Patent Publication Number: US-2021165061-A1

Title: Method for generating at least one image data set and one reference image data set, data carrier, computer program product and magnetic resonance system

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This patent application claims priority to European Patent Application No. 19212164.8, filed Nov. 28, 2019, which is incorporated herein by reference in its entirety. 
     BACKGROUND 
     Field 
     The disclosure relates to a method for generating at least one image data set and one reference image data set of an examination object from at least two raw data sets. The disclosure relates, moreover, to a computer program product, a data carrier and a magnetic resonance system with which said method can be carried out. 
     Related Art 
     In magnetic resonance imaging, measurement sequences are used for acquiring raw data sets from which image data sets are processed. With a measurement sequence, what is known as the k-space (for example defined as the spatial frequency space of the spin density distribution) is sampled, with measurement signals being acquired in a spatially encoded manner at a large number of read-out points. 
     Depending on the sampling pattern, reference is made, for example, to Cartesian, spiral or radial sampling of the k-space. The path with which the k-space is sampled in the process is called the k-space trajectory in this connection. 
     With Cartesian sampling, a read gradient is conventionally applied during the acquisition window, whereby an entire k-space row can be acquired. Depending on the measurement sequence, sampling is performed row for row with waiting times therebetween. With an Echo Planar Imaging (EPI) measurement sequence, by contrast, a large number of k-space rows are read out directly one after the other. 
     There can also be a waiting time or a multi-echo acquisition can be made between acquisition of the individual spokes, which do not correlate with the rows already described, in the case of radial sampling. 
     At least some of the k-space is sampled with a read gradient that varies over time in the case of spiral trajectories. 
     The sampling pattern is basically independent of the actual measurement sequence. A measurement sequence is a predefined sequence of radio frequency (RF) pulses, magnetic field gradients, acquisition windows and waiting times. Examples of such measurement sequences are a gradient echo (GE) measurement sequence, a spin echo (SE) measurement sequence, Fast Low Angle SHot (FLASH), a Fast Imaging with Steady Precession (FISP) measurement sequence, a True Fast Imaging with Steady Precession (TrueFISP) measurement sequence, a Turbo Spin Echo (TSE) measurement sequence, an Echo Planar Imaging (EPI) measurement sequence, and many others. 
     Each of said sampling patterns can be used in particular with a FLASH, FISP or TrueFISP measurement sequence. 
     Magnetic field gradients are used for the purpose of spatial encoding. Conventionally, a magnetic resonance system designed for imaging has three gradient coils for generating three gradient fields. These are perpendicular to each other to enable spatial encoding in all spatial directions. 
     Targeted application of gradients in a first and a second image direction, for example a read direction and a phase-encoding direction, defines the read-out points, in other words, the position of the measurement signals in the k-space. 
     If the actual gradient fields differ from the assumed ones, the read-out points are consequently also shifted in the k-space. For example, the gradients can have a strength different to the assumed one or their duration is shorter or longer than specified. Consequently, measurement signals are acquired at different positions in the k-space to the assumed ones, whereby artifacts are produced in the reconstructed image data set. 
     Such deviations can lead, in particular with spiral k-space trajectories or measurement sequences with multi-gradient echo read-outs, as with an EPI measurement sequence, to pronounced artifacts. 
     In reality, spiral trajectories are distorted, for example by gradient amplifier time shifts, eddy currents and accompanying gradient fields, also called concomitant gradient fields. These effects can be different from magnetic resonance system to magnetic resonance system and have to be precisely examined and optionally individually calibrated, therefore. 
     There is a large number of correction models for spiral trajectories, for example Tan et Meyer: Estimation of k-space trajectories in spiral MRI, Magn. Reson. Med., 2009, 61(6): 1396-1404. Furthermore, there are also more general methods, which describe any gradient pulses, for example Duyn J. H., Yang Y., Frank J. A., van der Veen J. W.: Simple correction method for k-space trajectory deviations in MRI, J Magn. Reson., 1998, 132(1):150-3 or Vannesjo S J, Haeberlin M, Kasper L, Pavan M, Wilm B J, Barmet C, Pruessmann K P: Gradient system characterization by impulse response measurements with a dynamic field camera, Magn. Reson. Med., 2013; 69(2):583-93. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES 
       The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate the embodiments of the present disclosure and, together with the description, further serve to explain the principles of the embodiments and to enable a person skilled in the pertinent art to make and use the embodiments. 
         FIG. 1  shows a magnetic resonance system according to an exemplary embodiment of the disclosure. 
         FIG. 2  shows a TrueFISP sequence diagram according to an exemplary embodiment of the disclosure. 
         FIG. 3  shows a spiral k-space trajectory according to an exemplary embodiment of the disclosure. 
         FIG. 4  shows a Cartesian k-space trajectory with unconnected k-space rows according to an exemplary embodiment of the disclosure. 
         FIG. 5  shows a Cartesian k-space trajectory with connected k-space rows according to an exemplary embodiment of the disclosure. 
         FIG. 6  shows a Magnetic Resonance Fingerprinting (MRF) method with a plurality of measurement sequences according to an exemplary embodiment of the disclosure. 
         FIG. 7  shows a corrected first raw data set according to an exemplary embodiment of the disclosure. 
         FIG. 8  shows a method for generating an image data set according to an exemplary embodiment of the disclosure. 
         FIG. 9  shows a method for generating an image data set according to an exemplary embodiment of the disclosure. 
     
    
    
     The exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings. Elements, features and components that are identical, functionally identical and have the same effect are  13  insofar as is not stated otherwise—respectively provided with the same reference character. 
     DETAILED DESCRIPTION 
     In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the embodiments, including structures, systems, and methods, may be practiced without these specific details. The description and representation herein are the common means used by those experienced or skilled in the art to most effectively convey the substance of their work to others skilled in the art. In other instances, well-known methods, procedures, components, and circuitry have not been described in detail to avoid unnecessarily obscuring embodiments of the disclosure. The connections shown in the figures between functional units or other elements can also be implemented as indirect connections, wherein a connection can be wireless or wired. Functional units can be implemented as hardware, software or a combination of hardware and software. 
     An object of the present disclosure to implement a method of simple and robust trajectory correction in particular for spiral k-space trajectories. 
     This object is achieved by the methods for generating at least one image data set and one reference image data set of an examination object from at least two raw data sets, according to exemplary aspects. In an exemplary embodiment, the method includes:
         providing a first raw data set, wherein the first raw data set is acquired with a magnetic resonance system and comprises measurement signals at a plurality of read-out points in the k-space, wherein the read-out points lie on a first k-space trajectory,   providing a second raw data set, wherein the second raw data set is acquired with the same magnetic resonance system and is acquired at the same examination object as the first raw data set and comprises measurement signals at a plurality of read-out points in the k-space, wherein the read-out points lie on a second k-space trajectory that is different from the first k-space trajectory,   reconstruction of a plurality of image data sets from the first raw data set, wherein before the reconstruction of each image data set, a separate distortion correction coefficient set is used, wherein a distortion correction coefficient set defines a shift of the read-out points of the measurement signals of the first raw data set in the k-space,   reconstruction of a reference image data set from the second raw data set,   comparison of the image data sets with the reference image data set, wherein on comparison, one similarity value respectively is generated, and   selection of the image data set with the greatest similarity value.       

     As is known, the k-space is the sampled frequency space. It is a mathematical data space in which the measurement signals can be represented in the form of k-space points. The Nyquist criterion defines how many k-space points have to be sampled to obtain a reconstruction without convolution artifacts. Such a k-space is also called “complete”. Subsampling leads, by contrast, to what are known as convolution artifacts. There are types of acquisition, for example parallel imaging, in which image data sets free from convolution artifacts can also be obtained from sub-sampled k-spaces with specific reconstruction methods. 
     Several possibilities exist for sampling the k-space. Different samplings can also result with Cartesian sampling over the sequence of acquisition of the k-space rows, therefore. The path in the k-space, which is defined by the gradient fields after the RF excitation(s), is what is known as the k-space trajectory. This can be visualized in k-space representations. The k-space trajectory shows the path of the acquisition of all measurement signals, therefore, which are included in a raw data set. A k-space trajectory can also comprise partial trajectories, therefore. 
     The k-space points, which were actually sampled, are called read-out points. The measurement signals, which were acquired, are stored in a raw data set (already mentioned). 
     A magnetic resonance system with a receive coil arrangement and gradient coils is designed to acquire raw data sets with different measurement sequences. Consequently, the image data sets reconstructed from a raw data set can have different contrasts. Different sampling patterns can also be used when the same measurement sequence is used, resulting in different k-space trajectories. 
     It has been found that some k-space trajectories are more sensitive to gradient deviations than others such that they have a greater tendency toward deviations between actual and desired trajectory than others. Two raw data sets are provided, therefore, which are acquired with different k-space trajectories. The k-space trajectory which is less sensitive to gradient deviations can be used to correct the more sensitive k-space trajectory. 
     This occurs by way of the targeted shift in the read-out points. The shift vectors of the individual read-out points are combined in a distortion correction coefficient set. With a distortion correction coefficient set, preferably the first raw data set is transformed into a corrected first raw data set and then reconstructed by a Fourier transform into an image data set. A separate image data set then exists for each distortion correction coefficient set. 
     These image data sets, which have all been reconstructed from the first raw data set using a different distortion correction coefficient set respectively, are then compared with a reference image data set reconstructed from the second raw data set. On comparison, a similarity value is generated and the image data set with the greatest similarity value continues to be used. 
     One of the distortion correction coefficient sets can also represent identity imaging, in other words, not implement a shift. Without gradient deviations, the image data set reconstructed using this distortion correction coefficient set is the one with the greatest similarity value. 
     The similarity value is advantageously defined by an edge comparison. When determining the similarity, primarily the structure is then considered and not signal intensity distributions. 
     The described method can be used in four repetition frequencies: 
     It can be carried out once in the calibration of a new type of magnetic resonance system. A detected distortion correction coefficient set can then be used for all magnetic resonance systems of this type. 
     It can be carried out on setup of any magnetic resonance system. Consequently, device-specific features can be taken into account. 
     If the position of the patient and the gradient settings associated therewith affect the correction, the method has to be carried out for each patient. 
     If, however, the actual measurement parameters are problematic, a separate implementation is necessary for each measurement sequence. 
     A distortion correction coefficient set is stored for future corrections in the first three cases. 
     In an exemplary embodiment, a trajectory can be used as the first k-space trajectory in which at least some of the k-space is sampled with read gradients that vary over time, in particular a spiral trajectory, in other words, a spiral k-space trajectory, can be used. As described in the introduction, spiral trajectories are sensitive to gradient deviations since the gradients used have a large number of changes and deviations add up. Each read-out point has a different shift, therefore. As has also been described above, a k-space trajectory denotes the entirety of all measurement signals, which are used for reconstruction of an image data set. A spiral k-space trajectory can have a spiral, therefore. It can also have at least two, in other words, a plurality of, spirals, however. 
     For the generation of spiral trajectories, measurement sequences are used, which are provided with two oscillating gradients. 
     Alternatively, a path with at least two connected k-space lines can be used as a first k-space trajectory. A k-space line can be a k-space row or a spoke. In an exemplary embodiment, two successive k-space lines respectively have a different, in particular an opposing, read-out direction. A k-space trajectory of this kind is present inter alia with multi-gradient echo measurement sequences such as an EPI measurement sequence. Gradient deviations, in particular of the read gradient, add up also in the case of measurement sequences of this kind. In an exemplary embodiment, the k-space-lines have as k-space rows a spacing in the phase-encoding direction, in other words, are sampled in a Cartesian arrangement. Alternatively, the k-space lines can also be obtained as spokes with radial sampling. 
     Connection of the k-space lines results by reading out a multi-gradient echo, therefore. The rows or spokes are actually connected in a k-space representation. 
     The k-space can be sampled in a single pass, as what is known as “single-shot” sampling, or in a segmented manner. The more k-space lines that are acquired in one pass, the greater the susceptibility in relation to gradient deviations. 
     Other complicated trajectories are conceivable, moreover, such as Rosette (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2746043/) or basically trajectories in which the read-out direction changes. 
     Advantageously, a Cartesian distribution of the read-out points can be used as the second k-space trajectory. This can preferably be acquired with a measurement sequence in which only a few or preferably a single gradient echo is acquired. The k-space trajectory then consists of individual, unconnected k-space rows or of blocks of a few unconnected rows. With Cartesian trajectories, the shift in the k-space caused by gradient deviations is typically exactly the same for each row since they all point in the same direction. Not many gradient echoes denotes fewer gradient echoes than with the first raw data set, in particular a maximum of 10 gradient echoes. Advantageously, a single gradient echo is acquired in one pass. 
     The feature of not many or of the single gradient echo(es) also includes all imaging spin echo and mixed measurement sequences. A gradient echo is generated at the same time as the spin echo by means of a read dephasing gradient and the following read gradients in the case of spin echo measurement sequences as well. 
     In an exemplary embodiment, only one of the first gradient echo signals or the first gradient echo signals is/are used for the reconstruction of the reference image data set. If a turbo spin echo measurement sequence is combined together with a 2 point Dixon method on acquisition of the raw data, then the second gradient echo respectively is used for generating a second image data set. Such gradient echoes are irrelevant to the reference image data set, however, which is advantageously reconstructed from the first gradient echoes respectively. 
     Alternatively, a path with unconnected spokes or not many connected spokes can be used as the second k-space trajectory. A radial sampling with a single or not many gradient echo signal(s) is also possible, therefore. Here too the few gradient echo signals are fewer gradient echo signals than with the first raw data set, preferably a maximum of 10 gradient echo signals. 
     In an exemplary embodiment, a model of the gradient deviation on acquisition of the first raw data set can be used in the generation of at least one distortion correction coefficient set. All causes of deviations, which are known, can be included in the model. It is not necessary for the deviations to be known exactly for this, otherwise they could be considered from the start when establishing the measurement parameters. 
     However, the model can also include whether the gradient moments have a tendency to be too big or too small, whether the deviation is merely present or is present more strongly in a particular direction, what effects eddy currents have, etc. Specifically adapted distortion correction coefficient sets can be generated in respect of the identified deviations, therefore. 
     If it is known, for example, that the gradient deviations cause a shift only in the read direction, a distortion correction coefficient set shifts the read-out points only in the read direction. Since the strength of the shift is not known, shifts of different strengths are “tested” and one image data set respectively is generated using one distortion correction coefficient set. 
     Furthermore, smaller shifts can be provided in the center of the k-space than further out, since the gradient strengths are lower in the center. All of the previous knowledge about the gradients can be packed into the distortion correction coefficient sets to achieve a distortion correction with optimally few distortion correction coefficient sets. 
     Alternatively or in addition, at least one distortion correction coefficient set can be generated with random numbers. In a first embodiment, the shifts for each distortion correction coefficient set are generated solely with random numbers. This is a type of brute force attack. This process can always be applied since it does not assume any previous knowledge. It is usually inefficient, however. 
     In a second alternative, use of the random numbers with the application of previous knowledge, for example the model of the gradient deviation on acquisition of the first raw data set, can be combined. Model errors can be intercepted without reaching, as it were blindly, for shift values. A combination can for example be obtained in that limit values are specified for the random numbers as a function of location. As described further above, the gradient deviations can be greater at the edge. There the limit values for the random numbers are likewise higher. In addition or as an alternative, shifts are determined with the random numbers only for directions in which a deviation is anticipated. Any model can be combined with random numbers, therefore to keep the models more robust against unconsidered effects. 
     Advantageously, a maximum shift can be specified for the measurement signals. The aim here is to keep the shifts in a physically expedient range. While limit values are already specified for the numbers as such when using random numbers, the shift resulting therefrom can be provided with additional conditions. For example, it is possible to specify that two read-out points located side by side on the k-space trajectory do not change place. Here too the aim is to limit the number of distortion correction coefficient sets used to an optimally low number. 
     In an exemplary embodiment, a plurality of partial data sets can be provided as the first raw data set, wherein the partial data sets are acquired with at least one different measurement parameter comprising the group: flip angle of the excitation pulse and/or repetition time. A particularly valuable application of the described method occurs with Magnetic Resonance Fingerprinting (MRF) or MR fingerprinting for short. With MR fingerprinting, image data sets with different measurement parameters are acquired directly one after the other to generate signal curves, which are influenced by a plurality of tissue and device parameters, for example by the T 1  relaxation time and/or the T 2  relaxation time and/or the RF pulse strength B 1  or the basic field strength B 0 . 
     Each signal curve is compared with the signal curves stored in what is known as a dictionary and the tissue and device parameters are thus determined. Each signal curve is obtained from a large number of MRF image data sets, and, more precisely, conventionally for each image element, one signal curve. 
     To be able to acquire the large number of MRF image data sets, and, more precisely, in the order of several hundred, in a reasonable period of time, it is known to use spiral trajectories when acquiring an MRF image data set. The situation can occur here that an MRF image data set is sub-sampled 48 times. To reduce the sub-sampling when generating a first raw data set, one spiral k-space trajectory can be formed from a plurality of spirals. For this, the spirals must have at least partially different read-out points, otherwise only an averaging would exist. For example, the spirals can be rotated towards each other by an angle. With four spirals, there can be a 90° turn in each case. 
     A spiral will be used for generating an MRF image data set, therefore and a plurality of, for example four, spirals form a first raw data set for generating an image data set. Consequently, the spirals can be used not only for an MRF method but simultaneously for a preceding distortion correction. 
     An MRF raw data set, the measurement signals located on a spiral, therefore, which is used to reconstruct an MRF image data set, represents a partial data set of a first raw data set thereby. 
     When providing or acquiring the MRF image data sets an additional task, therefore, is to obtain a correction for one or more first raw data set(s), which does not lengthen the measuring time, or at least not significantly. 
     In order to solve this problem it is proposed, therefore, when acquiring the MRF image data sets, to acquire some with spiral sampling and some with Cartesian sampling. The method described in the introduction should be supplemented such that measurement parameters are varied during the measurement. 
     In addition, the measurement sequence can also be varied, with the magnetization being taken over from measurement sequence to measurement sequence by direct succession of the measurement sequences in the case of MR fingerprinting. 
     An MRF data set acquired in this way is then composed of a plurality of first raw data sets and at least one second raw data set. Each of the first raw data sets can be composed of a plurality of partial data sets. A plurality of MRF image data sets is then generated therefrom. The process for obtaining a corrected k-space trajectory should be differentiated herefrom, however. In this case a whole first raw data set is used despite the different measurement parameters. In the whole first raw data set the read-out points are shifted by means of a distortion correction coefficient set and a single image data set—for each distortion correction coefficient set—is generated therefrom. If the k-space trajectory in which the similarity value is at a maximum is found, it can be used to reconstruct the plurality of MRF image data sets from the single k-space spiral trajectory. 
     While both the image data sets from the k-space spiral trajectory and the MRF image data sets from the spirals can be sub-sampled, the MRF image data sets are sub-sampled significantly more strongly. The use of the first raw data sets instead of the individual spirals means that only the calculation of expedient similarity values is possible. Distortion correction coefficient sets can be provided for any type of k-space trajectory, although with excessively sub-sampled raw data sets there is the problem that the image data sets reconstructed therefrom have excessive convolution artifacts. In this case it is no longer possible to create expedient similarity values with one reference image data set. 
     In an exemplary embodiment, a plurality of first raw data sets can be provided and a distortion correction coefficient set selected for a first raw data set, which is used for all first raw data sets or for partial data sets of the first raw data sets. This is similarly an application in the case of MRF fingerprinting, in particular when using different measurement sequences in the case of spiral sampling. For an image data set, that distortion correction coefficient set which leads to the greatest similarity value is then determined. This can then be applied to other first raw data sets to correct them as well. 
     Insofar as, previously, storage of the distortion correction coefficient set was irrelevant, this process represents a modification since it was not imperatively used further. For example, the distortion-corrected raw data set, in other words, the first raw data set with shifts already applied, could also be stored. 
     In an exemplary embodiment, after the reconstruction of at least two image data sets from the first raw data set using two different distortion correction coefficient sets, the resulting similarity values are considered when generating a third distortion correction coefficient set. In other words, the distortion correction coefficient sets and the image data sets can be generated iteratively. Using the image data sets an evaluation is made as to which distortion correction coefficient sets produce an improvement or which bring about a greater improvement and an attempt is then made to improve them further. For example, a first distortion correction coefficient set can perform shifts in the center of the k-space and a second set can perform shifts in the outer regions of the k-space. Depending on which of the image data sets generated from the first raw data set using the first and the second distortion correction coefficient sets has a greater similarity value, the distortion correction coefficient set, which is associated with the image data set with the greater similarity value, is modified. 
     In an exemplary embodiment, the first raw data set can be acquired with a first measurement sequence and the measurement sequence for acquisition of the second raw data set can be selected such that image data sets reconstructed from the first and the second raw data sets have, as far as possible, equal contrasts. The gradient deviations cause a change in the contours in an image data set reconstructed from a first raw data set. The distortion correction coefficient set is intended to reverse this change. A comparison, for example, of the edges in an image data set and in the reference image data set then shows how well the distortion correction has worked. In particular, the first raw data set and the second raw data set can be acquired with the same measurement sequence, but different k-space trajectories. 
     Advantageously, a distortion correction coefficient set can also define a phase shift. A phase shift can be produced by local off-resonances, in other words, by local magnetic field strengths different from the anticipated B 0 . These are caused by inhomogeneities of the basic magnetic field of the magnetic resonance system or by susceptibility differences of tissues of the patient. The phase is a further parameter, which is present in the distortion correction coefficient set. This makes the distortion correction slightly more complex, but ultimately even better. 
     In addition, the disclosure relates to a method for training an artificial neural network, comprising:
         receiving input training data, where the input training data is the first raw data set according to one or more embodiments,   receiving output training data, where the output training data is the second raw data according to one or more exemplary embodiments,   training the neural network with the input and output training data, in particular by back propagation, and   providing the trained neural network.       

     A plurality of first raw data sets is produced in the case of MRF. For each of these an optimum distortion correction coefficient set can be determined (as described above) for training the artificial neural network. This data can then also be used to train a neural network. 
     With the trained neural network, it is possible, instead of the fixed incorporation of raw data sets with a second k-space trajectory, in particular with Cartesian sampling, for generating reference image data sets, to use these only for training the artificial neural network and to then use MRF data sets again in which only k-space spiral trajectories were used when sampling the k-space. 
     In addition, the disclosure relates to a method for generating at least one image data set using a trained neural network as described, having the following steps:
         providing a first raw data set, wherein the first raw data set is acquired with a magnetic resonance system and comprises measurement signals at a plurality of read-out points in the k-space, wherein the read-out points lie on a first k-space trajectory,   applying the trained neural network as described to the first raw data set, whereby a corrected first raw data set is generated, and   reconstruction of an image data set from the corrected first raw data set.       

     Once the neural network is trained it can be used for the distortion correction of the first raw data sets. In the context of MRF, a plurality of MRF image data sets can be reconstructed, as described, from a corrected or distortion-corrected first raw data set. 
     Furthermore, the present disclosure relates to a computer program product, which can be loaded into a memory of a programmable controller or an arithmetic unit of a magnetic resonance system. With this computer program product, all or different embodiments (described above) of the inventive method can be carried out when the computer program product runs in the controller. The computer program product potentially requires program means, for example libraries and help functions, to implement the corresponding embodiments of the method. In other words, the aspects directed toward the computer program product is intended, in particular, to protect software with which one of the above-described embodiments of the inventive method can be carried out or which carries out this embodiment. The software can be a source code (for example C++), which still has to be compiled and linked or which only has to be interpreted, or an executable software code, which for execution merely has to be loaded into the corresponding arithmetic unit or controller. 
     In addition, the disclosure relates to a data carrier for a controller for controlling a computer, in particular a data generator of a magnetic resonance system and/or an evaluation unit, with data for carrying out the described method. Advantageously, the data generator can be an image generator. The evaluation unit can be part of the magnetic resonance system or be an external unit. The data carrier can then also be a permanently accessible memory of the magnetic resonance system. It does not have to be installed in the controller of the magnetic resonance system for this; it can also be designed as a storage service or Cloud server. 
     Said methods can be implemented in the controller as software or as (hard-wired) hardware. 
     In addition, the disclosure relates to a magnetic resonance system with a controller. The magnetic resonance system is characterized in that the controller is designed for carrying out the method as described. 
     Further advantageous embodiments of the inventive magnetic resonance system correspond to analogous embodiments of the inventive method. To avoid unnecessary repetitions, reference will be made to the corresponding method features and their advantages, therefore. 
       FIG. 1  shows a magnetic resonance (MR) system  1  according to an exemplary embodiment that includes a scanner  2  and a controller  3 . The scanner  2  has three gradient coils  4 ,  5  and  6  for generating gradient fields. There is also a transmit coil arrangement  7  present on the scanner  2 . The transmit coil arrangement  7  can be configured as a body coil. The transmit coil arrangement  7  can also be a transmit coil array, however. In an exemplary embodiment, the controller  3  includes processor circuitry  100  that is configured to perform one or more functions and/or operations of the controller  3 . For example, the processor circuitry  100  may be configured to execute one or more executable instructions (e.g. of the computer program product  10 ) to perform one or more functions and/or operations of the controller  3  (e.g. perform the method according to one or more aspects of the disclosure). The processor circuitry  100  may also be configured to control the overall operation of the MR system  1 . 
     The transmit coil arrangement  7  can also be used for signal reception. To increase the signal-to-noise ratio SNR it is also known, to use local coils, however. In particular, a coil array can be used as the receive coil arrangement  8  for carrying out parallel imaging. The measuring time can be shortened with a coil array. 
     The controller  3  of the magnetic resonance system  1  has a data carrier (memory)  9  on which a computer program product  10  for carrying out the described method is stored. A FISP measurement sequence  11  with spiral k-space sampling, a FLASH measurement sequence  12  with spiral k-space sampling, a TrueFISP measurement sequence  13  with spiral k-space sampling and a FISP measurement sequence  14  with Cartesian k-space sampling of unconnected rows can also be stored on the data carrier  9 . 
     Furthermore, an EPI measurement sequence  15  with Cartesian k-space sampling and a FLASH measurement sequence  16  with Cartesian k-space sampling can also be stored. 
     The FISP measurement sequence  11  can be used for acquiring a first raw data set  17 . A first raw data set  18  can be acquired with the FLASH measurement sequence  12 . The TrueFISP measurement sequence  13  can be used for acquisition of a first raw data set  19 . A second raw data set  20  can be obtained with the FISP measurement sequence  14  by contrast. 
     Similarly, a first raw data set  21  can be obtained with the EPI measurement sequence  15  and a second raw data set  22  with the FLASH measurement sequence  16 . 
     Further conventional components of the magnetic resonance system  1  such as a patient couch, etc. are not shown for the sake of clarity. 
       FIG. 2  shows a TrueFISP sequence diagram  23  for the TrueFISP measurement sequence  13 . The gradient axes are labeled, as is customary, with G R  for the read direction, G P  for the phase-encoding direction and G S  for the slice selection direction. ACQ designates the axis for the radio frequency pulses and acquisition windows. 
     To excite just one slice with the radio frequency pulse  24 , a slice selection gradient  25  is applied in the slice selection direction G S  at the same time as the radio frequency pulse  24 . To compensate the dephasing effect thereof on the magnetization in the transverse plane, a slice re-phasing gradient  26  directly follows the slice selection gradient  25 . In addition to this gradient there is also a slice dephasing gradient  27  in the slice selection direction G S . This ensures that the TrueFISP measurement sequence  13  is “balanced” over a repetition time T R . In other words, over a repetition time T R  the totals of the gradient moments in the slice selection direction G S  is zero. 
     A phase-encoding gradient  28  is used in the phase-encoding direction G P . Like the read gradient  29  in the read direction G R , this is applied so as to oscillate. This embodiment is used to sample the k-space spirally. 
     In order to balance the gradient moments there is a read-rewind gradient  30  and a phase-rewind gradient  31  at the end of the gradients applied so as to oscillate. These are shown in broken lines in order to better differentiate them from read gradient  29  and phase-encoding gradient  28 . The TrueFISP measurement sequence  13  is thereby “fully balanced” over a repetition time T R , in other words, over a repetition time T R  the totals of the gradient moments is zero in all directions. 
     The measurement signals  32  are acquired in the acquisition phase. 
     Acquisition of a further raw data set is begun with the radio frequency pulse  33 . This has a different flip angle to the preceding radio frequency pulse  24 .  FIG. 6  shows possible variations of the flip angle. 
     With a FISP measurement sequence  11 , at least the slice-dephasing gradient  27  is omitted in the comparison with the TrueFISP measurement sequence  13 , optionally also the read-rewind gradient  30 . A FISP measurement sequence  11  is only balanced in the phase direction G P , therefore and possibly in the read direction G R , but not in the slice selection direction G S . 
     In contrast to a TrueFISP sequence  13 , a FLASH measurement sequence  12  is not balanced in any direction. The slice-dephasing gradient  27 , the read-rewind gradient  30  and the phase-rewind gradient  31  are missing, therefore. 
     Owing to the otherwise high level of consistency, only a TrueFISP sequence diagram  23  was shown, therefore. The modifications for a FISP measurement sequence  11  and a FLASH measurement sequence  12  result as described. 
       FIG. 3  shows a k-space  34  with a spiral k-space trajectory  35  pertaining to the measurement sequences  11  to  13 , having spirals  35   a ,  35   b ,  35   c  and  35   d . The four spirals  35   a ,  35   b ,  35   c  and  35   d  have the same form, but are rotated in 90° steps towards each other. Consequently, denser sampling of the k-space  34  results in the center. 
     By way of example, some read-out points  36  at which measurement signals  32  are acquired are provided with reference numerals. This is a known visual representation for the spatial encoding of the measurement signals  32 . 
     By way of example, one phase  37   a ,  37   b ,  37   c  and  37   d  of the phase distribution  37  of the k-space trajectory  35  is shown in the known clock hand presentation at four read-out points  36 . One phase exists at each read-out point even if only some were displayed. Each read-out point  36  has its own phase. 
     The use of a plurality of spirals  35   a ,  35   b ,  35   c  and  35   d  as the spiral k-space trajectory  35  is one possible embodiment of a spiral trajectory. 
       FIG. 4  shows a k-space  34  with a k-space trajectory  38  pertaining to the measurement sequences  14  or  16 , having k-space rows  39   a  to  39   l . The read-out points  40  are located here on the k-space rows  39   a  to  39   l . The k-space trajectory  38  is composed of individual, unconnected k-space rows  39   a  to  39   l . The arrowheads at the ends of the k-space rows are intended to represent the uniformity of the read-out direction but have no significance otherwise. 
     The phases  41   a  to  411  of the k-space rows  39   a  to  39   l  are again represented by a clock hand presentation. The read-out points  40  of a k-space row always have the same phase in this case. 
       FIG. 5  shows a k-space  34  with a k-space trajectory  42  pertaining to the EPI measurement sequence  15 , having k-space rows  43   a  to  43   l . The read-out points  44  are located here on the k-space rows  43   a  to  43   l . The k-space trajectory  42  is composed of connected k-space rows  43   a  to  43   f  and  43   g  to  43   l , which are arranged in two blocks  45  and  46 . In other words, the measurement is segmented. As described above, the blocks  45  and  46  conventionally have more than two k-space rows. Only a few k-space rows are shown for the sake of clarity. 
     The phases  47   a  to  471  of the k-space rows  43   a  to  43   l  are represented by a clock hand presentation in  FIG. 5  too. The read-out points  44  of a k-space row always have the same phase in this case. 
     As already described several times, a k-space trajectory, the k-space trajectory  42  as well, therefore, comprises all measurement signals  32 , represented here by read-out points  44 , which are used when generating an image data set. It thereby includes the two blocks  45  and  46  and not just one of them. 
       FIG. 6  schematically shows an acquisition method for acquiring an MRF data set. The numbers of the acquired MRF image data sets are plotted on the axis  48  and different sizes on the axis  49 . As the first size, the flip angle is plotted in ° from 0° at the origin to 90° at axis point  50 . The axis  48  runs from the MRF image data set 1 to the MRF image data set 3,000. 
     The 3,000 MRF image data sets are distributed among 11 sections  51 ,  52 ,  53 ,  54 ,  55 ,  56 ,  57 ,  58 ,  59  and  60 . 
     In the first section  51 , the flip angle of the FISP measurement sequence  12 , which was used on acquisition, is plotted over the curve  62  for two hundred MRF image data sets. As is described in relation to  FIG. 2 , after the application of a radio frequency pulse with a particular flip angle, a complete spiral is acquired and then the next radio frequency pulse with the next flip angle is applied and a further spiral, rotated by 90°, is sampled.  FIG. 6  accordingly shows in section  51  a flip angle distribution, which corresponds to a sin 2  curve. The maximum flip angle is 24° and constant phases are used. 
     Purely by way of example, a line  63  is plotted for the hundredth MRF image data set. The corresponding flip angle is the maximum flip angle of the curve  62 . 
     Four hundred MRF image data sets are acquired in the second section  52  with the TrueFISP sequence  13  according to  FIG. 2 . Flip angles according to the curves  64  and  65  are used in the process. These extend to 45° in the case of curve  64  and to 72° in the case of curve  65 . 
     Purely by way of example for the section  52  as well, a line  66  is plotted for the four hundredth MRF image data set. Here the flip angle is 1°. 
     The use of two different phase cycles in section  52  represents a peculiarity. No phase cycle is used on running through the flip angles of the curve  64  and a 180° phase cycle is used on running through the curve  65 . 
     In the following section  53 , the flip angles for acquisition of 450 MRF image data sets with a FLASH measurement sequence  12  are indicated in the curve  67 . These angles are smaller than in the FISP or TrueFISP measurement sequence and run to 6°. Their distribution is also a sin 2  distribution. 
     In addition to the variation of the flip angle, a phase cycle for the implementation of RF spoiling is applied in the case of repeated run-throughs of the FLASH sequence. The phase is increased by multiples of 117° in this case. 
     The sequence of measurement sequences  11 ,  12  and  13  together form a block  61 . This is used a total of three times in  FIG. 6 . Solely the type of measurement sequence but not the number of MRF image data sets or the flip angle curves is applied here. 
     Two hundred MRF image data sets are again acquired in section  54  with a FISP sequence  11 . As in section  51 , the phase is constant, but the maximum flip angle is 45°. These lie on the curve  68 . 
     Two hundred MRF image data sets follow in section  55 , which are to be acquired with a TrueFISP measurement sequence  13 . Here a 90° phase cycle is used, the maximum flip angle is 50°. The flip angles are plotted on the curve  69 . 
     The next approximately 450 MRF image data sets in section  56  are to be acquired, as in section  53 , with a FLASH measurement sequence  12 . The curve  70  shows a sine distribution with a maximum value of 14°. 
     The curve  71  in section  57  runs to 72° and shows the flip angles of the radio frequency pulse  24  or  33  on the third use of a FISP measurement sequence  11 . The phase is constant in this run too. 
     A 270°-phase cycle is used when acquiring a further two hundred MFR image data sets in section  58  with a TrueFISP measurement sequence  13  according to  FIG. 2 . The flip angles, which are plotted in the curve  72 , run to 65°. 
     The next approximately 450 MRF image data sets in section  59  are acquired with the FLASH measurement sequence  16 . The curve  73  represents a flip angle characteristic to a maximum of 20°, again sine-distributed. 
     Up to this section, measurement sequences with spiral trajectories were always used for sampling the k-space. The acquired raw data sets are first raw data sets. The measurement signals sampled on spirals are each four successive MRF image data sets combined to form a first raw data set. The number of first raw data sets is a quarter of the number of MRF image data sets, therefore. 
     Cartesian sampling, as is shown in  FIG. 4 , is used below. 
     In the last section  60  there are two curves  74  and  75  for the acquisition of second raw data sets with a FISP measurement sequence  14 . These in turn represent flip angle characteristics. As in the preceding sections already, a constant phase is used in the FISP measurement sequence  18 . A plurality of k-space rows, which are used for generating an MRF image data set, can be acquired with the same flip angle. A second raw data set then comprises, as in the case of the first raw data set, a particular multiple of the plurality of k-space rows. For example, four k-space rows can be used for reconstruction of an MRF image data set and sixteen, in other words, four times four, for reconstruction of a reference image data set. Accordingly, the k-space trajectory of the second raw data set comprises sixteen unconnected k-space rows. 
     The section with the Cartesian-sampled second raw data sets forms a block  76 . 
     To summarize, it can be maintained that, irrespective of the specific number of images and the maximum flip angles in each case, preferably one sine-distributed flip angle characteristic is used in all sections. It is also possible for significantly fewer image data sets to be acquired in one section, but preferably at least 10. 
     The first and second raw data sets thus acquired together form an MRF data set. 
     There has to be only a single second raw data set present in order to carry out the method. In this example a plurality of second raw data sets is acquired to sample the curves  74  and  75 . The second raw data sets can then also be used for generating a plurality of MRF image data sets respectively. 
       FIG. 7  shows a corrected first raw data set  77  with a corrected k-space trajectory  78 . A distortion correction coefficient set  79  has been applied to a first raw data set. Of the distortion correction coefficient set  79 , by way of example the shift vectors  79   a  to  79   d  are provided with reference numerals. Consequently, the read-out points  36  are shifted to corrected read-out points  80 . 
     Each shift vector  79   a  to  79   d  causes a local shift of the read-out points  36  in the k-space  34  and/or a phase shift of the phase  37   a  to  37   c , with both shifts being shown in  FIG. 7 . It can also be designed as an identity shift, however. In this case it does not change anything. Both the read-out point and the phase are parameters of a single measurement signal, for which reason the distortion correction of these parameters can also be combined in each case in a single shift vector  79   a  to  79   d.    
     The effect of a distortion correction coefficient set  79  is shown by way of example for four spirals  78   a  to  78   d  of a spiral k-space trajectory  78 . With spiral sampling, it is possible to use a plurality of mutually offset or rotated spirals in addition to just one spiral. Of course, corresponding shift vectors and distortion correction coefficient sets  79  can be used for any k-space trajectories. 
       FIG. 8  shows a first embodiment for the reconstruction of image data sets. Starting from a first raw data set  17 , corrected first raw data sets  77 ,  84 ,  85  and  86  are generated by application of distortion correction coefficient sets  79 ,  81 ,  82  and  83 . One image data set  87 ,  88 ,  89  and  90  is generated from each corrected first raw data set  77 ,  84 ,  85  and  86 . 
     A reference image data set  91  is generated from the second raw data set  20  without modifications. 
     The image data sets  87 ,  88 ,  89  and  90  are compared with the reference image data set  91  and one similarity value a 87 , a 88 , a 89  and a 90  respectively determined. The image data set with the greatest similarity value is selected. 
     If this is, for example, the image data set  90 , and if this is part of an MRF data set, then MRF image data sets  90   a ,  90   b ,  90   c  and  90   d  are generated from individual spirals  78   a  to  78   d  of the k-space trajectory  78  of the image data set  90 . 
     In connection with MR fingerprinting the reference image data set  91  can similarly be acquired with varying measurement parameters on acquisition of the k-space rows. MRF image data sets  91   a ,  91   b ,  91   c  and  91   d  can then also be generated from the reference image data set  91 . A signal curve is created for each of the image elements from the MRF image data sets  90   a ,  90   b ,  90   c ,  90   d ,  91   a ,  91   b ,  91   c  and  91   d  and compared with a dictionary. In this way, values are obtained for, for example, T 1 , T 2 , B 0  and B 1  for each image element. 
     The MRF image data sets  90   a ,  90   b ,  90   c  and  90   d  are sub-sampled four times in the comparison with the image data set  90  and the MRF image data sets  91   a ,  91   b ,  91   c  and  91   d  are sub-sampled four times in the comparison with the reference image data set  91 . This is possible, however, since the MRF image data sets  90   a ,  90   b ,  90   c ,  90   d ,  91   a ,  91   b ,  91   c  and  91   d  are used, as described, for creating signal curves and not for direct consideration. 
       FIG. 9  shows a flowchart of a second embodiment for generating an image data set. 
     In step S 1 , a first raw data set  17  and a second raw data set  20  are provided. The first raw data set  17  is acquired with spiral sampling and the second raw data set  20  with Cartesian sampling. 
     In step S 2 , the k-space trajectory of the first raw data set  17  is shifted using a first distortion correction coefficient set  79  and a Cartesian intermediate data set is generated from the corrected k-space trajectory  78  by means of re-gridding. This intermediate data set is reconstructed by means of Fourier transform to form a first image data set  87 . 
     The re-gridding and the Fourier transform are customary reconstruction steps in the reconstruction of raw data sets with spiral trajectories. 
     A second image data set  88  is then reconstructed in step S 3  from the same first raw data set  17  using a second distortion correction coefficient set  81 . 
     In the following step S 4 , a reference image data set  91  is reconstructed from the second raw data set  20 . Similarity measures a 87  and a 88  are then determined in step S 5 . The distortion correction coefficient set pertaining to the image data set with the greater similarity measure is used as the starting basis when determining a third distortion correction coefficient set  82 . Let the first distortion correction coefficient set  79  be the one whose image data set had the greater similarity measure. For example, the shift vectors of the first distortion correction coefficient set  79  can then receive a positive “entry” and those of the second distortion correction coefficient set  81  a negative one. 
     In step S 6 , a third image data set  89  is reconstructed from the above-mentioned first raw data set  17  using the third distortion correction coefficient set  82  and a similarity measure a 89  determined in relation to the reference image data set  86 . Depending on whether the similarity measure a 89  is greater or smaller than the previous best similarity measure, the third distortion correction coefficient set  82  continues to be used or the first distortion correction coefficient set  79 . 
     Of course, at the beginning more than two distortion correction coefficient sets can be used to define a starting basis. In particular, the initial distortion correction coefficient sets can satisfy predefined specifications, for example shift only in the center or only in the outer region. 
     Starting from this iterative adjustment of the distortion correction coefficient sets the method can be continued until an abort criterion is met. This can be a maximum iteration step or the attainment of a predefined similarity measure. 
     The method can then conclude with the existence of just a first raw data set. 
     If, however, as  FIG. 6  shows, a large number of first raw data sets exists, the distortion correction coefficient set, which pertains to the image data set with the greatest similarity measure, can be stored in step S 7 . This is used to also correct distortions in other first raw data sets and reconstruct MRF image data sets therefrom. 
     As the first raw data set  17  a raw data set is used, which was acquired with the same measurement sequence as the second raw data set. This was the FISP measurement sequence in the example relating to  FIG. 6 . 
     To enable those skilled in the art to better understand the solution of the present disclosure, the technical solution in the embodiments of the present disclosure is described clearly and completely below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the embodiments described are only some, not all, of the embodiments of the present disclosure. All other embodiments obtained by those skilled in the art on the basis of the embodiments in the present disclosure without any creative effort should fall within the scope of protection of the present disclosure. 
     It should be noted that the terms “first”, “second”, etc. in the description, claims and abovementioned drawings of the present disclosure are used to distinguish between similar objects, but not necessarily used to describe a specific order or sequence. It should be understood that data used in this way can be interchanged as appropriate so that the embodiments of the present disclosure described here can be implemented in an order other than those shown or described here. In addition, the terms “comprise” and “have” and any variants thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or equipment comprising a series of steps or modules or units is not necessarily limited to those steps or modules or units which are clearly listed, but may comprise other steps or modules or units which are not clearly listed or are intrinsic to such processes, methods, products or equipment. 
     References in the specification to “one embodiment,” “an embodiment,” “an exemplary embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. 
     The exemplary embodiments described herein are provided for illustrative purposes, and are not limiting. Other exemplary embodiments are possible, and modifications may be made to the exemplary embodiments. Therefore, the specification is not meant to limit the disclosure. Rather, the scope of the disclosure is defined only in accordance with the following claims and their equivalents. 
     Embodiments may be implemented in hardware (e.g., circuits), firmware, software, or any combination thereof. Embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). For example, a machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others. Further, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be appreciated that such descriptions are merely for convenience and that such actions in fact results from computing devices, processors, controllers, or other devices executing the firmware, software, routines, instructions, etc. Further, any of the implementation variations may be carried out by a general-purpose computer. 
     For the purposes of this discussion, the term “processor circuitry” shall be understood to be circuit(s), processor(s), logic, or a combination thereof. A circuit includes an analog circuit, a digital circuit, state machine logic, data processing circuit, other structural electronic hardware, or a combination thereof. A processor includes a microprocessor, a digital signal processor (DSP), central processor (CPU), application-specific instruction set processor (ASIP), graphics and/or image processor, multi-core processor, or other hardware processor. The processor may be “hard-coded” with instructions to perform corresponding function(s) according to aspects described herein. Alternatively, the processor may access an internal and/or external memory to retrieve instructions stored in the memory, which when executed by the processor, perform the corresponding function(s) associated with the processor, and/or one or more functions and/or operations related to the operation of a component having the processor included therein. 
     In one or more of the exemplary embodiments described herein, the memory is any well-known volatile and/or non-volatile memory, including, for example, read-only memory (ROM), random access memory (RAM), flash memory, a magnetic storage media, an optical disc, erasable programmable read only memory (EPROM), and programmable read only memory (PROM). The memory can be non-removable, removable, or a combination of both.