Magnetic resonance fingerprinting quality assurance

Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an MRF scoring module (122). The MRF scoring module is configured for outputting an MRF quality score (126) in response to receiving MRF data (124) as input. The medical system further comprises a computational system (106) configured for controlling the medical system, wherein execution of the machine executable instructions causes the computational system to: receive (200) the MRF data; receive (202) the MRF quality score in response to inputting the MRF data into an MRF scoring module; append (206) the MRF quality score to the MRF data if the MRF quality score is within a predetermined range (128); and provide (208) a signal (132) if the MRF quality score is outside of the predetermined range.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. national phase application of International Application No. PCT/EP2021/068112 filed on Jul. 1, 2021, which claims the benefit of EP application Ser. No. 20/184,226.7 filed on Jul. 6, 2020 and is incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to Magnetic Resonance Imaging, in particular to magnetic resonance imaging fingerprinting.

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 B0 field or the main magnetic field. Various quantities or properties of the subject can be measured spatially using MRI. A newer MRI technique is Magnetic Resonance Fingerprinting (MRF).

MRF is a technique which allows the simultaneous measurement of MR parameters such as T1, T2, and other parameters as well as quantitative tissue properties such as the fractional quantity of different tissue types within a voxel. In MRF a pulse sequence is used where various parameters such as the flip angle, TE, and TR are systematically varied. The k-space data is repeatedly acquired during the execution of the pulse sequence and a series of images are reconstructed. A vector or “fingerprint” comprising the value of the voxel from each of the series of images is constructed. This vector or fingerprint is then compared to a dictionary of fingerprints for various MR parameters and for different tissue properties. The vector or “fingerprint” is referred to herein as MRF data. The MRF data may be either the series of images or the vector form of the data.

United States patent application publication US 2017261578 A1 discloses systems and methods for acquiring magnetic resonance fingerprinting (MRF) imaging data from a subject using a magnetic resonance imaging (MRI) system are provided. The method includes receiving an indication of an MRF imaging process to be performed by the MRI system and receiving a desired design objective for the MRF imaging process and a configuration metric associated with the MRF imaging process. The method further includes using the configuration metric to bound a variance of tissue parameter estimates associated with the MRF imaging process and determine imaging parameters that achieve the desired design objective. The method also includes performing the MRF imaging process using the determined imaging parameters to acquire MRF data using the MRI system.

The US patent application US2018/0217220 discloses a comparison to a dictionary of signal evolutions. On the basis of separate navigator data it is determined if the NMR data meet a criterion e.g. of acceptable patient respiration.

SUMMARY OF INVENTION

The invention provides for a medical system, a computer program, and a method in the independent claims. Various representative embodiments are also described below.

A drawback to MRF is that the comparison of the MRF data to the MRF dictionary can be computationally intensive. It may not always be practical to immediately reconstruct quantitative images or mappings from the MRF data while the subject is still within the MRI system being imaged. This can lead to the subject being recalled if the MRF data is in some way degraded. For example, the subject may have moved corrupting the data. By the time it is discovered that the data will not provide clinical quality images, the subject may have already left the site of the magnetic resonance imaging system.

Embodiments may provide for a means of scoring the MRF data prior to the reconstruction. This may enable the MRF data to be evaluated more quickly and may help reduce otherwise wasted magnetic resonance imaging system time. To achieve this the MRF data is input into an MRF scoring module which outputs an MRF quality score in response to inputting the MRF data. The MRF quality score may be used to annotate or label MRF data in a database if the MRF quality score is within a predetermined range. The MRF quality score could also be used to provide a signal if the MRF quality score is outside the predetermined range. The signal could for example be used to alert the operator or be integrated into a closed control loop.

As is described below, there are a variety of principles or algorithms which can be used to calculate the MRF quality score. Depending upon the time of clinical image or map being constructed different algorithms could be selected. For example, different algorithms could be selected to give an MRF quality score related to the correct positioning of the ROI, the signal to noise level, or even to the predicted presence of motion artifacts.

In one aspect the invention provides for a medical system that comprises a memory storing machine-executable instructions and an MRF scoring module. The MRF scoring module is configured for outputting an MRF quality score in response to receiving MRF data as input. The MRF quality score may for example be a numerical value that is assigned to rate the inputted MRF data. MRF stands for magnetic resonance imaging fingerprinting. In MRF a pulse sequence is constructed which varies a number of pulse sequence parameters. The k-space data is acquired at various intervals and then reconstructed into images. The MRF data may either be in the form of a series of images or else a signal, vector, or fingerprint, for each voxel constructed from the series of images. The MRF signal can then be compared to a number of dictionary entries. Various quantities such as a T1 or T2 time or even the concentration or type of material filling a voxel can also be within a magnetic resonance imaging fingerprinting dictionary. It may take a significant amount of computational power or time to match the MRF signal to an MRF dictionary. The MRF scoring module can for example be used to rate the MRF data before the complicated computational comparison between the signal and the dictionary is made.

The present invention pertains to a magnetic resonance fingerprinting (MRF) method in which MRF (k,t)-space data (MR signal evolutions) are acquired and a comparison to stored dictionary MRF datasets is made to reconstruct an image. From the voxel-wise comparison of the MRF (k,t)-space data to the dictionary datasets the tissue content of the voxels may be derived. According to the invention, an MRF quality score is returned by the MRF scoring module prior to the dictionary comparison to reconstruct the image. In a particular implementation the MRF quality score being outside a predetermined range may initiate or prompt to reacquire the MRF-space data because the MRF scoring module appears to indicate that the originally acquired MRF space data are corrupted or otherwise not suitable for the dictionary comparison. The MRF k-space data are rated before reconstruction that involves the comparison between the k-space data of the acquired signal and the dictionary. A clinical MRF image may be by matching the MRF data to the clinical MRF dictionary according to an MRF reconstruction algorithm by matching the MRF data to the clinical MRF dictionary according to an MRF reconstruction algorithm.

The magnetic resonance imaging system is configured to arrange for reconstruction of the set of magnetic resonance images from the echo signals in that reconstruction software is installed in the magnetic resonance examination system's computational system or in that the computational system has access to a remote reconstruction facility. The reconstruction software may be installed on a remote server, e.g. in the healthcare institution of even accessible to a data-network in that the reconstruction software may be available in ‘the cloud’, In these remote configurations the computational system is equipped with functionality to arrange for reconstruction of the set of magnetic resonance images at the remotely located reconstruction function. Moreover, reconstruction of the magnetic resonance image may be done by way of machine learning, for example by a trained neural network that may be incorporated on the computational system or may be accessible from a remote location.

The medical system further comprises a computational system that is configured for controlling the medical system. Execution of the machine-executable instructions causes the computational system to receive the MRF data. As was mentioned before this may be/is a series of images or it may be in the form of signals for each voxel. The MRF data is in image space. Execution of the machine-executable instructions further causes the computational system to receive the MRF quality score in response to inputting the MRF data into the MRF scoring module.

Execution of the machine-executable instructions further causes the computational system to append the MRF quality score to the MRF data if the MRF quality score is within a predetermined range. Execution of the machine-executable instructions further causes the computational system to provide a signal if the MRF quality score is outside of the predetermined range.

This embodiment may be beneficial because it provides for a means of ranking or rating the MRF data before the dictionary comparison is made. This for example may enable the reacquisition of the MRF data before a subject leaves the examination room. The MRF quality score can take different forms in different examples. In one example the MRF quality score could be a score for each individual voxel of the MRF data. In other examples it might be voxels for a score that is assigned to groups of voxels. For example, there might be a segmentation or a region of interest which is indicated within the MRF data.

In another embodiment the medical system further comprises a magnetic resonance imaging system. The memory further contains MRF pulse sequence commands configured for acquiring MRF k-space data according to an MRF magnetic resonance imaging protocol. Execution of the machine-executable instructions further causes the processor to acquire MRF k-space data by controlling the magnetic resonance imaging system with the MRF pulse sequence commands. Execution of the machine-executable instructions further causes the processor to reconstruct the MRF data from the MRF k-space data. In this example the medical system comprises the magnetic resonance imaging system used to acquire the MRF k-space data which is used to reconstruct the MRF data. This may be beneficial because it may augment a conventional magnetic resonance imaging system with a means of quickly evaluating the MRF data that it acquires.

In the above embodiment the medical system is a magnetic resonance imaging system. The medical system may take other forms in other examples. In one example the medical system could be a computer system or workstation that is used in a radiology department for evaluating radiological images. In other examples the medical system may be a remote or multiple computer systems that for example is used over the internet or in a cloud situation for providing image processing services.

In another embodiment execution of the machine-executable instructions further causes the computational system to control the magnetic resonance imaging system with the pulse sequence commands to reacquire the MRF k-space data if the signal is provided. That is, a reacquisition of the MRF k-space date is initiated or prompted in response to the MRF quality score being outside said predetermined range. This embodiment may be beneficial because the MRF scoring module may be used to provide a closed control loop which is able to improve the quality of the MRF data.

In another embodiment the MRF data comprises a series of complex valued images. The MRF scoring module is configured to construct a composite image by summing the series of complex valued images and provide the MRF quality score using the composite image. This embodiment may be beneficial because it may provide a very rapid and quick means for performing an initial evaluation of the MRF data.

When one looks at an individual image in the sequence of images that makes up an MRF data, typically very few details are visible and the image quality may be quite poor. Surprisingly, constructing a composite image by summing the series of complex valued images provides a composite image which may be used to evaluate a number of image parameters very rapidly. For example, segmentations can be used to determine the location of the subject within the image as well as also notice various types of magnetic resonance imaging artifacts in the image.

In another embodiment, a preview image or composite image of artificial contrast is generated by summing up all complex values of the fingerprint signals for each voxel. Summing up the values of the fingerprint signal averages out most undersampling artifacts, so that a sharp image is retrieved, although with an image contrast that cannot be interpreted directly for diagnosis. Motion artifacts will still be present in this image can be assessed automatically. The MRF scoring module is further configured to provide the MRF quality score by identifying motion artifact regions in the composite image. This task can be performed using AI-based detection and scoring of image artifacts, for example.

In another embodiment the MRF scoring module is further configured to provide the MRF quality score by identifying a desired region of interest within the composite image. For example, the composite image may have enough detail that segmentation algorithms can be applied to the composite image. This for example may enable the identification of various anatomical regions of the subject. This in turn may enable the automatic determination of a region of interest within the composite image. This for example may be useful in determining if the detail which is desired to be imaged is actually within the image. This may result in a great savings in computational time by enabling the region of interest or the position which is being imaged in the MRF pulse sequence to be adjusted and corrected. This for example may be done rapidly enough that it can be performed immediately after an examination by the subject and enable reacquisition of the MRF data if necessary.

In another embodiment the MRF data comprises phase encoding. The MRF scoring module is configured to calculate a magnetic field inhomogeneity map from the phase encoding of the MRF data. The MRF scoring module is further configured to determine inhomogeneities B0 field regions by thresholding the magnetic field inhomogeneity map. The MRF scoring module is further configured to provide the MRF quality score by correlating the inhomogeneities B0 field regions with the motion artifact regions. The pulse sequences used for performing magnetic resonance imaging fingerprinting can be modified so that they have phase encoding as one would have if one were measuring a B0 map for a conventional magnetic resonance imaging protocol. In this embodiment this is performed and this is used to identify regions where the B0 field is inhomogeneous. This may correlate to there being more artifacts in the magnetic resonance images reconstructed from the MRF data. By correlating the MRF quality score with these inhomogeneous B0 field regions it may provide a means of more accurately identifying if there will be problems later when images are reconstructed from the MRF data.

One very common implementation of MRF is based on a gradient-spoiled SSFP sequence using Fast Imaging with Steady State Precession (FISP) with spiral readout.

Using this implementation, the phase shift of all acquired values along the fingerprinting time series will depend on B0 in the same way. The average phase shift can thus serve as an estimate for B0. This value can be determined directly from the signals, without the need for dictionary matching.

In the original MRF implementation, which was based on a balanced SSFP sequence, B0 information is encoded in the phase shifts and can be extracted before dictionary matching. Here, the B0-related phase is accumulated throughout the MRF time series.

Image regions with strong magnetic field or phase fluctuations may be identified. This may be useful because the MRF post-processing could be potentially unreliable there.

In another embodiment the memory comprises an initial magnetic resonance fingerprinting dictionary. The label ‘initial’ is a label to identify a particular magnetic resonance fingerprinting dictionary. The MRF scoring module is configured to reconstruct an initial magnetic resonance fingerprinting image by matching the MRF data to the initial MRF dictionary according to an initial MRF reconstruction algorithm and then to provide the MRF quality score using the initial MRF image. One thing which is computationally intensive in MRF is the matching of the MRF signals to the dictionaries. In this case, the initial MRF dictionary may be a dictionary that has fewer entries. This would then mean that the matching process can proceed much more rapidly. The results may for example not be useful for clinical results, but the results may be good enough to evaluate whether the MRF data has sufficient quality to enable it to be used later to make clinical images.

In another embodiment the MRF scoring module is further configured to provide the MRF quality score by restricting the reconstruction of the initial MRF image to a predetermined parameter range and calculating the average signal-to-noise range from the initial MRF image. For example, if it is known what one is imaging or what types of tissue will be in the image, then it is likely that the various parameters which can be determined by MRF are within a predetermined range. Restricting the reconstruction of the initial MRF image to the predetermined parameter range may therefore enable a more rapid reconstruction. The signal-to-noise ratio can then be determined by comparing the values of the voxels in the MRF image to an expected range. This may then provide the signal-to-noise range directly.

In another embodiment the MRF scoring module is further configured to provide the MRF quality score by calculating an estimate of parameter values within predetermined regions of the initial MRF image. For example, the speed at which the image can be reconstructed may be increased if only a portion of the voxels are reconstructed.

In another embodiment the memory further comprises a chosen MRF dictionary with multiple dictionary entries. The MRF scoring module is configured to compute an inner product between the MRF signal for each voxel and each of the multiple dictionary entries. The MRF scoring module is further configured to provide the MRF quality score using the inner product. The MRF signal is constructed from the series of images. The MRF dictionary has for example matched different materials or tissue types with a signal that is either measured or calculated for the MRF pulse sequence that was used. A very quick numerical check is to take the inner product between the signal for each voxel and each dictionary entry. This inner product may then be a very quick measure which can inform one of what the closest match is. The MRF quality score in this example could for example be a score which identifies the closest match and it could also be a score which identifies how closely it is matched. For example, looking at what the closest match is and what the actual inner product is. If the inner product is higher then this means that it has a closer match.

The match quality can be calculated as difference of the inner product of the signal with its best match in the dictionary:
q=1−<s,d>where s and d are the normalized signal vector and best-matching dictionary entry, respectively. This quantity can be considered a reliability measure for the result of the fingerprinting reconstruction. For a smaller test dictionary, the value q will on average be lower than for a more densely sampled dictionary, but if the smaller dictionary represents the expected fingerprinting signal shapes sufficiently well, the measure can still be used to estimate the reliability of the results of a post-processing with a larger dictionary.

In another embodiment the MRF scoring module is configured to provide the MRF quality score as a signal-to-noise estimate calculated by applying a statistical measure to predetermined regions of the MRF data. For example, it may be known that a particular region is one particular tissue type. One can then take voxels from this one tissue type and have a reasonable expectation that they should provide very similar signals. By comparing the signals from this region one can apply statistical measure and notice how much they are varying. This may be used to provide an estimate of the signal-to-noise ratio.

In another embodiment execution of the machine-executable instructions further causes the computational system to receive an MRF scoring module configuration command with the MRF data. As was noted above, the MRF scoring module can use a variety of different means to provide a score or provide the MRF quality score. In various clinical situations different of these quantities may be useful. The providing of the MRF scoring module configuration command can be used for example to select how the MRF scoring module provides the MRF quality score. Execution of the machine-executable instructions further causes the computational system to configure an operational mode of the MRF scoring module using the MRF scoring module configuration command. In this example the operational mode would be a choice of one of the above described algorithms for providing the MRF quality score. This embodiment may be beneficial because it may provide a means of providing an MRF quality score that is meaningful for a particular clinical situation or question.

In another embodiment the medical system comprises a user interface. The signal is provided by the user interface. For example, there may be a dialogue box which appears on a graphical user interface that informs the operator that the MRF quality score is outside of a predetermined range. This for example may enable the operator to ignore the signal, reacquire the MRF k-space data, to stop the examination, or simply to accept whatever images are reconstructed and move on.

In another embodiment the memory further comprises a clinical MRF dictionary. The term ‘clinical’ here is used to identify a particular MRF dictionary. The clinical MRF dictionary is therefore an MRF dictionary. Execution of the machine-executable instructions further causes the computational system to reconstruct a clinical MRF image by matching the MRF data to the clinical MRF dictionary according to an MRF reconstruction algorithm if the MRF quality score is within the predetermined range. This may have the advantage that the final reconstruction is controlled by the MRF quality score. This may help to ensure that computational resources are not wasted.

The clinical MRF dictionary in some examples may have more entries than the initial MRF dictionary. In other examples, the clinical MRF dictionary has five times as many entries as the initial MRF dictionary. In other examples the clinical MRF dictionary has at least ten times as many entries as the initial MRF dictionary.

In another aspect the invention provides for a computer program comprising machine-executable instructions for execution by a computational system configured for controlling a medical system. The computer program comprises an MRF scoring module that is configured for outputting an MRF quality score in response to receive MRF data as input. Execution of the machine-executable instructions causes the computational system to receive the MRF data. Execution of the machine-executable instructions further causes the computational system to receive the MRF quality score in response to inputting the MRF data into an MRF scoring module. Execution of the machine-executable instructions further causes the computational system to append the MRF quality score to the MRF data if the MRF quality score is within a predetermined range. Execution of the machine-executable instructions further causes the computational system to provide a signal if the MRF quality score is outside of the predetermined range.

In another aspect the invention provides for a method of medical imaging. The method comprises receiving the MRF data. The method further comprises receiving the MRF quality score in response to inputting the MRF data into an MRF scoring module. The MRF scoring module is configured for outputting an MRF quality score in response to receiving MRF data as input. The method further comprises appending the MRF quality score to the MRF data if the MRF quality score is within a predetermined range. The method further comprises providing a signal if the MRF quality score is outside of the predetermined range.

‘Computer memory’ or ‘memory’ is an example of a computer-readable storage medium. Computer memory is any memory which is directly accessible to a computational system. ‘Computer storage’ or ‘storage’ is a further example of a computer-readable storage medium. Computer storage is any non-volatile computer-readable storage medium. In some embodiments computer storage may also be computer memory or vice versa.

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

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

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,

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

A Magnetic Resonance Imaging (MRI) image, MR image, or magnetic resonance imaging data 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.

DESCRIPTION OF EMBODIMENTS

FIG.1illustrates an example of a medical system100. The medical system100inFIG.1is shown as comprising a computer102. The medical system100could be integrated into a variety of other systems. For example, the medical system100could also be incorporated into or be part of a magnetic resonance imaging system. Additionally, the medical system100could be a workstation type computer such as is used by a radiologist or other medical expert to examine radiological images. The medical system100could also be a remote or cloud-based computing system for providing image processing services.

The medical system100is shown as comprising a computational system106. The computational system106is intended to represent one or more computing systems that may be located at one or more locations. The computational system106is connected to an optional hardware interface104. If the medical system100comprises other components the hardware interface104may be used to interface the computational system106to these additional components. The computer102is further shown as comprising an optional user interface108. The user interface108may provide a means for an operator or user to control and manipulate the function of the medical system100. The computer102is further shown as comprising a memory110. The memory110is intended to represent various types of memory which may be accessible to the computational system106.

The memory110is shown as containing machine-executable instructions120. The machine-executable instructions120enable the computational system comprising a processor (not shown) to control the operation and function of the medical system100as well as to perform various data and image processing tasks.

The memory110is shown as containing an MRF scoring module122. The MRF scoring module122is configured for receiving MRF data124as an input and then outputting an MRF quality score126. The MRF scoring module could function using a variety of different algorithms. The memory110is further shown as containing both the MRF data124and the MRF quality score126. The memory110is further shown as containing a predetermined range128. These for example may be numerical scores for particular voxels, individual voxels or statistical groups of voxels and may be compared to the MRF quality score126. The MRF quality score126may also for example be a single numerical value for the entire MRF data124.

In other examples it may be regions or sub-regions are assigned a particular MRF quality score126. In other examples the MRF data124may be segmented and an MRF quality score126may be assigned for a particular anatomical region. In any case, the various MRF quality score126or scores can be compared to a predetermined range128. If the MRF quality score126is within the predetermined range128or meets other conditions, then the MRF quality score126may be appended to the MRF data124to make an annotated MRF data130. In case the MRF quality score or parts of the MRF quality score126are outside of the predetermined range128, then a signal132may be produced. The signal132may for example be used for a control purpose, for example to control the reacquisition of magnetic resonance imaging k-space data. In yet other examples, the signal132may for example be used as a trigger to display a dialogue box using a graphical user interface, which may be a component of user interface108.

FIG.2shows a flowchart which illustrates a method of operating the medical system100ofFIG.1. First, in step200, the MRF data124is received. Next, in step202, the MRF quality score126is received in response to inputting the MRF data124into the MRF scoring module122. In step206the MRF quality score126is appended to the MRF data124if the MRF quality score126is within the predetermined range128. As an alternate step, in step208, the signal132is provided if the MRF quality score126is outside of the predetermined range128.

FIG.3illustrates a further example of a medical system300. The medical system300inFIG.3is similar to the medical system100inFIG.1except that it additionally comprises a magnetic resonance imaging system302.

The magnetic resonance imaging system302comprises a magnet304. The magnet304is a superconducting cylindrical type magnet with a bore306through 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 bore306of the cylindrical magnet304there is an imaging zone308where the magnetic field is strong and uniform enough to perform magnetic resonance imaging. A region of interest310is shown within the imaging zone308. The magnetic resonance data that is acquired typically acquired for the region of interest. A subject320is shown as being supported by a subject support322such that at least a portion of the subject320is within the imaging zone308and the region of interest310.

Within the bore306of the magnet there is also a set of magnetic field gradient coils314which is used for acquisition of preliminary magnetic resonance data to spatially encode magnetic spins within the imaging zone308of the magnet304. The magnetic field gradient coils312connected to a magnetic field gradient coil power supply314. The magnetic field gradient coils312are intended to be representative. Typically magnetic field gradient coils312contain 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 coils312is controlled as a function of time and may be ramped or pulsed.

Adjacent to the imaging zone308is a radio-frequency coil316for manipulating the orientations of magnetic spins within the imaging zone308and for receiving radio transmissions from spins also within the imaging zone308. 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 coil316is connected to a radio frequency transceiver318. The radio-frequency coil316and radio frequency transceiver318may be replaced by separate transmit and receive coils and a separate transmitter and receiver. It is understood that the radio-frequency coil316and the radio frequency transceiver318are representative. The radio-frequency coil316is intended to also represent a dedicated transmit antenna and a dedicated receive antenna. Likewise the radio frequency transceiver318may also represent a separate transmitter and receivers. The radio-frequency coil316may also have multiple receive/transmit elements and the radio frequency transceiver318may have multiple receive/transmit channels. For example if a parallel imaging technique such as SENSE is performed, the radio-frequency coil316will have multiple coil elements.

The transceiver318and the magnetic field gradient coil power supply314are shown as being connected to the hardware interface104of the computer102.

The memory110is shown as containing MRF pulse sequence commands330. The MRF pulse sequence commands330are according to an MRF magnetic resonance imaging protocol. The memory110is further shown as containing MRF k-space data332that has been acquired by the magnetic resonance imaging system302by controlling it with the MRF pulse sequence commands330. The memory110is further shown as containing a clinical MRF dictionary334. The clinical MRF dictionary334is an MRF dictionary. The memory110is further shown as containing a clinical MRF image336that has been reconstructed from the MRF data124using the clinical MRF dictionary334.

FIG.4shows a flowchart which illustrates one method of operating the medical system300ofFIG.3. The method starts with step400. In step400the MRF k-space data332is acquired by controlling the magnetic resonance imaging system302with the MRF pulse sequence commands330. Next, in step402, the MRF data124is reconstructed from the MRF k-space data332. The MRF data124is either a sequence of images or a signal that has been reconstructed from this sequence of images for each voxel.

The method inFIG.4then proceeds to steps200and202as was performed in the method illustrated inFIG.2. Next, the method proceeds to step204, which is a decision box with the question “Is the MRF quality score126within the predetermined range128?.” If the answer is yes, the method proceeds to step206as was illustrated inFIG.2. After step206has been performed step404may optionally be performed. In step404the clinical MRF image336is reconstructed by matching the MRF data124to the clinical MRF dictionary334. Returning back to step204, if the answer was no, then the method proceeds to step208as was illustrated inFIG.2. After the signal has been provided there are several options. In one example a user interface may be displayed which gives an operator a number of options as to how to proceed. Another option is illustrated with step406. In step406the method returns back to step400. Essentially the signal causes the magnetic resonance imaging system to reacquire the MRF k-space data332and repeat the method.

Processing MRF data can take considerable time and a multitude of quality aspects can be defined that impact the diagnosis. It is therefore difficult to check data quality immediately after data acquisition. The invention proposes an automated quality check of the acquired MRF data, depending on the clinical question and the diagnostic requirements. By implementing the method proposed here, an operator will know immediately if the data quality is insufficient and the data acquisition needs to be repeated.

Furthermore, the scores are stored as metadata with the acquired data set, making it available for post-processing and reviewing purposes.

As was described in the introduction, MR Fingerprinting (MRF) is an acquisition method for multi-parametric quantitative imaging. MRF can captures multiple tissue properties in a single acquisition. The resulting signals are matched against a dictionary of known signals to retrieve the multiple parameters. Different dictionaries or matching techniques may be used for different applications and clinical questions. The size of the dictionary and matching time can grow significantly when a larger number of parameters are encoded. In addition to producing multiple quantitative maps, MRF can be used to generate synthetic conventional image contrasts, to determine the composition of tissue within a voxel, or to classify tissue types.

In conventional MR imaging, a single contrast-weighted image or parameter map is derived from each measurement. The resulting image is usually computed directly and shown to the operator, so that image quality can be checked immediately. Further, in many cases a conventional MR contrast serves a specific diagnostic purpose (for example, a T1-weighted image within a specific protocol may be used for observing the anatomy, while a diffusion-weighted image may be used to identify physiological properties of tissue).

Since MRF encodes multiple parameters at once and there is a multitude of options how to analyze and present the acquired data, it is more difficult for the operator to decide if the quality of the acquired data is sufficient for all diagnostic purposes. Especially junior or less trained technologists may have difficulties estimating the data quality. Furthermore, some analysis techniques, such as multi-compartment analysis, can take a considerable calculation time, so that an immediate feedback is not always possible.

Examples may overcome these problems by implementation of an automated quality check (via the MRF scoring module122) of the acquired MRF data124, depending on the clinical question (MRF scoring module configuration command) and the diagnostic requirements. By implementing the method proposed here, an operator could know immediately if the image quality is insufficient and the data acquisition needs to be repeated.

Furthermore, the scores (MRF quality score126) are stored as metadata (annotated MRF data130) with the acquired data set (MRF data124), making it available for post-processing and reviewing purposes. Examples may contain one or more of the following features:A method to map a pre-defined clinical question (MRF scoring module configuration command) to requirements for quality scores (MRF quality score126) for different diagnostic aspectsFor each diagnostic aspect (and each MRF sequence implementation), a definition of a method to estimate a quality score (MRF quality score126) of acquired data (MRF data comprising the MRF quality score126)A method to calculate an overall quality score (MRF quality score126) and present a proposal to the operator

For each diagnostic aspect, a quality measure (algorithm in the MRF scoring module122) is defined. In some examples, the higher the quality score derived for this measure, the better the quality is considered to be for the corresponding diagnostic aspect. Quality measures are defined in a way that they can be calculated from the acquired MRF data with limited computational effort, so that they can be evaluated immediately after data acquisition.

Data processing methods for quick evaluation of quality include, but are not limited to:(A) Summing up the complex-valued images of the MRF time series(B) Matching the MRF data to a small dictionary that is not sufficiently resolved for diagnostics but still delivers insights into quality aspects(C) Retrieving phase information from the MRF image series

The following table lists some possible diagnostic aspects and examples of associated quality measures, referring to the above data processing methods (A)-(C).

Example for preparation of qualityDiagnostic aspectmeasureExample quality measureCorrect choice ofFrom (A), use template matching orFraction of ROI covered byimage geometrymachine learning to determine the positionimage or fraction of imageof the ROI within the imaging volumecovered by ROI (plusmargin), whatever value issmallerMotion artifacts thatFrom (A), use spatial frequency analysis orFor each artifact type: 1 ifmake diagnosticallydeep learning approaches to identifynot detectable, <1 ifimportant patternsprevalence of specific motion artifactsdetectable, with lowerless visible (this canvalues being more severebe several differenttypes)Reliability ofFrom (C), determine regions where largeAverage of reliabilities of allquantitative valuesfield inhomogeneities may affect theimage regions, where foraccuracy of the quantitative values. Fromeach region 1 means no field(A), determine regions where imagefluctuation and no artefact,artifacts are relevant (see above).<0 if values are assumed tobe affected by fieldinhomogeneities or artifactsNoise level orFrom (B) and with prior knowledge aboutSNR of quantitativefluctuations ofthe parameter range expected for a certainparameter averaged overquantitative valuesimage region, determine quantitative valuesmultiple image regionswith high resolution for a restricted valuerange.General noise mapEstimated noise from fingerprint signalsEstimated SNR in differentimage regionsCorrect range andFrom (B), use low-resolution dictionary toDeviation from expecteddistribution ofproduce coarse parameter maps that allowvalue range or deviationquantitative valuesto estimate if the quantitative values infrom expected histogram ofdifferent parts of the image are within thevaluesexpected range.Overall reliability ofFrom (B), determine average or standardAverage match quality formatching resultsdeviation of match quality q for all voxel inall voxels that do not containa certain region.fluids (i.e. all voxels with T1below a certain thresholdand signal strength above acertain threshold)

For each clinical question, a number of quality requirements are defined. A quality requirement is the minimum quality score that a specific diagnostic aspect needs to achieve in order to be sufficient for the diagnostic purpose.

FIG.5illustrates a further example of the method. The method starts with start block500. Next, in step502, the magnetic resonance fingerprinting sequence is set up and quality requirements for specific clinical questions are retrieved. The method then proceeds to step400, where the measurement is performed.

Then, in step504, for each diagnostic aspect a quality score is calculated. For example, the MRF scoring module122may have multiple algorithms that it can perform. Then, in step506, the quality scores are compared with quality requirements. This is equivalent to comparing the MRF quality score126to the predetermined range128. There may be a predetermined range128for each individual test that is performed. The method then proceeds to decision box at step204and the question is whether all quality scores are sufficient. If the answer is no the method then proceeds to provide a signal and proceeds to step508, where the scoring is shown to the operator and an action is proposed. In step510the operator has a number of decisions.

The operator can choose to repeat and then go to step400and repeat the measurement. In other cases, the operator can abort the measurement and proceed to step512, where the examination is stopped and the data is discarded. After step512the method proceeds to step514, where the method ends. Returning back to step510, the operator can also decide to accept that the quality score is insufficient and the method proceeds to step206, where the MRF quality score126is stored as meta data with the image or MRF data124for further reference. The method then proceeds to step516where the data is kept and used for processing and reading. After step516the method proceeds to step518where the method ends. Returning back to step204, if all the quality scores are sufficient then the method proceeds to step206and then steps206,516and518proceed as was previously described.

An overview of the methods illustrated inFIG.5is:The MRF sequence is set up and the quality requirements for the clinical question are retrieved.A measurement is performed and quality scores for the different diagnostic aspects are calculated.The quality scores are compared with the respective quality requirements.If all quality scores are sufficient, store them as metadata with the images and continue processingIf not all quality scores are sufficient, provide the operator with information about the scores and ask for decisionThe operator may decide to repeat the measurement, stop the examination, or accept the images in spite of insufficient quality

By storing the quality scores as metadata with the images, a later search for images with specific quality scores becomes possible. Furthermore, when processing of the MRF data in different ways at a later time (e.g. dictionary matching, creation of synthetic contrasts, multi-compartment analysis), the available quality scores can serve as an indication if the selected processing method can deliver useful results.

In another example, the described method is used for data acquisition methods other that MR Fingerprinting that produce multiple contrasts, require long computation times, or are difficult to evaluate by technologists.

REFERENCE SIGNS LIST

100medical system102computer104hardware interface106computational system108user interface110memory120machine executable instructions122MRF scoring module124MRF data126MRF quality score128predetermined range130annotated MRF data132signal200receive the MRF data202receive the MRF quality score in response to inputting the MRF data into an MRF scoring module204append the MRF quality score to the MRF data if the MRF quality score is within a predetermined range206Is the MRF quality score within the predetermined range?208provide a signal if the MRF quality score is outside of the predetermined range300medical system302magnetic resonance imaging system304magnet306bore of magnet308imaging zone310region of interest312magnetic field gradient coils314magnetic field gradient coil power supply316radio-frequency coil318transceiver320subject322subject support330MRF pulse sequence commands332MRF k-space data334clinical MRF dictionary336clinical MRF image400acquire MRF k-space data by controlling the magnetic resonance imaging system with the MRF pulse sequence commands402reconstruct the MRF data from the MRP k-space data500start502Set up MRF sequence and retrieve quality requirements for the specific clinical question504for each diagnostic aspect, calculate quality score506compare quality scores with quality requirement508show scoring to operator and propose action510operator decision512stop examination, discard data514end516keep data and use for processing and reading518end