Patent Description:
Dynamic contrast-enhanced (DCE-) MRI (Magnetic Resonance Imaging) captures multiple single-shot images of tissue perfusion after contrast injection, aiming at identifying hypoperfusion in an organ of interest. Key factors for clinical application of dynamic MRI are image resolution and morphologic (slice) coverage, which can usually only be increased when using higher acceleration factors. Use of high acceleration factors (><NUM>) demands more complex reconstruction frameworks employing regularization through time, making use of data redundancy across the dynamic series. However, regularization methods generally do not perform well in the presence of motion, with the two types of motion commonly encountered being cardiac (contraction of the heart) and respiratory. Additionally, post-processing of reconstructed image data, such as absolute quantification of perfusion, also requires the heart to be in the same motion state across the whole dynamic series.

It is generally assumed that cardiac motion is frozen across the whole dynamic series by acquiring each image for a given slice position at the same distance to the ECG trigger. However, this assumption may break down for individual frames in the case of mis-triggering, which can have different causes and is a common phenomenon especially if the patient is pharmacologically stressed during the exam. Mis-triggering occurs when an incorrect trigger event in the detected trigger events is used for the image acquisition. Frames impacted by mis-triggering may display any cardiac phase different to the rest of the dynamic series, which poses a challenge to motion compensation, both in the context of regularized reconstructions and/or for post-processing such as quantitative perfusion MRI.

Thus, the task at hand is to identify and exclude frames impacted by mis-triggering, ideally based only on ECG acquisition timestamps, because reconstructed image data, on which the different cardiac phases would be obvious, may not be available before a regularized reconstruction or further post-processing is executed.

Different methods exist to include motion information into the reconstruction, i.e. implicitly, e.g. assuming a "low-rank" periodicity, or explicitly, e.g. in the form of motion fields. While cardiac motion is periodic (e.g. in cine imaging), intermittent variations in cardiac contraction states across a dynamic acquisition due to mis-triggering are not periodic, and thus no a-priori assumption on the motion can be made, which challenges approaches assuming periodicity.

Explicit motion estimation and correction algorithms are usually optimized for tackling respiratory motion, with cardiac motion (deformation) manifesting at a smaller spatial scale. Optimizing an algorithm to simultaneously estimate motion at different scales is challenging, because a priori-assumptions on the spatial scale of the motion are made implicitly by controlling hyperparameters such as the smoothness of the resulting motion fields. Even if there were motion estimation or compensation algorithms that can handle respiratory and extreme cardiac motion at the same time, correcting cardiac motion in practice means warping e.g. a systolic into a diastolic frame. Different cardiac contraction states, however, represent different physiological states of tissue perfusion and blood volume, so that a series containing a mix of both is ultimately not meaningful in the context of a perfusion analysis.

Rejection of mis-triggered data before final reconstruction and/or additional post-processing could in some situations be based on already reconstructed images, e.g. preliminary reconstructions or motion fields. Classification of cardiac phases with the purpose of rejecting mis-triggered data in an image series is, however, not straightforward, and training a neural network would need large amounts of training data. The latter problem for a neural network-based approach would also apply for the purpose of rejecting mis-triggered data using only the ECG trace.

Reference is made to the prior art document <NPL>. This prior art document describes that R-waves in the ECG are not always correctly detected, thus leading to missed or false ECG triggers. The reference document proposes to implement an algorithm to estimate the missed ECG trigger times by averaging the RR-interval duration of <NUM> neighboring triggers.

A need exists to overcome the above identified problems and to provide a way to identify MR images in a series of MR images which were acquired based on a wrong trigger event based solely on the ECG trace.

According to a first aspect a method for processing a plurality of cardiac MR data sets is provided as defined in claim <NUM>. The method comprises the step of providing the plurality of cardiac MR data sets, which were acquired over a plurality of cardiac cycles wherein each cardiac MR data set has a corresponding timestamp at which the cardiac MR data set was acquired. Furthermore, each of the cardiac MR data sets was acquired based on a trigger event triggering the acquisition of the corresponding cardiac MR data set. Furthermore, a series of time differences between the acquisitions of the cardiac MR data sets is determined based on the timestamps. In the series of time differences a default time difference is determined representing the time difference between two consecutive cardiac MR data sets, which were acquired using correct trigger events present in the trigger events. Furthermore, in the time differences at least one pair of incorrect time differences is determined in the series of time differences wherein each incorrect time difference represents the time difference between two consecutive MR data sets, in which at least one MR data set from the <NUM> consecutive MR data sets was acquired using an incorrect trigger event present in the trigger events. Furthermore, approved MR data sets comprising only cardiac MR data sets from the plurality of cardiac MR data sets are determined which were acquired using the correct trigger event based on the at least one pair of incorrect time differences. The approved MR data sets can then be further processed.

It was found that the series of time differences especially the pair of incorrect time differences can form a basis to identify MR data sets, which were acquired based on an incorrect trigger event. A missed trigger or an incorrect trigger leads to a specific pattern in the temporal distance between the acquisition of consecutive or successive MR data sets. It was especially found that MR data sets, which are affected by an incorrect trigger event are encapsulated by a pair of incorrect time differences. It can be interpreted as corresponding to the fact that the image acquisition enters the wrong trigger cycle and then exits the wrong trigger cycle.

According to the invention, the determining of the approved MR data sets comprises determining, in the plurality of cardiac MR data sets, first MR data sets which were acquired between the at least one pair of incorrect time differences. Furthermore, according to the invention, the approved MR data set is then determined while excluding the first MR data sets from the plurality of cardiac MR data sets. The first MR data sets correspond to the data sets, which were acquired between missed trigger events. Accordingly the images generated from the first MR data sets might have another cardiac phase compared to the approved MR data sets so that these images and data sets are excluded. All the MR data sets acquired after entering the wrong trigger cycle and until exiting the wrong trigger cycle are removed.

The method can furthermore comprise the step of determining, based on the timestamps, for the at least one pair of time differences, a time interval between the acquisition of cardiac MR data sets having incorrect time differences. The cardiac MR data sets which were acquired between the at least one pair of incorrect time differences is only confirmed as belonging to the first MR data sets if the time interval is smaller than a threshold value. When the image acquisition enters a wrong trigger cycle, it can be assumed that within a certain time, the image acquisition returns to the correct trigger event. Accordingly, images are only excluded when the image acquisition in the time interval between the pair of time differences is smaller than a defined number, by way of example a number smaller than <<NUM> acquisitions, smaller than <NUM> acquisitions, smaller than <NUM> acquisitions or smaller than any number between <NUM> and <NUM> acquisitions.

The step of determining the default time difference can comprise the step of determining an average time difference in the series of time differences and the default time difference is then determined based on the average time difference. The default time difference corresponding to the time difference occurring between two correctly identified trigger events is assumed to be present in the major cases of the acquired MR data sets. Accordingly, the default time difference will be close to the average time difference.

Furthermore, it is possible to carry out a clustering step in which the series of time difference is clustered and the default time difference is determined based on the clustering based on an assumption that the cluster having the highest number of time differences is used for determining the default time difference. Here again, it is assumed that most of the acquired MR images are acquired based on the correct trigger event, so that the clustering helps to identify the default time difference. If the acquisition conditions are very poor and it is hardly possible to identify the correct trigger event, the acquired MR image data will likely not provide fruitful results. Only when the major part of the cardiac MR data sets was acquired based on the correctly identified trigger events, the data provide a useful basis for a further processing.

Aditionally it is possible to determine integer multiples of the default time difference and the cardiac MR data sets having a time difference in the series of time differences corresponding to integer multiples of the default time difference are confirmed as belonging to the approved MR data sets. It is assumed that the trigger events occur in a regular pattern and it might be possible that one or two trigger events were missed for the image acquisition, however the image acquisition is based on the correct trigger events. Accordingly, data sets which were acquired corresponding to an integer multiple of correct trigger events are considered as belonging to the approved MR data sets even though the time difference is larger than the default time difference.

Furthermore, it is possible to identify single time differences in the series of time differences which are located at a beginning or at an end of an acquisition of the cardiac MR data sets and which differ from the default time difference by more than a threshold. The cardiac MR data sets belonging to the identified single time differences are excluded when determining the approved MR data sets. At the beginning of the acquisition, it is also possible that the acquisition is started with an incorrect trigger event and after a while, the correct trigger event is used. Here no pair of incorrect time differences can be detected, but the acquisition starts with an incorrect trigger event or finishes with an incorrect trigger event. The MR data sets belonging to those single time differences are also excluded for the further processing of the MR data sets.

The plurality of cardiac MR data sets can contain a number of cardiac MR images, but may also contain a number of cardiac MR raw data sets. Accordingly, it is not absolutely necessary to do an image reconstruction, the above mentioned analysis might only be carried out on the raw data sets as only the timestamp is needed from the different data sets.

Preferably, each time difference in the at least one pair of incorrect time differences has a time period larger than a time period of the default time difference. This means that the entering and exiting of the incorrect trigger events leads to a prolonged time distance between the acquisition of two consecutive MR data sets. The processing of the approved MR data sets can contain the step of determining a perfusion parameter using the approved MR data sets.

According to a further aspect a processing unit which is configured to process the cardiac MR data sets is provided as defined in claim <NUM>. Additionally, a computer program comprising program code is provided as defined in claim <NUM>.

The foregoing and additional features and effects of the application will become apparent from the following detailed description, when read in conjunction with the accompanying drawings in which like reference numerals refer to like elements.

It is to be understood the following description of embodiments is not to be taken in a limiting sense. The scope of the invention is defined by the claims and not intended to be limited by the embodiments described hereinafter or by the drawings, which are to be illustrative only.

The drawings are to be regarded as being schematic representations, and elements illustrated in the drawings are not necessarily shown to scale. Rather, the various elements are represented such that their function and general purpose becomes apparent to a person skilled in the art. Any connection or coupling between functional blocks, devices, components of physical or functional units shown in the drawings and described hereinafter may be implemented by an indirect connection or coupling. A coupling between components may be established over a wired or wireless connection. Functional blocks may be implemented in hardware, software, firmware, or a combination thereof.

<FIG> shows a schematic view of an MR system <NUM>, which comprises a magnet <NUM> generating a polarization field B0. An object under examination <NUM> lying on a table <NUM> is moved into the center of the MR system <NUM> where MR signals after RF excitation can be detected by receiving coils <NUM> which can comprise different coil sections wherein each coil section is associated with a corresponding detection channel <NUM>. By applying RF pulses and magnetic field gradients, the nuclear spins in the object <NUM> and especially in the part located in the receiving coil <NUM>, here the heart are exited and location coded and the currents induced by the relaxation can be detected. The way how MR images are generated and how the MR signals are detected using a sequence of RF pulses and the sequence of magnetic field gradients are known in the art so that a detailed explanation thereof is omitted.

The MR system comprises a control module <NUM> which is used for controlling the MR system. The control module <NUM> comprises a gradient control unit <NUM> for controlling and switching the magnetic field gradients, an RF control unit <NUM> for controlling and generating the RF pulses for the imaging sequences. An image sequence control unit <NUM> is provided which controls the sequence of the applied RF pulses and magnetic field gradients and thus controls the gradient control unit <NUM> and the RF control unit <NUM>. In a memory <NUM>, computer programs needed for operating the MR system and the imaging sequences necessary for generating the MR images can be stored together with the generated MR images. The generated MR images can be displayed on a display <NUM> wherein an input unit <NUM> is provided used by a user of the MR system to control the functioning of the MR system. A processing unit <NUM> can coordinate the operation of the different functional units show in <FIG> and can comprise one or more processors which can carry out instructions stored on the memory <NUM>. The memory includes the program code to be executed by the processing unit <NUM>. The processing unit can, based on the detected images reconstruct MR images.

The processing unit <NUM> can, as discussed below process a set of cardiac MR data in such a way that incorrect trigger events or missed triggers are identified in a reliable and effective way.

The proposed solution solves the problem of rejecting mis-triggered data in cardiac MR data sets prior to reconstruction of any image data by analyzing logged trigger events and identifying a signature of mis-triggered data, that is specific to cardiac perfusion MR. This can be applied for both, (pharmacological) stress and rest perfusion data. Preferably, the method discussed below is applied in dynamic perfusion examinations, however it might be applied to any other cardiac MR image where several MR data sets are acquired over different cardiac cycles.

<FIG> shows in more detail the interplay of correct and incorrect ECG trigger events, which can perturb a dynamic cardiac magnetic resonance perfusion examination. The data points and MR data sets to be rejected can be identified by identifying prolonged pairs in the time series of differences between successive acquisition times. For simplicity only one slice position is indicated per RR interval.

<FIG> shows how several data MR data sets <NUM> to <NUM> are acquired based on an ECG trigger signal of the heart. The R-wave in the ECG signal is shown by reference numerals <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM> and plays the role of the correct trigger event. However, especially in the field of magnetic resonance imaging the ECG signal is perturbed by artefactual trigger events such as the events <NUM>, <NUM>, <NUM>, <NUM> and <NUM> shown in <FIG>. During the acquisition of an image, a trigger lock time is present, in which no trigger event is detected, and in which no trigger event can trigger the acquisition of the next MR image. In the example shown in <FIG>, the system misses the correct trigger event <NUM> and the next data acquisition is triggered based on trigger event <NUM>, which is an incorrect trigger. In view of the trigger lock time, the correct triggers <NUM> and <NUM> are not detected. In the example shown the system then misses the trigger event <NUM>, which is again an artefactual trigger and the next image is acquired based on a correct trigger <NUM>. Images <NUM>, <NUM> and <NUM> are images, which are acquired in the correct cardiac phase (the systole) as shown by reference numerals <NUM> and <NUM> whereas images <NUM> and <NUM> are acquired in the wrong cardiac phase, by way of example in the diastole compared to the correct cardiac phase the systole.

<FIG> furthermore shows that the time difference between the acquisition of the images varies in dependence of the fact whether a correct or wrong trigger event is used. As can be deduced from <FIG>, the time difference <NUM> is a correctly identified time difference and corresponds to a default time difference. The following time difference is a prolonged time difference <NUM> followed by another time difference <NUM>, which seems to be in the same range as the time difference <NUM>. However, the corresponding images are acquired in the wrong cardiac phase. When the system returns to the correct trigger events another prolonged time difference <NUM> is present.

Accordingly as shown in <FIG> a missed trigger leads to a specific pattern in the temporal distance between the acquisition of successive dynamic MR data sets. While a trigger lock prevents detection of triggers during image acquisition, wherein only one slice position is shown in <FIG> for simplicity, artefactual triggers due to disturbances on the ECG trace may be accepted after a correct R-wave trigger is missed, pushing the trigger period into a shifted cycle until the artefactual trigger itself is missed. Accordingly, dynamic image acquisitions affected by missed trigger events are encapsulated by a pair of prolonged distances in time to the following acquisition, which corresponds to entering and exiting the shifted artefactual triggering cycle. Accordingly, by detecting a pair of incorrect time differences, here the time differences <NUM> and <NUM> it is possible, to identify MR data sets, which were acquired based on an incorrect or missed trigger event even though the time difference itself between missed triggers, here the time difference <NUM>, seems to be similar to the time difference <NUM>.

Accordingly, the MR data sets <NUM> and <NUM> can be detected using the following procedure:
in step number <NUM> multiple MRI images of one or more slice-positions are acquired as a dynamic series with the acquisition of each frame being triggered by an ECG trigger signal or an alternative cardiac triggering device. Furthermore, for each image data set the corresponding acquisition timestamp is determined and recorded.

In a second step the following processing is carried out either before reconstruction of the image data, by way of example for temporarily regularized reconstructions or before further post-processing the acquired images, by way of example for an absolute quantification of the flow:.

Referring to <FIG> a first situation (case A) is shown in the upper two graphs, which is called the first case in the following where at the beginning three clusters of time differences were identified cluster <NUM>, <NUM> and <NUM>. As it was found that cluster <NUM> corresponds to a multiple of the cluster <NUM>, the time difference occurring in a cluster <NUM> is assumed to be a correct time difference, namely a multiple of the correct trigger distance. Cluster <NUM> is a prolonged trigger interval and is not a multiple of the correct trigger interval so it can be determined that the corresponding images were acquired at the wrong cardiac cycle.

The same is true for the upper right part of <FIG> showing a cluster <NUM> and a cluster <NUM>. Cluster <NUM> represents the correct trigger interval whereas cluster <NUM> represents the wrong trigger interval.

In the next step <NUM>. f) it is necessary to identify pairs of missed triggers as the pair <NUM> shown in the left upper part and the pair <NUM> shown in the upper right part of <FIG>. Referring to <FIG>, these <NUM> data points could correspond to the prolonged time difference <NUM> and <NUM> shown in <FIG>. Accordingly, it is necessary to exclude the data acquisitions between these two time differences. Accordingly, the time differences occurring between these two pairs here including the first end excluding the second partner of the pair, the time differences <NUM> and 60a or <NUM> and 61a, and the corresponding MR data sets have to be identified. Even though the time differences <NUM> and <NUM> seem to be based on the correct trigger interval, they belong to the wrong cardiac phase or cycle so that it is necessary to exclude those data sets from the image processing. The identification of the pair of time differences <NUM> or <NUM> shown in <FIG> helps to identify the time differences <NUM> and <NUM> occurring between the corresponding pair. The image data acquisitions belonging to the time differences <NUM> and 60a as well as <NUM> and 61a now should be excluded from the further processing.

A possible exception to the encapsulation between pairs represents the case in which one of the two missed trigger events is outside of the dynamic image acquisition, accordingly at the end or at the start of the image acquisition. As a consequence it is advisable to also exclude image acquisitions before/after a single missed trigger at the beginning/end of the series of acquisitions. Referring to <FIG>, this means that a single missed trigger <NUM> occurs at the end of the acquisition. Based on the assumption that the images after this trigger are based on the wrong cardiac cycle all the MR data sets occurring after this single time difference are excluded.

For the determination of the pairs of incorrect time differences, one can furthermore assume that the distance between incorrect pairs should not be larger than a certain number of successive wrong triggers, the number being a number between <NUM> and <NUM> such as a number between <NUM> and <NUM>. Accordingly it can be assumed that the data points 67a and 61a shown in <FIG> do not correspond to a single pair of incorrect time differences as the distance between those image acquisitions is too large.

Summarising, in step <NUM>. g) for each pair found in step <NUM>. f, accordingly for each pair <NUM> or <NUM> shown in <FIG>, the data acquisitions in between those missed triggers are excluded from the further processing. At the end approved MR data sets are determined which contain all the MR data sets which were acquired based on the correct trigger interval or at least on a multiple of the correct trigger interval. The data sets which were acquired between the pair of incorrect time differences can be called first data sets, which are excluded from the further processing. In a further third step it is then possible to reconstruct or process the image data, namely the approved MR data sets after the data acquired in the wrong cardiac phase was excluded as discussed above. In a further optional step, it is possible to reconstruct and display also the rejected data separately to avoid a loss of any diagnostic information.

Case B as shown in <FIG> shows another situation where only a single missed trigger is detected, which is however a multiple of the correct trigger interval. Accordingly, no significant cluster difference occurs and none of the data has to be rejected. The same is true for the lower right part of <FIG>.

<FIG> summarizes some of the major steps carried out in the above-discussed identification of MR data sets, which were acquired at the wrong cardiac phase of the cycle.

In a first step MR data the cardiac MR data sets are provided, which were acquired over several cardiac cycles (step S71). Those data set comprise data sets, which were acquired based on the correct trigger event and based on an incorrect trigger event. In step S72 time differences are determined between the different acquisitions based on the timestamps of the acquisitions. Accordingly a series of time differences is generated. In a further step, in the series of time differences a default time difference is determined representing the time difference between two consecutive cardiac MR data sets, which were acquired using a correct trigger event (step S73). As discussed above, this determination of the default time difference can be based on autocorrelation or clustering of the time series as it is assumed that the major part of the images were acquired with the correct trigger event. If it is detected that one cluster is not much larger than the other cluster, it is also possible to discard the complete data set as it might be assumed that the complete image acquisition cannot provide diagnostic relevant information. Based on the default time difference is also possible to determine pairs of incorrect time differences in step S74, as discussed above in connection with <FIG> the pairs of time differences <NUM> or <NUM>. Based on this pair of incorrect time differences it is possible to determine the approved data sets in step S75, which only contain the cardiac MR data sets, which were acquired based on a correct trigger event. In step S76, the approved data set can be further processed meaning that an image reconstruction is carried out or the image data of further processed such as the determination of a perfusion parameter.

Claim 1:
A method for processing a plurality of cardiac MR data sets, the method comprising:
- providing the plurality of cardiac MR data sets(<NUM>-<NUM>) which were acquired over a plurality of cardiac cycles, each cardiac MR data set having a corresponding time stamp at which the corresponding cardiac MR data set was acquired, wherein each of the cardiac MR data sets was acquired based on a trigger event triggering an acquisition of the corresponding cardiac MR data set,
- determining a series of time differences (<NUM>-<NUM>) between the acquisitions of the cardiac MR data sets based on the time stamps,
- determining, in the series of time differences, a default time difference (<NUM>) representing the time difference between <NUM> consecutive cardiac MR data sets which were acquired using correct trigger events present in the trigger events,
- determining, in the series of time differences, at least one pair of incorrect time differences (<NUM>,<NUM>), each incorrect time difference representing the time difference between <NUM> consecutive MR data sets in which at least one MR data set from the <NUM> consecutive MR data sets was acquired using an incorrect trigger event present in the trigger events,
- determining approved MR data sets comprising only cardiac MR data sets from the plurality of cardiac MR data sets which were acquired using the correct trigger event, based on the at least one pair of incorrect time differences,
- processing the approved MR data sets,
characterized in that the determining of the approved MR data sets comprises determining, in the plurality of cardiac MR data sets, first MR data sets (<NUM>, <NUM>) which were acquired between the at least one pair of incorrect time differences and determining the approved MR data sets while excluding the first MR data sets from the plurality of cardiac MR data sets.