Acquisition of diffusion-weighted measurement data with non-trapezoidal gradient pulse shapes for diffusion coding

Abstract

An inventive method for testing the feasibility of acquiring diffusion-weighted measurement data of a test object using a magnetic resonance system with a measurement protocol with non-trapezoidal gradient pulse shapes for diffusion coding comprises the steps a) Loading a non-trapezoidal gradient pulse shape, b) Loading pre-prepared characteristics for the loaded non-trapezoidal gradient pulse shape, c) Receiving a condition that must be met during the execution of the acquisition of the diffusion-weighted measurement data, d) Determine at least one gradient amplitude relevant for the gradient pulse shape based on the charged characteristics and the condition, e) Checking the feasibility of acquiring diffusion-weighted measurement data of the object under investigation using the magnetic resonance system with a measurement protocol with the non-trapezoidal gradient pulse shape based on at least one specific relevant gradient amplitude.

Description

[0001] The invention relates to an improved planning of the acquisition of diffusion-weighted measurement data with non-trapezoidal gradient pulse shapes for diffusion coding.

[0002] Magnetic resonance imaging (MRI) is a well-known technique used to generate images of the interior of an object. In simplified terms, the object is positioned in a magnetic resonance imaging (MRI) scanner within a relatively strong, static, homogeneous background magnetic field, also known as the B0 field, with field strengths ranging from 0.2 Tesla to 7 Tesla and higher. This causes the object's nuclear spins to align with the background magnetic field. To trigger measurable nuclear spin resonances, high-frequency excitation pulses (RF pulses) are applied to the object. The resulting nuclear spin resonances are measured as so-called k-space data using specially designed coils, and MR images or spectroscopic data are then derived from these measurements. The alternating magnetic field generated by the excitation pulses applied via at least one transmitting coil is also referred to as the B1 field.To spatially encode the measurement data, rapidly switched magnetic gradient fields, or gradients for short, are superimposed on the underlying magnetic field. The temporal evolution of such a gradient field, e.g., along a gradient axis, can also be described as a gradient pulse shape. A scheme used that describes a temporal sequence of applied RF pulses and switched gradients is called a pulse sequence, or simply a sequence. The recorded measurement data are digitized and stored as complex numerical values ​​in a k-space matrix. A corresponding MR image can be reconstructed from the k-space matrix containing these values, for example, using a multidimensional Fourier transform.

[0003] A magnetic resonance imaging scan typically consists of a large number of individual partial measurements, in which raw data are recorded from different layers of the object under investigation, from which volume image data can then be reconstructed.

[0004] Furthermore, many examinations require multiple, i.e., a whole series of magnetic resonance imaging (MRI) scans of the subject, during which a specific measurement parameter is varied. The effect of this parameter on the subject is then observed based on these measurements, allowing for subsequent diagnostic conclusions. A series of MRI scans typically consists of at least two, but usually more than two. Ideally, the measurement parameter is varied in such a way that the contrast of a specific material type excited during the measurements—for example, a tissue type within the subject or a chemical substance that is significant for most or certain tissue types, such as water—is influenced as strongly as possible by the variation of the measurement parameter. This ensures that the effect of the measurement parameter on the subject is particularly clearly visible.

[0005] A typical example of a series of magnetic resonance images acquired while varying a measurement parameter that strongly influences contrast is diffusion-weighted imaging (DWI). Diffusion refers to the Brownian motion of molecules in a medium. In DWI imaging, multiple images with different diffusion directions and weightings are typically acquired and combined. The strength of the diffusion weighting is usually defined by the so-called "b-value." The diffusion images with different diffusion directions and weightings, or the images combined from them, can then be used for diagnostic purposes.Thus, by appropriately combining the recorded diffusion-weighted images, parameter maps with special diagnostic significance can be generated, such as maps that represent the "Apparent Diffusion Coefficient (ADC)" or the "Fractional Anisotropy (FA)".

[0006] Diffusion imaging is often based on echoplanar imaging (EPI) because of the short acquisition time of EPI sequences per image and their robustness to motion.

[0007] In diffusion-weighted imaging, additional gradients are inserted into a pulse sequence to visualize or measure the diffusion properties of tissue. These gradients cause tissues with rapid diffusion (e.g., cerebrospinal fluid, CSF) to experience greater signal loss than tissues with slower diffusion (e.g., grey matter in the brain). The resulting diffusion contrast is becoming increasingly important clinically, and applications now extend far beyond the classic early detection of ischemic strokes.

[0008] For many years, measurements of diffusion properties in different tissue types using magnetic resonance imaging have been an indispensable tool in clinical diagnostics. Typically, diffusion coding with two or more diffusion gradients with trapezoidal gradient pulse shapes, as described by Stejskal and Tanner in "Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient," J. Chem. Phys. 42: pp. 288-292 (1965), is employed because it is a) efficient in utilizing the performance limits of a gradient system and b) easy to describe both within the framework of diffusion models and for technical calculations (e.g., regarding stimulation or limitations of the gradient system).

[0009] Recently, however, there has been an increased interest in more complex gradient pulse shapes in the research and diagnostics sector, particularly for tensor-weighted diffusion measurements, which promise access to new contrast properties "by design" as described, for example, in the article by Szczepankiewicz et al., "Gradient Waveform Design for Tensor-Valued Encoding in Diffusion MRI", J. Neurosc. Methods 348: p. 109007 (2021).

[0010] However, the increasing complexity of the gradient pulse shapes used here poses major challenges to the preparation, planning and execution of such measurements.

[0011] When a user processes measurement protocols, for example, in the context of tensor-weighted diffusion measurements with complex gradient pulse shapes, the user is typically presented with a possible value range for each measurement parameter within a group of parameters to be processed. Each value range is categorized, for example, into at least one of the following categories: "non-adjustable parameter values," "conditionally adjustable parameter values," and "unrestricted adjustable parameter values." This categorization can be displayed to the user in a suitable format, such as color-coded, to facilitate the selection of appropriate parameter values.

[0012] Parameters can be assigned to category A ("non-adjustable parameter value") if a parameter value assigned to this category A for a modified measurement parameter would result in an unexecutable measurement protocol. Such parameter values ​​can, for example, be locked for the user, preventing them from being selected for the corresponding measurement parameter. However, parameter values ​​assigned to this category A can still be displayed or otherwise brought to the user's attention, for instance, if they could be adjusted if the measurement protocol being processed undergoes a corresponding change elsewhere, such as a change to at least one other measurement parameter within the protocol, particularly one that is dependent on the modified measurement parameter. Such dependencies between different measurement parameters further increase the complexity of processing measurement protocols.

[0013] An assignment to category B ("conditionally adjustable parameter value") can occur if a parameter value assigned to this category B for a modified measurement parameter requires an automated, and in particular clearly defined, change to the respective parameter value of at least one other measurement parameter besides the modified measurement parameter in order to make the measurement protocol executable. The required automated change can then occur, for example, as soon as the user sets a parameter value assigned to category B for a modified measurement parameter, so that the adjustments necessary for the measurement protocol to be executable are carried out automatically.

[0014] An assignment to category C ("unrestricted") is possible if a parameter value assigned to this category C for a processed measurement parameter has no impact on the executability of the processed measurement protocol. A parameter value assigned to this category C for a processed measurement parameter can be selected without the need for further adjustments.

[0015] To enable users to assign a measurement protocol to at least one of categories A, B, or C while it is being processed, complex calculations are necessary. This is particularly important because every change to the value of a measurement parameter can alter the assignment of possible parameter values ​​to categories A ("not adjustable"), B ("conditionally adjustable"), and C ("fully adjustable") for at least one other measurement parameter, and potentially for many others.

[0016] The calculations can include, in particular, the determination of a gradient amplitude G(t) necessary for a given diffusion weighting (b-value).

[0017] In diffusion-weighted MR imaging, a so-called b-matrix B in the diffusion tensor model describes the diffusion-induced decrease of an MR signal S: S=S0 exp(−B_:D_).

[0018] Here, S0 represents a signal amplitude without diffusion weighting, and D is the diffusion tensor. For symmetric tensors, the product B : D (dyadic product) is defined as follows: B:D=bxxDxx+byyDyy+bzzDzz+2 bxyDxy+2 bxzDxz+2 byzDyz

[0019] Ultimately, the diffusion tensor, also called the weighting tensor, describes a characteristic of diffusion motion, for example, anisotropy due to a microscopic environment present in the object under investigation, and the b-matrix describes a characteristic of the diffusion encoding. One trace of the b-matrix provides a measure of the diffusion weighting, is invariant under rotations, and is also referred to as the b-value. b=Trace(B_)=bxx+byy+bzz.

[0020] The elements b ij The b-matrix B is determined according to the following calculation rule, where the integral over the total time of the diffusion coding, i.e., for example, from excitation by an RF excitation pulse at a time t=0 until the acquisition of the RF signal, which is recorded as measurement data, after the echo time TE has elapsed, i.e., at t=TE): B_=0∫TEdt' q(t') qT(t') with the gyromagnetic ratio γ and the q-space vector q : q(t')=γ 0∫t'dt'' G(t'') with a gradient G(t") and with q T as the transposed vector of the vector q.

[0021] It follows that the elements b ij the b-matrix for an arbitrary gradient pulse sequence G(t) = G f(t) = G (f x (t), f y (t), f z (t)) can be calculated as follows: bij=γ2G20∫TEdt'(0∫t'dt''fi(t''))(0∫t'dt''fj(t''))with i,j∈{x,y,z}

[0022] For trapezoidal gradient pulse shapes, and gradient pulse shapes composed of trapezoidal gradient pulse shapes, analytical approaches and simple approximations are known that can perform sufficiently fast calculations of the elements bij of a b-matrix during measurement planning when processing parameter values ​​of measurement parameters in a measurement protocol. However, for complex, especially non-trapezoidal, gradient pulse shapes, these established techniques can no longer be used due to the significantly increased complexity.

[0023] The b-matrix (the weighting tensor) must also be determined during a measurement, for example, so that it can be stored as a DICOM parameter along with image information derived from the acquired diffusion-weighted measurement data. Because the calculations need to be performed in short, real-time windows during an ongoing measurement, the computational effort required to determine the b-matrix is ​​of great importance. This effort is generally too high with known methods to perform the required calculation for non-trapezoidal gradient pulse shapes in real time. While determining a b-matrix (or a b-value) is fundamentally possible, for example,Based on a specific definition of a non-trapezoidal gradient pulse shape, the switching process would be performed stepwise, for example, on a time grid of a gradient unit of the magnetic resonance system, taking into account all gradients relevant for diffusion weighting, possibly including contributions from trapezoidal imaging gradients. However, during interactive measurement planning, and especially during real-time measurement acquisition of diffusion-weighted data, this requires control computers with exceptionally high computing power. This solution is problematic due to both the high energy consumption and the cost of such high-performance computers.

[0024] Calculations for assigning parameter values ​​of measurement parameters of a measurement protocol to one of the aforementioned categories may additionally or alternatively include a check of the feasibility of a measurement with the measurement protocol with regard to physiological limitations, in particular peripheral or cardiac stimulations.

[0025] For trapezoidal pulse shapes, analytical approaches and simple approximations are already known, allowing such calculations to be performed quickly during measurement planning when processing parameter values ​​of a measurement protocol. However, these established techniques are no longer applicable to complex, especially non-trapezoidal, gradient pulse shapes.

[0026] Approximations are already known with which some gradient pulse shapes can be approximated by "enveloping" trapezoidal gradient pulse shapes under certain conditions. For example, for sinusoidal or cosine-like gradient pulse shapes, an approximation using trapezoidal gradient pulse shapes is relatively straightforward as long as the number of half-waves is sufficiently small, for example, less than 100. Then the aforementioned calculations can be performed sufficiently quickly using such an approximation.

[0027] Furthermore, US20240295621A1 discloses, for example, a method for trapezoidal gradient pulse shapes that, during measurement preparation, calculates an assignment of a maximum permissible rate of rise S, dependent on a gradient amplitude G, for specific pulse sequences, such as "monopolar," "bipolar," or "oscillating" sequences of trapezoidal gradient pulse shapes, and adjusts the ramp durations of the trapezoidal gradient shapes accordingly during measurement planning. However, this approach is not applicable to non-trapezoidal gradient pulse shapes due to their complex (and predetermined) shape with a constantly varying rate of rise.

[0028] For more complex, especially non-trapezoidal, gradient pulse shapes, which can be characterized / defined by several hundred or thousand data points, this approach is no longer viable. Furthermore, the results of the calculations are generally too conservative, since envelopes exhibit a higher stimulation potential than the actual pulse shapes.

[0029] It would be conceivable, in principle, to disregard some of the aforementioned limitations that can restrict the feasibility of a measurement for acquiring diffusion-weighted data. Regarding compliance with physiological limitations, one could rely on monitoring mechanisms typically integrated into magnetic resonance imaging (MRI) systems for this purpose. However, the disadvantage of such an approach is that the feasibility of a measurement cannot be guaranteed at the start of the measurement, and thus, if such limitations are exceeded during the measurement and detected by such a monitoring mechanism, this could lead to its termination.

[0030] Furthermore, a subsequent feasibility check after a measurement has been planned, e.g., immediately before the measurement begins, would also be possible. However, this would often force a user to modify their measurement plan at very short notice, potentially requiring significant changes to parameter values. In a clinical setting, this is not a practical solution.

[0031] Another fundamental way to minimize the computational effort required for assigning parameter values ​​to measurement parameters within a measurement protocol would be to provide pre-tested protocols (with fixed parameter values ​​for the measurement parameters to be set). However, this approach lacks the necessary flexibility in clinical applications when planning a measurement, where parameter values ​​for the measurement parameters of planned measurement protocols—for example, regarding desired spatial resolution, number of slices, or contrast-determining parameters such as echo time (TE) or repetition time (TR)—often need to be optimized specifically for the individual case.

[0032] In principle, a subsequent (e.g., one-time) feasibility check of a modified measurement protocol immediately before starting the measurement would also be conceivable. However, this would often force a user to modify their measurement plan at very short notice (and possibly necessitating significant changes to parameter values). This is hardly practical in a clinical setting.

[0033] Another possibility would be to provide pre-tested measurement protocols (with defined measurement parameters) before any measurement, for example, once during the installation of a magnetic resonance imaging (MRI) system. However, this approach lacks the flexibility required in clinical applications for customizing measurement protocols (e.g., regarding spatial resolution, number of slices, or contrast-determining parameters such as echo time (TE) or repetition time (TR)) for individually performed measurements.

[0034] The purpose of the described method is to enable the user to plan measurements with non-trapezoidal gradient pulse shapes for diffusion coding in a simplified, possibly interactive, manner.

[0035] The problem is solved by a method for testing the feasibility of acquiring diffusion-weighted measurement data of a test object using a magnetic resonance system, a measurement protocol with non-trapezoidal gradient pulse shapes for diffusion coding according to claim 1, a magnetic resonance system according to claim 10, a computer program according to claim 11, and an electronically readable data carrier according to claim 12.

[0036] An inventive method for testing the feasibility of acquiring diffusion-weighted measurement data of a test object using a magnetic resonance system with a measurement protocol with non-trapezoidal gradient pulse shapes for diffusion coding comprises the steps a) Loading a non-trapezoidal gradient pulse shape, b) Loading pre-prepared characteristics for the loaded non-trapezoidal gradient pulse shape, c) Receiving a condition that must be met during the execution of the acquisition of the diffusion-weighted measurement data, d) Determine at least one gradient amplitude relevant for the gradient pulse shape based on the charged characteristics and the condition, e) Checking the feasibility of acquiring diffusion-weighted measurement data of the object under investigation using the magnetic resonance system with a measurement protocol with the non-trapezoidal gradient pulse shape based on at least one specific relevant gradient amplitude.

[0037] The inventive loading of prepared characteristics for at least one non-trapezoidal gradient pulse shape of the measurement protocol allows for a rapid check of the feasibility of acquiring diffusion-weighted measurement data of the object under investigation and a rapid assignment of possible parameter values ​​for the measurement data to be set in the measurement protocol to a category indicating the feasibility of the measurement protocol on the magnetic resonance system while adhering to the loaded limit values. By assigning the parameters to the at least one category, it is made easier for a user to find parameter values ​​for the measurement parameters to be set in a measurement protocol to be used during the planning of a measurement and thus to obtain a measurement protocol that can be executed on the magnetic resonance system with the selected parameter values.

[0038] Characteristics for any pulse shape can be prepared without specific design requirements (e.g., periodicity). Determining the characteristics of non-trapezoidal pulse shapes can be performed on any computing unit and at any time before a measurement. This allows prepared characteristics for non-trapezoidal gradient pulse shapes to be loaded into a memory of the magnetic resonance system, either during commissioning or at any time after commissioning.

[0039] In particular, the method described here allows for very fast calculation of diffusion weights, especially b-values ​​and b-matrices, both during measurement planning and during measurement execution. The method is applicable without restrictions to any temporal sequence of trapezoidal and non-trapezoidal pulse shapes within the measurement protocol and can be adapted for any rotation without the need for recalculations.

[0040] Likewise, the method described here allows for the rapid determination of a maximum permissible gradient amplitude to verify compliance with physiological limits, in particular a maximum permissible stimulation, thus ensuring the feasibility of a planned measurement, whereby the loading of characteristics that have already been calculated in advance, e.g. during measurement preparation, enables particularly fast (interactive) measurement planning.

[0041] Overall, the method described herein thus facilitates and accelerates the feasibility check of a measurement for acquiring diffusion-weighted measurement data with a non-trapezoidal gradient pulse shape during the planning of a measurement to be carried out with the magnetic resonance system.

[0042] A magnetic resonance system according to the invention comprises a magnet unit, a gradient unit, a radio frequency unit and a control unit designed for carrying out a method according to the invention, with an allocation unit.

[0043] A computer program according to the invention implements a method according to the invention on a control device when it is executed on the control device. For example, the computer program includes instructions that, when the program is executed by a control device, e.g., a control device of a magnetic resonance system, cause this control device to execute a method according to the invention. The control device can be in the form of a computer.

[0044] The computer program can also be in the form of a computer program product that can be directly loaded into a memory of a control device, with program code means to execute a method according to the invention when the computer program product is executed in a computing unit of the control device.

[0045] A computer-readable storage medium according to the invention comprises instructions which, when executed by a control device, e.g. a control device of a magnetic resonance system, cause it to execute a method according to the invention.

[0046] The computer-readable storage medium can be designed as an electronically readable data carrier which includes electronically readable control information stored on it, which includes at least one computer program according to the invention and is designed in such a way that, when the data carrier is used in a control unit of a magnetic resonance system, it carries out a method according to the invention.

[0047] The advantages and explanations given regarding the procedure also apply analogously to the magnetic resonance system, the computer program product and the electronically readable data carrier.

[0048] Further advantages and details of the present invention will become apparent from the exemplary embodiments described below and from the drawings. The examples shown do not constitute a limitation of the invention. They show: Fig. 1 a schematic flowchart of a method according to the invention for testing the feasibility of acquiring diffusion-weighted measurement data of a test object with a magnetic resonance system using a measurement protocol with non-trapezoidal gradient pulse shapes for diffusion coding, Fig. 2 A schematic representation of a gradient with a non-trapezoidal gradient pulse shape GF on three axes Gx, Gy, Gz, Fig. 3 Another schematic representation of a gradient with a non-trapezoidal gradient pulse shape GF on three axes Gx, Gy, Gz, Fig. 4 a schematically illustrated magnetic resonance system according to the invention.

[0049] Fig. Figure 1 is a schematic flowchart of a method according to the invention for testing the feasibility of acquiring diffusion-weighted measurement data of an object under investigation using a magnetic resonance system with a measurement protocol with non-trapezoidal gradient pulse shapes for diffusion coding.

[0050] A non-trapezoidal gradient pulse shape GF is loaded (Block 101).

[0051] Pre-loaded characteristics CGF for the loaded non-trapezoidal gradient pulse shape GF are loaded (Block 103). Such pre-loaded characteristics CGF can include, for example, section-specific terms for calculating a diffusion quantity, such as a diffusion tensor and / or a diffusion weighting (b-value), and / or maximum permissible gradient amplitudes Gmax, particularly for various constraints N, which are associated with a permissible stimulation of a test object.

[0052] A condition B, which must be met during the execution of the acquisition of diffusion-weighted measurement data with the non-trapezoidal gradient pulse shape GF, is received (Block 105). A condition can, for example, include a desired diffusion parameter, such as a diffusion weighting, and / or a maximum permissible stimulation of the object under investigation.

[0053] Based on the charged characteristics CGF and the condition B, at least one gradient amplitude Gr relevant for the charged gradient pulse shape GF is determined (Block 107).

[0054] For example, the received condition may include a maximum allowable stimulation, and in particular at least one constraint on the maximum allowable stimulation, and a maximum gradient amplitude Gr may be determined with which the allowable stimulation is not exceeded.

[0055] To assess the feasibility of a maximum permissible stimulation loaded as a condition, known stimulation models can be used. For example, the SAFE model described in the article by Hebrank et al., “SAFE model - a new method for predicting peripheral nerve stimulations in MRI”, Proceedings of the 8th Annual Meeting of ISMRM, Denver, 2000, or a simpler but more conservative limitation of the maximum slew rate dB / dt at each time point of the gradient pulse shape GF, e.g., according to the standard IEC 60601-2-23, are possible stimulation models.

[0056] Each of these models is capable of calculating, for any gradient amplitude profile, G(t) = G f(t) = (f x (t), f y (t), f z (t)) of a gradient pulse shape to make a statement A about whether the execution is permissible or not, which can be expressed in pseudo-code as: A(G f(t),{Ni})=true / false.

[0057] The condition can include at least one constraint for a maximum permissible stimulation and for each constraint an associated maximum permissible stimulation.

[0058] A constraint can be a constraint from the group consisting of an orientation of the axes of a gradient unit of the magnetic resonance system relative to the object under investigation, a stimulation type and an operating mode to be selected.

[0059] In such a feasibility study using a stimulation model, constraints N can be considered. i This may be relevant. For example, the orientation of the x, y, z axes of gradient unit 5 relative to an object of investigation, such as a patient, can be included in the examination.

[0060] Here, N can be used as a constraint. iIn particular, the positioning of a patient as an examination subject may be requested, for example, prone, supine or lateral positioning, and the patient's orientation (head first or feet first).

[0061] Furthermore, an alignment of the main magnetic field relative to the x, y, z axes of the gradient unit can be specified as a constraint N. i This may be relevant, for example, there are magnetic resonance systems with horizontally or vertically oriented main magnetic field.

[0062] Furthermore, investigation-specific constraints N can be applied to each measurement. i, such as operating modes, may be relevant. Various levels of permitted stimulation are known here, for example, in addition to a "normal operating mode" with a first probability of stimulation, there is a "controlled increased operating mode" in which higher levels of stimulation of the subject are permitted and stimulation occurs with a higher probability, and which therefore advantageously requires confirmation by a user.

[0063] Additionally or alternatively, a stimulation type can be specified as a constraint N. i relevant, whereby, for example, the stimulation types of peripheral nerve stimulation (PNS) and cardiac stimulation (CNS) can be distinguished.

[0064] For non-trapezoidal gradient pulse shapes, a stimulation potential can be calculated using one of the aforementioned models on a sufficiently narrow grid of support points - for example, 1µs, 10µs or 100µs - assuming a constant gradient amplitude on each of the three axes within the grid interval.

[0065] Conversely, a maximum permissible gradient amplitude for a gradient G(t) of the non-trapezoidal gradient pulse shape GF can also be determined, with which the execution is (just) permissible and which can be encompassed by the loaded characteristics CGF. Such calculations can be performed in advance, for example, during measurement preparation. Therefore, the increased computational effort due to the complex non-trapezoidal gradient pulse shapes plays only a minor role in these calculations.

[0066] In a simple embodiment, for a gradient with a non-trapezoidal gradient pulse shape with a gradient amplitude G(t) = G f(t) for each set {N i} of constraints during measurement preparation using a search algorithm, the amplitude G max The limit under which execution is still permissible must be determined. For this purpose, an extensive search algorithm can be used, which could be represented in pseudocode as:

[0067] For stimulation models without a time characteristic, i.e., stimulation models that do not take into account a history of previously switched gradient pulses, this approach is already sufficient to determine the maximum permissible gradient amplitude Gmax for the chosen set of constraints.

[0068] The SAFE model mentioned above tests a stimulation potential, for example, by processing (numerically) differentiated, axis-specific gradient amplitude profiles with different filter functions and combining the results into a temporal stimulation profile. This profile is then compared with limit values ​​to determine feasibility.

[0069] For such stimulation models with time characteristics, it is necessary to make additional assumptions about previously switched gradient pulses, because the maximum gradient amplitudes are calculated in advance, at a time when details of the gradients later used in the imaging measurement protocol are not yet known.

[0070] For example, it can be assumed that immediately before the gradient with the non-trapezoidal gradient pulse shape GF, which serves for diffusion coding, a representative trapezoidal imaging gradient B(t) with an amplitude characteristic of imaging (e.g., 50% of the hardware-side maximum) and a rise rate characteristic of the imaging process (e.g., 100% of the hardware-side maximum) is applied. Alternatively, two or more successive imaging gradients B can be applied. n (t) with alternating amplitudes are assumed: these generally have a higher stimulation potential and thus provide a more reliable (conservative) consideration of possible consequences of switched gradients in imaging.

[0071] Fig. Figure 2 is a schematic representation of a gradient with a non-trapezoidal gradient pulse shape GF on three axes Gx, Gy, Gz. The gradient amplitude with the gradient pulse shape GF in the Gx direction is shown as a double-dotted dash-dotted line, the gradient amplitude with the gradient pulse shape GF in the Gy direction as a single-dotted line, and the gradient amplitude with the gradient pulse shape GF in the Gz direction as a dashed line. Furthermore, a possible imaging gradient Bx(t) is shown for illustration purposes, which can be assumed to be connected directly before the gradient with the gradient pulse shape GF. The depicted imaging gradient Bx(t) has, by way of example, a positive polarity in the period t0 and t1 and a negative polarity in the immediately following period from t1 to t2.For example, an imaging gradient Bx(t) can be a readout gradient train for recording echo signals generated by the measurement protocol using an echo-planar (EPI) imaging technique.

[0072] Instead of specifying a fixed rate of increase, as described above, e.g., according to standard IEC 60601-2-23, an initial step can be used to determine a maximum rate of increase in a known manner, e.g., using a known stimulation model. This maximum rate should result in stimulation within the permissible limits after a predetermined number of alternating trapezoidal imaging gradients. This preparation allows verification that a predetermined non-trapezoidal pulse sequence can be executed after a representative number M (e.g., M=2) of imaging gradients, and the corresponding maximum permissible amplitude G can be determined. max to be determined.

[0073] For example, in the pseudo-code above, instead of A(G * f(t), {N i}) simply A(∑m=1…M Bm(t−tm−1)+G*f(t−tM),{Ni}) Considered, assuming that the imaging gradients B m (t-tm-1) at time t m-1 start and the last imaging gradient B m (t-tm-1) at t M ends.

[0074] A determination of the maximum permissible gradient amplitudes Gmax can be performed separately for different constraints {N i} can be carried out. The results can be used, for example, as follows: For a stimulation type, especially PNS or CNS, as a constraint, a separate execution of the respective calculation with PNS and CNS modeling can be carried out, whereby maximum permissible gradient amplitudes G max,PNS , G max,CNSto be determined. Since both limit values ​​usually have to be met, the smaller of the two values ​​can simply be included in the loaded characteristics in order to check feasibility when planning a measurement.

[0075] For an operating mode as a constraint, a separate calculation can also be performed, e.g. for operating modes "normal" (0th) and "controlled increased" (1st), whereby maximum permissible gradient amplitudes G max,0th , G max,1st Both values ​​can be determined. They can be included in the loaded characteristics, and they also have an assignment to the respective operating mode, so that if, for example, by selecting a measurement protocol, possibly a patient registration and / or a user input, an operating mode is defined for a measurement to be performed, the corresponding value can be used to check executability.

[0076] Even for an orientation of the gradient axes relative to the object under investigation, especially a patient as the object of investigation, as a constraint, a separate execution of the respective calculation for different orientations can be carried out, whereby maximum permissible gradient amplitudes {G max,ar The gradient amplitude G(t) of a gradient with a non-trapezoidal gradient pulse shape can be determined for different orientations. For this purpose, the amplitude G(t) of a gradient with a non-trapezoidal gradient pulse shape can be written as a normalized curve f(t) with a scaling G as G(t) = G f(t). The x, y, z axes of the gradient unit can, for example, be permuted in separate calculations, i.e., with different assignments of the, in particular normalized, non-trapezoidal gradient pulse shapes f. 1 / 2 / 3 (t) to the axes x, y, z. The obtained values ​​for {G max,ar} can include all (possibly separately for each limit value and type) of loaded characteristics, such that during the planning of a measurement, the value of {G max,ar} can be used for the feasibility check, which is currently relevant for the measurement to be performed. Although this approach requires more memory, it allows for a maximum permissible gradient amplitude Gmax to be calculated from {G max,ar} can be used. Alternatively, only the smallest of the values ​​of {G} can be used. max,ar} as maximum permissible gradient amplitude Gmax = min{G max,ar The characteristics loaded are included and used to check executability. This approach reduces the required storage space, reduces the complexity of choices, and allows a measurement to be tested independently of the actual storage of the object under investigation.

[0077] Since it is not known a priori for which gradient axis Gx, Gy, Gz the combination of trapezoidal imaging gradients B m (t-tm-1) and non-trapezoidal diffusion gradients of the gradient pulse shape GF the maximum stimulation potential and thus the smallest permissible G max exhibits, the application of imaging gradients B can be provided for. m (t-tm-1) assumes simultaneous stimulation of Gx, Gy, and Gz on all axes. However, this – the SAFE model combines the stimulation potential of all axes – can lead to an excessively strong restriction of G. max lead.

[0078] It is therefore proposed to perform separate calculations, each assuming a specific imaging axis, so that the imaging gradient B m (t-tm-1): can be written as: Bx,m(t)=(Bm(t),0,0),By,m(t)=(0,Bm(t),0),Bz,m(t)=(0,0,Bm(t)).

[0079] The in Fig. The imaging gradient Bx(t) shown in Figure 2 runs, for example, along the Gx axis.

[0080] Using the same approach as before (without imaging gradients), separate maximum gradient amplitudes G can be determined. max,x , G max,y , G max,z be determined, of which at least the smallest (min{G max,x , G max,y , G max,z}) can be loaded as the maximum permissible gradient amplitude Gmax as a characteristic of the gradient pulse shape GF.

[0081] Similarly, different polarities of the imaging gradients can be taken into account: B'x,m(t)=(−Bm(t),0,0),B'y,m(t)=(0,−Bm(t),0),B'z,m(t)=(0,0,−Bm(t)), Depending on the shape of the non-trapezoidal gradient pulse, either the positive or the negative polarity of the imaging gradient may exhibit the higher stimulation potential.

[0082] If a sequence of several sections of non-trapezoidal gradient pulse shapes is used, for example before and after RF refocusing pulses to be applied, this can also be taken into account analogously in the preparation for a measurement in advance of planning a measurement to be carried out, so that the following can be written for statement A of the stimulation model used: A(∑m=1…N Bm(t−tm−1)+G*∑k=1…K fk(t−tM+k−1),{Ni}), where the sections are each at time t M+k-1 start.

[0083] If time intervals between sections are not defined during measurement planning, for example due to different durations of refocusing modules depending on other measurement parameters of the measurement protocol, separate calculations can be performed with different time intervals P between two sections, and, for example, at least the minimum result can be used as a limit for the maximum permissible gradient amplitude Gmax. Generally, a number of representative time intervals (e.g., P ∈ {0 ms, 5 ms, 10 ms, 20 ms}) are sufficient, the duration of which is advantageously based on a time characteristic of the stimulation model.

[0084] In Fig. Figure 2 shows an example of a sequence of two sections of non-trapezoidal gradient pulse shapes GF with a time interval P between the two sections.

[0085] During measurement planning, at least one of the loaded characteristics CGF of the non-trapezoidal gradient pulse shape GF, a predefined maximum permissible gradient amplitude, can be used to verify the feasibility of the planned measurement. For example, the verification can include assigning parameter values ​​of adjustable measurement parameters of the measurement protocol used to at least one of the categories A, B, or C described above. This allows, for example, the identification and appropriate labeling of permissible value ranges of measurement parameters, such as the b-values, using the predefined maximum permissible gradient amplitudes Gmax. For instance, b-values ​​requiring higher gradient amplitudes than the determined maximum permissible gradient amplitude Gmax can be assigned to the category "not adjustable."No complex stimulation considerations are necessary during measurement planning, as these have already been carried out in advance, enabling fast and interactive operation.

[0086] It may be possible to inform the user of any existing limitations on possible parameter values, for example, as a notification when reading in the non-trapezoidal gradient pulse shape GF or as a tooltip during measurement planning. The user can then use this as an opportunity to adjust the non-trapezoidal gradient pulse shapes GF.

[0087] A determined maximum permissible gradient amplitude Gmax can be further scaled by a safety factor S < 1 (e.g., S = 0.90 or S = 0.95). This reduces the probability of measurement failures, particularly in cases where a preliminary calculation to determine the maximum permissible gradient amplitude Gmax fails to capture a specific measurement protocol with a particularly high stimulation potential.

[0088] Additional approaches to testing stimulation limits can be combined. For example, to reduce the stimulation potential, the amplitude of diffusion gradients with a non-trapezoidal gradient pulse shape (GF) can be limited in advance, while the stimulation potential of imaging gradients, such as an echo-planar readout gradient train, is only checked at the start of the measurement. In this way, the task of stimulation monitoring can be solved in two steps: During preparation, an adapted maximum permissible gradient amplitude (Gmax) of the diffusion gradients is ensured, and during measurement planning, feasibility within given stimulation limits encompassed by the loaded conditions is guaranteed.This limits the possible solution space in a subsequent check of the imaging gradients at the start of the measurement in such a way that, in the event of an exceedance of existing limit values, a user can quickly be offered a suitable selection of changes to the parameter values ​​of the measurement parameters to be set, for example longer ramps of the EPI readout gradients.

[0089] Gradient pulse shapes GF, defined on axes Gx, Gy, Gz, can be transformed into a different coordinate system by a suitable rotation. This can be done, for example, from a logical coordinate system used to plan a desired image volume ROI (region of interest) to a physical coordinate system, such as the x, y, z axes of gradient unit 5 in magnetic resonance imaging (MRI) system 1, or for directional assignments to desired diffusion encoding directions. When rotating non-trapezoidal gradient pulse shapes, it is important to note that their stimulation potential can change significantly due to the rotation. Therefore, for more complex stimulation models with a temporal characteristic (such as the SAFE model), it is advisable to consider rotations during the pre-calculation. The necessary calculations can be performed extensively for arbitrary rotations.In three-dimensional space, any rotation can be described by three independent parameters, for example, the Euler angles. To account for arbitrary rotations, the calculations described above can be extended using stimulation models for a multitude of discrete angle combinations {α. i , β i , γ k} with a fixed number of steps I, J, K: α i = 2π * i / I, β j = 2π * j / J, γ k= 2π * k / K. From these results, at least the smallest value of the maximum permissible gradient amplitudes obtained for the various rotations can then be included as the maximum permissible gradient amplitude of the loaded characteristics. Alternatively, a more restricted and therefore less computationally intensive calculation can be performed for different rotations. This option requires that a) the non-trapezoidal gradient pulse shape GF is described in physical coordinates of the x, y, z axes of a gradient unit, and b) directional assignments of the diffusion encoding (with associated rotation matrices R) are available. m ) are already known during measurement preparation. In this case, it suffices to perform the previously described calculations with the respective transformed pulse shapes G'. m (t) = GR m f(t) only for the necessary direction assignments R mto carry out. From these results, at least the smallest value of the maximum permissible gradient amplitudes obtained for the various rotations can then be included as the maximum permissible gradient amplitude of the charged characteristics.

[0090] For identical non-trapezoidal gradient pulse shapes GF on all axes G n (t) = G f(t) V n with the direction vectors V n = (v n,x , v n,y , v n,zIt may suffice to limit the measurement preparation to considering the case of maximum stimulation. For this purpose, a "worst-case scenario" can be assumed. In common simulation models, such as the SAFE model, a maximum stimulation potential for direction vectors V with |V| = 1 occurs when the gradient activity is entirely on one axis, i.e., V = (1, 0, 0), V = (0, 1, 0), or V = (0, 0, 1). Therefore, it is sufficient to consider precisely these cases during measurement preparation. Alternatively, restricted calculations can also be performed, assuming that a) the non-trapezoidal gradient pulse shape is described in physical coordinates of the x, y, z axes of the gradient unit, and b) the direction vectors V n are already known during measurement planning. The previously described calculations are then performed using the respective transformed pulse shapes G. n (t) = G f(t) V ncarried out and, in turn, at least the smallest of the maximum permissible gradient amplitudes determined in this way must be covered by the charged characteristics.

[0091] It may be necessary, when determining maximum permissible gradient amplitudes, to consider not only stimulation limitations but also, simultaneously or successively, limitations of the gradient unit. In this context, the maximum achievable slew rates on each of the x, y, and z axes are particularly relevant. Thus, in addition to statement A (for testing the stimulation), another statement B (for testing the slew rates) must be considered, which can be expressed in pseudo-code as:

[0092] A simple check could look like this in pseudo-code: B:max(|Si(t)|≤Si,max i∈{x,y,z}with S(t)=G*df(t) / dt

[0093] Provided that normalized gradient profiles f(t) of the non-trapezoidal gradient pulse shape are available on a sufficiently fine grid, e.g. with a grid of T raster For values ​​of 1µ, 10µs or 100µs, the derivative to be calculated can be determined numerically: S(tk)=G*(f(tk+1)−f(tk)) / Traster

[0094] A received condition can include a desired b-value, and a gradient amplitude required to achieve the desired b-value with the gradient pulse shape can be determined as the relevant gradient amplitude.

[0095] The gradient pulse shape GF can be decomposed into sections, particularly those that do not overlap in time, and the loaded characteristics for these sections can include section-specific terms for determining elements of a b-matrix with the desired b-value. To check whether individual sections i overlap in time, a simple, automatic calculation can be performed for each normalized curve f during the planning and / or execution of the measurement. i (t) be checked whether f holds i (t) = 0, for t < T i and t > T i+1 If this is not the case, an error message may be displayed and / or parameter values ​​that would lead to an overlap may be assigned to "not adjustable" and marked accordingly.

[0096] Fig. Figure 3 is a schematic representation of a gradient with a non-trapezoidal gradient pulse shape GF on three axes Gx, Gy, Gz, where as in Fig. 2. The gradient amplitude with the gradient pulse shape GF in the Gx direction is shown as a double-dotted dash-dotted line, the gradient amplitude with the gradient pulse shape GF in the Gy direction as a single-dotted line, and the gradient amplitude with the gradient pulse shape GF in the Gz direction as a dashed line. A decomposition of the gradient pulse shape GF can be performed at time points T0, T1, T2, and T3 (=TE) as described below.

[0097] Preparing section-specific terms as characteristics CGF of the gradient pulse shape GF can include forming a normalized profile f(t) of the gradient amplitude of the gradient pulse shape GF.

[0098] For example, a gradient pulse shape GF can first be decomposed into two sections for the sake of simplicity, so that a gradient pulse sequence G(t) resulting from the decomposition can be defined as follows for any gradient pulse sequence G(t). G(t)=G1(t)=G f1(t) for 0≤t <T1 G(t)=G2(t)=G f2(t) for T1≤t <TE →bij=γ2G20∫TEdt'(0∫t'dt''fi(t''))(0∫t'dt''fj(t'')) =γ2G2(0∫T1dt'(0∫t'dt''fi(t''))(0∫t'dt''fj(t''))+ T1∫TEdt'(0∫t'dt''fi(t''))(0∫t'dt''fj(t'')))

[0099] The first term (for which t' ∈ [0, T1] holds) represents the b-matrix elements of the first section: bij,1=γ2G20∫T1dt'(0∫t'dt''fi(t''))(0∫t'dt''fj(t''))

[0100] The second term can be further decomposed: bij=bij,1+γ2G2T1∫TEdt'(0∫t'dt''fi(t''))(0∫t'dt''fj(t''))=bij,1+γ2G2T1∫T Edt'(0∫T1dt''fi(t'')+T1∫t'dt''fi(t''))(0∫T1dt''fj(t'')+T1∫t'dt''fj(t''))

[0101] With M i,1 = 0∫ T1 dt" f i (t") and M j,1 = 0∫ T1 dt" f j (t") yields: bij=bij,1+γ2G2T1∫TEdt'(Mi,1Mj,1+Mi,1 T1∫t'dt''fj(t'')+Mj,1 T1∫t'dt''fi(t'')+T1∫t'dt''fj(t'')T1∫t'dt''fj(t''))

[0102] The last term (for which t' ∈ [T1, TE] holds) represents the b-matrix elements of the second section: bij,2=γ2G2T1∫TEdt'(T1∫t'dt''fi(t''))(T1∫t'dt''fj(t''))

[0103] With K i,2 = T1 ∫ TE dt' T1 ∫ t' dt" f j (t") and K j,2 = τ1∫ TE dt' τ1∫ t' dt" f j (t") ultimately results in: bij=bij,1+bij,2+γ2G2((TE−T1)Mi,1Mj,1+Mi,1Ki,2+Mj,1Ki,2)

[0104] The crucial point of this decomposition is that each of these terms now only relates to one of the two sections. This makes it possible to perform section-specific pre-calculations independently during measurement preparation, and to load the results of these pre-calculations, which include characteristics CGF of the gradient pulse shape GF, and use them during the planning and / or execution of a measurement to calculate a combined b-matrix that no longer requires significant computational effort.

[0105] In this case, the section-specific terms b can be used for a first section 1. ij,1 , M i,1 , M j,1 and for a second section 2 the selection-specific terms bi j,2 , K i,2 , K j,2 can be calculated in advance in preliminary calculations.

[0106] It is irrelevant whether G1(t) and / or G2(t) are trapezoidal or non-trapezoidal. For gradients with a non-trapezoidal gradient pulse shape, the calculations can be performed on a sufficiently dense grid of support points—for example, 1 µs, 10 µs, or 100 µs—assuming a constant gradient amplitude on each of the three axes within the grid interval: the integrations then simplify to summations. Since the pre-calculations are only required once during measurement preparation, the associated increased computational effort is negligible.

[0107] The approach can be extended to more than two sections as desired: the sections can be considered successively and the preliminary calculations accumulated. For example, the gradient pulse shape GF can be in Fig. 3 can also be divided into three sections with G(t)=G1(t)=G f1(t) for 0≤t <T1 G(t)=G2(t)=G f2(t) for T1≤t <T2 G(t)=G3(t)=G f3(t) for T2≤t <TE

[0108] The preliminary calculations for sections 1 and 2 can be performed analogously to the above, where TE is replaced by T2. This yields: b ij,1&2 : Calculation as described above (TE is replaced by T2) Wed / j,1&2:Wed / j,1&2=0∫T2dt'' fi / j(t'')=0∫T1dt'' fi / j(t'')+T1∫T2dt''fi / j(t'')=Wed / j,1+Wed / j,2

[0109] For the new third section 3, the terms b ij,3 , K i,3 , K j,3 pre-calculated, resulting in: →bij=bij,1&2+bij,3+γ2G2((TE−T2)Wed,1&2Mj,1&2+Wed,1&2Ki,3+Mj,1&2Ki,3)

[0110] It should be noted that a complete pre-calculation of the b-matrix elements during measurement preparation is not possible. This is due, firstly, to the fact that the time interval between sections can change during measurement preparation. For example, the duration of a refocusing module between two sections, e.g., between section 1 and section 2, can change depending on the parameter values ​​set for the measurement protocol. Secondly, the contributions of imaging gradients to the b-matrix, such as spoiler or slice coding gradients, can vary depending on the parameter values ​​set for the measurement protocol. The previously described decomposition and distribution of the computational operations is therefore essential for interactive measurement planning.If the preliminary calculations are performed on normalized gradient profiles f(t), the b-matrix elements for any actual gradient amplitudes G can be easily determined by appropriate scaling of the results.

[0111] The rapid determination of b-matrices described here can be advantageously applied both in measurement planning, particularly for the rapid calculation of a gradient amplitude required for a given b-value, and during measurement execution, e.g., for the rapid calculation of all b-matrix elements for a currently measured image, especially for storage in the DICOM header of the current image and / or use, for example, for diffusion tensor calculations. Thus, diffusion tensors applied during the measurement can be calculated based on loaded characteristics. The key to this is the efficient decomposition of the calculation procedure, which allows all time-consuming calculation steps to be performed only once during measurement preparation.

[0112] During measurement planning, it is generally not necessary to consider rotations of the non-trapezoidal gradient pulse shapes GF. This is because, during measurement planning, only the b-value (i.e., the trace of the b-matrix) is relevant for assigning parameter values, e.g., in certain ranges, to at least one category: "not adjustable," "conditionally adjustable," or "fully adjustable." The trace of the b-matrix is ​​invariant under rotations R: Trace(RBR). -1 ) = Trace(B).

[0113] During the measurement process, rotations of non-trapezoidal gradient pulse shapes GF into a different coordinate system, e.g., a physical one, and / or for directional assignments, must be taken into account. For diffusion tensor calculations (and for storing data in DICOM format), all elements bij of the b-matrix must be known. Calculating the b-matrix elements while considering a rotation R is easily accomplished by transforming (tensor or vector rotation) the pre-calculated quantities. This results in: [b'ij]=B_'=R_B_R_−1 [M'i]=M'=R_M [K'i]=K'=R_K

[0114] With the transformed quantities b' ij , M' i , K' i (i, j ∈ {x', y', z'}) the previously described steps for combining contributions from multiple sections can be carried out unchanged - now taking rotation into account.

[0115] For identical, non-trapezoidal gradient pulse shapes GF on all axes G n (t) = G f(t) V n with the direction vectors V n = (v n,x , v n,y , v n,z The elements of the b-matrix can be determined in the same way. A rotation matrix R can be used. n are determined in such a way that they form a single-axis gradient pulse shape assumed in the pre-calculation, for example on the Gx-axis: G x (t) = G f(t) (1, 0, 0), just transformed into the desired direction assignment of the diffusion direction: Gn(t)=R_n Gx(t),with Rn=(vn,x,vn,y,vn,x0,0,00,0,0)

[0116] When calculating b-matrices, combinations of sections with trapezoidal and non-trapezoidal gradient pulse shapes can be considered. For non-trapezoidal gradient pulse shapes, the pre-calculations described above can be applied to raster intervals. The exact shape of the trapezoidal gradients to be switched is usually only known during measurement planning (imaging gradients change in shape and amplitude depending on resolution parameters). Associated b-matrix elements b ij.trap However, due to the piecewise linear shaping, they can be quickly determined using known methods. This also applies to any additional dimensions M that may be required when combining with non-trapezoidal sections. i,trap and K i,trap .

[0117] By decomposing the gradient pulse shape into sections and by decomposing the necessary calculations for determining the diffusion values ​​into time-consuming components that are performed once during measurement preparation and into quickly manageable components that are calculated repeatedly during measurement planning and / or measurement execution, the planning of the measurement and the assignment of parameter values ​​of measurement parameters from the measurement protocol into the described categories A, B and C is accelerated and simplified.

[0118] If RF pulses are injected between the sections, their effect can be taken into account in the successive calculation of the b-matrix, for example, as follows. After the injection of an RF excitation pulse, all quantities accumulated up to the time of injection (b) can be calculated. ij = 0, M i / j= 0). After applying an RF refocusing pulse, e.g., to generate a spin echo, the gradient amplitudes in all subsequent sections can be inverted. With multiple refocusings, an alternating inversion of the polarities can be achieved according to (-1) nThis process occurs after the application of RF pulses for storage, e.g., of longitudinal magnetization, for example, to generate a stimulated echo. Until the next RF recovery pulse is applied, all subsequent sections of the gradient pulse shape can be ignored, as these have no effect on the diffusion encoding in the stored magnetization state. After the application of an RF recovery pulse, all subsequent sections of the gradient pulse shape can be considered again, after inverting the gradient amplitudes. Ultimately, the effect of a pair of RF storage and RF recovery is equivalent to that of an RF refocusing pulse, except that any gradient activity between storage and recovery has no effect on the magnetization.

[0119] As is known in the prior art, imaging gradients with a small influence on the b-matrix (for example, a slice selection gradient and an associated slice rephasing gradient applied immediately sequentially) can be ignored to further accelerate the computation. Other imaging gradients with a significant influence can be taken into account, such as spoiler gradients (to suppress unwanted signal paths). This applies particularly to gradients for which the zero moment they generate is only compensated at a late time. This is the case, for example, for a pair of spoiler gradients with a first spoiler gradient before an RF storage pulse and a second spoiler gradient after an RF recovery pulse.

[0120] The feasibility of acquiring diffusion-weighted measurement data DWMD of the object under investigation with the magnetic resonance system 1 using a measurement protocol with the non-trapezoidal gradient pulse shape GF is checked on the basis of at least one specific relevant gradient amplitude Gr (query 100).

[0121] If the test yields a positive result (query 100, y), a measurement to acquire diffusion-weighted measurement data (DWMD) with diffusion coding using the gradient pulse shape (GF) can be performed (block 111). A positive result is obtained when parameter values ​​can be found for all measurement parameters of the measurement protocol used that allow the measurement to be carried out under all required boundary conditions.

[0122] If the test yields a negative result, the gradient amplitude of the diffusion gradient to be switched with the non-trapezoidal gradient pulse shape GF can be reduced, or other parameter values ​​of the measurement parameters, e.g., possible b-values, of the measurement protocol can be adjusted, in particular assigned to at least one of the aforementioned categories A, B, and C, so that a set of parameter values ​​of the measurement parameters of the measurement protocol can be found that allows the measurement to be carried out. Alternatively, for example, a different non-trapezoidal gradient pulse shape GF can be loaded and the procedure repeated with this, or adjustments can be made to other suitable parameter values ​​of the measurement parameters of the measurement protocol, or at least suggested.

[0123] Fig. Figure 4 schematically represents a magnetic resonance system 1 according to the invention. This comprises a magnet unit 3 for generating the basic magnetic field, a gradient unit 5 for generating the gradient fields, a radio frequency unit 7 for irradiating and receiving radio frequency signals, and a control device 9 designed for carrying out a method according to the invention.

[0124] In the Fig.Figure 4 shows only a rough schematic representation of these subunits of the magnetic resonance system 1. The high-frequency unit 7 can consist of several subunits and, for example, comprise several coils. In particular, the high-frequency unit 7 can comprise a body coil that is permanently integrated into the magnetic resonance system 1 and, in turn, can comprise, for example, two antenna elements 7.1 and 7.2. Furthermore, the high-frequency unit 7 can comprise one or more different local coils 7*, which can be designed either only for transmitting high-frequency signals or only for receiving the triggered high-frequency signals, or for both, and which themselves can comprise several antenna elements and associated coil channels.

[0125] To examine a test object U, for example a patient or a phantom, it can be placed on a table L in the magnetic resonance imaging (MRI) system 1 within its measurement volume. Layers S1 or S2 represent exemplary target volumes of the test object, from which echo signals can be recorded and acquired as measurement data.

[0126] The control unit 9 serves to control the magnetic resonance system 1 and can, in particular, control the gradient unit 5 by means of a gradient controller 5' and the radio frequency unit 7 by means of a radio frequency transmit / receive controller 7'. The radio frequency unit 7 can comprise several channels on which signals can be transmitted or received.

[0127] The high-frequency unit 7, together with its high-frequency transmit / receive control 7', is responsible for generating and transmitting a high-frequency alternating field to manipulate the spins in a region to be manipulated (for example, in layers S to be measured) of the object under investigation U. The center frequency of the high-frequency alternating field, also referred to as the B1 field, is generally set as close as possible to the resonance frequency of the spins to be manipulated. Deviations from the center frequency to the resonance frequency are referred to as off-resonance. To generate the B1 field, controlled currents are applied to the RF coils in the high-frequency unit 7 by means of the high-frequency transmit / receive control 7'.

[0128] Furthermore, the control unit 9 includes a planning unit 15 for carrying out a test according to the invention. The control unit 9 is designed overall to carry out a method according to the invention.

[0129] A computing unit 13, encompassed by the control unit 9, is designed to perform all the necessary calculations for the required measurements and determinations. Intermediate results and final results required for this purpose, or determined in the process, can be stored in a storage unit S of the control unit 9. The units shown here are not necessarily to be understood as physically separate units, but merely represent a subdivision into conceptual units, which can also be realized, for example, in fewer or even just a single physical unit.

[0130] Via an input / output device (I / O) of the magnetic resonance system 1, control commands can be sent to the magnetic resonance system by a user, for example, and / or results from the control device 9, such as image data, can be displayed.

[0131] The method described herein may also be in the form of a computer program comprising instructions that execute the described method on a control unit 9. Likewise, a computer-readable storage medium may be present, comprising instructions that, when executed by a control unit 9 of a magnetic resonance system 1, cause it to execute the described method.

[0132] Regardless of the grammatical gender of a particular term, persons with male, female or other gender identities are included. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] US 20240295621A1

[0027] Cited non-patent literature

[0000] Stejskal and Tanner in “Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient,” J. Chem. Phys. 42: pp. 288-292 (1965)

[0008] Szczepankiewicz et al., “Gradient Waveform Design for Tensor-Valued Encoding in Diffusion MRI,” J. Neurosc. Methods 348: p. 109007 (2021

[0009] Hebrank et al., “SAFE model - a new method for predicting peripheral nerve stimulations in MRI”, Proceedings of the 8th Annual Meeting of ISMRM, Denver, 2000

[0055] IEC 60601-2-23

[0055]

Claims

[1] Method for testing the feasibility of acquiring diffusion-weighted measurement data of a test object using a magnetic resonance system with a measurement protocol with non-trapezoidal gradient pulse shapes for diffusion coding comprising the steps a) Loading a non-trapezoidal gradient pulse shape, b) Loading pre-prepared characteristics for the loaded non-trapezoidal gradient pulse shape, c) Receiving a condition that must be met during the execution of the acquisition of the diffusion-weighted measurement data, d) Determine at least one gradient amplitude relevant for the gradient pulse shape based on the charged characteristics and the condition, e) Checking the feasibility of acquiring diffusion-weighted measurement data of the object under investigation using the magnetic resonance system with a measurement protocol with the non-trapezoidal gradient pulse shape based on at least one specific relevant gradient amplitude. [2] Method according to claim 1, wherein the condition comprises a desired b-value and a gradient amplitude required to achieve the desired b-value with the gradient pulse shape is determined as the relevant gradient amplitude. [3] Method according to claim 2, wherein the gradient pulse shape is decomposed into sections, in particular non-overlapping sections, and the characteristics for the sections comprise section-specific terms for determining elements of a b-matrix with the desired b-value. [4] Method according to claim 3, wherein the preparation of the section-specific terms as characteristics of the gradient pulse shape comprises forming a normalized gradient amplitude profile of the gradient pulse shape. [5] Method according to any of the preceding claims, wherein the condition comprises a maximum permissible stimulation (and at least one constraint for the maximum permissible stimulation) and a maximum gradient amplitude is determined with which the permissible stimulation is not exceeded. [6] Method according to claim 5, wherein the condition comprises at least one constraint for a maximum permissible stimulation and for each constraint an associated maximum permissible stimulation. [7] Method according to claim 6, wherein a constraint is a constraint from the group consisting of an orientation of the axes of a gradient unit of the magnetic resonance system relative to the object under investigation, a stimulation type, a selected operating mode. [8] Method according to one of the preceding claims, wherein, if the test yields a positive result, a measurement is carried out to acquire diffusion-weighted measurement data using diffusion coding with the gradient pulse shape. [9] Method according to claim 8, wherein diffusion tensors used in the measurement are calculated on the basis of loaded characteristics during the measurement. [10] Magnetic resonance system (1) comprising a magnet unit (3), a gradient unit (5), a radio frequency unit (7) and a control unit (9) with a radio frequency transmit / receive control (7') and with a planning unit (15), wherein the control unit (9) is configured to perform a method according to any one of claims 1 to 9 on the magnetic resonance system (1). [11] Computer program comprising commands which, when the program is executed by a control device (9) of a magnetic resonance system (1), cause it to execute the method according to any one of claims 1 to 9. [12] Computer-readable storage medium comprising instructions which, when executed by a control device (9) of a magnetic resonance system (1), cause it to execute the method according to any one of claims 1 to 9.

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