Prospective motion correction

Abstract

Methods for magnetic resonance imaging with prospective motion correction, wherein the method comprises: - Acquisition of initial magnetic resonance data (211) representing an initial sub-area (190-1) of an investigation area (181), - Determining motion correction parameters (220) based on a comparison of at least the first magnetic resonance data (211) with reference magnetic resonance data (215) that map the investigation area (181), - Acquisition of second magnetic resonance data (212) that map a second sub-area (190-2) of the investigation area (181), with prospective motion correction based on the motion correction parameters (220), - Generating a magnetic resonance image based on a combination of at least the first magnetic resonance data (211) and the second magnetic resonance data (212).

Description

[0001] Embodiments relate to a method for magnetic resonance imaging with prospective motion correction and a corresponding magnetic resonance system. In particular, embodiments relate to magnetic resonance imaging techniques in which motion correction parameters for acquiring magnetic resonance data depicting a sub-area of ​​the examination area are determined based on previously acquired magnetic resonance data depicting another sub-area of ​​the examination area.

[0002] Magnetic resonance (MR) imaging generates an MR image that depicts a region of interest. Typically, a certain amount of time (scan time) is required to acquire the corresponding MR data. It is possible for the subject to move during the scan time. This movement often reduces the quality of the MR image, resulting in motion artifacts, for example.

[0003] Techniques for reducing motion artifacts (motion correction) are known. Motion correction is often also referred to as motion compensation. Reduction can mean the complete or partial elimination of motion artifacts. In particular, so-called prospective motion corrections are known. With these techniques, information about motion is obtained during the acquisition of MR data (measurement) and this information is then used for motion correction during the subsequent measurement. Typically, parameters of a measurement sequence are adjusted in such a way as to counteract the motion.

[0004] Prospective motion correction can be distinguished, for example, from retrospective motion correction, where the motion correction is carried out after the measurement has been completed, meaning that the parameters of the measurement sequence can no longer be adjusted.

[0005] Purely retrospective techniques suffer from an inherently limited accuracy of motion correction; prospective adjustment of the measurement sequence parameters is not possible. As a result, certain information may not be measured at all and must be reconstructed or interpolated using specific model assumptions. This can lead to inaccuracies. Furthermore, such techniques require significant computing power.

[0006] Techniques are also known in which so-called navigator measurements are interspersed during the measurement time, enabling prospective motion correction; see, for example, A. van der Kouwe et al., “Real-time rigid body motion correction and shimming using cloverleaf navigators” in Magn. Reson. Med. 56 (2006) 1019–1032 or MD Tisdall et al., “Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI” in Magn. Reson. Med. 68 (2012) 389–399. Additional navigator images are acquired to detect and correct the motion. Furthermore, the motion is typically modeled using an extended Kalman filter for state change; see, for example, N. White et al., “PROMO: Real-time prospective motion correction in MRI using image-based tracking” in Magn. Reson. Med. Med 63 (2010) 91 - 105. The use of navigators can increase the complexity of the measurement sequence.It may therefore be necessary to adjust the timing of the measurement sequence so that the navigators can be inserted.

[0007] Furthermore, techniques for prospective motion correction based on external camera systems are known. In these techniques, motion can be detected by a camera system. However, this typically requires significantly more hardware; see, for example, M. Zaitsev et al., “Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system,” in Neuroimage 31 (2006) 1038–1050. Moreover, the hardware components used must be operational even in the high background magnetic field of the MRI scanner and should not cause artifacts, such as those resulting from susceptibility jumps.

[0008] For example, prospective motion correction is known in connection with functional magnetic resonance imaging (fMRI). In fMRI, a time series of volumetric MRI images is typically generated, and the change in image intensity (BOLD contrast) caused by changes in blood flow (blood oxygenation level) is analyzed. During the measurement, the patient performs various tasks, the so-called paradigm, to selectively activate specific brain regions. These tasks include viewing a visual stimulus—e.g., flickering checkerboard patterns or emotionally evoking images—performing motor tasks—e.g., finger tapping—and perceiving auditory stimuli. Statistical analysis of the correlation between the change in BOLD contrast in the generated MRI images and the simultaneously performed paradigm yields the activation of the respective brain regions in the form of statistical outcome maps.

[0009] Even relatively small movements, especially head movements, e.g., on the order of millimeters or less during the measurement of neurofunctional images with fMRI, can lead to significant motion artifacts and impairments of the generated statistical result maps. This can result in a loss of physiological information.

[0010] To correct patient movement, prospective and / or retrospective motion corrections can be applied. In fMRI, in particular, motion correction is a central part of image processing, increasing the detected activation and thus the agreement with the paradigm by up to 20% and the size of the activation area by up to 100% (see, e.g., TR Oakes et al., "Comparison of fMRI motion correction software tools," in Neuroimage 28 (2005) 529-543). Techniques are known in which motion correction parameters for subsequently acquired MR data are generated based on an MR image from a time series of MR images. See S. Thesen et al., "Prospective Acquisition Correction for Head Motion With Image-Based Tracking for Real-Time fMRI," in Magn. Reson. Med. 44 (2000) 457-465. Fig. As shown in Figure 1, the measurement of the MR image (n+1) with prospective motion correction is performed based on motion correction parameters of the measurement of the MR image (n). For this purpose, the MR data of the MR image (n+1) are registered against reference MR data.

[0011] However, this approach has certain disadvantages and limitations. For example, significant patient movement may occur during the acquisition time for one or more MRI images. In such cases, determining motion correction parameters for a subsequently acquired MRI image may be impossible or severely limited. In particular, it may not be possible to find a clear transformation for determining the motion correction parameters.

[0012] Therefore, there is a need for improved techniques for prospective motion correction. In particular, there is a need for techniques that enable accurate motion correction even for movements that occur on a comparatively short timescale.

[0013] This task is solved by the features of the independent claims. The dependent claims define embodiments.

[0014] According to one aspect, the present invention relates to a method for MR imaging with prospective motion correction. The method comprises acquiring first MR data depicting a first sub-area of ​​the examination area. The method further comprises determining motion correction parameters based on a comparison of at least the first MR data with reference MR data depicting the examination area. The method further comprises acquiring second MR data depicting a second sub-area of ​​the examination area. The acquisition of the second MR data is performed with prospective motion correction based on the motion correction parameters. The method further comprises generating an MR image based on a combination of at least the first MR data and the second MR data.

[0015] In other words, the examination area for determining the motion correction parameters cannot be considered as a whole, but rather divided into individual blocks or sub-areas. This allows for the comparatively accurate consideration of movements that occur on a short timescale compared to the acquisition time of the entire MR image. This is achieved because the acquisition time is divided into the acquisition of the first and second MR data points. It is not necessary for the entire examination area to consist of a disjoint combination of the individual sub-areas. Advantageously, individual sub-areas can overlap, thereby increasing the robustness of the method. For example, sub-areas for which MR data are acquired sequentially can overlap.

[0016] The reference MR data can be predefined, for example. Alternatively, the procedure could still include acquiring the reference MR data using a reference measurement sequence. The reference MR data can, for example, have a different resolution than the first and second MR data. For instance, the reference MR data can have a lower resolution than the first and second MR data. Generally, it is not necessary for the reference MR data to be acquired using the same measurement sequence as the first and second MR data. For example, if the first and second MR data are acquired using the same fMRI measurement sequence, it is not necessary for the reference MR data to also have BOLD contrast.

[0017] In particular, it may be desirable to acquire the reference MRI data using the reference measurement sequence in such a way that the acquisition time for the reference MRI data is comparatively short. It may also be desirable if there is no or no significant patient movement during the acquisition of the reference MRI data. This can help avoid systematic errors in motion correction.

[0018] For example, it is possible to acquire the reference MR data before acquiring the first MR data—e.g., in a calibration sequence. At the same time, it may be desirable to have a relatively short time interval between acquiring the reference MR data and the first MR data, so that, for example, a systematic shift of the patient due to a sudden movement is comparatively unlikely. In the case of fMRI, it may be advantageous to acquire the reference MR data only at the beginning of a given paradigm, since starting the experiment could cause unintentional patient movement. Optionally, additional time points can be added at the start of the experiment in this context, but these are not used for subsequent analysis.

[0019] In principle, it is therefore possible to use a different acquisition sequence for the reference MR data than for the acquisition of the first and second MR data. In particular, the acquisition sequence used for the reference MR data may allow for a shorter acquisition time. For example, the acquisition sequence for the reference MR data may be a Fast Low Angle Shot Magnetic Resonance Imaging (FLASH) sequence. Reducing the acquisition time of the reference MR data minimizes motion during acquisition. This avoids systematic errors in motion correction. Changes in the contrast of the reference MR data compared to the first and second MR data—such as T1 or T2 contrast versus BOLD contrast—can be accounted for by using a similarity measure, based on, for example, transinformation, to detect motion during comparison.The similarity measure is used for mutual information. Such a similarity measure is typically independent of the contrast of the MR data being compared. This makes it robust against changes in contrast in the reference MR data. Optionally, as described above, the reference MR data can be acquired with a reduced resolution compared to the first and second MR data. This reduced resolution can be increased, for example, by interpolation before comparison during the determination of the motion correction parameters, and then by post-processing to a value corresponding to the resolution of the first and second MR data. This also reduces the acquisition time for the reference MR data.

[0020] The first sub-area can be different from the second sub-area. This means that the first sub-area includes at least some points that the second sub-area does not. The first and second sub-areas can overlap or be disjoint, considered in terms of time and / or space. For example, it is possible that the first sub-area is connected, i.e., not interrupted by the second sub-area. However, it is also possible that the first sub-area is not connected, i.e., interrupted by the second sub-area. Both the first and second sub-areas can be smaller than the area under investigation and be encompassed by it. The first sub-area can be adjacent to or incongruous with the second sub-area.

[0021] By considering both the first and second sub-areas, it is possible to determine the motion correction parameters for the second MR data based on the first MR data, with the acquisition time for the first MR data being shorter than the measurement time of the MR image. This means that patient movement can be more accurately accounted for when determining the motion correction parameters; time averaging, etc., is less pronounced.

[0022] In general, a wide variety of prospective motion correction techniques can be applied. In particular, the motion correction parameters can describe a rigid transformation of the initial MR data onto the reference MR data. In a rigid transformation, it is typically assumed that the transformation can be described with sufficient accuracy by translational and rotational degrees of freedom. Specifically, it is typically assumed that the motion does not cause any deformation—e.g., compression, shearing, etc.—of the area under investigation. For example, the number of degrees of freedom considered in the rigid transformation may be limited. For instance, three rotational and three translational degrees of freedom may be considered. Often, reflections and / or intensity changes can also be taken into account within the rigid transformation.For example, in addition to translational and rotational degrees of freedom, a degree of freedom relating to intensity could optionally be considered (intensity scaling). Typically, rigid transformations are distinguished from affine transformations or elastic transformations (non-rigid transformations).

[0023] Using a rigid transformation to determine motion correction parameters can reduce the required computing power, especially compared to non-rigid transformations. In particular, it can allow for relatively quick determination of the motion correction parameters. This reduces the measurement time, which consists of the acquisition time for the MR data and the dead time required to determine the motion correction parameters.

[0024] Prospective motion correction can be performed using well-established techniques. For example, motion correction parameters can be used as control parameters for a sequence controller, which selects the parameters of a corresponding measurement sequence that provides the first and second MR data. Based on these motion correction parameters, gradient pulses, which select a specific slice of the scan area when acquiring the first or second MR data, can be adjusted to compensate for the motion. The slice position can be selected using the motion correction parameters to compensate for the detected motion.

[0025] The first and second MR data points can be combined—for example, after a Fourier transform of the raw data into image space—to generate the MR image. In general, it is possible to consider further MR data points, i.e., to subdivide the examination area into further sub-areas beyond the first and second sub-areas. This can allow for a particularly precise determination of the motion correction parameters by taking comparatively short movements into account. For example, the procedure can also include the acquisition of third MR data points that depict a third sub-area of ​​the examination area. In this case, it may be possible to apply the motion correction techniques described above to these third MR data points as well.For example, the procedure can further include determining additional motion correction parameters based on a comparison of at least the second MR data with the reference MR data. It may then be possible to acquire the third MR data with prospective motion correction based on these additional motion correction parameters. The generation of the MR image can then be based on a combination of at least the first, second, and third MR data.

[0026] The preceding section primarily illustrated techniques for prospective motion correction. It is also possible to apply techniques for retrospective motion correction. Retrospective motion correction can be applied to the entire scan area or to specific sub-areas. For example, generating the MRI image can include determining retrospective motion correction parameters. Determining these parameters can be based on comparing the initial MRI data with reference MRI data or with other MRI data covering the scan area. Retrospective motion correction of the initial MRI data can then be performed based on these parameters. Similar techniques can be applied to the second or third MRI data sets.It is evident from the above that it is not necessary to use the same reference MR data for both prospective and retrospective motion correction.

[0027] It would be possible to determine the retrospective motion correction parameters for correcting the initial MR data by considering additional or different MR data than the initial MR data itself. In general, both prospective and retrospective motion correction can be performed; the two techniques can be carried out completely independently. This means that it is not necessary to refer back to sub-areas. For example, while prospective motion correction divides the scan area into sub-areas or blocks, retrospective motion correction can consider the entire scan area independently. Furthermore, a different reference volume or reference MR data could be chosen for retrospective motion correction. This is possible because all MR data are available retrospectively in their entirety.

[0028] Retrospective motion correction can involve, for example, regridding and / or interpolation of the acquired MR data with respect to the patient's movement indicated by the retrospective motion correction parameters. Such techniques are generally familiar to experts, so further details are unnecessary here.

[0029] Combining prospective motion correction with retrospective motion correction allows for particularly precise motion correction of the MR image. This improves the quality of the MR image.

[0030] It is often desirable to depict various physical processes within the area under investigation with a certain temporal resolution. Therefore, it may be possible to repeat the preceding techniques for acquiring another MR image after a specific repetition interval. For example, acquiring the first MR data, determining the motion correction parameters, acquiring the second MR data, and generating the MR image to obtain a time series of MR images can be repeated multiple times, e.g., after a set repetition interval.

[0031] The preceding section illustrated techniques in which the examination area is divided into two or more sub-areas. For MR data acquired for a specific sub-area, prospective motion correction can be performed based on motion correction parameters determined from previously acquired MR data for another sub-area. This can also apply if the MR data are considered when generating an MR image and the previously acquired MR data are considered when generating a further MR image.

[0032] In various scenarios, the MR data acquired immediately beforehand can be used to determine the motion correction parameters for the subsequently acquired MR data.

[0033] In various scenarios, it may be possible to determine the motion correction parameters of subsequently acquired MR data from only a single sub-area of ​​the scan. However, it is also possible to determine the motion correction parameters based on MR data from multiple sub-areas. For example, the corresponding motion correction parameters for certain MR data could be determined based on two, three, or four previously acquired MR data from other sub-areas. In other words, it may be possible to consider several previous sub-areas when determining the motion correction parameters. This can increase the robustness of the determination process and reduce the probability of outliers.The fact that movements typically occur continuously can be taken into account. Continuous movements are particularly likely to occur when the patient is not supposed to move or is immobilized during the measurement period.

[0034] In general, the techniques described above can be applied to a wide variety of MR imaging techniques. For example, the prospective motion correction techniques described above can be used in perfusion or angiography MR imaging. They can also be applied in chemical-shift MR imaging and in fMRI.

[0035] For example, the first MR data can be acquired with a first measurement sequence, and the second MR data with a second measurement sequence. It is possible, for instance, for the first and second measurement sequences to be identical, while varying parameters of the sequence, such as gradient pulse amplitude, scanned slices, etc.

[0036] For example, it is possible that the first measurement sequence and the second measurement sequence involve fMRI and that the first MR data and the second MR data show a BOLD contrast.

[0037] In general, it is possible for the first and second measurement sequences to be three-dimensional. However, it is also possible for the first measurement sequence to be a two-dimensional sequence that scans at least one layer of the area under investigation to acquire the first MR data. Similarly, it is possible for the second measurement sequence to be a two-dimensional sequence that scans at least one layer of the area under investigation to acquire the second MR data.

[0038] For example, the first measurement sequence to acquire the initial MR data can scan a single slice of the study area. Determining the motion correction parameters can involve, for example, performing a slice-to-volume registration (see, e.g., EB Beall et al., “SimPACE: Generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: A new, highly effective slicewise motion correction” in NeuroImage 101 (2014) 21-34) of the initial MR data against the reference MR data. A similar approach can be used for the second MR data.

[0039] In general, it is therefore possible that the first MR data may only sample a relatively small number of k-space points or slices. This can reduce the motion component during the acquisition of the first MR data. The acquisition time for the first MR data, or the second MR data, can be particularly short. At the same time, it may be necessary to exercise particular care when comparing the first MR data with the reference MR data: due to the reduced data set of the first MR data, the comparison—e.g., in the form of registration—may be comparatively more sensitive to noise or local image artifacts.

[0040] This reduction of the data basis described above, e.g. to only one scanned layer, can also be applied to the second MR data.

[0041] Particularly in such cases as described above, it may be desirable to divide the area of ​​investigation into a comparatively large number of sub-areas.

[0042] It is also possible that the first measurement sequence, used to acquire the first MR data, scans multiple layers of the examination area. Similarly, the second measurement sequence, used to acquire the second MR data, can also scan multiple layers of the examination area. The first and second measurement sequences can scan the examination area in relation to each other, for example, in an interleaved, layer-by-layer manner. In other words, it is possible that the first sub-area is interrupted by the second sub-area. This interleaved scanning can mean, for example, that spatially adjacent layers of the MR image are assigned—e.g., alternately—to the first and second MR data; thus, anatomically adjacent layers are not measured in temporal proximity.

[0043] Several effects can be achieved by staggering the acquisition of the first and second MRI data points. It is possible for a relatively long interval to elapse between the acquisition of MRI data for spatially adjacent strata – during which, for example, MRI data for other, more distant strata are acquired. In this way, interactions between spatially adjacent strata that distort the BOLD contrast can be reduced in fMRI.

[0044] Furthermore, it is possible to determine the motion correction parameters with exceptional accuracy. Typically, in nested acquisition scenarios, the layers of the first MR data are spaced further apart or distributed over a larger area – this is because at least one layer of the second MR data is positioned between two layers of the first. MR data relating to layers distributed over a comparatively large area can, in turn, be compared with the reference MR data with exceptional reliability. This allows for a particularly precise determination of the motion correction parameters.

[0045] It is evident from the above that the prospective motion correction techniques described herein offer particular advantages when applied to nested scanned areas. However, these techniques can also be used in scenarios without nested scanning of the scanned area. In such cases, the assumption of a rigid transformation can be particularly well fulfilled, especially for measurements with comparatively long durations.

[0046] For example, the first measurement sequence for acquiring the first MR data can scan multiple layers of the study area, and the second measurement sequence for acquiring the second MR data can also scan multiple layers of the study area. The first and second measurement sequences do not necessarily have to scan the study area in a layer-by-layer nested manner relative to each other. For example, the first and second measurement sequences can scan the study area relative to each other in an anatomical order or according to spatial position in an ordered ascending or descending sequence.

[0047] According to a further aspect, the invention relates to an MRI system configured for MRI imaging with prospective motion correction. The MRI system comprises a transmit / receive unit and a processing unit. The transmit / receive unit is configured to acquire first MRI data representing a first sub-area of ​​the examination area. The processing unit is configured to determine motion correction parameters based on a comparison of at least the first MRI data with reference MRI data. The reference MRI data represent the examination area. The transmit / receive unit is further configured to acquire second MRI data representing a second sub-area of ​​the examination area with prospective motion correction based on the motion correction parameters. The processing unit is further configured to generate an MRI image based on a combination of at least the first MRI data and the second MRI data.

[0048] The MR system according to the aspect currently discussed can be set up to carry out the MR imaging method according to a further aspect of the present invention.

[0049] For such an MR system according to the aspect currently discussed, effects can be achieved that are comparable to the effects that can be achieved for the MR imaging method according to another aspect of the present invention.

[0050] The features set out above and those described below can be used not only in the corresponding explicitly set out combinations, but also in further combinations or in isolation, without leaving the scope of protection of the present invention.

[0051] The properties, features and advantages of this invention described above, as well as the manner in which they are achieved, will become clearer and more easily understood in connection with the following description of the exemplary embodiments, which are explained in more detail in conjunction with the drawings. Fig. Figure 1 illustrates a technique for prospective motion correction according to the state of the art. Fig. 2 illustrates disadvantages and limitations of the technology according to Fig. 1. Fig. Figure 3 is a schematic view of an MR system set up to carry out concepts and techniques according to the invention. Fig. Figure 4 illustrates a technique for prospective motion correction according to various embodiments of the present invention. Fig. Figure 5 illustrates a technique for prospective motion correction according to various embodiments of the present invention. Fig. Figure 6 illustrates a technique for prospective motion correction according to various embodiments of the present invention. Fig. Figure 7 is a flowchart of a process according to various embodiments of the present invention. Fig. Figure 8 is a flowchart of a process according to various embodiments of the present invention.

[0052] The present invention is explained in more detail below with reference to preferred embodiments and the drawings. In the figures, identical reference numerals denote identical or similar elements. The figures are schematic representations of various embodiments of the invention. Elements depicted in the figures are not necessarily shown to scale. Rather, the various elements depicted in the figures are represented in such a way that their function and general purpose are understandable to a person skilled in the art. Connections and couplings between functional units and elements shown in the figures can also be implemented as indirect connections or couplings. A connection or coupling can be implemented as a wired or wireless connection. Functional units can be implemented as hardware, software, or a combination of hardware and software.

[0053] The following describes techniques for prospective motion correction in MR imaging. In this process, the imaging area is divided into several sub-areas. MR data are acquired sequentially for the different sub-areas, e.g., in a nested scheme. Based on the MR data acquired for the (n)th sub-area, prospective motion correction can be performed for the (n+m)th sub-area, where m >= 1.

[0054] These techniques are used in various fMRI scenarios. Typically, a time series of MR images, each with a BOLD contrast, is acquired. Successive MR images are acquired with a repetition time (TR). The acquisition time for an MR image is less than or equal to the repetition time (TR). Typically, fMRI data acquisition follows a nested scanning scheme. This means that anatomically adjacent slices are not measured sequentially, as this would typically result in excessive crosstalk between them. To minimize such artifacts, the time interval between the acquisition of MR data between anatomically adjacent slices is typically maximized. In practice, this is typically achieved by using an even number of slices to be scanned (e.g.,(36 layers) in a first pass, all layers with odd indices are sampled before all layers with even indices are sampled in a second pass; layer numbering starting at zero can be assumed. This means that the layers are sampled in the following order: 1, 3, 5, 7,..., 31, 33, 35, 0, 2, 4,..., 30, 32, 34. This principle applies accordingly for odd numbers of layers; for example, the layers with even indices can be measured first.

[0055] Furthermore, the temporal arrangement of the acquisition times for the MR data for the different slices is typically uniformly distributed with maximum spacing. In a typical example, an examination area consisting of N = 48 slices would be acquired after each repetition time TR = 3000 ms. Due to the temporally uniform distribution of the different slices, it follows that a slice is sampled every 62.5 ms, namely 48 x 62.5 ms = 3000 ms. As described above, a nested sampling scheme can now be applied, which means that the slice with index 1 would be sampled at time T1 = 0 ms and the slice zero at time T0 = 1500 ms; here, the slice with index 0 denotes the first slice to be sampled in the second half, which is sampled after 24 x 62.5 ms.

[0056] Such a scanning scheme typically offers advantages in terms of reducing artifacts in fMRI. However, such a pre-existing scanning scheme can have adverse effects regarding motion correction. Typically, a rigid transformation model can be assumed for motion correction. This may be the case due to the high time requirements, particularly for prospective motion correction. Additionally, the anatomical context can also justify the assumption of a rigid transformation model. Since the brain is completely enclosed by the skull, the rigid transformation model can describe the actual movements relatively well.

[0057] In Fig. Figure 1 illustrates an example of prospective motion correction. First, reference MR data 215 are acquired for an examination area 181 of a subject 101 or a patient 101. The reference MR data 215 are acquired using a two-dimensional measurement sequence, i.e., different slices 200 are scanned. This is in Fig. 1 shown on the left.

[0058] Then an fMRI measurement sequence is acquired to acquire MR data 216a for an MR image of the time series of MR images ( Fig. 1, middle). After the repetition time TR, 230, further MR data 216b are acquired for the next MR image ( Fig. 1, right). The acquisition of the MR data 216a, 216b is carried out for different layers 200-1, 200-2 in a nested scanning scheme as explained above (in Fig. 1 illustrates the nested layers with solid and dashed lines.

[0059] The acquisition of the MR data 216b for the next MR image is performed with prospective motion correction. This prospective motion correction is based on motion correction parameters 220, which are determined by comparing the MR data 216a of the previously acquired MR image with the reference MR data 215 (in Fig. 1 are the motion correction parameters 220 illustrated as a change in the position of the i-th voxel after the j-th degree of freedom).

[0060] In such a scenario, the time interval between acquiring MR data for two anatomically adjacent slices is approximately half the repetition time TR, 230. Typical values ​​for the repetition time TR, 230 are on the order of 2000 ms to 3000 ms. It is possible that patient movement occurs during the acquisition of the MR data 216a, 216b for one of the MR images. For example, the movement could be caused by breathing or a slight shift of the head. Such movement typically has a significant impact on the information content of the MR data.

[0061] Since this is typically a small, continuous movement not consciously caused by the patient, the assumption of a rigid transformation for temporally sequentially scanned slices—in the example above, with a time interval of 62.5 ms—is justified. However, this assumption of a rigid transformation is often not met, or not well met, for a nested scanning scheme for anatomically adjacent slices; in the scenario discussed here, there is a significantly larger time interval between scanning these slices—in the example, half the repetition time TR, 230, i.e., 1500 ms. In particular, the time interval for scanning anatomically adjacent slices is typically on the same order of magnitude as the duration of a patient's breathing movement. Thus, the anatomy captured in the image can deviate significantly from the intended position of the slice.The assumption of a rigid transformation may not be fulfilled, or only to a limited extent.

[0062] This fact is based on the Fig. 2 illustrated. In Fig. 2. It is assumed that the repetition time TR,230 takes on a value of 3000 ms. In Fig. Figure 2, on the left, shows the initial situation, i.e., before movement 225. Typically, the patient breathes continuously. However, for the sake of simplicity, it is assumed here that after a certain time interval of 1500 ms, the patient inhales suddenly, resulting in a translational movement 225 of the area under investigation in the z-direction (in Fig. 2 oriented upwards) caused by +1 mm (in Fig. Figure 2 is shown in the middle, with the arrow illustrating movement 225 shown enlarged. After a time interval of 1500 ms following movement 225, the patient suddenly exhales, causing an opposite translation, i.e., in the z-direction, of -1 mm (in Fig. 2 not shown). Otherwise, no movement takes place. The resolution of the examination area is, for example, 2 mm in the z-direction; anatomically adjacent layers are abutting each other and are not spaced apart. This causes the layers 200-2 scanned in the first interval of the nested scanning scheme to overlap with the layers 200-1 scanned in the second interval of the nested scanning scheme. The overlap is 50% in this example (in Fig. 2 (shown on the right). The assumption of a rigid transformation for the registration of the MR data 216a to the reference MR data 215 is violated.

[0063] The motion correction parameters for acquiring the MR data are determined using 216b of the following MR image (in Fig. Even if the assumption of a rigid transformation for determining the motion correction parameters is made (2 not shown), adverse effects can occur. In such a case, the determined motion correction parameters represent a compromise for mapping the examination area to a reference time point, whereby the MR data 216a for slices 200-1, acquired in the first half of the repetition time TR, 230, and the MR data 216a of slices 200-2, acquired in the second half of the repetition time TR, 230, make conflicting contributions to determining the motion correction parameters. For example, optimization can be performed within the framework of determining the motion correction parameters. In the scenario discussed here, the optimization can, for example, be aimed at achieving identity with reference MR data 215 (in Fig. 2 not shown) converge - which determines the patient's movement state 101 in the first half of the repetition time TR, 230 (in Fig. 2 shown on the left). However, it would also be possible that the optimization converges to translation z = -1 mm, which corresponds to the movement state of patient 101 after performing movement 225 in the second half of the repetition time TR, 230 (in Fig. (2 shown on the right). Convergence to any intermediate value of the aforementioned values ​​is also possible. This means that the motion correction parameters 220 can only be determined with a certain degree of uncertainty in this case. A result of the prospective motion correction is comparatively imprecise or not very deterministic.

[0064] Another adverse effect can occur when applying retrospective motion correction based on previously detected motion 225. The motion correction parameters found for the rigid transformation can be applied to the MR data 216a to retrospectively correct the motion. Interpolation from the measured MR data to a new grid can be performed. In the Fig. In the illustrated example 2, the image content of anatomically adjacent layers 200-1 and 200-2 is thereby mixed. It should be noted that before the interpolation between the MR data, the information content of each layer 200-1 and 200-2 was correct; only the distance between adjacent layers 200-1 and 200-2 does not uniformly correspond to the actual anatomical layer spacing of 2 mm.

[0065] Techniques that can reduce or avoid such adverse effects are explained below. According to such techniques, patient movements during the measurement period have a significantly smaller impact on the quality of the MR image. In contrast to the preceding [reference] regarding the Fig. 1 and Fig. In the techniques described above, the examination area 181 is not considered completely when determining the motion correction parameters 220, but is divided into individual sub-areas. This addresses the problem that anatomically adjacent layers 200-1, 200-2 are scanned at different times with a significant time interval in a nested scanning scheme, and significant movement 225 may have occurred in between.

[0066] Such techniques can be implemented, for example, using an MR system 100, as is the case in Fig. As shown in Figure 1, the following can be implemented: The MRI system 100 has a magnet 110 that defines a tube 111. The magnet 110 can generate a basic magnetic field parallel to its longitudinal axis. The basic magnetic field can exhibit inhomogeneities, i.e., local deviations from a target value. An examination object, here a patient 101, can be moved into the magnet 110 on a table 102. The MRI system 100 also has a gradient system 140 for generating gradient fields, which are used for MRI imaging and for spatial coding of acquired raw data. Typically, the gradient system 140 comprises at least three separately controllable gradient coils 141 that are positioned relative to each other in a well-defined manner. The gradient coils 141 make it possible to apply and switch gradient pulses or gradient fields along specific spatial directions (gradient axes). The gradient fields can, for example,These gradients are used for slice selection, frequency coding (in the readout direction), and phase coding. This allows for spatial coding of the raw data. The spatial directions, which are parallel to the slice selection gradient fields, phase coding gradient fields, and readout gradient fields, do not necessarily have to coincide with the machine coordinate system. Rather, they can be defined, for example, with respect to a k-space trajectory, which in turn may be determined based on specific requirements of the respective MR measurement sequence and / or based on the anatomical characteristics of the subject.

[0067] To excite the polarization or alignment of the nuclear spins or longitudinal magnetization resulting from the fundamental magnetic field, an RF coil arrangement 121 is provided, which can radiate an amplitude-modulated RF excitation pulse into the subject 101. This allows a transverse magnetization to be generated. To generate such RF excitation pulses, an RF transmitter unit 131 is connected to the RF coil arrangement 121 via an RF switch 130. The RF transmitter unit 131 can comprise an RF generator and an RF amplitude modulation unit. The RF excitation pulses can tilt the transverse magnetization from its resting state in a layer-selective manner (1D), a location-selective manner (2D / 3D), or globally.

[0068] Furthermore, an RF receiver 132 is coupled to the RF coil assembly 121 via the RF switch 130. MR signals of the relaxing transverse magnetization, e.g., by inductive coupling into the RF coil assembly 121, can be acquired as MR data via the RF receiver 132. The MR data can image an examination area 190. The MR data can correspond to the raw data; however, it is also possible for the raw data to be further processed to obtain the MR data. For example, the raw data present in k-space can be Fourier-transformed to obtain MR data in image space.

[0069] In general, it is possible to use separate RF coil arrangements 121 for radiating the RF excitation pulses using the RF transmitter unit 131 and for acquiring the MR data using the RF receiver unit 132. For example, a volume coil 121 can be used for radiating RF pulses, and a surface coil (not shown), consisting of an array of RF coils, can be used for acquiring raw data. For example, the surface coil for acquiring raw data can consist of 32 individual RF coils and thus be particularly suitable for parallel imaging techniques. Such techniques are known to those skilled in the art, so no further details need to be explained here.

[0070] The MR system 100 also features a control unit 150, which may include, for example, a screen, a keyboard, a mouse, etc. User input can be captured and output to the user via the control unit 150. For example, it may be possible to adjust individual operating modes or operating parameters of the MR system 100 by the user and / or automatically and / or remotely using the control unit 150.

[0071] Furthermore, the MR system 100 has a computing unit 160. The computing unit 160 can, for example, be configured to perform various calculations within the scope of MR imaging with prospective motion correction.

[0072] In Fig. Figure 4 illustrates such a prospective motion correction technique. First, the reference MR data 215 are acquired. For example, the reference MR data can be acquired using an fMRI measurement sequence and thus exhibit BOLD contrast. However, it is also possible that the reference MR data 215 do not exhibit BOLD contrast. For example, the reference MR data 215 can be acquired using a FLASH measurement sequence. In the scenario of Fig. 4. An investigation area 181 was established.

[0073] Subsequently, the first MR data 211 are acquired. In the scenario presented here, the first MR data 211 have the same resolution as the reference MR data 215, however, only a sub-area 190-1 (in Fig. The area of ​​investigation 181 is depicted (shown with the dotted line in Figure 4). Based on a comparison of the initial MR data 211 with the reference MR data 215, motion correction parameters 220 are determined. For example, the motion correction parameters 220 can describe a rigid transformation of the initial MR data 211 onto the reference data 215. A recording can be performed, for example, to determine the motion correction parameters 220.

[0074] These motion correction parameters 220 are used for prospective motion correction when acquiring second MR data 212, which depict a second sub-area 190-2 of the examination area 181 (in Fig. 4 (shown with the dotted line), used.

[0075] In the scenario of Fig. 4. It is possible to generate an MR image based on a combination of the first MR data 211 and the second MR data 212. In other words, a combination of the first sub-area 190-1 and the second sub-area 190-2 completely covers the examination area 181, with the first sub-area 190-1 being complementary to the second sub-area 190-2.

[0076] However, it would also be possible that, for example, third-party MR data (in Fig. 4 not shown); in principle, it is possible that the examination area 181 is subdivided into more than two sub-areas 190-1, 190-2. In such a case, it would be possible that, based on the second MR data 212, motion correction parameters 220 are determined, which are used for a prospective motion correction of subsequently acquired third MR data.

[0077] More than three sub-areas can also be considered. In each case, the previously acquired MR data can be used for prospective motion correction, followed by subsequently acquired MR data from another sub-area. In other words, prospective motion correction can be performed cascade-wise with respect to the sub-areas.

[0078] Out of Fig. As can be seen in Figure 4, a reference measurement sequence used to acquire the reference MR data 215 scans several slices 200 of the study area 181. The acquisition of the reference MR data 215 forms the basis for the subsequent detection of motion 225. For example, a Gauss-Newton method can be used to detect the motion. This may require determining a gradient image of the study area 181 using the reference measurement sequence. Calculating the gradient image based on the reference MR data 215 is typically relatively susceptible to the motion 225 that occurs during the acquisition of the reference MR data 215. This is especially true if the slices 200 of the reference MR data 215 are to be acquired in a nested manner. For this reason, according to the scenario of Fig. 4. The examination area 181 is not scanned in a nested manner with respect to the slices 200 by the reference measurement sequence, but rather in a strictly ascending order. This means that the different slices 200 of the reference MR data 215 are scanned in anatomical order according to their spatial position. Therefore, continuous movements, for example, due to the patient's breathing 101, can only slightly and uniformly expand or compress the observed volume.

[0079] Out of Fig. Figure 4 further shows that both a first measurement sequence, with which the first MR data 211 of the first sub-area 190-1 are acquired, and a second measurement sequence, with which the second MR data 212 of the second sub-area 190-2 are acquired, are two-dimensional measurement sequences that provide MR data for different layers 200-1, 200-2. The first and second measurement sequences scan the investigation area 181 in a layer-by-layer, interleaved manner relative to each other. In other words, between adjacent layers 200-1 of the first MR data 211, there is a layer 200-2 of the second MR data 212. Fig. Thus, acquiring the first MR data 211 for the first sub-area 190-1 corresponds to a first pass of the nested scanning scheme; similarly, acquiring the second MR data 212 of the second sub-area 190-2 corresponds to a second pass of the nested scanning scheme. For example, according to a corresponding scanning scheme, the slices 200-1 of the first MR data 211 could be scanned in an anatomical sequence (in Fig. 4 e.g. from bottom to top). The same can apply to the second MR data 212. This makes it possible in particular to maximize a time interval between the acquisition of MR data 211, 212 for anatomically adjacent slices 200-1, 200-2 (in Fig. 4 is a temporal sequence in which the different layers 200-1, 200-2 are sampled (not indexed).

[0080] In the scenario of Fig. 4. When determining the motion correction parameter 220, the system does not wait for the complete acquisition of the MR data 211, 212 for an MR image. Instead, the motion 225 is detected after each pass of the nested scanning scheme. In the scenario of Fig. 4. The two sub-areas 190-1 and 190-2 do not include anatomically adjacent layers 200-1 and 200-2, which are scanned with a comparatively large time interval. Therefore, the assumption of a rigid transformation is justified.

[0081] It is possible that the acquisition of the first MR data 211, the determination of the motion correction parameters 220, the acquisition of the second MR data 212 and the generation of the MR image to generate a time series of MR images are repeated multiple times (in Fig. (4 not shown). For example, it may be possible to use motion correction parameters 220 based on the second MR data 212, which are used for a prospective motion correction of the first MR data 211 of the subsequently acquired MR image. In other words, the second pass of the nested scanning scheme – corresponding to slices 200-2 of the second MR data 212 – can trigger a second execution of the determination of the motion correction parameters 220 for the first pass of the nested scanning scheme of the subsequently generated MR image.

[0082] Using such techniques as described above in relation to Fig. As described in section 4, several advantageous effects can be achieved. First, during a pass of the nested scanning scheme, the reduced acquisition time required for the corresponding MR data 211, 212 allows for comparatively less movement 225 of the patient 101. Furthermore, there are no longer any anatomically adjacent slices 200-1, 200-1 containing contradictory image content (cf. Fig. 1, Fig. 2) Furthermore, the motion correction parameters used are relatively up-to-date (see below). Fig. 1) and are also obtained from consistent MR data 211.

[0083] It is possible that the MR image generated from the first MR data 211 and the second MR data 212 is subjected to retrospective motion correction. For example, generating the MR image may further include determining retrospective motion correction parameters based on a comparison of the first MR data 211 with the reference MR data 215 or with other MR data covering the examination area 181. Similarly, the retrospective motion correction parameters 220 may be determined alternatively or additionally based on a comparison of the second MR data 212 with the reference MR data 215 or the other MR data. Then, a retrospective motion correction of the first MR data 211 or the second MR data 212 can be performed based on the determined retrospective motion correction parameters 220. It would also be possible, for example, that the retrospective movement correction does not operate based on sub-areas 190-1 and 190-2. For example,A complete MR image could always be motion-corrected using retrospective motion correction.

[0084] For example, retrospective motion correction can involve interpolation of the first MR data 211 and / or the second MR data 212. The interpolation can take into account that in the scenario of Fig. 4. No unique transformation exists that can be applied to both the first MR data 211 and the second MR data 212. Instead, different transformations exist for the first MR data 211 of the first sub-area 190-1 and the second MR data 212 of the second sub-area 190-2. This can mean that interpolation methods based on an isotropic grid cannot be used. Instead, methods can be applied that can interpolate a non-uniformly scanned volume—here, the study area 181, which consists of the first sub-area 190-1 and the second sub-area 190-2—into a uniform grid, the so-called target voxel system. This can be comparatively computationally intensive.For prospective motion correction, however, the resulting additional effort is negligible, since the interpolation within the retrospective motion correction does not need to be performed in real time. For example, it is possible to determine the relevant information used for retrospective motion correction when determining the motion parameters 220 from the initial MR data 211 within the prospective motion correction.

[0085] The preceding discussion referred to Fig. 4 discusses a scenario in which the first measurement sequence for acquiring the first MR data 211 and the second measurement sequence for acquiring the second MR data 212 scan the examination area 181 in a layer-by-layer, interleaved manner relative to each other. However, it is also possible that the first measurement sequence for acquiring the first MR data 211 and the second measurement sequence for acquiring the second MR data 212 do not scan the examination area 181 in a layer-by-layer, interleaved manner (see Fig. 5). In Fig. 5. The first sub-area 190-1 and the second sub-area 190-2 are contiguous and adjacent to each other. In particular, there is no layer 200-1 of the second MR data 190-2 between any two nearest layers 200-1 of the first MR data 211.

[0086] Furthermore, it is from Fig. It is evident from Figure 5 that the resolution of the reference MR data 215 is lower than the resolution of the first MR data 211 and the resolution of the second MR data 212. This allows the reference measurement sequence for acquiring the reference MR data 215 to be performed relatively quickly. Therefore, the movement 225 during the acquisition of the reference MR data 215 can be relatively small.

[0087] It is also possible that the first measurement sequence to acquire the first MR data 211 scans a single layer 200-1 of the examination area 181 (see Fig. 6). In Fig. Figure 6 further shows that motion correction parameters 220, determined based on a comparison between the second MR data 212 with the reference MR data 215, can be used for the prospective motion correction of a third measurement sequence to acquire third MR data 213. While in Fig. 6. The acquisition of MR data 211, 212, 213 for three sub-areas 190-, 190-2, 190-3 is shown; in general, the examination area 181 could be divided into a larger number of sub-areas 190-1 - 190-3. In particular, it may be desirable for sub-areas 190-1 - 190-3 to completely cover the examination area 181.

[0088] The preceding discussion referred to Fig. Section 6 describes a scenario in which the motion correction parameters 220, used for prospective motion correction when acquiring the third MR data 213, are determined based on a comparison of the second MR data 212 with the reference MR data 215. This allows comparatively recent MR data to be used as the basis for determining the motion correction parameters for further MR data; the motion correction takes the current motion state into account with comparative accuracy.

[0089] However, it would also be possible to determine the motion correction parameters 220, which are used to acquire the third MR data 213, based both on a comparison of the second MR data 212 with the reference MR data 215 and on a comparison of the first MR data 211 with the reference MR data 215. Since the data set on which the comparison is based is larger in this case, the motion correction parameters 220 can be determined with greater accuracy. Furthermore, it may be possible to reduce outliers in the motion correction parameters 220 because averaging is performed over a comparatively longer period.

[0090] In Fig. Figure 7 shows a flowchart of a method for MR imaging with prospective motion correction according to various embodiments. First, in step S1, the initial MR data 211 are acquired. Then, in step S2, the motion correction parameters 220 are determined. In step S2, the motion correction parameters 220 are determined based on a comparison of the initial MR data 211 acquired in step S1 with the reference MR data 215. For example, the reference MR data 215 can be predefined; optionally, the reference MR data 215 could be acquired, for example, before step S1 is performed.

[0091] Then, in step S3, the second MR data 212 is acquired with prospective motion correction. The prospective motion correction is based on the motion correction parameters determined in step S2.

[0092] Subsequently, in step S4, the MR image is determined based on the first MR data 211 and based on the second MR data 212.

[0093] For example, to obtain a time series of MR images, steps S1-S4 could be repeated for another MR image. In this process, it would be possible, for instance, to determine current motion correction parameters based on the second MR data from step S3, which would then be used for the subsequently acquired MR data of the next MR image.

[0094] In Fig. Figure 8 shows a flowchart of a method for MR imaging with prospective motion correction according to various embodiments of the present invention. The method begins in step T1. In step T1, the reference MR data 215 are acquired. For example, the reference MR data in step T1 can be acquired using a FLASH measurement sequence. It is not necessary for the reference MR data to have, for example, a BOLD contrast. This is particularly unnecessary if the generated MR image is used for fMRI and itself has a BOLD contrast.

[0095] In step T2, initial MR data is then acquired as current MR data. For example, the initial MR data can be acquired using an fMRI measurement sequence and exhibit the BOLD contrast.

[0096] Step T3 checks whether further MRI data are needed. For example, step T3 can be used to verify whether the MRI data collected so far adequately covers examination area 181. It would also be possible to compare the number of MRI data collected so far with a predefined threshold. If further MRI data are required, step T4 is performed.

[0097] In step T4, the current MR data – i.e., in the first pass of the initial MR data from step T2 – are registered against the reference MR data from step T1. Based on this registration, current rigid motion correction parameters are determined.

[0098] Then, in step T5, further MR data are acquired with prospective motion correction based on the current rigid motion correction parameters from step T4.

[0099] Then, in step T6, the additional MR data are set as the current MR data.

[0100] Then, in step T3, it is checked again whether further MR data are needed. If so, the current MR data from step T6 are registered as the reference MR data 215 in step T4.

[0101] Once it is determined in step T3 that no further MR data are needed to generate the MR image, a retrospective motion correction is performed in step T7. In step T7, the motion correction parameters already determined in step T4 can be used. Alternatively or additionally, it would also be possible to determine specific motion correction parameters 220.

[0102] In the scenario of Fig. In step T7, the motion correction is performed after the measurement time. Generally, retrospective motion correction can also be performed during the measurement time, although even then the results of the retrospective motion correction are not fed back into the measurement sequence in order to counteract motion during the measurement itself. For example, it is possible to perform retrospective motion correction during the measurement time. This allows the motion component that occurs between the measurement of one data area and the measurement of the next data area to be corrected early on. Such a motion component often cannot be corrected by prospective motion correction, as prospective motion correction always lags slightly behind the current state of motion, for example, due to the time required for step T6 (reconstruction into image space), step T4 (feedback to the measurement sequence), and tracking of the gradient system.

[0103] In step T8, the MR image is generated based on the MR data acquired in steps T2 and T5.

[0104] In general, it is possible to repeat steps T1-T8 for further MR images in a time series. It may be possible to acquire the initial MR data from step T2 of the different MR images with prospective motion correction, using motion correction parameters last determined for the preceding MR image.

[0105] In summary, the preceding sections described techniques that enable the robust detection of a patient's movements and the use of this information for both prospective and retrospective motion correction. In particular, such techniques can offer significant advantages for nested scanning schemes of two-dimensional measurement sequences.

[0106] Several beneficial effects can be achieved. First, patient movements during the measurement can be modeled more accurately. In particular, the determined motion correction parameters do not represent a compromise in terms of complete volume acquisition of an examination area. Instead, the desired temporal resolution of the motion correction parameters can be flexibly selected by choosing the sub-areas, where, for example, a minimal update interval of the motion correction parameters corresponds to sampling a single slice. Such a technique, representing continuous motion through piecewise rigid transformation, reduces the error in determining the motion correction parameters relative to the actual trajectory of the movement. At the same time, however, the comparatively large additional effort of non-rigid registration is avoided.

[0107] A second advantageous effect is the acceleration of the update cycle for motion correction parameters. This makes it possible to detect motion and determine motion correction parameters immediately after acquiring MR data for a single sub-area of ​​the scan. In particular, it is not necessary to acquire complete MR data for the entire scan area before motion can be detected. This typically reduces the computational resources required to determine the motion correction parameters, as the number of MR data points to consider is reduced. Furthermore, this can be particularly useful in the case of prospective motion correction, as it allows patient movement to be compensated for during the acquisition time of an MR image. The technique by S. Thesen et al. referenced above typically has a delay of approximately four seconds with a repetition time (TR) of 2000 ms (see [reference]).the aforementioned publication of theses, . Fig. 1) The techniques presented here can significantly reduce the time required to determine the motion correction parameters, depending on the number of sub-areas into which the examination area is divided. This means that the subsequent interpolation to correct the image data has to correct a considerably smaller amount of motion, which also significantly reduces interpolation effects.

[0108] A third advantage is the increased quality of the motion-corrected data. As mentioned above regarding Fig. As shown in Figure 2, patient movement during MR data acquisition typically results in the anatomy actually depicted in a slice not corresponding to the expected anatomy due to the transformation. The techniques presented here are able to significantly reduce this error, so that the motion-corrected data depict the anatomy with considerably reduced motion artifacts. Furthermore, in the case of fMRI, the calculated statistical result maps or activation maps gain considerable significance through the techniques presented here. In addition to the reasons already mentioned, a reduction in the additional smearing of the MR data caused by movement during interpolation can also contribute to this. As discussed above Fig.As shown in Figure 2, a relatively simple movement in the z-direction typically leads to an elongation of the anatomy between a scanned layer with an even index and a subsequently scanned layer with an odd index. Conversely, between a layer with an odd index and a subsequently scanned layer with an even index, the anatomy is compressed.

[0109] A further beneficial effect is improved consistency between anatomically adjacent slices. Typically, the techniques discussed here result in significantly less anatomical distortion due to movement. This can provide added diagnostic value. The problem of patient movement during nested scans and its impact on the diagnosis of multiple sclerosis was investigated, for example, in an article by E.L. Gedamo in J. Magn. Reson. Imag. 36 (2012) 332-343. The motion artifacts described there as detrimental, which result in parts of the anatomy not being visualized, can be reduced by the techniques discussed here. Such effects can be particularly advantageous for motion detection techniques that determine patient movement using gradient imaging.

[0110] Naturally, the features of the embodiments and aspects of the invention described above can be combined with one another. In particular, the features can be used not only in the combinations described, but also in other combinations or individually, without leaving the scope of the invention.

Claims

[1] Method for magnetic resonance imaging with prospective motion correction, comprising: - Acquisition of initial magnetic resonance data (211) representing an initial sub-area (190-1) of an investigation area (181), - Determining motion correction parameters (220) based on a comparison of at least the first magnetic resonance data (211) with reference magnetic resonance data (215) that map the investigation area (181), - Acquisition of second magnetic resonance data (212) that map a second sub-area (190-2) of the investigation area (181), with prospective motion correction based on the motion correction parameters (220), - Generating a magnetic resonance image based on a combination of at least the first magnetic resonance data (211) and the second magnetic resonance data (212). [2] Method according to claim 1, wherein the acquisition of the first magnetic resonance data (211) is done with a first measurement sequence and the acquisition of the second magnetic resonance data (212) is done with a second measurement sequence, where the first measurement sequence and the second measurement sequence involve functional magnetic resonance imaging and the first magnetic resonance data (211) and the second magnetic resonance data (212) exhibit a “Blood Oxygenation level dependent”, BOLD contrast. [3] Method according to claim 1 or 2, wherein the first measurement sequence is a two-dimensional measurement sequence which scans at least one layer (200, 200-1, 200-2, 200-3) of the investigation area (181) to acquire the first magnetic resonance data (211), wherein the second measurement sequence is a two-dimensional measurement sequence which scans at least one layer (200, 200-1, 200-2, 200-3) of the investigation area (181) to acquire the second magnetic resonance data (212). [4] Method according to claim 3, wherein the first measurement sequence to acquire the first magnetic resonance data (211) scans a single layer (200, 200-1, 200-2, 200-3) of the investigation area (181), including determining the motion correction parameters (220): - Performing a layer-to-volume recording of the first magnetic resonance data (211) against the reference magnetic resonance data (215). [5] Method according to claim 3, wherein the first measurement sequence to acquire the first magnetic resonance data (211) scans several layers (200, 200-1, 200-2, 200-3) of the investigation area (181), wherein the second measurement sequence to acquire the second magnetic resonance data (212) scans several layers (200, 200-1, 200-2, 200-3) of the investigation area (181), wherein the first measurement sequence and the second measurement sequence scan the investigation area (181) in a layer-wise nested manner in relation to each other. [6] Method according to claim 3, wherein the first measurement sequence to acquire the first magnetic resonance data (211) scans several layers (200, 200-1, 200-2, 200-3) of the investigation area (181), wherein the second measurement sequence to acquire the second magnetic resonance data (212) scans several layers (200, 200-1, 200-2, 200-3) of the investigation area (181), wherein the first measurement sequence and the second measurement sequence do not scan the investigation area (181) in a layer-wise nested manner in relation to each other. [7] A method according to any of the preceding claims, the method further comprising: - Acquiring the reference magnetic resonance data (215) with a reference measurement sequence, wherein the reference magnetic resonance data (215) have no BOLD contrast and / or a different resolution than the first magnetic resonance data (211) and the second magnetic resonance data (212). [8] A method according to any of the preceding claims, the method further comprising: - Determining further motion correction parameters (220) based on a comparison of at least the second magnetic resonance data (212) with the reference magnetic resonance data (215), - Acquisition of third magnetic resonance data (213) that represent a third sub-area (190-3) of the investigation area (181), with prospective motion correction based on the further motion correction parameters (220), wherein the generation of the magnetic resonance image is based on a combination of at least the first magnetic resonance data (211), the second magnetic resonance data (212) and the third magnetic resonance data (213). [9] Method according to claim 8, wherein the determination of the further motion correction parameters (220) is further based on a comparison of the first magnetic resonance data (211) with the reference magnetic resonance data (215). [10] Method according to any of the preceding claims, wherein generating the magnetic resonance image further comprises: -Determining retrospective motion correction parameters (220) based on a comparison of the initial magnetic resonance data (211) with the reference magnetic resonance data (215) or with further magnetic resonance data that map the investigation area (181), - Performing a retrospective motion correction of the initial magnetic resonance data (211) based on the retrospective motion correction parameters (220). [11] Method according to any of the preceding claims, wherein the motion correction parameters (220) describe a rigid transformation of the first magnetic resonance data (211) to the reference magnetic resonance data (215). [12] Method according to one of the preceding claims, wherein the acquisition of the first magnetic resonance data (211), the determination of the motion correction parameters (220), the acquisition of the second magnetic resonance data (212) and the generation of the magnetic resonance image to obtain a time series of magnetic resonance images is repeated multiple times. [13] Method according to one of the preceding claims, wherein the first sub-area (190-1) is different from the second sub-area (190-2). [14] Magnetic resonance imaging system (100) equipped for magnetic resonance imaging with prospective motion correction, the magnetic resonance system (100) comprises: - a transmit / receive unit (130, 131, 132) that is set up to acquire initial magnetic resonance data (211) that represent an initial sub-area (190-1) of an investigation area (181), - a computing unit (160) that is set up to determine motion correction parameters (220) based on a comparison of at least the first magnetic resonance data (211) with reference magnetic resonance data (215) that map the investigation area (181), wherein the transmit / receive unit (130, 131, 132) is still set up to acquire second magnetic resonance data (212) that represent a second sub-area (190-2) of the study area (181) with prospective motion correction based on the motion correction parameters (220), wherein the computing unit (160) is still set up to generate a magnetic resonance image based on a combination of at least the first magnetic resonance data (211) and the second magnetic resonance data (212). [15] Magnetic resonance system (100) according to claim 14, wherein the magnetic resonance system (100) is further configured to perform a method according to any one of claims 1-13.

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