be referred to as antennas. Further, the transmitter and receiver coils can also be integrated into a single transceiver coil that performs both functions. It is understood that the use of the term transceiver coil also refers to systems where separate transmitter and receiver coils are used. The transmitted RF field is referred to as the Bl field.

United States patent US 7,570,051 B2 disclsoes the system for the contrast optimization of MRT images. A storage unit with predetermined exerimental values for values of parameters of equations for the combination of a number of various MRT images are stored with regard to a number of selectable anatomical areas of an examination subject.

United States patent US 6,584,216 Bl discloses a method for standardizing the image intensity scale for magnetic resonance imaging. The paper 'Simulation procedure to determine nuclear mangnetic resoance imageing pulse sequence paramters for optimal tissue contrast' by C.N. de Graaf and Ch.J.G. Bakker in J.Nucl.Med. 27(1986)281-286 concerns a simulation to compute new image contrast from other measured contrast.

SUMMARY OF THE INVENTION

The invention provides for a medical instrument, a computer program product, and a method in the independent claims. Embodiments are given in the dependent claims. 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, wireline, 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 will be 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 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, gear sticks, steering wheel, pedals, wired glove, dance pad, 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, Bistable 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 (MR) data is defined herein as being the recorded measurements of radio frequency signals emitted by atomic spins by 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 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.

In one aspect the invention provides for a medical instrument comprising a magnetic resonance imaging system for acquiring magnetic resonance data from a subject within an imaging zone. The medical instrument further comprises a memory for storing machine-executable instructions and pulse sequence data. Pulse sequence data as used herein encompasses data or information which may be transformed or used directly to control a magnetic resonance imaging system to acquire the magnetic resonance data. In other words the pulse sequence data may either be commands or data which may be transformed into commands to control the operation and function of the magnetic resonance imaging system to acquire magnetic resonance data. The pulse sequence data specifies a magnetic resonance imaging protocol for controlling the magnetic resonance imaging system to acquire the magnetic resonance data. That is, the pulse sequence data are data or information to control the MR data acquisition (e.g. such as the actual gradient waveforms and RF pulses). The pulse sequence data are determined, parametrised by so-called scan parameters which define the technical settings of the operation of the magnetic resonance imaging system; examples are the gradient strength and slew rate and the RF pulse flip angle. The medical instrument further comprises at least one processor for controlling the medical instrument. Execution of the instructions causes the processor to receive constraint data for a set of examination parameters. These examination parameters specify at a higher level the ways to operate the magnetic resonance imaging system. The examination parameters include e.g. imaging parameters on the basis of which the pulse sequence data can be modified. Examples of the examination parameters are: signal-to-noise ratio, contrast type, such as BOLD, Tl or T2, image volume or field-of-view, image resolution, maximum acquisition time, image filtering aspects, SAR, the target organ or clinical application. Further, the intended reading physical may be an examination parameter to account for personal preferences of the reading physician. The pulse sequence data are e.g. modified to satisfy constraint data. Examples of these constraint data are desired image contrast.

According to the invention, the user may impose constrains, represented by the constraint data upon he examination parameters so as to set the magnetic resonance imaging system to produce images that are within the constraints. A model based on the Bloch equations is employed to determine by simulation the scan parameters for the pulse sequence data for the constraint data. Using eh determined scan parameters the pulse sequence data are specified or modified which are then employed to operate the magnetic resonance imaging system within the imposed constraint, e.g. a desired image contrast type. The invention enables to specify the contrast data, instead of the scan parameters. This requires less technical skills of the user, who may better concentrate on the clinical aspects of the examination. Moreover, as the scan parameters are obtained by the model simulation, so that the magnetic resonance image system needs to operate only once to obtain the image data satisfying the constraint. Also the image reconstruction may be included in the model simulation. It may be practical to employ a preliminary image, such as a low-resolution survey image to account for size, shape and position in the magnetic resonance image system's examination zone. Such a preliminary image is often available for a variety of reasons, e.g. for fine tuning the RF transceivers.

Execution of the instructions further cause the processor to execute a magnetic resonance imaging system model to determine a set of scans parameters for the pulse sequence data that satisfy the set of examination parameters. The magnetic resonance imaging system model is configured to model the acquisition of magnetic resonance data from the subject. The scan parameters comprise adjustments of the pulse sequence data.

The magnetic resonance imaging system model may for instance be a model that solves the so called Bloch equations to simulate the acquisition of magnetic resonance data. The solution of the Bloch equations is described in the paper Balac and Chupin, "Fast approximate solution of Bloch equation for simulation of RF artifacts in Magnetic Resonance Imaging", Mathematical and Computer Modelling 48 (2008), pp. 1901-1913,

doi: 10.1016/j.mcm.2007.05.021. The solution of the Block equations to simulate a magnetic resonance imaging system is also described in H. Benoit-Cattin, and G. Collewet,

"Numerical implementation of the Bloch equation to simulate magnetization dynamics and imaging", International CONFERENCE ISMRM'06, Seattle, USA, May, 2006.

Execution of the instructions further causes the processor to modify the pulse sequence data using the scan parameters. Execution of the instructions further causes the processor to acquire the magnetic resonance data using the modified pulse sequence data. That is to say the pulse sequence data is modified using the scan parameters and then the pulse sequence data is used to acquire the magnetic resonance data. Execution of the instructions further causes the processor to reconstruct a magnetic resonance image using the magnetic resonance data.

Embodiments may be beneficial because a set of examination parameters which may for example specify certain ways to operate the magnetic resonance imaging system or specific properties of a finished magnetic resonance image may be specified ahead of time. Instead of running the magnetic resonance imaging system and then further adjusting the scan parameters a model is used instead to predict what the measured magnetic resonance data will be like. This allows the control of the magnetic resonance imaging system by specifying the constraint data instead of specifying the scan parameters in the pulse sequence data.

In some examples the constraint data may be used to adjust the overall protocol and means for which the magnetic resonance data is acquired. A protocol as used herein encompasses a combination of scans or operations by a magnetic resonance imaging system to fulfill or achieve a clinical objective or clinical task which may be specified by constraints or instructions. The term protocol may also be referred to as an exam card.

In another embodiment the examination parameters comprise image parameters. An image parameter as used herein is a property or measure of an image such as the magnetic resonance image. The magnetic resonance imaging system model is configured to generate simulated magnetic resonance imaging data for the subject. Execution of the instructions further cause the processor to calculate a set of simulated image parameters from the simulated magnetic resonance data. Execution of the instructions further cause the processor to iteratively determine the scan parameters by repeatedly applying the magnetic resonance imaging system model and repeatedly calculating the set of simulated image parameters. This embodiment may be beneficial because the pulse sequence data can be modified in a way such that the constraint data is satisfied. For example if the particular contrast was desired in the magnetic resonance imaging it is possible that someone would have to run the magnetic resonance imaging system over and over again repeatedly to adjust the pulse sequence data such that the desired contrast is obtained. By simulating this the proper contrast can be obtained with running the magnetic resonance imaging system only once. This may result in a large saving of resources such as electricity and also valuable time in running the magnetic resonance imaging system. This may allow a larger number of patients to be used in the magnetic resonance imaging system in a given period such as a day.

In various examples the magnetic resonance imaging system model could be executed many times or the results could be pre-calculated. That is to say the execution of the magnetic resonance imaging system could be performed even before the subject is put into a magnetic resonance imaging system. The various examination parameters can be measured from the simulated magnetic resonance data and by running the system multiple times standard multi-dimensional search algorithms may be used.

In another embodiment the magnetic resonance imaging system is further configured to model the reconstruction of the simulated magnetic resonance imaging data into a simulated magnetic resonance image. In other words the magnetic resonance imaging system model is configured for doing post-processing of the simulated magnetic resonance image. The magnetic resonance imaging system model is further configured for determining a set of image reconstruction parameters during the repeated applying of the magnetic resonance imaging system model that satisfy the set of image parameters using the simulated magnetic resonance image. The simulated magnetic resonance image may be calculated from the simulated magnetic resonance data. Execution of the instructions further cause the processor to reconstruct the magnetic resonance image using the magnetic resonance image data according to the image reconstruction parameters. In this embodiment, post-processing parameters such as a filter choice or other imaging processing algorithms can be varied also. This may be beneficial because a desired appearance of a magnetic resonance image may be due not just to the way in which the magnetic resonance data is acquired but also how the image is processed. In this example both the optimization of the acquisition parameters used in the pulse sequence and also the post-processing settings or techniques used in the image are used together and they are optimized together.

The imaging processing parameters which may be used for post-processing the magnetic resonance image could include the reconstruction interpolation or zero-padding, the coil combination used for acquisition, the uniformity corrections, the coil profile, noise correction, anti-ringing processing, water and fat processing techniques, default windowing during the acquisition of data, and the coil combination. In some examples the various imaging techniques may also be varied such as using various parallel imaging or even compressed sensing may in some cases be considered an examination parameter.

In another example execution of the instructions further cause the processor to acquire preliminary magnetic resonance imaging data. In more detail, the magnetic resonance imaging system model is configured for constructing a subject volume using the preliminary magnetic resonance image, wherein the magnetic resonance imaging system model is configured for generating the simulated magnetic resonance imaging data for the subject volume. Execution of the instructions further causes the processor to reconstruct a preliminary magnetic resonance image. The magnetic resonance imaging system is operable for modeling the subject using the preliminary magnetic resonance image. Execution of the instructions further causes the processor to execute the magnetic resonance imaging system model using the preliminary magnetic resonance image. The preliminary magnetic resonance data could for example be such information as a scout scan or during a SENSE calibration. When subjects are placed into a magnetic resonance imaging system they may have difference sizes and different locations. Using a preliminary magnetic resonance data it can be estimated where the location of the subject is and even the size of the subject such that the model more accurately models the actual subject to be imaged. This may result in the magnetic resonance imaging system model to be more accurate.

In another embodiment the magnetic resonance imaging system model is configured for constructing a subject volume using the preliminary magnetic resonance image. The magnetic resonance imaging system model is configured for generating the simulated magnetic resonance imaging data for or from the subject volume. In this embodiment the preliminary magnetic resonance data is used to make an estimate of the volume or location of the subject and then this is used directly in the magnetic resonance imaging system model. This may result in improved accuracy of the magnetic resonance imaging system model.

In another embodiment the magnetic resonance imaging system model comprises pre-calculated scan parameters classified according to a number of subject volumes. The number of subject volumes may for instance be a list or a group of subject volumes for which the magnetic resonance imaging system model has been used to pre- calculate the scan parameters. The magnetic resonance imaging system model is operable for selecting one of the number of subject volumes using the preliminary magnetic resonance imaging data. The preliminary magnetic resonance imaging data can be used to estimate the size and/or location of the subject. This information may then be used to select the pre- calculated scan parameters which best match the measurement of the subject. The pre- calculated results can also be grouped for ranges of desired constraints. This system may match the shape and then try to find pre-calculated constraints that approximate what is specified in the constraint data.

In another embodiment the at least one processor comprises at least one control processor and at least one modeling processor. The medical instrument further comprises a server. The medical instrument further comprises a controller. The server comprises the at least one modeling processor and the controller comprises the at least one control processor. The medical instrument further comprises a network connection for connecting the at least one control processor with the at least one modeling processor. The at least one modeling processor is configured for executing the magnetic resonance imaging system model. In this embodiment the control of the magnetic resonance imaging system is separate from the modeling of the magnetic resonance data. This may be beneficial because external computing sources may be used to efficiently execute the magnetic resonance imaging system model. It may also enable multiple magnetic resonance imaging systems to use a single modeling engine. In some examples there may be a display for displaying a user interface. Constraints may be displayed as a ranking or a slider for individual values. A ranking may also be given for a particular constraint to specify how important it is to satisfy that particular constraint.

In another embodiment the set of examination parameters comprise a signal- to-noise ratio. In another embodiment the set of examination parameters comprise a contrast constraint such as diffusion, profusion, BOLD, Tl, T2, and etc.

In another embodiment the set of examination parameters comprise an image volume constraint. This may also be abstracted from or for smart scans such as the type of scan such as a prostate scan or a lymph node scan.

In another embodiment the set of examination parameters comprise a resolution constraint or how many pixels or voxels there are for a particular distance or volume.

In another embodiment the examination parameters comprise a maximum image acquisition time. This may for example be how long the scan time in the magnetic resonance imaging system is limited to.

In another embodiment the set of examination parameters comprise a minimum image acquisition time.

In another embodiment the set of examination parameters further comprise any number of image parameters. Examples of image parameters may include, but at not limited to: a noise ratio in the image, a maximum contrast, a contrast ratio of the image, an average brightness of the image.

In another embodiment the set of examination parameters comprises a maximum allowed specific absorption ratio or SAR.

In another embodiment the set of examination parameters comprise parameters that are shared by a set of images for a higher level specification of an exam such as an exam card optimization. An ExamCard as used herein encompasses a set of instructions to be performed on a subject to acquire magnetic resonance data.

In another embodiment the set of examination parameters comprise constraints for a set of imaging protocols. This for instance may be for a whole class of protocols or methods of acquiring magnetic resonance data.

In another embodiment the set of examination parameters further comprise specification of a target organ.

In another embodiment the set of examination parameters comprise a specification of a target application.

In another embodiment the set of examination parameters comprise a total image time.

In another embodiment the set of examination parameters comprise a specification of a reading physician. This may for instance identify a particular physician as a physician may have certain preferences as to how the magnetic resonance data is acquired or how it is processed.

In another embodiment the set of examination parameters further comprise image filtering preferences.

In another embodiment the set of examination parameters further comprise contrast preferences.

In some examples the set of scan parameters may include pulse sequence programming parameters supported by a particular instrument which may be the temporal gradient and transmit RF pulse train data points, receiver coil sampling windows and settings. This may also include physiology signal conditioning settings and trigger points. This may also include the loop structure of the sequences such as how often particular loops of a pulse sequence are repeated, and abstractions thereof. This may also include such data as is condensed to more conventional parameters such as TR or the repetition time, TE the time to echo, the flip angle, and the FOV or field of view, acquisition and reconstruction resolution parameters that can be decomposed into said low-level pulse sequence programming parameters.

In another embodiment the memory further comprises a constraint library. Execution of the instructions further comprises receiving a constraint selection. The constraint selection indicates the constraint data. The constraint library may for instance comprise a set of constraint data one of which the constraint data is a member of.

In another embodiment execution of the instructions further cause the processor to display a selector on a user interface. The selector is configured for selecting the constraint selection. Know there can be a separate constraint for each radiologist or clinic or user of the magnetic resonance imaging system. There may also be a selection of a magnetic resonance imaging protocol from the set of protocols so that for each protocol there is a separate constraint data that can be recalled.

In some examples there may be a step of selection a magnetic resonance protocol first and then selecting from a set of available constraints. There may be a user interface. The user interface may be configured such that it can receive a selection of a magnetic resonance imaging protocol on a graphical user interface. The user interface may also be configured to receive the set of constraints on the graphical user interface. This may provide for a simplified user interface which makes the magnetic resonance imaging system more easy to operate and requires less cognitive burden. Instead of having to specify the various scan parameters of the pulse sequence a set of desired results such as the image contrast can be specified instead. This makes the magnetic resonance imaging system easier to operate and requires less training.

In some examples the medical instrument may be operable without a specialized user interface as described in the previous paragraph. There may be an optional graphical user interface used for the constraint training phase and/or for inputting data for specifying the operation of the system. For instance data or information such as the target organ, i.e., prostate, type of diffusion imaging needed or other information may be input. Data may for instance be optionally fed to the system using the Digital Imaging and

Communications in Medicine (DICOM) protocol or similar networked information-system, where the referring physician has provided the necessary information for fully automated scanning instead of using a graphical user interface.

In a simplified version the user or operator of the magnetic resonance imaging system may just enter the constraints manually onto a simplified user interface.

In another embodiment the memory further comprises an image database. The image database comprises multiple data records. Each data record comprises a trial image and pre-determined constraint data. Execution of the instructions further causes the processor to determine a preferred image from the image database by repeatedly receiving two or more of the multiple data records. Execution of the instructions further cause the processor to repeatedly display each trial image from the multiple data records simultaneously on a display.

Execution of the instructions further causes the processor to receive a selection of a selected image from the displayed trial images. Execution of the instructions further causes the processor to add the pre-determined constraint data for the preferred image to the set of constraint data. Execution of the instructions further cause the processor to use a decision tree algorithm or an iterative algorithm to determine the preferred image during the repeated selection of the selected image. In this example a subject is guided to select a preferred image which is in a database. The pre-determined constraint data associated with each of the trial images is constraint data which would lead to acquisition of the trial image in the magnetic resonance imaging system. By selecting the preferred image the operator of the system is selecting constraint data to be used later to acquire magnetic resonance data.

Instead of selecting or setting various scan parameters the subject selects a preferred format or appearance of an image and this is used to determine constraint data which is later used for the acquisition of further magnetic resonance data. In another embodiment the instructions may implement a "learn-by-doing" mode, where the viewer of the images has an interaction tool to mark those patient images he likes/dislikes (thumb-up/down), which can be relayed back to the constraints engine to bias subsequent optimizations of the same type? This could also be performed implicitly - images annotated and exported to a Picture Archiving and Communication System (PACS) can be considered to have received a virtual 'thumbs up' or implicit approval.

In another embodiment the memory further comprises an image database. The image database comprises multiple data records. Each data record comprises a trial image and a pre-determined constraint data. Execution of the instructions further cause the processor to display an image selector user interface with one or more slider user interface objects. Execution of the instructions further causes the processor to display preferred image selector on the image selector user interface. Execution of the instructions further cause the processor to determine a preferred image from the image database by repeatedly receiving a trial set of constraints from the one or more slider user interface objects, selecting a selected database record from the multiple data records by matching the trial set of constraints to the pre-determined constraint data, displaying the trial image from the selected database record, and adding the pre-determined constraint data for the selected database record to the set of constraint data if the trial image is selected as a preferred image using the preferred image selector.

In another embodiment execution of the instructions further cause the processor to repeatedly retrieve pre-determined constraint data from the image database. Execution of the instructions further cause the processor to execute the magnetic resonance imaging system model to determine temporary scan parameters for the pulse sequence data that satisfy the pre-determined constraint data. The magnetic resonance imaging system model is configured to model the acquisition of magnetic resonance data from the test object. The temporary set of scan parameters comprises adjustments of the pulse sequence data. Execution of the instructions further cause the processor to repeatedly modify the pulse sequence data using the temporary scan parameters. Execution of the instructions further causes the processor to repeatedly acquire temporary magnetic resonance data using the pulse sequence. Execution of the instructions further causes the processor to repeatedly reconstruct the trial image using the temporary magnetic resonance data.

Execution of the instructions further cause the processor to repeatedly store the trial image with the pre-determined constraint data in the image database. In this example the trial image and the pre-determined constraint data are acquired by making measurements on a test object which is placed into the magnetic resonance imaging system. This may be beneficial because it can be assured that particular constraints will result in particular image properties for that particular magnetic resonance imaging system. This also makes the determination of the constraint data to be an empirical process.

In another aspect the invention provides for a computer program product comprising machine-executable instructions for execution by a processor controlling the medical instrument. The medical instrument comprises a magnetic resonance imaging system for acquiring magnetic resonance data from a subject within and imaging zone. The medical instrument further comprises a memory for pulse sequence data. The pulse sequence data specifies a magnetic resonance imaging protocol for controlling the magnetic resonance imaging system to acquire the magnetic resonance data. Execution of the instructions causes the processor to receive constraint data for a set of examination parameters.

Execution of the instructions further causes the processor to execute a magnetic resonance imaging system model to determine a set of scan parameters for the pulse sequence data that satisfy the set of examination parameters. The magnetic resonance imaging system model is configured to model the acquisition of magnetic resonance data from the subject. The scan parameters comprise adjustments of the pulse sequence data. Execution of the instructions further causes the processor to modify the pulse sequence data using the scan parameters. Execution of the instructions further causes the processor to acquire the magnetic resonance data using the pulse sequence data. Execution of the machine-executable instructions further causes the processor to reconstruct a magnetic resonance image using the magnetic resonance data.

In another example the invention provides for a method of operating a medical instrument. The medical instrument comprises a magnetic resonance imaging system for acquiring magnetic resonance data from a subject within an imaging zone. The medical instrument further comprises a memory for storing pulse sequence data. The pulse sequence specifies a magnetic resonance imaging protocol for controlling the magnetic resonance imaging system to acquire the magnetic resonance data. The method comprises the step of receiving constraint data for a set of examination parameters.

The method further comprises the step of executing a magnetic resonance imaging system model to determine a set of scan parameters for the pulse sequence data that satisfy the set of examination parameters. The magnetic resonance imaging system model is configured to model the acquisition of magnetic resonance data from the subject. The scan parameters comprise adjustments of the pulse sequence data. The method further comprises the step of modifying the pulse sequence data using the scan parameters. The method further comprises the step of acquiring the magnetic resonance data using the scan parameters. The method further comprises the step of reconstructing the magnetic resonance image using the magnetic resonance data.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following preferred embodiments of the invention will be described, by way of example only, and with reference to the drawings in which:

Fig. 1 illustrates an example of a medical instrument,

Fig. 2 shows a flow chart which illustrates a method of operating the medical instrument of Fig. 1,

Fig. 3 illustrates an example of a graphical user interface,

Fig. 4 illustrates a further example of a graphical user interface, Fig. 5 illustrates a further example of a medical instrument,

Fig. 6 shows a block diagram,

Fig. 7 shows a block diagram which illustrates a method,

Fig. 8 illustrates the functioning of a medical instrument,

Fig. 9 shows an example of how data can be formatted such as in a Google

Protobuf 900 class hierarchy,

Fig. 10 shows an example of how some scan parameters 1000 that have been received from the server may be formatted using the Protobuf data format,

Fig. 11 shows two magnetic resonance images, and

Fig. 12 shows two further magnetic resonance images. DETAILED DESCRIPTION OF THE EMBODIMENTS

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.

Fig. 1 illustrates an example of a medical instrument 100. The medical instrument 100 comprises magnetic resonance imaging system 102 with a magnet 104. The magnet 104 is a superconducting cylindrical type magnet 104 with a bore 106 through it. The use of different types of magnets is also possible for instance 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 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 106 of the cylindrical magnet 104 there is an imaging zone 108 where the magnetic field is strong and uniform enough to perform magnetic resonance imaging.

Within the bore 106 of the magnet there is also a set of magnetic field gradient coils 110 which is used for acquisition of magnetic resonance data to spatially encode magnetic spins within the imaging zone 108 of the magnet 104. The magnetic field gradient coils 110 connected to a magnetic field gradient coil power supply 112. The magnetic field gradient coils 110 are intended to be representative. Typically magnetic field gradient coils 110 contain three separate sets of coils for spatially 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 110 is controlled as a function of time and may be ramped or pulsed.

Adjacent to the imaging zone 108 is a radio-frequency coil 114 for manipulating the orientations of magnetic spins within the imaging zone 108 and for receiving radio transmissions from spins also within the imaging zone 108. 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 114 is connected to a radio frequency transceiver 116. The radio-frequency coil 114 and radio frequency transceiver 116 may be replaced by separate transmit and receive coils and a separate transmitter and receiver. It is understood that the radio -frequency coil 114 and the radio frequency transceiver 116 are representative. The radio -frequency coil 114 is intended to also represent a dedicated transmit antenna and a dedicated receive antenna. Likewise the transceiver 116 may also represent a separate transmitter and receivers. The radio -frequency coil 114 may also have multiple receive/transmit elements and the radio frequency transceiver 116 may have multiple receive/transmit channels.

The magnetic field gradient coil power supply 112 and the transceiver 116 are connected to a hardware interface 128 of computer system 126. The computer system 126 further comprises a processor 130. The processor 130 is connected to the hardware interface 128, a user interface 132, computer storage 134, and computer memory 136. The computer storage 134 is shown as containing pulse sequence data 140. The pulse sequence data 140 enables the processor 130 to acquire magnetic resonance data. The computer storage 134 is further shown as containing constraint data 142 that has either been received from a program, a network, or has been entered into the user interface 132. The computer storage 134 is shown as further containing scan parameters 144 that have been calculated by a magnetic resonance imaging system model. The computer storage 134 is further shown as containing magnetic resonance data 146. The magnetic resonance data 146 has been acquired using the pulse sequence data 140 to control the magnetic resonance imaging system 102. The computer storage 134 is further shown as containing a magnetic resonance image 148 that has been reconstructed from the magnetic resonance data 146. The computer storage 134 is shown as optionally containing a constraint library 150. The constraint library 150 contains a set of constraints that can be selected. For example the constraint data 142 may have been received by selecting one of the set of constraints from the constraint library 150. The computer storage 134 is further shown as containing an optional image database 152. The image database 152 may for example contain a database of different images and constraint data that is associated with each image. For example the image can be selected and then the appropriate constraint data may be retrieved or associated with the image.

The computer memory 136 is shown as containing a control module 160. The control module 160 contains computer-executable code which enables the processor 130 to control the operation and function of the medical instrument 100 and the magnetic resonance imaging system 102. For instance the control module 160 may execute a method of acquiring magnetic resonance data. The computer memory 136 is further shown as containing image reconstruction module 162. The image reconstruction module 162 contains computer- executable code which enables the processor to reconstruct the magnetic resonance image 148 using the magnetic resonance data 146. The computer memory 136 is also shown as further containing a magnetic resonance imaging system module 164 that was used to calculate the scan parameters 144 using the constraint data 142.

Fig. 2 shows a flowchart which illustrates an example of a method of operating the medical instrument 100 of Fig. 1. First in step 200 constraint data 142 for a set of examination parameters is received. Next in step 202 the magnetic resonance imaging system model 164 is executed to determine the set of scan parameters 144 using the constraint data 142. The magnetic resonance imaging system model 164 is configured to model the acquisition of magnetic resonance data 146 from the subject 118. The scan parameters 144 comprise adjustments of the pulse sequence data 140.

Next in step 204 the pulse sequence data 140 is modified using the scan parameters 144. Next in step 206 the magnetic resonance data 146 is acquired using the pulse sequence data 140. The pulse sequence data 140 has been modified using the scan parameters 144. Finally in step 208 the magnetic resonance image 148 is reconstructed from the magnetic resonance data 146.

Fig. 3 shows an example of a user interface 300. The user interface 300 may be a graphical user interface. The user interface 300 may optionally contain a selector 302 for selecting a magnetic resonance protocol. Once the magnetic resonance protocol 302 is selected it may display a number of constraints which may be selected using the constraint selector 304. For instance the constraint selector can show a number of icons or a drop-down menu which would display available constraints. The protocol selector 302 may also display various types of controls for selecting the particular magnetic resonance protocol. Not shown in this Fig. but other things may be used to order or select the constraints 304 too. For instance there may be a selector which indicates a particular attending physician or a particular clinic to pre-select or group the constraints 304 which are possible to select.

Fig. 4 shows a further example of a user interface 400. The user interface 400 may be a graphical user interface that is used to select a new constraint to add to the constraint library 150. For instance there may be a trial image 402 which is displayed on the user interface 400. There may be a number of selectors 404, 406, 408 which are used to select different examination parameters. As the various values of the constraints are varied using the selectors 404, 406, 408 the processor retrieves a different trial image 402 that best matches those constraints from the image database 152. When a preferred image is found using the user interface 400 the user can click button 410 to add the constraints that match with the trial image 402 to the constraint library 150.

Fig. 5 shows a further example of a medical instrument 500. The medical instrument 500 is similar to that shown in Fig. 1, however the function of the computer 126 has also been expanded to move some of the functions onto an external server 126'. The computer or controller 126 has a network interface 500 as does the server 126'. The network connectors 500 connect to a network 502 which enables the controller or computer 126 and the server 126' to exchange data. The server 126' has components which are equivalent to the computer 126. Components of the server 126' are labeled with the same numbers as the computer 126, however the numbers have a prime after them. The computer memory 136' is shown as containing the magnetic resonance imaging system model 164 and also the image reconstruction module 162. The computer memory 136' may also further contain an image processing module which is not shown.

The computer storage 134' is shown as containing the constraint data 142 which has been received via the network 502. Using the constraint data 142 the model 164 is run repeatedly to search for scan parameters which satisfy the constraints 142. The computer storage 134' can be shown as containing several instances of simulated magnetic resonance data 504, 504' that has been run during multiple iterations. The computer storage 134' is further shown as containing simulated magnetic resonance images 506, 506' that have been reconstructed using the simulated magnetic resonance data 504, 504'. These for instance may be useful for calculating simulated image parameters 508 or image reconstruction parameters 510 both of which are shown as being stored in the computer storage 134'. The computer storage 134' is shown as optionally containing preliminary magnetic resonance image 512 that was acquired using the magnetic resonance imaging system 102.

For example the preliminary magnetic resonance image 512 may be a scout scan or SENSE coil calibration. The computer storage 134' is further shown as optionally containing a subject volume 514 that was calculated using the preliminary magnetic resonance image 512. This for instance may be used as an input to the model 164 to make it more accurate. The computer storage 134' is also shown as containing a set of pre-calculated scan parameters 516 that may be recalled by the model 164 or as an alternative to the model 164. For instance the pre-calculated scan parameters 516 could be selected by matching a closest calculated value to the determined subject volume 514.

MR imaging protocols may be calculated as a part of the user interaction with the MRI scanner GUI. User adjusts parameters on the provided prototype protocols in order to fine-tune the protocols to match the imaging needs and morphology of the patient.

Software algorithms running on the GUI console validate the new parameter values against hardware performance and patient safety (specific absorption rate SAR, gradient induced currents dB/dt), while trying to optimize the protocol with given, often conflicting, constraints (shortest execution, maximal signal-to-noise etc).

The current designs suffer from ever-increasing algorithmic complexity and non-intuitive optimization dead-ends, where the automaton is no longer able to find a solution to the parameter space and user needs to solve resulting parameter conflicts manually. The latter requires great skill and in-depth knowledge of the MRI physics and the underlying software implementation. Examples may provide a method where the validation and optimization is decoupled from the problem setting, that is, from the description of the protocol prototype and modification of its parameters. This may be beneficial as it allows execution of the optimization on the background, greater flexibility in selection of optimization algorithms, and provision of input material to centralized machine- learning algorithms for further protocol optimizations and MRI installation site monitoring.

In one example, the protocol prototype description is also decoupled from the parameter modifications, which allows two different types of users to operate on protocol design: protocol designers can describe the protocol structure with high level programming languages, set the optimization criteria or choices for the optimization criteria, and publish the editable protocol parameters. Protocol operators can use the produced prototypes and fine-tune them by editing the optimization choices and published parameters. This is beneficial as user interfaces can be tailored for different skill-sets of the users.

In one example, the protocol operator is replaced by an knowledge-atlas automaton, which automates protocol slice positioning parameter settings (current state of art: Philips Smart exams) and uses prescribed patient data from physician to automatically select the needed protocol prototypes for the MRI examination (e.g., target region of interest, needed contrasts and main optimization criteria as input data; mapped to available protocol prototypes). This may be beneficial as the operator no longer needs to interact with the system under normal operating conditions.

In one example, the validation and optimization calculations are performed remotely via networking to another server, server farm, or cloud-based solution. This may be beneficial as the computation resources can be centralized at the hospital/enterprise/global level. The operator machine can also be turned into a thin client with very modest hardware requirements. This has also the added benefit of allowing data-mining on the resulting calculation requests, such as usage statistics.

In one example, the resulting scan measurement data from the usage of the realized protocol is characterized, e.g., with signal-to-noise, contrast-to-noise, or other usage information (e.g., DICOM exported/discarded), and the characteristics transmitted to the remote calculation resources for machine learning for atlas-based optimization algorithms.

In one example, the physics models for the optimization algorithms can be configured using the calibration data of the MRI scanner and combined with initial MR data acquisitions from the patient. This is beneficial for the centralized computation units that are able to use complex physical models in optimization algorithms, which are then tailored for the current patient.

Fig. 6 shows how the imaging protocol preparation for scanning may be divided into three layers. The first layer is the protocol type description 600. The second layer is the protocol instant description 602, the third layer is the protocol realization 604. Protocol prototype description is done with a protocol description language, which is a domain specific language tailored for MRI pulse programming.

In the example of Fig. 6, the protocol prototype description 600and patient MR data is encoded into a hierarchical data model that can be efficiently transferred to remote calculation resources. This is beneficial as the amount of data from the initial scans and the data model can be reduced into machine readable intermediate form that can be transmitted with optimized bandwidth to global computation resources.

Protocol instance description 602 uses the published variation points in the protocol description to customize the protocol prototype further, if the defaults are not satisfactory for imaging purpo ses .

Protocol realization 604 uses the knowledge about the hardware capabilities, patient characteristics (such as weight, age, previously acquired MR data), and the provided protocol instance data, to perform a multi- variable optimization task, which results in success/failure and new variable settings for the protocol instance that comply with the physical realm requirements.

This may provide for the decoupling of the layers 600, 602, 604. This allows, e.g., protocol development process to skip the protocol instance description step for the validation of the protocol on a wide variety of environment settings:

Fig. 7 shows a flowchart which illustrates a method of selecting scan parameters using a magnetic resonance imaging system model. First in step 700 a prototype description modification is detected. Next in step 702 validation sets of hardware and patient parameters are generated. A number of variation points may be set into the prototype instance so that the model can run for various values and determine which values to search for the end parameters. In 704 a description algorithm is run against a set of the variation points. The data is then transformed into an intermediate data format data. Step 706 is performed by executing the magnetic resonance imaging system model which may also be referred to here as a realizer. Box 708 is a decision box which is to determine if more sets of data need to be run in order to converge or arrive at an acceptable set of scan parameters. If the answer is yes then the method returns back to step 704. If the answer is no then the scan parameters are displayed in step 710. The scan parameters may be received or displayed.

One example of decoupling between protocol problem setting and validation/optimization is the usage of a machine readable intermediate data format for passing the data from the higher level programming language into efficient optimization algorithms running in native binary format. One example of the hierarchical, machine readable data model, which is network-efficient, is google protobuf protocol.

One example of protocol prototype description with a higher level language is Ruby, which can be used to create a Domain Specific Language (DSL) for physicists and clinical scientists to describe protocol composition and the needed optimization criteria.

One example of a thin client implementation is an electronic kiosk, which serves as the user interface for MRI. The kiosk software has a network connection to MRI hardware. The hardware is equipped with a server with highly parallel processing units, such as Xeon Phis or GPUs, for optimizing the protocols.

One example of the complex physics mode used for centralized optimizations: acquisition of the SENSE coil channel sensitivity maps, combined with a three orthogonal 2D scout images, can be used to calculate SENSE factors and the needed coil elements for reduced SENSE artifacts.

Fig. 8 is a functional diagram which illustrates a method of operating a magnetic resonance imaging system. In step 800 there is a data input 800 using user interface which is used to receive constraints 802. These constraints are used to construct protocol prototype instance using the received constraints 802 and Ruby scripts 806 describing the physics of the protocol prototype and constraints available for for the user interface. Other scripting languages are computer programming languages such as Python or other languages may also be used. These are then sent to a computer 126 which may also be in contact with other serves 126' used for computationally intensive tasks. The tasks can use the a-priori installed set of hardware parameters 808 about the hardware capabilities and/or patient and coil data 810 which can be used for limiting the model to the physically meaningful solutions and for post-processing or for controlling the magnetic resonance imaging system.

Fig. 9 shows an example of how data can be formatted such as in a Google

Protobuf 900 class hierarchy. This is one example of how the data may be formatted to exchange between the computer 126 and the server 126' in Fig. 5. Other formatting languages such as XML may also be used for exchanging data. There may be unique identifiers, such as UUIDs ( ISO/IEC 9834-8:2005), to link to pre-stored data on servers at a computing center 904,which reduces the needed data transfer capabilities as resending of large amounts of data is not needed.

Fig. 10 shows an example of how some scan parameters 1000 that have been received from the server may be formatted using the Protobuf data format.

Representing Magnetic Resonance (MR) images is always a balance between different types of artifacts. For example, Gibbs ringing can be avoided by applying anti- ringing filters. Applying too strong filtering will result in so-called 'blurring', resolution loss. In case filtering is too weak, residual ringing is visible. A similar example holds for uniformity. With the introduction for multi-element receiver coils, the realized SNR became very spatially dependent. Applying a perfect signal uniformity correction, gives the perception of noise enhancement in areas with low coil sensitivity, also known as noise breakthrough. Without uniformity correction, the image is very non-uniform. In this respect, the total perception of the image is the balance between signal uniformity and noise uniformity. Examples are given below for spine. More examples can be given, e.g. denoising, distortion, windowing, etc. The balance between these different types of artifacts, determine the total image perception and is controlled by system settings. So, the total image perception can be optimized by applying the correct settings.

Fig. 11 shows two magnetic resonance images 1100, 1102 that have been generated from the same magnetic resonance data. Image 1100 shows a magnetic resonance image of a spine. The image in Fig. 1100 has no uniformity correction and the spine is hardly visible without windowing. The anterior noise is not visible. Image 1102 shows strong uniformity correction that has been used. The spine is nicely visible, but the anterior noise is strongly enhanced. This gives image 1102 a noisy perception.

Fig. 12 shows two magnetic resonance images 1200, 1202 that are from the same magnetic resonance data but have different image processing. Image 1200 has weak ringing filtering. Some ringing is visible, but the image appears sharp. In image 1202 strong ringing filtering has been applied to the image. There is no ringing visible, but the image appears smooth compared to the weakly filtered image.

The main issue with image perception is that it is observer dependent. Each observer has his own preferences, e.g. caused by the training he has followed and the reference he had. The image perception of users of machines from different vendors differ as the reference of the users is totally different. By applying general settings, a group of observers can be satisfied, but also a large group of observers can be dissatisfied because they had different training. A solution to this problem is to give the user complete control to all related parameters, but this leads to very complicated user interface with an abundant set of parameters. This is difficult to understand and does require a lot of training.

In an example: At the system installation, perform a calibration procedure to optimize system settings. This can be very similar to the procedure e.g. used for first TV use. A result of this training is a set of parameter that will be used for the acquisition,

reconstruction and image processing procedures for the given MR scanner. The calibration procedure can be system specific, but also observer specific. For the latter case, the image observer will need to be known before the acquisition starts. It might even be thought of anatomy or contrast specific settings.

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.

Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of 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 fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measured cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied 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.

LIST OF REFERENCE NUMERALS

100 medical instrument

102 magnetic resonance imaging system

104 magnet

106 bore of magnet

108 imaging zone

110 magnetic field gradient coils

112 magnetic field gradient coil power supply

114 radio-frequency coil

116 transceiver

118 subject

120 subject support

126 computer system

126' server

128 hardware interface

130 processor

130' processor

132 user interface

134 computer storage

136 computer memory

140 pulse sequence data

142 constraint data

144 scan parameters

146 magnetic resonance data

148 magnetic resonance image

150 constraint library

152 image database

160 control module

162 image reconstruction module

164 magnetic resonance imaging system model

200 receive constraint data for a set of examination parameters

202 execute a magnetic resonance imaging system model to determine a set of scan parameters for the pulse sequence data that satisfy the set of examination parameters 204 modify the pulse sequence data using the scan parameters

206 acquire the magnetic resonance data using the pulse sequence data

208 reconstruct a magnetic resonance image using the magnetic resonance data

300 user interface

302 magnetic resonance protocol selector

304 constraint selector

400 user interface

402 trial image

404 selector

406 selector

408 selector

410 button

500 network connector

502 network

504 simulated magnetic resonance data

504' simulated magnetic resonance data

506 simulated magnetic resonance image

506' simulated magnetic resonance image

508 simulated image parameters

510 image reconstruction parameters

512 preliminary magnetic resonance image

514 subject volume

516 pre-calculated scan parameters

600 protocol prototype description

602 protocol instance description

604 protocol realization

800 data input

802 receive constraints

804 construct protocol prototype instance

806 ruby scripts

808 hardware parameters

810 control commands

900 Google Protobuf class hierarchy

902 link to pre-stored data at servers 904 computing center server with database

1000 scan parameters

1100 magnetic resonance image

1102 magnetic resonance image

1200 magnetic resonance image

1202 magnetic resonance image

Claims

CLAIMS:

1. A medical instrument (100, 500) comprising:

a magnetic resonance imaging system (102) for acquiring magnetic resonance data (146) from a subject (118) within an imaging zone (108);

a memory (136, 136') for storing machine executable instructions (160, 162, 164) and pulse sequence data (140), wherein the pulse sequence data specifies a magnetic resonance imaging protocol for controlling the magnetic resonance imaging system to acquire the magnetic resonance data;

at least one processor (130, 130') for controlling the medical instrument, wherein execution of the instructions causes the at least one processor to:

receive (200) constraint data (142) for a set of examination

parameters;

execute (202) a magnetic resonance imaging system model (164) to determine a set of scan parameters (144) for the pulse sequence data that satisfy the set of examination parameters, wherein the magnetic resonance imaging system model is

configured to model the acquisition of magnetic resonance data from the subject, wherein the scan parameters comprise adjustments of the pulse sequence data;

modify (204) the pulse sequence data using the scan parameters;

acquire (206) the magnetic resonance data using the pulse sequence data; and

reconstruct (208) a magnetic resonance image using the magnetic resonance data.

2. The medical instrument of claim 1, wherein the examination parameters comprise image parameters, wherein the magnetic resonance imaging system model is configured to generate simulated magnetic resonance imaging data (504, 504') for the subject, wherein execution of the instructions causes the at least one processor to calculate a set of simulated image parameters from the simulated magnetic resonance imaging data, wherein execution of the instructions further cause the at least one processor to iteratively determine the scan parameters by repeatedly applying the magnetic resonance imaging system model and repeatedly calculating the set of simulated image parameters.

3. The medical instrument of claim 2, wherein the magnetic resonance imaging system model is further configured to model the reconstruction of the simulated magnetic resonance imaging data into a simulated magnetic resonance image (506, 506'), wherein the magnetic resonance imaging system model is further configured for determining a set of image reconstruction parameters (510) during the repeated applying of the magnetic resonance imaging system model that satisfy the set of image parameters using the simulated magnetic resonance image, and wherein execution of the instructions further causes the at least one processor to reconstruct the magnetic resonance image using the magnetic resonance data according to the image reconstruction parameters.

4. The medical instrument of claim 1, 2, or 3, wherein execution of the instructions further causes the at least one processor to acquire preliminary magnetic resonance imaging data, wherein execution of the instructions further causes the at least one processor to reconstruct a preliminary magnetic resonance image (512), wherein the magnetic resonance imaging system model is operable for modeling the subject using the preliminary magnetic resonance image, and wherein execution of the instructions further causes the at least one processor to execute the magnetic resonance imaging system model using the preliminary magnetic resonance image.

5. The medical instrument of claim 4, wherein imaging protocol preparation for scanning is divided into three layers, the first layer including protocol type description , The second layer including protocol instant description and the third layer includes protocol realization 604.

6. The medical instrument of claim 4, wherein the magnetic resonance imaging system model comprises pre-calculated scan parameters (516) classified according to a number of subject volumes, wherein the magnetic resonance imaging system model is operable for selecting one of the number of subject volumes using the preliminary magnetic resonance imaging data.

7. The medical instrument of any one of the preceding claims, wherein the at least one processor comprises at least one control processor (130) and at least one modeling processor (130'), wherein the medical instrument further comprises a server (126'), wherein the medical instrument further comprises a controller (126), wherein the server comprises the at least one modeling processor, wherein the controller comprises the at least one control processor, wherein the medical instrument further comprises a network connection (502) for connecting the at least one control processor with the at least one modeling processor, wherein the at least one modeling processor is configured for executing the magnetic resonance imaging system model.

8. The medical instrument of any one of the preceding claims, wherein the set of examination parameters comprises any one of the following: a signal to noise ratio, a contrast constraint , an image volume constraint, a resolution constraint, a maximum image acquisition time, a minimum image acquisition time, a contrast to noise ratio, maximum contrast, contrast ratio, average brightness, a maximum allowed specific absorption ratio, parameters that are shared by a set of images for a higher level ExamCard optimization, constrains for a set of imaging protocols, specification of a target organ, specification of a target applicatoin, total imaging time, specifcation of a reading physician, image filtering preferences, contrast preferences, and combinations thereof.

9. The medical instrument of any one of the preceding claims, wherein the memory further comprises a constraint library (150), wherein execution of the instructions further comprises receiving a constraint selection, wherein the constraint selection indicates the constraint data.

10. The medical instrument of claim 9, wherein execution of the instructions further causes the at least one processor to display a selector on a user interface (304), wherein the selector is configured for selecting the constraint selection.

11. The medical instrument of claim 9 or 10, wherein the memory further comprises an image database (152), wherein the image database comprises multiple data records; wherein each data record comprises a trial image and pre-determined constraint data; wherein execution of the instructions further cause the at least one processor to determine a preferred image from the image database by repeatedly: retrieve two or more of the multiple data records,

display each trial image from the multiple data records simultaneously on a display, and

receive a selection of a selected image from the displayed trial images; and wherein execution of the instruction further causes the at least one processor to add the predetermined constraint data for the preferred image to the set of constraint data; and wherein execution of the instructions further causes the at least one processor to use a decision tree algorithm or an iterative algorithm to determine the preferred image during the repeated selection of the selected image.

12. The medical instrument of claim 9 or 10, wherein the memory further comprises an image database (152), wherein the image database comprises multiple data records; wherein each data record comprises a trial image (402) and pre-determined constraint data;

wherein execution of the instructions further cause the at least one processor to display an image selector user interface (400) with one or more slider user interface objects (404, 406, 408),

wherein execution of the instructions further cause the at least one processor to display an preferred image selector on the image selector user interface,

wherein execution of the instructions further cause the at least one processor to determine a preferred image from the image database by repeatedly:

receiving a trial set of constraints from the one or more slider user interface objects;

selecting a selected database record from the multiple data records by matching the trial set of constraints to the pre-determined constraint data;

displaying the trial image from the selected database record;

adding the pre-determined constraint data for the selected database record to the set of constraint data if the trial image is selected as the preferred image using a preferred image selector (408).

13. The medical instrument of claim 11 or 12, wherein execution of the instructions further causes the at least one processor to repeatedly:

retrieve pre-determined constraint data from the image database, execute the magnetic resonance imaging system model to determine temporary scan parameters for the pulse sequence data that satisfy the pre-determined constraint data, wherein the magnetic resonance imaging system model is configured to model the acquisition of magnetic resonance data from the test object, wherein the temporary set of scan

parameters comprise adjustments of the pulse sequence data;

modify the pulse sequence data using the temporary scan parameters;

acquire temporary magnetic resonance data using the pulse sequence;

reconstruct the trial image using the temporary magnetic resonance data; and store the trial image with the pre-determined constraint data in the image database.

14. A computer program product comprising machine executable instructions (160,

162, 164) for execution by at least one processor (130, 130') controlling a medical instrument (100, 500), wherein the medical instrument comprises a magnetic resonance imaging system (102) for acquiring magnetic resonance data (146) from a subject (118) within an imaging zone (108), wherein the medical instrument further comprises a memory (134) for pulse sequence data (140), wherein the pulse sequence data specifies a magnetic resonance imaging protocol for controlling the magnetic resonance imaging system to acquire the magnetic resonance data,

wherein execution of the instructions causes the at least one processor to:

receive (200) constraint data (142) for a set of examination parameters;

execute (202) a magnetic resonance imaging system model to determine a set of scan parameters (144) for the pulse sequence data that satisfy the set of examination parameters, wherein the magnetic resonance imaging system model is configured to model the acquisition of magnetic resonance data from the subject, wherein the scan parameters comprise adjustments of the pulse sequence data;

modify (204) the pulse sequence data using the scan parameters; acquire (206) the magnetic resonance data using the pulse sequence data; and reconstruct (208) a magnetic resonance image (148) using the magnetic resonance data.

15. A method of operating a medical instrument (100, 500), wherein the medical instrument comprises a magnetic resonance imaging system (102) for acquiring magnetic resonance data (146) from a subject (118) within an imaging zone (108), wherein the medical instrument further comprises a memory (134) for storing pulse sequence data (140), wherein the pulse sequence data specifies a magnetic resonance imaging protocol for controlling the magnetic resonance imaging system to acquire the magnetic resonance data,

wherein the method comprises the steps of:

- receiving (200) constraint data for a set of examination parameters;

executing (202) a magnetic resonance imaging system model to determine a set of scan parameters for the pulse sequence data that satisfy the set of examination parameters, wherein the magnetic resonance imaging system model is configured to model the acquisition of magnetic resonance data from the subject, wherein the scan parameters comprise adjustments of the pulse sequence data;

modifying (204) the pulse sequence data using the scan parameters;

acquiring (206) the magnetic resonance data using the pulse sequence data; and

reconstructing (208) a magnetic resonance image using the magnetic resonance data.