Patent ID: 12189011

DETAILED DESCRIPTION OF THE EMBODIMENTS

In the following, like numbered elements in the figures are either similar elements or perform an equivalent function. Elements which have been discussed previously will not necessarily be discussed in later figures if the function is equivalent.

Various structures, systems and devices are schematically depicted in the figures for purposes of explanation only and so as to not obscure the present invention with details that are well known to those skilled in the art. Nevertheless, the attached figures are included to describe and explain illustrative examples of the disclosed subject matter.

FIG.1is a schematic diagram of a medical analysis system100. The medical analysis system100comprises a control system111that is configured to connect to a scanning imaging system (or acquisition component)101. The control system111comprises a processor103, a memory107each capable of communicating with one or more components of the medical system100. For example, components of the control system111are coupled to a bidirectional system bus109.

It will be appreciated that the methods described herein are at least partly non-interactive, and automated by way of computerized systems. For example, these methods can further be implemented in software121, (including firmware), hardware, or a combination thereof. In exemplary embodiments, the methods described herein are implemented in software, as an executable program, and is executed by a special or general-purpose digital computer, such as a personal computer, workstation, minicomputer, or mainframe computer.

The processor103is a hardware device for executing software, particularly that stored in memory107. The processor103can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the control system111, a semiconductor based microprocessor (in the form of a microchip or chip set), a micro-processor, or generally any device for executing software instructions. The processor103may control the operation of the scanning imaging system101.

The memory107can include any one or combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and non-volatile memory elements (e.g., ROM, erasable programmable read only memory (EPROM), electronically erasable programmable read only memory (EEPROM), programmable read only memory (PROM). Note that the memory107can have a distributed architecture, where various components are situated remote from one another, but can be accessed by the processor103. Memory107may store an instruction or data related to at least one other constituent element of the medical analysis system100.

The control system111may further comprise a display device125which displays characters and images and the like e.g. on a user interface129. The display device125may be a touch screen display device.

The medical analysis system100may further comprise a power supply108for powering the medical analysis system100. The power supply108may for example be a battery or an external source of power, such as electricity supplied by a standard AC outlet.

The scanning imaging system101may comprise at least one of MRI, CT and PET-CT imagers. The control system111and the scanning imaging system101may or may not be an integral part. In other terms, the control system111may or may not be external to the scanning imaging system101.

The scanning imaging system101comprises components that may be controlled by the processor103in order to configure the scanning imaging system101to provide image data to the control system111. The configuration of the scanning imaging system101may enable the operation of the scanning imaging system101. The operation of the scanning imaging system101may for example be automatic.FIG.4shows an example of components of the scanning imaging system101being an MRI system.

The connection between the control system111and the scanning imaging system101may for example comprise a BUS Ethernet connection, WAN connection, or Internet connection etc.

In one example, the scanning imaging system101may be configured to provide output data such as images in response to a specified measurement. The control system111may be configured to receive data such as MR image data from the scanning imaging system101. For example, the processor103may be adapted to receive information (automatically or upon request) from the scanning imaging system101in a compatible digital form so that such information may be displayed on the display device125. Such information may include operating parameters, alert notifications, and other information related to the use, operation and function of the scanning imaging system101.

The medical analysis system100may be configured to communicate via a network130with other scanning imaging systems131and/or databases133. The network130comprises for example a wireless local area network (WLAN) connection, WAN (Wide Area Network) connection LAN (Local Area Network) connection or a combination thereof. The databases133may comprise information relates to patients, scanning imaging systems, anatomies, scan geometries, scan parameters, scans etc. The databases133may for example comprise an electronic medical record (EMR) database comprising patients' EMR, Radiology Information System database, medical image database, PACS, Hospital Information System database and/or other databases comparing data that can be used for planning a scan geometry. The databases133may for example comprise training datasets for the training performed by the present subject matter.

The memory107may further comprise an artificial intelligence (AI) component150. The AI component150may or may not be part of software component121. The AI component150may for example comprise a trained machine learning model160. The trained machine learning model160may be configured to receive a set of X dimensional arrays and to provide an image reconstruction output that represents one of the set of arrays e.g. the set of X dimensional arrays may comprise 3 arrays Arr1, Arr2, and Arr3, wherein the trained model may reconstruct an image output of one array Arr1 of the arrays, while the other arrays Arr2 and Arr3 may be used for taking into account, in the reconstruction, their correlation with Arr1. Thus, the three arrays may be input to the trained models in order to receive an image reconstruction output of the array Arr1. The image reconstruction output may be a subset or portion of a final output. For example, the image reconstruction output may be a range of images out of the total volume. There may be a mapping of a subrange of M dimensions input to a subrange of the P dimensional output.

The machine learning model160may for example be trained by providing multiple sets of XD arrays, and associating each set of X dimensional arrays with a fully sampled k-space reconstructed image that corresponds to the array (Arr1) of the set of X dimensional arrays.

FIG.2is a flowchart of a method for reconstructing magnetic resonance images of a target volume such as a brain of a subject. A MM system (e.g. as shown inFIG.4) may be configured to acquire data by scanning or imaging a target volume of a subject.

An M dimensional array comprising acquired data (by the MM system) may be received in step201. The acquired data may for example be k-space data or other image data such as aliased image data. The acquired data may for example comprise at least one dimension that represents one of the three directions of the target volume. The M dimensions may comprise further dimensions such as a time dimension. In one example, M>=2.

Assuming for simplification of the description that M=3 which corresponds to the spacial dimensions e.g. kx, ky and kz. In this case, the M dimensional array may, for example, be defined as mr(x, y, z), wherein x has a number of Sx values, y has Sy values and z has Sz values i.e. the array mr has a size of Sx*Sy*Sz.

At least one dimension of the M dimensions may be selected or identified in step203(i.e. K dimensions may be selected where K>=1). K=1 in this example, but it is not limited to. For example, the selected dimension may be a user selected dimension e.g. a user input may be received. The user input indicates the selected dimension. In another example, an automatic selection of the dimension may be performed e.g. a random selection may be performed. For simplification of description, the selected dimension may be the dimension x of the array mr. The selected dimension has a respective set of values in the M dimensional array. The set of values comprises Sx values, where Sx is, for example, the number of phase encodings in case the selected dimension is a dimension that corresponds to the phase encoding.

The dimension may be selected so that the M−1 dimensional arrays have dimension M−1=X, where X is the dimension of the set of arrays that are used as input to the trained machine learning model160. Each array of the set of M−1 dimensional arrays may comprise at least one dimension that represents the spatial frequency information in one of the three directions of the target volume.

A subset of values of the set of values may be determined in step205. Following the above example, the subset of values may for example be a subset of the Sx values. The subset of values may be determined as follows: select a first value (or current value) of the subset from the set of Sx values, and further select the remaining values of the subset. The remaining values and the current value may or may not form a range (they are consecutive values of the array). In one example, the remaining values may be chosen so that the current value is the center value. Assuming for example that the set of values are provided in the following order: v1, v2, v3, . . . vSx. If the current value is v4, the remaining values may be v3 and v5 or may be v2, v3, v5 and v6, wherein v4 is the center value of the subset defined by v3, v4 and v5 or by v2, v3, v4, v5 and v6. If in one example, the current value being processed is the first value v1 (or last value vSx) the determined subset may comprise v1 and values beyond v1 such as v2 and v3.

Each value vx of the set of Sx values may define a 2D array mrx(y, z) associated with the value vx. The 2D array mrx(y, z) may represent a 2D slice at the value vx. For example, value v4 may be associated with 2D array mr4(y, z), v3 may be associated with 2D array mr3(y, z) and v2 may be associated with 2D array mr2(y, z) and so on. In one example, the remaining values associated with each current value may be determined by determining the correlations between the 2D array of each value of the remaining values with the 2D array of the current value. Following the above example, if the current center value is v4, the method may determine for each value of the values v1 to vSx (excluding v4) a correlation value between the 2D array associated with the each value and the 2D array mr4(y, z). Only values vx associated with a correlation value higher than a predefined threshold may be chosen. Those chosen values and the current value may form the subset.

In one example, the subset of values may comprise a predefined number of values e.g. 3. That is, the subset may comprise a current center value and the surrounding two values. This may be advantageous as it may enable an optimal selection method while still considering the correlation between the remaining values and the center value.

For each value of the subset, an M−1 dimensional array comprising the acquired data corresponding to the value may be determined in step207. This may result in a set of M−1 dimensional arrays. Following the above example, if the determined subset of step205is [v3, v4, v5], the three arrays mr3(y, z), mr4(y, z) and mr5(y, z) may be the determined M−1 dimensional arrays of the three values of the subset [v3, v4, v5] respectively.

The set of M−1 dimensional arrays may be input to the trained machine learning model160in order to receive in step209an output of a reconstructed image from the trained machine learning model. Following the above example, a 2D image of the 2D slice mr4(y, z) associated with the current value v4 may be reconstructed.

In one example, steps205-209may be repeated for further subsets of values of the selected dimension. Following the above example, the first execution of the present method may be performed for a value v1 of the set of values v1 to vSx. Steps205-209may be repeated for each value of the set of values v2 to Sx. This may result in Sx reconstructed 2D slices.

FIG.3depicts a block diagram illustrating an excepted gain in increase in memory cost301and computational load303, versus the input data size provided to the trained machine learning model.FIG.3shows three different input size namely one 2D slice, five 2D slices and a full 3D volume.FIG.3provides an illustration of the expected gain, ‘costs’ in term of memory, and computational load versus input data size.

FIG.4illustrates a magnetic resonance imaging system700as an example of the medical system100. The magnetic resonance imaging system700comprises a magnet704. The magnet704is a superconducting cylindrical type magnet with a bore706in 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 or a sealed 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 subject718to be imaged, the arrangement of the two sections area similar to that of a Helmholtz coil. Inside the cryostat of the cylindrical magnet there is a collection of superconducting coils. Within the bore706of the cylindrical magnet704there is an imaging zone or volume or anatomy708where the magnetic field is strong and uniform enough to perform magnetic resonance imaging.

Within the bore706of the magnet there is also a set of magnetic field gradient coils710which is used during acquisition of magnetic resonance data to spatially encode magnetic spins of a target volume within the imaging volume or examination volume708of the magnet704. The magnetic field gradient coils710are connected to a magnetic field gradient coil power supply712. The magnetic field gradient coils710are intended to be representative. Typically, magnetic field gradient coils710contain three separate sets of coils for the 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 coils710is controlled as a function of time and may be ramped or pulsed.

MRI system700further comprises an RF coil714at the subject718and adjacent to the examination volume708for generating RF excitation pulses. The RF coil714may include for example a set of surface coils or other specialized RF coils. The RF coil714may be used alternately for transmission of RF pulses as well as for reception of magnetic resonance signals e.g., the RF coil714may be implemented as a transmit array coil comprising a plurality of RF transmit coils. The RF coil714is connected to one or more RF amplifiers715.

The magnetic field gradient coil power supply712and the RF amplifier715are connected to a hardware interface of control system111. The memory107of control system111may for example comprise a control module. The control module contains computer-executable code which enables the processor103to control the operation and function of the magnetic resonance imaging system700. It also enables the basic operations of the magnetic resonance imaging system700such as the acquisition of magnetic resonance data.

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 interne, 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.

A ‘computer memory’ or ‘memory’ is an example of a computer-readable storage medium. A computer memory is any memory which is directly accessible to a processor. A ‘computer storage’ or ‘storage’ is a further example of a computer-readable storage medium. A 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.

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

100medical system101scanning imaging system103processor107memory108power supply109bus111control system121software125display129user interface133databases150AI component160machine learning model201-209method steps301memory cost303computational load700magnetic resonance imaging system704magnet706bore of magnet708imaging zone710magnetic field gradient coils712magnetic field gradient coil power supply714radio-frequency coil715RF amplifier718subject