Patent ID: 12196832

DESCRIPTION OF EMBODIMENTS

Like numbered elements in these figures are either equivalent elements or perform the same function. Elements which have been discussed previously will not necessarily be discussed in later figures if the function is equivalent.

FIG.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.

While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments.

Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measured cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

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