GB2616847A - Method for motion correcting a magnetic resonance image - Google Patents

Abstract

A method of applying motion correction to an MR image comprising receiving imaging data corresponding to a target and motion data corresponding to a pose of the target, wherein the imaging data and motion data are captured concurrently by a radio receiver of an MR scanner; generating a motion calibration model based on the motion data; motion correcting the image data using the calibration model; and generating a diagnostic image using the motion corrected target image data. The imaging data may be related to a readout RF pulse and the motion data may be related to a constant RF signal. The frequency of the constant RF signal may be near a minimum or maximum frequency of the frequency range of the readout RF pulse. The received imaging data may correspond to a 3D gradient echo acquisition with optimised phase encoding measurement order. The motion data may be extracted for every phase encoding measurement by taking an inverse FFT in the readout direction. Also disclosed is a system for carrying out the method.

Description

METHOD FOR MOTION CORRECTING A MAGNETIC RESONANCE IMAGE

The present invention relates to computer implemented method for motion correcting a magnetic resonance 'MR image and a magnetic resonance imaging system in which the corresponding method may be utilised. In particular, the presently discussed techniques provide a means to produce motion corrected reconstructions of MRI scans -such as applied in the brain -in a very flexible way with little modification to existing acquisition protocols.

Background

In the field of magnetic resonance imaging, motion corrected brain imaging is a well-recognised problem with many proposed mitigation methods. Subject motion degrades many Magnetic Resonance Imaging (MRI) scans. Motion causes image artefacts that can obscure the desired image content, thereby reducing the utility of the data.

The basic solutions to the problem are to enlist patient support (i.e., the patient is asked to stay still), perform data averaging (at the cost of increased scan time), and/or perform repeat scans (to replace a damaged acquisition with a better one).

More recent solutions involve prospective motion correction On which motion is detected in real time and scanner acquisition geometry is updated to keep the object still in the imaging coordinate system) and retrospective correction (in which data from an external motion measurement is used to inform a correction).

External motion measurement systems rely on a variety of different physical phenomenon. One example is optical systems involving one or more cameras, often combined with targets affixed to the subject; for example the subject's teeth. Another example is nuclear magnetic resonance (NMR) probe systems that use fiducials connected to the person or use NMR as a means to detect very subtle magnetic field shifts secondary to a change in subject pose.

A recent development in the field of external motion systems are passive radio frequency (RF) systems which use the native RF receiver system of the scanner to detect signal changes caused by changes in subject pose. A particular instance of this applied to thoracic applications is the Siemens "pilot tone" imaging system, which is used to detect chest wall movement and cardiac pulsation, but requires a set-up phase in which a calibration model is created to link the RF signatures detected to subject pose changes. The pilot tone approach is described by, inter alia, U32020/013313419, EP3,413,075, DE10 2015 224,158, and US2018/013513139A1.

However, the present pilot tone protocol requires a considerable setup phase in which a patient is scanned during different poses in order to build a calibration for motion correcting. This can be disconcerting for the patient, who must spend longer in the MRI machine, and there is also no guarantee that the calibration will accurately reflect the patient motion during diagnostic image capture (because the movements could be different).

Hence it is still highly desirable to develop improved techniques for MRI motion correction, particularly when scanning the brain.

Summary

The present disclosure aims to overcome at least some of the deficiencies with current motion correction imaging protocols, whether those deficiencies are those described above or will otherwise be appreciated from the discussion herein.

Broadly, the present disclosure provides means to produce motion corrected reconstructions of MRI scans -e.g., applied in the brain -in a very flexible way with little or no modification to the underlying acquisition protocol. That is, the present techniques are formulated to work with existing image acquisition systems. More specifically, the present techniques utilise additional RF signals detected by the scanner RF receiver system just as with pilot tone.

The motion correction technique discussed herein combines the benefits of high time resolution motion detection available from optical systems with the advantages on operating without any need for markers or other devices that have to be assembled on or around the patient, and without impacting on the flexibility or efficiency of the examination. The proposed technique can work with anatomical imaging and functional/diffusion imaging. The proposed technique has minimal cost for deployment, requiring only low power RF generators and cabling, will save the customer costs by reducing the number of rescans required and will improve image quality without any change to present acquisition protocols. Thus, there will be performance, efficiency and cost saving benefits.

Accordingly, in one aspect of the invention there is provided a method for motion correcting a magnetic resonance 'MR image of a target. The method comprises receiving imaging data corresponding to the target and motion data corresponding to a pose of the target -the imaging data and motion data having been captured concurrently by at least one radiofrequency 'RE' receiver unit of an MR scanner -and then generating a motion calibration model based on the motion data which has been captured concurrently with the imaging data. The method includes motion correcting the target image data using the calibration model and generating a diagnostic image using the motion corrected target image data. It will be appreciated that the above method may be implemented by a computer, which may be a control device for the MR scanner or separate to it.

Preferably the imaging data takes the form of a plurality of k-space data lines, and motion correcting the target image data may further comprise correcting each k-space data line using the calibration model. That is, motion states can be predicted for each scanner repetition time.

Suitably, in one example the imaging data is related to a signal generated by an excitation system of the scanner producing the data, for example by a readout RF pulse applied to the target. In one example the motion data is related to a predefined (preferably constant) RF signal applied to the target by an external radio frequency source. The RF signal is monotoned and is preferably selected to be near a maximum or minimum frequency of a frequency range of the apparent diffusion coefficient 'ADC' map of the scan.

In one example the imaging data corresponds to a 3D gradient echo acquisition with an optimised phase encoding measurement order, and for every phase encoding measurement, the motion data may be extracted by taking an inverse fast Fourier transform WET' in the readout direction of the scanner. In one example a phase of the motion data captured by a first coil is subtracted from a phase of motion data captured by any other coil used in the data acquisition. This has been shown to avoid generator or sequence-related phase contamination.

In one example the method includes estimating a set of discrete motion states that occurred during data acquisition by the MR scanner, and the motion calibration model is suitably generated based on the set of discrete motion states in addition to the received motion data.

In one example the calibration model is linear, comprising a calibration matrix (of transform coefficients) of size based on a number of receiver channels and degrees of freedom of movement of the target. Moreover the matrix may be calculated using a least squares fit comparison between the estimated motion states and the motion data. Non-linear calibration models and other fitting techniques (such as a machine learning algorithm) may also be employed.

In one aspect of the invention there is provided a computer program comprising instructions which, when executed by a computer, carry out the method above and/or techniques otherwise described herein. In one aspect of the invention there is provided a computer readable medium having stored thereon this computer program. In another aspect of the invention, there is provided a non-transitory data carrier carrying code which, when implemented by a computer controlling an operation of a magnetic resonance scanner, carries out the above method(s) and/or techniques discussed herein.

In one aspect of the invention, there is provided a magnetic resonance imaging system. The system comprises a radiofrequency 'RE' generator configured to generate a predefined (preferably constant) SF signal, a scanner comprising an excitation system and at least one SF receiver unit, and a controller (comprising at least one processor). The controller is configured to receive, from the at least one RF receiver unit, imaging data corresponding to an RE signal from the target stimulated by the excitation system, and motion data corresponding to a pose of the target stimulated by the predefined SF signal. It will be appreciated that the imaging data and motion data are captured concurrently. The controller generates a motion calibration model based on the motion data captured concurrently with the imaging data, and then motion corrects the imaging data using the calibration model.

Finally the controller generates a diagnostic image using the motion corrected target image data.

Suitably the controller may operate the scanner in a 3D gradient echo acquisition mode with an optimised phase encoding measurement order. It will also be appreciate that the system may comprise a display configured to output the generated diagnostic image.

Brief Description of the Drawings

The present disclosure will now be described by way of example only with reference to the accompanying drawings, in which: Figure 1 shows an example Magnetic Resonance Imaging MRI' system; Figure 2 shows an example controller of the MRI system; Figure 3 shows example diagnostic images achieved by the MRI system; Figure 4 shows example motion correction graphs; and Figure 5 summaries an example method of motion correction.

Detailed Description

System. Figure 1 shows an example Magnetic Resonance Imaging MRI' system 100. The imaging system 100, e.g. an MRI apparatus, captures image data or signals which can be used to generate an image using a magnetic resonance scanner 120.

The MR scanner 120 is arranged in the typical cylindrical bore fashion, and includes a magnet (not shown) for establishing a stationary magnetic field. The magnet can include a permanent magnet, a superconducting magnet or other type of magnet; in the present examples it is envisaged that the magnet may generate a stationary magnetic field of at least 7T (Tesla). The MR scanner 120 may comprise a gradient arrangement (not shown) configured to apply a magnetic field gradient, and in particular a gradient arrangement configured to generate magnetic field gradients along three mutually orthogonal direction x, y, z. The gradient arrangement comprises one or more coils (not shown) used to apply magnetic gradients for localization during MR imaging.

The system 100 comprises one or more radiofrequency 'RF' receiver units 122 which are typically implemented as part of the scanner 120. In this example the receiver units 122 are RF coils positioned at various angles around the head area of a subject 10. That is, in this example, the head, or more specifically the brain, is designated as the target of the imaging system 100. Accordingly, the system 100 comprises an excitation system (not shown), such as one or more RF transmit coils, which generate an excitation readout pulse which stimulates RF emission in the target to provide a signal 124 which is captured by the receiver coils 122. Such a process will of course be very familiar to those in the art of MRI and more generally nuclear magnetic resonance NMR'.

The system comprises an RF generator 150 configured to generate a constant (mono) radiofrequency signal 152 which permeates an area in which the scanner is located, or at least probes an area in which the target and receivers 122 are located. The mono-signal 152 is suitably generated at a frequency which is off-resonance (so does not cause excitation in the target) and is toward a minimum or maximum of a frequency range at which the scanner 120 is set to sample; that is, the RF signal 152 is towards an edge of the frequencies being excited by the excitation system (corresponding to the edge of the apparent diffusion coefficient 'ADC' map of the scan). In this way the constant RF signal 152 will be far away from the target signal both during capture and later Fourier transformation. The signal 152 may be suitably broadcast from an RF antenna 154 coupled to the RF generator 150; for example a monopole antenna. It will also be appreciated that a non-constant RF signal 152 could be utilised, provided that the form of that signal is known during an imaging sequence. That is, in principle the present techniques could be applied with any predefined RF signal 152.

The MR scanner 120 and RF generator 150 are controlled by a controller 130. Here the controller 130 is configured to also provide image processing (as will be discussed shortly), however it will be appreciated that a general control device for the MR scanner 120, RF generator 150, and an image processor for receiving and analysing images could be different devices (or networks of devices). Thus, in this example, signals captured by the RF coils 122 are sent to the controller 130. The output of the image processing, e.g. a generated image, may be output to a user 140 via any suitable user interface 142, e.g. a screen on a computer or other electronic device Figure 2 is a block diagram of components of the controller 130. The controller 130 could be a desktop computer, a workstation, a server, or a laptop computer. The controller 30 could be formed from multiple devices -e.g. multiple servers. The steps (or tasks) discussed previously may be split across the computer, one or more servers, or the cloud. The controller 130 may include one or more processors 131, one or more memory devices 132 (generically referred to herein as memory 132), one or more input/output ("I/O') interface(s) 133, one or more data ports 134, and data storage 135. The data storage 135 may store one or more operating systems (0/S) 137; and one or more program modules, applications, engines, computer-executable code, scripts, or the like. The controller 30 may further include one or more buses 136 that functionally couple various components of the controller 30.

Motion Correction. The signals received by the RF coils 122 vary differentially in amplitude and phase between receiver channels as the subject changes head pose: i.e. translations and rotations of the head away from a base position for rigid motion. The present techniques aim to build a model that relates these signal changes to rigid body position coordinate changes.

In existing techniques, such a model is built in a training step, for example using a series of head images with the subject in different poses. This can be achieved by asking the subject to move their head while being imaged using a fast acquisition method, but that requires both the subject to deliberately move and an extra period of data acquisition at the beginning of the examination. The model built from the training data has to be assumed to remain valid for the subsequently acquired data that is to be motion corrected. This assumption might break down when calibration and acquisition are distant in time.

Here, the system 100 is configured to derive the model for motion correction directly from the target imaging data to be reconstructed.

In general, the present techniques are applicable to any imaging sequence that provides 3D coverage of a target (multislice or true 3D). In a preferred example, the system 100 is configured to operate a 3D gradient echo acquisition with an optimised phase encoding order. Such an acquisition favours joint estimation of motion history and motion free imaging directly from the captured data.

More specifically, the present techniques build upon the DISORDER framework to estimate a set of discrete motion states that occurred during the data acquisition. Each of these motion states must be of sufficient duration to allow robust estimation from the k-space data -typically a motion state might be an average pose over about 4 seconds.

Formerly, DISORDER motion correction divides the k-space acquisition (i.e., the signal data from the RF coils 122) into temporal groups (sweeps) of readouts (each acquired per repetition time TR') that sample uniformly across k-space. Each sweep n is treated as a different motion state with rigid motion parameters zn that can be jointly optimised together with the image x: 35, 12 (Se, 27,) = argmin,""Eq1AqFST(zq)x-y 42, (1) where q is the running variable that sums over all sweeps with T(zq),S,F,Aq and yq respectively associated rigid motion, coil sensitivities, Fourier operator, sampling structure, and measured multi-shot k-space data In addition to the usual k-space acquisition (i.e., the DISORDER acquisition), the receiver coils 122 also receive motion signal generated by (i.e., k-space data corresponding to) the mono-frequency 152. These received (motion) signals vary slightly with target (head) position and can provide updated motion information every repetitio time. A signal corresponding to the mono-frequency 152 is extracted for every phase encoding (PE) measurement by taking an inverse fast Fourier transform IFFT' in the readout direction, and in some example implementations the phase of the motion signal from the first coil is subtracted from the signals captured by any other coil's to avoid generator or sequence-related phase contamination.

In one example a linear model can be used to predict motion parameters based on the received signal from the mono-frequency 152, represented by a matrix that relates signals received on each receiver channel to the 6 degrees freedom (3 translations and 3 rotations) of rigid body movement.

More formerly, the received mono-frequency signals pTR, can be used to build a model to predict rigid motion parameters z7R, as ZTRt = CPTRC 2) where pTRt is an N+1 element complex vector comprising N-channels for signal receipt for the TR at time t, and a 1 channel to allow for signal offset. So for N=32 channels, C is a 6x33 matrix with coefficients for each of the 6 rigid motion parameters in each row. C can then be estimated as C±', = argnlinx,C Eq lAqFST(Cpq)x-Yq 112 (3) where pq = XtEWqPTRL is the averaged PT signal within the time window wq of shot q. Here it can be seen that once a set of discrete motion states has been estimated using DISORDER, these can be used to directly solve for the calibration matrix using least squares fit. This is an over-determined inverse problem since there are typically many more estimated motion states than elements in the calibration matrix C. Beneficially, whereas in Equation 1 each motion state is estimated by optimising for an independent quantity, the matrix C is assumed to be constant for the full acquisition, which can stabilise motion states over time. Moreover, in comparison to previous pilot tone techniques, the calibration matrix optimally encodes the mono-frequency signal data during the target acquisition, so there is no potential mismatch between the optimal models in the calibration and target phases.

Figure 3 shows brain reconstruction images showing the improved motion correction of the above technique (C) in comparison to no correction (A) and DISORDER motion correction only (B). Here, full brain reconstruction is performed at 1mm resolution. The present technique improves image quality and reduces motion artefacts as indicated in red. At 0.53mm3 the use of the above techniques results in improved sharpness and contrast (outlined in red).

Figure 4 shows another comparison between DISORDER motion estimates alone and the motion estimates achieved by the present disclosure. Al shows motion parameters from the DISORDER data-driven optimisation for every shot independently. A2 shows motion parameters zq = Cpq after calibrating C on the DISORDER estimates (i.e., the present techniques). A3 shows the difference between Al & A2. It can be seen that the present techniques provide cleaner parameters as different motion states are linked via the calibration matrix C and avoids single shots to get in local minima (e.g. Rot-LR at t=1000s). B shows a zoom-in of the temporal resolution for each method.

Since the motion parameters of the present techniques are encoded per TR, they provide finer temporal resolution and potentially allows correction for individual motion states for each k-space data line (i.e. at the TR level); this yields opportunities to correct for motion on a time scale of a few milliseconds, which is a significant improvement over prior art techniques.

By predicting the motion parameters (e.g., equation (2)) directly from the constant RF signal 152 (that is of course modified by patient pose), the present techniques allow for jointly optimising the calibration model (matrix) and the final image from the acquired k-space data. Providing the calibration model (matrix) can be accurately estimated, the present techniques allow motion corrected images to be produced from virtually any acquisition sequence that has 3D coverage (including both true 3D and multi-slice methods). The approach is completely non-contact, without any need for markers or for sequence changes, so the patient experience and efficiency of the examination is unchanged.

Although the above discussion focusses on a linear model and classic optimisation methods (e.g., least squares fit), it will be appreciated that the present techniques are also applicable with non-linear models (i.e., by using an appropriate different form of equation (2)) and artificial intelligence 'Al' based reconstruction methods (e.g., equation (3) could be replaced by an Al fitting technique). The present techniques have been tested at 7T, but are equally applicable at other field strengths and with more complex RF injection strategies (i.e., a different approach to the constant RF signal 152). The present technique has been tested on rigid (head) motion, but is also applicable to non-rigid motions of the subject.

Figure 5 summarises the above method(s) for motion correcting an MR image. At step 501 data (e.g., k-space data) is received which has been captured by the RF coils 122. The data includes a component corresponding to the target being imaged by the scanner and a component corresponding to the target pose; importantly this data is captured concurrently during a diagnostic scan. More specifically the imaging data corresponds to a signal received by the coils 122 corresponding to the readout excitation pulse applied to the target, while the motion data corresponds to a signal received by the coils at the same time but corresponding to the constant (monotone) RF signal 152 injected into the scanner during examination of the target. At step 502, a calibration model is generated based on the motion data which is captured concurrently with the target data. More specifically, the techniques above (e.g., equations (1)-(3)) may be applied to generate a calibration matrix for motion correcting the target image data. At step 503, the calibration model is applied to the target image data, and at step 504 the motion corrected image data is used to generate a diagnostic image (e.g., by Fourier transform).

It will be appreciated that the method may be suitably implemented by a computer. It is particularly envisaged that the method will be implemented by the controller 130 of the system 100. Suitably the processor(s) 131 may be configured to access the memory 132 and execute computer-executable instructions loaded therein corresponding to the above techniques. For example, the processor(s) 131 may be configured to execute computer-executable instructions of various program modules, applications, engines, or the like to cause or facilitate various operations to be performed in accordance with the above techniques.

It should be appreciated that the systems, engines, and program modules depicted in the Figures are merely illustrative and not exhaustive and that processing described as being supported by any particular engine or module may alternatively be distributed across multiple engines, modules, or the like, or performed by a different engine, module, or the like. In addition, various program module(s), script(s), plug-in(s), Application Programming Interface(s) (API(s)), or any other suitable computer-executable code hosted locally on the system and/or hosted on other computing device(s) accessible via one or more of the network(s), may be provided to support the provided functionality, and/or additional or alternate functionality. Further, functionality may be modularized differently such that processing described as being supported collectively by the collection of engines or the collection of program modules may be performed by a fewer or greater number of engines or program modules, or functionality described as being supported by any particular engine or module may be supported, at least in part, by another engine or program module. In addition, engines or program modules that support the functionality described herein may form part of one or more applications executable across any number of devices of the system in accordance with any suitable computing model such as, for example, a client-server model, a peer-to-peer model, and so forth. In addition, any of the functionality described as being supported by any of the engines or program modules may be implemented, at least partially, in hardware and/or firmware across any number of devices.

It should further be appreciated that the system may include alternate and/or additional hardware, software, or firmware components beyond those described or depicted without departing from the scope of the disclosure. More particularly, it should be appreciated that software, firmware, or hardware components depicted as forming part of the system are merely illustrative and that some components may not be present or additional components may be provided in various embodiments. While various illustrative engines have been depicted and described as software engines or program modules, it should be appreciated that functionality described as being supported by the engines or modules may be enabled by any combination of hardware, software, and/or firmware. It should further be appreciated that each of the above-mentioned engines or modules may, in various embodiments, represent a logical partitioning of supported functionality. This logical partitioning is depicted for ease of explanation of the functionality and may not be representative of the structure of software, hardware, and/or firmware for implementing the functionality. Accordingly, it should be appreciated that functionality described as being provided by a particular engine or module may, in various embodiments, be provided at least in part by one or more other engines or modules. Further, one or more depicted engines or modules may not be present in certain embodiments, while in other embodiments, additional engines or modules not depicted may be present and may support at least a portion of the described functionality and/or additional functionality. Moreover, while certain engines modules may be depicted or described as sub-engines or sub-modules of another engine or module, in certain embodiments, such engines or modules may be provided as independent engines or modules or as sub-engines or sub-modules of other engines or modules.

The operations described and depicted in the illustrative methods may be carried out or performed in any suitable order as desired in various example embodiments of the disclosure. Additionally, in certain example embodiments, at least a portion of the operations may be carried out in parallel.

Although specific embodiments of the disclosure have been described, one of ordinary skill in the art will recognize that numerous other modifications and alternative embodiments are within the scope of the disclosure. For example, any of the functionality and/or processing capabilities described with respect to a particular system, system component, device, or device component may be performed by any other system, device, or component. Further, while various illustrative implementations and architectures have been described in accordance with embodiments of the disclosure, one of ordinary skill in the art will appreciate that numerous other modifications to the illustrative implementations and architectures described herein are also within the scope of this disclosure.

Certain aspects of the disclosure are described above with reference to block and flow diagrams of systems, methods, apparatuses, and/or computer program products according to example embodiments. It will be understood that one or more blocks of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and the flow diagrams, respectively, may be implemented by execution of computer-executable program instructions. Likewise, some blocks of the block diagrams and flow diagrams may not necessarily need to be performed in the order presented, or may not necessarily need to be performed at all, according to some embodiments. Further, additional components and/or operations beyond those depicted in blocks of the block and/or flow diagrams may be present in certain embodiments.

Accordingly, blocks of the block diagrams and flow diagrams support combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, may be implemented by special-purpose, hardware-based computer systems that perform the specified functions, elements or steps, or combinations of special-purpose hardware and computer instructions.

Program modules, applications, or the like disclosed herein may include one or more software components including, for example, software objects, methods, data structures, or the like. Each such software component may include computer-executable instructions that, responsive to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.

A software component may be coded in any of a variety of programming languages. An illustrative programming language may be a lower-level programming language such as an assembly language associated with a particular hardware architecture and/or operating system platform. A software component comprising assembly language instructions may require conversion into executable machine code by an assembler prior to execution by the hardware architecture and/or platform.

Another example programming language may be a higher-level programming language that may be portable across multiple architectures. A software component comprising higher-level programming language instructions may require conversion to an intermediate representation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to, a macro language, a shell or command language, a job control language, a script language, a database query or search language, or a report writing language. In one or more example embodiments, a software component comprising instructions in one of the foregoing examples of programming languages may be executed directly by an operating system or other software component without having to be first transformed into another form.

A software component may be stored as a file or other data storage construct. Software components of a similar type or functionally related may be stored together such as, for example, in a particular directory, folder, or library. Software components may be static (e.g., pre-established or fixed) or dynamic (e.g., created or modified at the time of execution).

Software components may invoke or be invoked by other software components through any of a wide variety of mechanisms. Invoked or invoking software components may comprise other custom-developed application software, operating system functionality (e.g., device drivers, data storage (e.g., file management) routines, other common routines and services, etc.), or third-party software components (e.g., middleware, encryption, or other security software, database management software, file transfer or other network communication software, mathematical or statistical software, image processing software, and format translation software).

Software components associated with a particular solution or system may reside and be executed on a single platform or may be distributed across multiple platforms. The multiple platforms may be associated with more than one hardware vendor, underlying chip technology, or operating system. Furthermore, software components associated with a particular solution or system may be initially written in one or more programming languages, but may invoke software components written in another programming language.

Computer-executable program instructions may be loaded onto a special-purpose computer or other particular machine, a processor, or other programmable data processing apparatus to produce a particular machine, such that execution of the instructions on the computer, processor, or other programmable data processing apparatus causes one or more functions or operations specified in the flow diagrams to be performed. These computer program instructions may also be stored in a computer-readable storage medium (CRSM) that upon execution may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement one or more functions or operations specified in the flow diagrams. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process.

Additional types of CRSM that may be present in any of the devices described herein may include, but are not limited to, programmable random access memory (PRAM), SRAM, DRAM, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the information and which can be accessed. Combinations of any of the above are also included within the scope of CRSM. Alternatively, computer-readable communication media (CRCM) may include computer-readable instructions, program modules, or other data transmitted within a data signal, such as a carrier wave, or other transmission. However, as used herein, CRSM does not include CRCM.

Although embodiments have been described in language specific to structural features and/or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments. Conditional language, such as, among others, "can," "could," "might," or "may," unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments could include, while other embodiments do not include, certain features, elements, and/or steps. Thus, such conditional language is not generally intended to imply that features, elements, and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and/or steps are included or are to be performed in any particular embodiment.

Attention is directed to all papers and documents which are filed concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference.

All of the features disclosed in this specification (including any accompanying claims and drawings), and/or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive.

Each feature disclosed in this specification (including any accompanying claims and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.

The invention is not restricted to the details of the foregoing embodiment(s). The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

Claims (17)

1. CLAIMS1. A computer implemented method for motion correcting a magnetic resonance 'MR' image of a target, comprising: receiving imaging data corresponding to the target and motion data corresponding to a pose of the target, wherein the imaging data and motion data are captured concurrently by at least one radiofrequency RE' receiver unit of an MR scanner; generating a motion calibration model based on the motion data captured concurrently with the imaging data; motion correcting the target image data using the calibration model; and generating a diagnostic image using the motion corrected target image data.
2. 2. The method of claim 1, wherein the imaging data is related to a readout RF pulse applied to the target of the MR resonance scanner.
3. 3. The method of claim 1 or 2, wherein the motion data is related to a constant RF signal applied to the target 4.
4. The method of claim 3 when dependent on claim 2, wherein a frequency value of the constant RF signal is near a maximum or minimum frequency of a frequency range being used by the readout RF pulse.
5. The method of any preceding claim, wherein the received imaging data corresponds to a 3D gradient echo acquisition with an optimised phase encoding measurement order.
6. 6. The method of claim 5 when dependent on at least claim 3, wherein for every phase encoding measurement, the motion data is extracted by taking an inverse fast Fourier transform IFFT in the readout direction.
7. 7. The method of claim 6, further comprising subtracting a phase of motion data captured by a first coil from a phase of motion data captured by any other coil.
8. 8. The method of any preceding claim, further comprising estimating a set of discrete motion states that occurred during data acquisition by the MR scanner, and the generating the motion calibration model is further based on the set of discrete motion states.
9. 9. The method of any preceding claim, wherein the calibration model is linear and comprises a matrix of coefficients based on a number of receiver channels and degrees of freedom of movement of the target.
10. 10. The method of claims 8 and 9, wherein generating the matrix is generated based on a least square fit between the estimated motion states and the motion data.
11. 11. The method of any preceding claim, wherein the imaging data comprises a plurality of k-space data lines, and motion correcting the target image data using the calibration model comprises correcting each k-space data line.
12. 12. A computer program comprising instructions which, when executed by a computer, carry out the method of claims 1 to 11.
13. 13. A computer readable medium having stored thereon the computer program of claim 12.
14. 14. A non-transitory data carrier carrying code which, when implemented by a computer controlling an operation of a magnetic resonance scanner, carries out the method of any of claims 1 to 11
15. 15. A magnetic resonance imaging system, comprising: a radiofrequency RF' generator configured to generate a predefined RF signal; a scanner comprising an excitation system and at least one RF receiver unit; and a controller, comprising at least one processor, and configured to: receive, from the at least one RF receiver unit, imaging data corresponding to an RF signal from the target stimulated by the excitation system, and motion data corresponding to a pose of the target stimulated by the predefined RF signal, wherein the imaging data and motion data are captured concurrently; generate a motion calibration model based on the motion data captured concurrently with the imaging data; motion correct the imaging data using the calibration model; and generate a diagnostic image using the motion corrected target image data.
16. 16. The system of claim 15, wherein the controller controls the scanner to operate a 3D gradient echo acquisition with an optimised phase encoding measurement order.
17. 17. The system of claims 15 or 16, further comprising a display configured to output the generated diagnostic image.