Systems and methods for dynamic field monitoring using NMR probes

Abstract

A method for monitoring a magnetic field of a magnetic resonance imaging (MRI) system includes determining a pulse sequence of the MRI system, the pulse sequence having a repetition time (TR) and a readout window repeated for a plurality of repetitions with the TR. A plurality of field measurement segments are also determined, each associated with one of the plurality of repetitions and having a trigger time and a field measurement duration defining a measurement window. The measurement windows collectively span the readout window. The MRI system is used to perform the pulse sequence, measuring MRI data during the readout window while detecting field data using an NMR probe system during the associated field measurement segment. The field data are stitched based on each trigger time of the plurality of field measurement segments, generating a characterization of the magnetic field of the MRI system through the readout window.

Description

Client Ref. UMN 2024 118SYSTEMS AND METHODS FOR DYNAMIC FIELD MONITORING USING NMR PROBESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on, claims priority to, and incorporates herein by reference for all purposes, U.S. Provisional Patent Application No. 63 / 636,537 filed on April 19, 2024. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under EB025144 and EB027061 awarded by the National Institutes of Health. The government has certain rights in the invention. BACKGROUND

[0003] Dynamic field monitoring using NMR probes has shown utility for various MRI applications owing to its ability to measure higher-order systematic and physiologically induced field fluctuations. However, measurement duration is limited by signal loss caused by T2*decay within the probe, dephasing by strong gradients, or both, indicating that long-duration or ultrahigh-resolution readout gradients cannot be characterized. Attempts have been made to circumvent such limitations using specialized field monitoring systems, including specialized field probes and software. However, such systems are not commercially or widely available. Other attempts to address this problem include measurement of the gradient impulse response function (GIRF) that can be used to predict high order dynamic field changes based on the readout gradients that are applied. However, such approach requires the assumption that the gradient system is linear, which is typically not true in practice. Thus, new systems and methods are needed to improve the performance of field monitoring, especially when using long duration readouts or strong encoding gradients. SUMMARY OF THE DISCLOSURE

[0004] The present disclosure addresses the aforementioned drawbacks by providing a system and method for improved field monitoring using nuclear magnetic resonance (NMR) probes.

[0005] Some aspects of the present disclosure provide a method for monitoring a -1- QB\920171.00647\95776343.4Client Ref. UMN 2024 118magnetic field of a magnetic resonance imaging (MRI) system. The method includes determining a pulse sequence of the MRI system. The pulse sequence has a repetition time and a readout window that is repeated for a plurality of repetitions with the repetition time. The readout window has a readout start time and a readout duration. The method further includes determining a plurality of field measurement segments. Each of the plurality of field measurement segments is associated with one of the plurality of repetitions, and each of the plurality of field measurement segments has a trigger time and a field measurement duration, thereby defining a measurement window. The measurement windows of the plurality of field measurement segments collectively span the readout window. The method further includes using the MRI system to perform the pulse sequence by measuring MRI data during the readout window for the plurality of repetitions while detecting field data using an NMR probe system during the associated field measurement segment. The method further includes stitching together the field data based on each trigger time of the plurality of field measurement segments, thereby generating a characterization of the magnetic field of the MRI system through the readout window.

[0006] Other aspects of the present disclosure provide a system for monitoring a magnetic field of an MRI system. The system includes an MRI system, an NMR probe system, and a control system. The control system is configured to determine a pulse sequence of the MRI system. The pulse sequence has a repetition time and a readout window that is repeated for a plurality of repetitions with the repetition time. The readout window has a readout start time and a readout duration. The control system is further configured to determine a plurality of field measurement segments. Each of the plurality of field measurement segments is associated with one of the plurality of repetitions, and each of the plurality of field measurement segments has a trigger time and a field measurement duration, thereby defining a measurement window. The measurement windows of the plurality of field measurement segments collectively span the readout window. The control system is further configured to control the MRI system to perform the pulse sequence by measuring MRI data during the readout window for the plurality of repetitions while detecting field data using the NMR probe system during the associated field measurement segment. The control system is further configured to stitch together the field data based on each trigger time of the plurality of field measurements segments, thereby generating a characterization of the magnetic field of the MRI system QB\920171.00647\95776343.4Client Ref. UMN 2024 118through the readout window.

[0007] These are but a few, non-limiting examples of aspects of the present disclosures. Other features, aspects and implementation details will be described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.

[0009] FIG. 1A illustrates two non-limiting examples of the disclosed stitching method.

[0010] FIG.1B provides a flowchart setting forth steps of an example process for monitoring a magnetic field of a magnetic resonance imaging system.

[0011] FIG. 2A illustrates an example pulse sequence used experimentally in accordance with the present disclosure.

[0012] FIG.2B illustrates another example pulse sequence used experimentally in accordance with the present disclosure.

[0013] FIG.3A illustrates another example pulse sequence used experimentally in accordance with the present disclosure.

[0014] FIG.3B illustrates another example pulse sequence used experimentally in accordance with the present disclosure.

[0015] FIG. 4A shows an example of experimental field measurements in the x direction acquired using the methods described in the present disclosure.

[0016] FIG. 4B shows an example of experimental field measurements in the y direction acquired using the methods described in the present disclosure.

[0017] FIG. 4C shows an experimental example plotting k-space trajectories measured using the methods described in the present disclosure and compared to standard methods.

[0018] FIG.4D shows another example of experimental field measurements in the x direction acquired using the methods described in the present disclosure.

[0019] FIG.4E shows another example of experimental field measurements in the y direction acquired using the methods described in the present disclosure. QB\920171.00647\95776343.4Client Ref. UMN 2024 118

[0020] FIG.4F shows another experimental example plotting k-space trajectories measured using the methods described in the present disclosure and compared to standard methods.

[0021] FIG. 5A shows an experimental example plotting dynamic field measurements for spherical harmonic terms up to second order measured in accordance with the present disclosure.

[0022] FIG. 5B shows another experimental example plotting dynamic field measurements for spherical harmonic terms up to second order measured in accordance with the present disclosure.

[0023] FIG. 6A shows a simulated experimental example acquired using the methods described in the present disclosure and compared to standard methods.

[0024] FIG.6B shows another simulated experimental example acquired using the methods described in the present disclosure and compared to standard methods.

[0025] FIG.7A shows an in vivo experimental example acquired using the methods described in the present disclosure and compared to standard methods.

[0026] FIG. 7B shows another in vivo experimental example acquired using the methods described in the present disclosure and compared to standard methods.

[0027] FIG. 8 is a block diagram of an example magnetic resonance imaging (“MRI”) system that can implement the methods described in the present disclosure.

[0028] FIG.9 is a block diagram of an example field monitoring system.

[0029] FIG. 10 is a block diagram of an example imaging system that can implement the methods of the present disclosure.

[0030] FIG.11 is a block diagram of example components that can implement the system of FIG.10. DETAILED DESCRIPTION

[0031] Before any aspects of the present disclosure are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and QB\920171.00647\95776343.4Client Ref. UMN 2024 118variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. Unless specified or limited otherwise, the terms “mounted,” “connected,” “supported,” and “coupled” and variations thereof are used broadly and encompass both direct and indirect mountings, connections, supports, and couplings. Further, “connected” and “coupled” are not restricted to physical or mechanical connections or couplings.

[0032] The present disclosure provides systems and methods for NMR or MRI field monitoring with improved quality. The method can advantageously be applied in the context of long readout trains or in the presence of strong encoding gradients while maintaining robust performance.

[0033] Ultrahigh-field (UHF) MRI systems operating at 7 T and above have significant advantages over those at lower field strengths, providing enhanced signal-to- noise ratio (SNR) and improved tissue contrast. However, UHF MRI faces its own challenges, including increased susceptibility to system imperfections, such as eddy currents. Eddy currents, especially those associated with the image readout, can become a problem when pursuing high resolution imaging, which is a major driver for UHF MRI. For high-resolution imaging, the image readout is often accomplished with relatively long gradients that are also relatively large in amplitude. This can lead to increased eddy currents, which in turn can result in strong image artifacts when uncorrected. One effective way to characterize eddy currents (for their subsequent correction) is by dynamic field monitoring.

[0034] Dynamically monitoring the magnetic field can be achieved using field monitoring systems or NMR probe systems. Such systems measure field data that represent the magnetic field through time and space within the scanning volume. These measurements can be used to model the magnetic field and correct the MRI data (e.g., imaging data, spectroscopic data, and so forth). Dynamic field monitoring using NMR probes has shown utility for various MRI applications, improving image quality in both Cartesian and non-Cartesian acquisitions. This is largely due to its ability to measure time-resolved high-order dynamic field changes associated with the readout gradients within each TR. These high-order dynamic field measurements can be incorporated into image reconstruction based on the expanded signal model to correct for unwanted field perturbations, resulting in improved image quality with reduced artifacts.

[0035] However, the quality of these measurements is often limited by the signal QB\920171.00647\95776343.4Client Ref. UMN 2024 118level of the field data. This signal level can be especially low when measuring readouts with long durations or in the presence of large gradients (e.g., used for high spatial resolution imaging), which cause intravoxel dephasing. Thus, the application of dynamic field monitoring to characterize a long-duration or ultrahigh-resolution readout remains challenging. This is largely due to the maximum effective measurement duration being limited by T2*decay within the probe, dephasing caused by strong gradients, or both. These limitations indicate that long-duration or ultrahigh-resolution readout gradients cannot be effectively characterized using current concurrent and prospective field monitoring approaches. These limitations can be addressed by other proposed dynamic field monitoring methods, including continuous field monitoring with rapid re-excitation of NMR probe sets. However, continuous field monitoring requires specialized hardware including short-lived NMR probes (e.g., with sub-millisecond T2values) and cannot be fulfilled using a standard commercially available field monitoring system that relies on long-lived NMR probes (e.g., with T2 values on the order of tens of milliseconds).

[0036] The present disclosure provides systems and methods for dynamically monitoring magnetic fields, especially for long-duration readouts, ultrahigh-resolution readouts, or both. Compared to existing approaches, the described method has the advantages of utilizing currently available field monitoring hardware. Also advantageously, the disclosed method does not require the assumption that the gradient system is linear. Moreover, the disclosed method is compatible with both concurrent and post-monitored field monitoring schemes. For example, with concurrent monitoring, field monitoring can be performed during the human scan while the participant is being imaged to capture all field perturbations, including those due to physiology. The field can also be monitored prospectively, where the dynamic field changes associated with the readout gradients are measured in a separate calibration session before or after the human or other scan.

[0037] The disclosed method can additionally be used in various MR applications, such as body or neuro MRI, spectroscopy, quantitative imaging, imaging with various contrasts (T1, T2, T2*, diffusion weighted imaging, contrast-enhanced imaging, and so forth), functional MRI, and so forth. Moreover, the method can be applied using various types of 2D or 3D readouts, such as spiral, echo planar imaging (EPI), radial, multishot, and so forth. The field data acquired using the described method can be used with various field models and correction algorithms. For example, the field data can be used for QB\920171.00647\95776343.4Client Ref. UMN 2024 118comprehensive image reconstruction incorporating high-order dynamic field measurements. As a non-limiting example, 0th-2ndorder field corrections can be performed. However, the described method is not limited in the field corrections that can be achieved, and higher order harmonics may be used.

[0038] The described method provides an effective means to extend and improve traditional field monitoring approaches and is shown capable of making quality high- order dynamic field measurements for challenging readout gradients, thereby holding a promise to many imaging applications, especially those at ultrahigh field in pursuit of ultrahigh resolutions.

[0039] A novel data stitching method is described, as illustrated in FIG. 1A. This method combines data from multiple sub-readout length segments of the readout trajectory and combines those segments to generate a full-length trajectory measurement for each TR. As described, the low signal levels of the field data can be prevented by sampling the field data using several (e.g., NFM) field measurement segments that collectively span the readout duration. In this way, the readout can be repeated (e.g., for increased averaging) while measuring the field data during the various field measurement segments. The field data can then be stitched together to characterize the magnetic field through the whole readout.

[0040] The MRI pulse sequence may include a series of readouts or readout windows, each with a readout duration. These readout windows can be repeated to measure varying spatial encodings, averages, contrasts, and so forth. The readout windows may also be repeated in order to characterize the field during NFMsegments. The readout window may be described by a readout start time (TRO) and a readout duration (DRO). Each field measurement segment (i) can be described by a trigger time (Ti) and a duration (Di) that defines a field measurement window (e.g., [Ti, Ti+ Di]). These field measurement windows can be defined such that the combination of the NFMsegments spans the whole readout window. For example, T , T DT , T D . After the readout window is repeated NFM times, the field data can bestitched together based on the trigger times of each field measurement window, thereby characterizing the field data throughout the whole readout window.

[0041] In some implementations, field data can be recorded during recording windows that are at least as long as the corresponding field measurement segments, as illustrated in FIG.1A. In this way, stitching the field data may include discarding some of QB\920171.00647\95776343.4Client Ref. UMN 2024 118the acquired field data. For example, parts of field data with low signal may be discarded and replaced with data acquired during other field measurements segments. Thus, in some implementations, the temporal span of the field measurement segments can be chosen retrospectively based on the signal levels. In some implementations, the recording windows may have constant durations while the field measurement segments can have variable durations to simplify the data measurement process. In any case, once stitched together, the field measurement segments can characterize the full readout window. This field data can be used to correct the MRI data acquired during the readout windows of the pulse sequence.

[0042] Referring now to FIG.1B, a process 100 that can be used for monitoring a magnetic field of an MRI system. As a non-limiting example, process 100 can be used to measure dynamic field changes during long-duration or ultrahigh-resolution readout gradients, which would otherwise be difficult to measure using a standard field monitoring system.

[0043] Process 100 includes choosing an appropriate pulse sequence for the desired imaging task in process block 102. In some implementations, this pulse sequence may advantageously provide high resolution images and have longer readout durations or larger spatial gradients than would be possible with standard field monitoring methods. Determining the pulse sequence includes determining gradient strengths, a readout window, and corresponding repetition times (TR) that can be used to achieve the desired resolution and other imaging parameters. Each readout window has a start time and readout duration within the respective TR. As a non-limiting example, a 2D spiral gradient echo sequence can be designed with time-optimal spiral gradient waveforms using the optimal control algorithm.

[0044] Process block 104 includes determining field measurements segments that correspond to the readout windows of each TR of the pulse sequence. Determining the field measurements segments includes setting trigger times and durations for each field measurement segment. These durations may be constant or variable across TRs. Process block 104 may also include setting recording windows that contain corresponding field measurement segments. The two windows may be equal, or the field measurement segment may make up a subset of the recording window, as illustrated in FIG.1A. In this way, the duration of each field measurement segment may be determined prospectively or after data acquisition (e.g., as in process block 106) as a subset of the data recorded QB\920171.00647\95776343.4Client Ref. UMN 2024 118during the recording window.

[0045] The field measurement segments can be defined based on the pulse sequence parameters. For example, if the readout durations of the pulse sequence are long, more field measurement segments with shorter durations can be used to maintain high signal of the stitched data. Similarly, when the pulse sequence requires large encoding gradients, more segments with shorter durations can be used. These durations may be equal for each of the NFM segments, providing a constant-segment stitching mode, or variable (e.g., based on the gradient waveform), providing a variable-segment stitching mode. In some implementations, it may be preferable to use the constant-segment mode when the T2*relaxation is the dominant mechanism underlying the probe signal decay, such as for low-resolution, long readouts where the probe signal decay due to gradient- induced intra-probe dephasing is negligible. It may be preferable to use the variable- segment mode when gradient-induced dephasing is the dominant mechanism underlying the probe signal decay, such as for high-resolution, short readouts where the probe signal decay due to T2*relaxation is negligible. As another non-limiting example, the variable- segment mode may be preferable to provide convenience, circumventing trial and error that may be needed to identify the minimum segment needed in the constant-segment mode.

[0046] In either case, the entire readout gradient can be characterized by stitching multiple segment-specific dynamic field measurements obtained across a matched number of consecutive TRs corresponding to a certain segmentation of the readout gradient.

[0047] In some implementations, the trigger times and durations can be set to maintain a desired signal level (e.g., based on the T2*decay time of the NMR probe system, a threshold defining some percentage of the initial NMR probe signal, and so forth). This signal level can be determined empirically or using an intra-voxel signal dephasing model that characterizes the loss of the NMR probe system based on the gradient moment throughout the pulse sequence. In some implementations a unified signal model can be used in which both intra-voxel dephasing and T2* decay mechanisms are used to determine the signal level. The trigger times and durations may also be determined to balance high signal level with low total scan time duration (e.g., limited to NFM = 4). The trigger times and durations may also be determined based on a maximum desired field measurement segment length (e.g., 5 ms, 10 ms, 15 ms, 20 ms, 25 ms, 30 ms, 35 ms, and QB\920171.00647\95776343.4Client Ref. UMN 2024 118so forth), while the readout duration can advantageously be allowed to exceed such segment length. As a non-limiting example, segments of the same duration can be measured from TR to TR, with the number of segments being determined based on the mono-exponential signal T2*decay model. As another non-limiting example, segments of variable durations can be measured from TR to TR, with the number of segments being determined based on the intra-voxel dephasing model accounting for the physical properties of the field probes in use. For example, the durations can be determined by ensuring that the maximum phase angle,, is smaller than the value as dictated by the intra-voxel dephasing model such that the residual signal retained would be higher thana prescribed threshold. In other words,, 1,2, … , , whereis the residual signal threshold, is the number of segments, r is the probe radius, and is the k-value of the ithsegment spanning from timeto time , which is given by with denoting the gyromagnetic ratio of the probe sample and g(t)denoting the readout gradient.

[0048] In some implementations, it may be desirable to choose a more conservative residual signal threshold to retain more residual signal from intra-probe dephasing. This may be useful to compensate for additional signal decay due to T2*relaxation, especially for segments with longer durations. Moreover, in some implementations, T2*relaxation can be considered when predicting probe signal decay to improve robustness and accuracy.

[0049] Whether the field measurement segments have equal or variable durations, in some implementations, field monitoring can occur for a constant recording window that is long enough to accommodate the maximum field measurement segment duration. This constant recording window may simplify the practical implementation of the method and to provide compatibility with existing field camera systems. In this case, for each segment, field data recorded outside of the field measurement segment can be discarded.

[0050] The MRI data and field data are measured in process block 106. The MRI system is used to perform the pulse sequence, measuring MRI data for multiple TRs (e.g., for multiple contrasts or multiple averages). The NMR probe system can be used simultaneously to measure field data during the associated field measurement segments according to trigger times determined in process block 104. In some implementations, QB\920171.00647\95776343.4Client Ref. UMN 2024 118these trigger times can be provided to the system using TTL triggers.

[0051] In some implementations, the field data can be provided as the raw or processed (e.g., filtered) spectroscopic NMR data measured by the NMR probe system. This data can be retrospectively processed to model the dynamic magnetic field corresponding to the time series through the readout window. In other implementations, the field data can be provided by the NMR probe system as k or phase coefficients.

[0052] The field data can be stitched together in process block 108. The data are combined based on the time during the readout window at which they were acquired, which depends on the corresponding trigger times and field measurement segment durations. Stitched together, the field data characterize the magnetic field throughout the full readout window. Stitching the data together may also include discarding data recorded during a recording window that does not correspond to a field measurement segment (e.g., because the signal level is too low).

[0053] In some implementations, block 108 includes processing the field data, which may be performed before or after combining the data together or both. As a non- limiting example, the field data may be provided as raw NMR data, and block 110 may include stitching the data together, and then processing the stitched raw NMR data to model the spatial distribution of the magnetic field through the readout window using known methods. As another non-limiting example, the field data may be provided as high- order k coefficients (e.g., provided by the NMR probe system). In this example, the field data can be processed by calculating the derivatives (e.g., using differential approximation) to derive corresponding TR-specific high-order gradient or dynamic coefficients. Then, the gradient coefficients can be stitched together to recover a complete time course of gradient coefficients across the readout window. This stitched gradient coefficient time course can then be integrated to form the complete time course of corresponding high-order k coefficients that can be used for image reconstruction. In any case, the stitched and processed field data can provide correction data that can be applied to correct the MRI data, as in process block 110.

[0054] Once correction data is produced for the full readout window, it can be applied in process block 110 to correct the MRI data over multiple TRs. Several correction methods can be used. As one non-limiting example, high-order field dynamics can be incorporated into the image reconstruction pipeline while accounting for coil sensitivities, field dynamics (e.g., including up to second-order spherical harmonic terms) QB\920171.00647\95776343.4Client Ref. UMN 2024 118and static off-resonances based on the expanded signal model. The image reconstruction can be formulated as a regularized least-squares optimization problem according to:

[0055] arg

[0056] where m is the image to be reconstructed, s is the multi-coil signal, E the encoding matrix based on the expanded signal model, and the regularization parameter. As a non-limiting example, the reconstruction problem in Equation [1] can be solved using the conjugate gradient (CG) algorithm. For improved computation efficiency, GPU- enabled operations can be used to speed up the evaluation of the expanded signal model. In some implementations, the synchronization delay between the field measurement and MRI data measurement can be estimated and corrected using a model-based approach. With this approach, the synchronization delay between field measurements and MRI data acquisition can be estimated in a data-driven manner by repeating image reconstruction processes to find the delay that results in the best image quality. The synchronization delay may differ for each readout, and it may be desirable to characterize such delay for each given acquisition protocol.

[0057] Examples

[0058] The following non-limiting examples provide example implementations and highlight the effectiveness of the disclosed systems and methods for measuring high- order dynamic field changes. In these examples, the approach was implemented with 2D single-shot spiral sequences. However, other readout types (e.g., 3D, spiral, echo planar imaging (EPI), radial, multishot, simultaneous multi-slice (SMS), stack of spirals, and so forth) may also be used. The examples include demonstration at 10.5 T using simulation experiments and validation at 7 T using in-vivo human experiments. The examples show that the disclosed methods promote various imaging applications, especially those at ultrahigh field targeting ultrahigh resolution, and complement existing dynamic field measurement techniques. The examples also demonstrate that both constant-segment and variable-segment modes are effective in characterizing their corresponding challenging readout spiral, leading to sensible high-order dynamic field measurements when compared to the standard field monitoring approach.

[0059] Example 1

[0060] As a non-limiting example, the stitching method is described herein in the context of acquiring long-duration readouts and using ultrahigh-resolution readout gradients, where standard field monitoring approaches do not work. In this example, data QB\920171.00647\95776343.4Client Ref. UMN 2024 118were obtained at 10.5 Tesla (T) using a 2D spiral readout. Results show that the disclosed method can be used to characterize a readout as lengthy as ~88 ms or a readout targeting a resolution as high as 0.3 mm while using standard fluorine-19 probes (of 0.4 mm in radius and ~23 ms in T2*).

[0061] Methods

[0062] Simulation experiments were conducted to demonstrate the usefulness of the disclosed data stitching method. MRI signals were simulated for image reconstruction. Two extreme 2D spiral readout scenarios, as illustrated in FIGS.2A and 2B were considered: 1) a long-duration readout shown in FIG.2A and 2) a short readout targeting ultrahigh resolution shown in FIG.2B. In scenario 1, the readout gradient was designed to image at 1-mm isotropic resolution with full k-space sampling, resulting in a long readout of ~88 ms. In scenario 2, the readout gradient was designed to achieve 0.3- mm isotropic resolution with 30-fold k-space under-sampling, leading to a short readout of ~21 ms but a maximum gradient moment of as high as ~1 mT·s / m. In both scenarios the nominal FOV was set to 150 × 150 m2, and TR was set to 500 ms.

[0063] For scenario 1 with T2*decay being a dominating limiting factor, the readout gradient was characterized in the constant-segment mode. The segmentation was determined such that each segment would not exceed 22 ms in length (a quarter of the total readout and within one T2*decay time of the field probes used), resulting in a total of four segments with the same duration. For scenario 2 with signal dephasing being the main limiting factor, the readout gradient was characterized in the variable-segment mode. The segmentation was determined by setting the residual signal threshold to = 0.41 to ensure each segment would maintain at least 41% of the initial probe signal while assuming no T2*decay and = 0.4 mm (the probe radius), leading to a total of 36 segments of variable durations.

[0064] For both scenarios, the readout gradients were characterized on a Siemens 10.5 T plus MR scanner (Siemens, Erlangen, Germany) equipped with whole-body gradients (capable of 70 mT / m maximum amplitude and 200 T / m / s maximum slew rate). Dynamic field monitoring was conducted using a clip-on field camera (Skope MRT, Zurich, Switzerland), with 16 fluorine-19 NMR probes optimally placed in a scaffold. For comparison, dynamic field measurements were also obtained using the standard field monitoring approach.

[0065] For each scenario, noise-free MR signals were calculated using an open- QB\920171.00647\95776343.4Client Ref. UMN 2024 118source framework for MRI simulations written in Julia and extended to enable 1) MR simulation with high-order field dynamics (by incorporating dynamic field changes up to second-order spherical harmonic terms into the calculation of the effective magnetic field in the z direction), and 2) multi-coil signal simulation for parallel imaging (by taking into account multiple coil sensitivity maps). In either scenario, MR signal was simulated by considering up to second-order dynamic field measurements associated with the corresponding readout gradient obtained using the disclosed stitching method. In both cases, MR signal was simulated for a single TR from a representative axial slice (located at the gradient isocenter) of a digital brain phantom dictated by proton density and assuming no transverse magnetization relaxation throughout the sequence. To simulate effects of static off-resonances for a more realistic situation, a 2D quadratic U-shaped B0 map was synthesized to have a maximum value of 150 Hz and a minimum value of 63 Hz across the brain region.

[0066] In both scenarios, MR simulation was carried out assuming uniform transmit B1 across the FOV. In scenario 1, MR simulation also assumed a single-channel signal reception with uniform sensitivity across the FOV owing to the fully-sampled k- space. In scenario 2, owing to the highly under-sampled k-space, MR simulation was performed for a thought experiment where 256-channel signal reception was considered for parallel imaging. To this end, 256 real-valued Gaussian-shaped coil sensitivity maps were artificially created, each spanning the entire FOV with scattered hot spots. In either scenario, all maps including proton density B0 and coil sensitivities were sampled at 0.1- mm in-plane resolution (higher than the nominal image resolution) to better simulate partial volume and intra-voxel dephasing effects in the subsequent image reconstruction. All MR simulations were performed on a server and accelerated using a single NVIDIA GeForce RTX 3090 GPU.

[0067] For both scenarios, simulated complex-valued MR signal was further contaminated by adding Gaussian noise to both real and imaginary parts to synthesize noisy complex-valued MR signal to mimic a more realistic situation. Noisy synthetic MR signal was then used to reconstruct the image using up to second-order field dynamics measured with the disclosed stitching method. The reconstruction performances were evaluated by calculating quality assessment metrics including Normalized Root Mean Square Error (NRMSE) and Structural Similarity Index (SSIM) values all in reference to proton density serving as the gold standard. The image reconstruction results were QB\920171.00647\95776343.4Client Ref. UMN 2024 118compared to those reconstructed using the same synthetic data but using nominal gradient waveforms and using up to second-order field dynamics obtained with the standard field monitoring approach. All image reconstructions were performed using 20 CG iterations and = 1e-9.

[0068] Results

[0069] When characterizing the long readout gradient, the use of the disclosed method eliminated discontinuities associated with T2*signal loss, generating a plausible gradient waveform and a sensible trajectory across the entire k-space, as shown in FIGS. 4A-4C. In contrast, the k-space trajectory measured using the standard approach became corrupted towards the end of the readout. For the ultrahigh resolution short readout, similar results were observed, as shown in FIGS. 4D-4F, demonstrating efficacy for eliminating discontinuities associated with gradient-induced probe dephasing. FIGS. 4A and 4B show the gradient waveforms in x and y, respectively, measured using the disclosed stitching method for the long readout scenario. The differences from what was measured using the standard approach are also plotted. FIG.4C shows the corresponding k-space trajectories. FIGS. 4D and 4E show the gradient waveforms in x and y, respectively, measured using the disclosed stitching method for the ultrahigh resolution short readout scenario. The differences from what was measured using the standard approach are also plotted. FIG.4F shows the corresponding k-space trajectories. In both scenarios, the stitching method effectively corrected the errors observed with the standard approach, leaving to a more sensible trajectory without erroneous k-space traversal.

[0070] When comparing dynamic field measurements for other spherical harmonic terms, the use of the disclosed method resulted in more sensible time courses than using the standard approach, as shown in FIG.5A for the long readout scenario and FIG. 5B for the ultrahigh resolution short readout scenario. Using the stitching method effectively avoided the large fluctuations observed with the standard approach, especially toward the end of the readout, resulting in a more plausible field measurement throughout the entire readout.

[0071] The disclosed stitching method led to best reconstruction quality, as shown in FIGS. 6A and 6B, producing images visually identical to the reference and effectively eliminating artifacts observed for reconstruction using nominal readout gradients or field measurement with the standard approach. Quantitatively, when comparing to using QB\920171.00647\95776343.4Client Ref. UMN 2024 118field measurement with the standard approach, the NRMSE reduced by ~55% (0.127 vs. 0.282 for the standard approach) and SSIM increased by ~141% (0.480 vs.0.199 for the standard approach) for the long readout scenario, as shown in FIG. 6A. The NRMSE reduced by ~5% (0.073 vs.0.077 for the standard approach) and SSIM increased by ~8% (0.653 vs. 0.605 for the standard approach) for the ultrahigh resolution short readout scenario, as shown in FIG.6B.

[0072] Example 2

[0073] To further validate the disclosed approach, human experiments were performed at 7 T. This example includes two spiral readout schemes: a low-resolution readout that could also be measured with the standard field monitoring approach and another high-resolution readout that could not. Thus, this example demonstrates that the method provides similar dynamic field measurement as the standard approach when dealing with an “easy” readout gradient, and the method extends to work in the case where the standard approach cannot be used.

[0074] Methods

[0075] To validate the described data stitching method, human experiments were conducted on a MAGNETOM 7 T MR scanner (Siemens, Erlangen, Germany) equipped with the same whole-body gradients as the 10.5 T scanner. Human data were collected using the commercial NOVA 8-channel transmit 32-channel receive head RF coil operating in its Circularly Polarized (CP) mode. A healthy adult who signed an informed consent form approved by local IRB was scanned. Dynamic field monitoring was performed in a separate session using a Dynamic Field Camera (Skope MRT, Zurich, Switzerland) with optimal integration of 16 high-precision proton NMR probes (of 0.4 mm in radius and ~35 ms in T2*).

[0076] Two representative 2D spiral readout schemes were considered: 1) a short readout, as shown in FIG. 3A, and 2) a long readout,, as shown in FIG. 3B, both with fourfold k-space undersampling (R = 4). In scheme 1, the readout gradient was designed to image at 1-mm isotropic resolution, resulting in a relatively short readout of ~29 ms for which the standard field monitoring approach was expected to work. In scheme 2, the readout gradient was designed to accomplish higher 0.5-mm isotropic resolution, giving rise to a relatively long readout of ~86 ms for which the standard field monitoring approach was expected to fail. Other relevant imaging parameters were kept constant for both readout schemes, including FOV = 200 × 200 mm², slice thickness = 2 mm, flip angle QB\920171.00647\95776343.4Client Ref. UMN 2024 118= 90°, TE = 5 ms, TR = 500 ms, bandwidth = 1 MHz, and a fat-saturation flip angle of 110°.

[0077] For scheme 1, the readout gradient was characterized in the constant- segment mode using four segments with the same duration. For scheme 2, the readout gradient was characterized in the variable-segment mode where the segmentation was determined using the same intra-voxel signal dephasing model as in the simulation study previously described, leading to a total of 188 segments of variable durations. For comparison, dynamic field measurements were also obtained using the standard field monitoring approach.

[0078] For image reconstruction, fully-sampled multi-echo 2D gradient echo (GRE) images were acquired to estimate coil-sensitivity and B0 maps. For either readout scheme, multi-echo GRE images were collected with matched in-plane resolution, FOV and slice thickness, other relevant imaging parameters being: 6 echoes, TE1= 3.06 ms, TE = 1.02 ms, and TR = 25 ms. Coil sensitivity maps were estimated from the first echo using ESPIRiT whereas the B0 map was estimated from all echoes using a regularized field mapping method.

[0079] For each readout scheme, image reconstruction was performed using up to second-order field dynamics measured with the described stitching method. The result was compared to that obtained with the same data but using up to second-order field dynamics measured with the standard field monitoring approach. For both reconstructions, the eddy current compensation applied to the raw data by the scanner to counteract eddy current b0 (i.e., the zero-th order spherical harmonic term) was reversed. This was done by simulating eddy current b0based on the nominal gradient waveforms using the same multi-exponential predictive model as used by the scanner. For comparison, image reconstruction was also conducted using the nominal gradient waveforms.

[0080] Moreover, all reconstructions were carried out with synchronization delay correction. This was accomplished using two steps, with step 1 aiming to estimate thesynchronization delay ( ) and step 2 to reconstruct images with estimatedsynchronization delay. In step 1, the delay was estimated using an iterative procedure with a jump factor of 6 and a minimum of 1 ns. During each iteration, the reconstruction problem in Equation [1] was solved using the CG algorithm. In step 2, the estimated delay was used to synchronize dynamic field measurements with the MR data through interpolation, and final images were reconstructed by solving the same QB\920171.00647\95776343.4Client Ref. UMN 2024 118reconstruction problem in Equation [1] with 20 CG iterations and = 10-9.

[0081] Results

[0082] The stitching method yielded nearly identical gradient measurements when characterizing the short, ~29-ms readout where the standard approach worked nicely, giving rise to the same k-space spiral trajectory. It also produced accurate gradient measurements when characterizing the long, ~86-ms readout where the standard approach did not work, leading to a k-space trajectory starting to degrade toward the end of the readout. Similar results were observed when comparing measurements of other spherical harmonic terms. Correspondingly, for the short readout, the disclosed method resulted in comparable image reconstruction when using the standard approach, as shown in FIG. 7A. It however led to improved reconstruction with reduced artifacts for the long readout where the use of the standard approach resulted in erroneous field measurements giving rise to degraded reconstruction, as shown in FIG. 7B and highlighted in the zoomed-in portion.

[0083] Example System

[0084] Referring particularly now to FIG.8, an example of an MRI system 600 that can implement the methods described herein is illustrated. The MRI system 600 includes an operator workstation 602 that may include a display 604, one or more input devices 606 (e.g., a keyboard, a mouse), and a processor 608. The processor 608 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 602 provides an operator interface that facilitates entering scan parameters into the MRI system 600. The operator workstation 602 may be coupled to different servers, including, for example, a pulse sequence server 610, a data acquisition server 612, a data processing server 614, and a data store server 616. The operator workstation 602 and the servers 610, 612, 614, and 616 may be connected via a communication system 640, which may include wired or wireless network connections.

[0085] The MRI system 600 also includes a magnet assembly 624 that includes a polarizing magnet 626, which may be a low-field magnet. The MRI system 600 may optionally include a whole-body RF coil 628 and a gradient system 618 that controls a gradient coil assembly 622.

[0086] The pulse sequence server 610 functions in response to instructions provided by the operator workstation 602 to operate a gradient system 618 and a QB\920171.00647\95776343.4Client Ref. UMN 2024 118radiofrequency (“RF”) system 620. Gradient waveforms for performing a prescribed scan are produced and applied to the gradient system 618, which then excited gradient coils in an assembly 622 to produce the magnetic field gradients (e.g., , , and ) that can be used for spatially encoding magnetic resonance signals. The gradient coil assembly 622 forms part of a magnet assembly 624 that includes a polarizing magnet 626 and a whole-body RF coil 628.

[0087] RF waveforms are applied by the RF system 620 to the RF coil 628, or a separate local coil to perform the prescribed magnetic resonance pulse sequence. Responsive magnetic resonance signals detected by the RF coil 628, or a separate local coil, are received by the RF system 620. The responsive magnetic resonance signals may be amplified, demodulated, filtered, and digitized under direction of commands produced by the pulse sequence server 610. The RF system 620 includes an RF transmitter for producing a wide variety of RF pulses used in MRI pulse sequences. The RF transmitter is responsive to the prescribed scan and direction from the pulse sequence server 610 to produce RF pulses of the desired frequency, phase, and pulse amplitude waveform. The generated RF pulses may be applied to the whole-body RF coil 628 or to one or more local coils or coil arrays.

[0088] The RF system 620 also includes one or more RF receiver channels. An RF receiver channel includes an RF preamplifier that amplifies the magnetic resonance signal received by the coil 628 to which it is connected, and a detector that detects and digitizes the and quadrature components of the received magnetic resonance signal. The magnitude of the received magnetic resonance signal may, therefore, be determined at a sampled point by the square root of the sum of the squares of the and components:

[0089] and the phase of the received magnetic resonance signal may also be determined according to the following relationship:

[0090] The pulse sequence server 610 may receive patient data from a physiological acquisition controller 630. By way of example, the physiological acquisition controller 630 may receive signals from a number of different sensors connected to the patient, including electrocardiograph (“ECG”) signals from electrodes, or respiratory QB\920171.00647\95776343.4Client Ref. UMN 2024 118signals from a respiratory bellows or other respiratory monitoring devices. These signals may be used by the pulse sequence server 610 to synchronize, or “gate,” the performance of the scan with the subject’s heartbeat or respiration.

[0091] The pulse sequence server 610 may also connect to a scan room interface circuit 632 that receives signals from various sensors associated with the condition of the patient and the magnet system. Through the scan room interface circuit 632, a patient positioning system 634 can receive commands to move the patient to desired positions during the scan.

[0092] The digitized magnetic resonance signal samples produced by the RF system 620 are received by the data acquisition server 612. The data acquisition server 612 operates in response to instructions downloaded from the operator workstation 602 to receive the real-time magnetic resonance data and provide buffer storage, so that data are not lost by data overrun. In some scans, the data acquisition server 612 passes the acquired magnetic resonance data to the data processor server 614. In scans that require information derived from acquired magnetic resonance data to control the further performance of the scan, the data acquisition server 612 may be programmed to produce such information and convey it to the pulse sequence server 610. For example, during pre-scans, magnetic resonance data may be acquired and used to calibrate the pulse sequence performed by the pulse sequence server 610. As another example, navigator signals may be acquired and used to adjust the operating parameters of the RF system 620 or the gradient system 618, or to control the view order in which k-space is sampled. In still another example, the data acquisition server 612 may also process magnetic resonance signals used to detect the arrival of a contrast agent in a magnetic resonance angiography (“MRA”) scan. For example, the data acquisition server 612 may acquire magnetic resonance data and processes it in real-time to produce information that is used to control the scan.

[0093] The data processing server 614 receives magnetic resonance data from the data acquisition server 612 and processes the magnetic resonance data in accordance with instructions provided by the operator workstation 602. Such processing may include, for example, reconstructing two-dimensional or three-dimensional images by performing a Fourier transformation of raw k-space data, performing other image reconstruction algorithms (e.g., iterative or backprojection reconstruction algorithms), applying filters to raw k-space data or to reconstructed images, generating functional QB\920171.00647\95776343.4Client Ref. UMN 2024 118magnetic resonance images, or calculating motion or flow images.

[0094] Images reconstructed by the data processing server 614 are conveyed back to the operator workstation 602 for storage. Real-time images may be stored in a data base memory cache, from which they may be output to operator display 602 or a display 636. Batch mode images or selected real time images may be stored in a host database on disc storage 638. When such images have been reconstructed and transferred to storage, the data processing server 614 may notify the data store server 616 on the operator workstation 602. The operator workstation 602 may be used by an operator to archive the images, produce films, or send the images via a network to other facilities.

[0095] The MRI system 600 may also include one or more networked workstations 642. For example, a networked workstation 642 may include a display 644, one or more input devices 646 (e.g., a keyboard, a mouse), and a processor 648. The networked workstation 642 may be located within the same facility as the operator workstation 602, or in a different facility, such as a different healthcare institution or clinic.

[0096] The networked workstation 642 may gain remote access to the data processing server 614 or data store server 616 via the communication system 640. Accordingly, multiple networked workstations 642 may have access to the data processing server 614 and the data store server 616. In this manner, magnetic resonance data, reconstructed images, or other data may be exchanged between the data processing server 614 or the data store server 616 and the networked workstations 642, such that the data or images may be remotely processed by a networked workstation 642.

[0097] Referring now to FIG.9, an example field monitoring system 700 is shown, which may be used in accordance with some aspects of the systems and methods described in the present disclosure. The field monitoring system 700 or NMR probe system can be used to measure the magnetic field achieved in the scanning volume through time. In this way, the system can provide dynamic field correction, in which the MRI data can be corrected based on the realized field. The field probe system may include a field camera 702 that has one or more (e.g., N) field probes 704. These probes 704 can be placed throughout the scanning volume within the MRI system 710 in order to measure a spatially varying field. Each probe 704 may include an MR sample, such as19F, a fluorine compound, or another sample capable of producing an NMR signal upon excitation. Each probe may also include a radiofrequency (RF) system that can excite the QB\920171.00647\95776343.4Client Ref. UMN 2024 118sample and measure the signal produced by the sample.

[0098] The system further includes an NMR probe control system 708 in communication with the field camera 702 and the MRI or other NMR (e.g., magnetic resonance spectroscopy) system 710. In this way, the NMR probe control system 708 can trigger 706 the field camera 702 to measure the field at the appropriate times based on the timing of the pulse sequence applied by the MRI system 710. The timing of such triggers 706 can be determined as previously described.

[0099] The NMR probe control system 708 can store the NMR probe data, which can be used to model the spatially and temporally varying field. In one non-limiting example, the field can be modeled in space with 0th, 1st, and 2ndorder spherical harmonics. In some implementations, the NMR probe control system 708 can provide the raw NMR probe data to another processor to model the field perturbations. In other implementations, the NMR probe control system 708 can process (e.g., filter, model the field, and so forth) the NMR probe data and supply field data (e.g., k coefficients) to another processor or the MRI system 710 that can be used to correct MRI data. The control system 708 can also receive and store MRI data (e.g., k-space data) in order to correct the MRI data based on the measured field. The NMR probe control system 708 can provide the corrected MRI data back to the MRI system 710 or another processor for further data processing and image reconstruction.

[0100] Referring now to FIG.10, an example of an MRI system 800 is shown, which may be used in accordance with some aspects of the systems and methods described in the present disclosure. As shown in FIG.10, a computing device 850 can receive one or more types of data (e.g., signal evolution data, k-space data, receiver coil sensitivity data) from data source 802. In some configurations, computing device 850 can execute at least a portion of an imaging system 804 to reconstruct images from magnetic resonance data (e.g., k-space data) acquired. In some configurations, the imaging system 804 can implement an automated pipeline to provide MRI images, field monitoring data, other MRI or NMR data, spectroscopic data, etc.

[0101] Additionally or alternatively, in some configurations, the computing device 850 can communicate information about data received from the data source 802 to a server 852 over a communication network 854, which can execute at least a portion of the imaging system 804. In such configurations, the server 852 can return information to the computing device 850 (and / or any other suitable computing device) indicative of an QB\920171.00647\95776343.4Client Ref. UMN 2024 118output of the imaging system 804.

[0102] In some configurations, computing device 850 and / or server 852 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device 850 and / or server 852 can also reconstruct images from the data.

[0103] In some configurations, data source 802 can be any suitable source of data (e.g., measurement data, images reconstructed from measurement data, processed image data), such as an MRI system, another computing device (e.g., a server storing measurement data, images reconstructed from measurement data, processed image data), and so on. In some configurations, data source 802 can be local to computing device 850. For example, data source 802 can be incorporated with computing device 850 (e.g., computing device 850 can be configured as part of a device for measuring, recording, estimating, acquiring, or otherwise collecting or storing data). As another example, data source 802 can be connected to computing device 850 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some configurations, data source 802 can be located locally and / or remotely from computing device 850, and can communicate data to computing device 850 (and / or server 852) via a communication network (e.g., communication network 854).

[0104] In some configurations, communication network 854 can be any suitable communication network or combination of communication networks. For example, communication network 854 can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), other types of wireless network, a wired network, and so on. In some configurations, communication network 854 can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in FIG.10 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on.

[0105] Referring now to FIG.11, an example of hardware 900 that can be used to QB\920171.00647\95776343.4Client Ref. UMN 2024 118implement data source 802, computing device 850, and server 852 in accordance with some configurations of the systems and methods described in the present disclosure is shown.

[0106] As shown in FIG. 11, in some configurations, computing device 850 can include a processor 902, a display 904, one or more inputs 906, one or more communication systems 908, and / or memory 910. In some configurations, processor 902 can be any suitable hardware processor or combination of processors, such as a central processing unit (“CPU”), a graphics processing unit (“GPU”), and so on. In some configurations, display 904 can include any suitable display devices, such as a liquid crystal display (“LCD”) screen, a light-emitting diode (“LED”) display, an organic LED (“OLED”) display, an electrophoretic display (e.g., an “e-ink” display), a computer monitor, a touchscreen, a television, and so on. In some configurations, inputs 906 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0107] In some configurations, communications systems 908 can include any suitable hardware, firmware, and / or software for communicating information over communication network 854 and / or any other suitable communication networks. For example, communications systems 908 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 908 can include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0108] In some configurations, memory 910 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 902 to present content using display 904, to communicate with server 852 via communications system(s) 908, and so on. Memory 910 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 910 can include random-access memory (“RAM”), read-only memory (“ROM”), electrically programmable ROM (“EPROM”), electrically erasable ROM (“EEPROM”), other forms of volatile memory, other forms of non-volatile memory, one or more forms of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some configurations, memory 910 can have encoded thereon, or QB\920171.00647\95776343.4Client Ref. UMN 2024 118otherwise stored therein, a computer program for controlling operation of computing device 850. In such configurations, processor 902 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server 852, transmit information to server 852, and so on. For example, the processor 902 and the memory 910 can be configured to perform the methods described herein.

[0109] In some configurations, server 852 can include a processor 912, a display 914, one or more inputs 916, one or more communications systems 918, and / or memory 920. In some configurations, processor 912 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some configurations, display 914 can include any suitable display devices, such as an LCD screen, LED display, OLED display, electrophoretic display, a computer monitor, a touchscreen, a television, and so on. In some configurations, inputs 916 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0110] In some configurations, communications systems 918 can include any suitable hardware, firmware, and / or software for communicating information over communication network 854 and / or any other suitable communication networks. For example, communications systems 918 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 918 can include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0111] In some configurations, memory 920 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 912 to present content using display 914, to communicate with one or more computing devices 850, and so on. Memory 920 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 920 can include RAM, ROM, EPROM, EEPROM, other types of volatile memory, other types of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some configurations, memory 920 can have encoded thereon a server program for controlling QB\920171.00647\95776343.4Client Ref. UMN 2024 118operation of server 852. In such configurations, processor 912 can execute at least a portion of the server program to transmit information and / or content (e.g., data, images, a user interface) to one or more computing devices 850, receive information and / or content from one or more computing devices 850, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on.

[0112] In some configurations, the server 852 is configured to perform the methods described in the present disclosure. For example, the processor 912 and memory 920 can be configured to perform the methods described herein.

[0113] In some configurations, data source 802 can include a processor 922, one or more data acquisition systems 924, one or more communications systems 926, and / or memory 928. In some configurations, processor 922 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some configurations, the one or more data acquisition systems 924 are generally configured to acquire data, images, or both, and can include an MRI system. Additionally or alternatively, in some configurations, the one or more data acquisition systems 924 can include any suitable hardware, firmware, and / or software for coupling to and / or controlling operations of an MRI system. In some configurations, one or more portions of the data acquisition system(s) 924 can be removable and / or replaceable.

[0114] Note that, although not shown, data source 802 can include any suitable inputs and / or outputs. For example, data source 802 can include input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, and so on. As another example, data source 802 can include any suitable display devices, such as an LCD screen, an LED display, an OLED display, an electrophoretic display, a computer monitor, a touchscreen, a television, etc., one or more speakers, and so on.

[0115] In some configurations, communications systems 926 can include any suitable hardware, firmware, and / or software for communicating information to computing device 850 (and, in some configurations, over communication network 854 and / or any other suitable communication networks). For example, communications systems 926 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 926 can include hardware, firmware, and / or software that can be used to establish a wired QB\920171.00647\95776343.4Client Ref. UMN 2024 118connection using any suitable port and / or communication standard (e.g., VGA, DVI video, USB, RS-232, etc.), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0116] In some configurations, memory 928 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 922 to control the one or more data acquisition systems 924, and / or receive data from the one or more data acquisition systems 924; to generate images from data; present content (e.g., data, images, a user interface) using a display; communicate with one or more computing devices 850; and so on. Memory 928 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 928 can include RAM, ROM, EPROM, EEPROM, other types of volatile memory, other types of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some configurations, memory 928 can have encoded thereon, or otherwise stored therein, a program for controlling operation of medical image data source 802. In such configurations, processor 922 can execute at least a portion of the program to generate images, transmit information and / or content (e.g., data, images, a user interface) to one or more computing devices 850, receive information and / or content from one or more computing devices 850, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), and so on.

[0117] In some configurations, any suitable computer-readable media can be used for storing instructions for performing the functions and / or processes described herein. For example, in some configurations, computer-readable media can be transitory or non- transitory. For example, non-transitory computer-readable media can include media such as magnetic media (e.g., hard disks, floppy disks), optical media (e.g., compact discs, digital video discs, Blu-ray discs), semiconductor media (e.g., RAM, flash memory, EPROM, EEPROM), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and / or any suitable tangible media. As another example, transitory computer-readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and / or any suitable intangible media.

[0118] As used herein in the context of computer implementation, unless QB\920171.00647\95776343.4Client Ref. UMN 2024 118otherwise specified or limited, the terms "component," "system," "module," "controller," "framework," and the like are intended to encompass part or all of computer-related systems that include hardware, software, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being, a processor device, a process being executed (or executable) by a processor device, an object, an executable, a thread of execution, a computer program, or a computer. By way of illustration, both an application running on a computer and the computer can be a component. One or more components (or system, module, and so on) may reside within a process or thread of execution, may be localized on one computer, may be distributed between two or more computers or other processor devices, or may be included within another component (or system, module, and so on).

[0119] In some implementations, devices or systems disclosed herein can be utilized or installed using methods embodying aspects of the disclosure. Correspondingly, description herein of particular features, capabilities, or intended purposes of a device or system is generally intended to inherently include disclosure of a method of using such features for the intended purposes, a method of implementing such capabilities, and a method of installing disclosed (or otherwise known) components to support these purposes or capabilities. Similarly, unless otherwise indicated or limited, discussion herein of any method of manufacturing or using a particular device or system, including installing the device or system, is intended to inherently include disclosure, as embodiments of the disclosure, of the utilized features and implemented capabilities of such device or system.

[0120] As used herein, the phrase "at least one of A, B, and C" means at least one of A, at least one of B, and / or at least one of C, or any one of A, B, or C or combination of A, B, or C. A, B, and C are elements of a list, and A, B, and C may be anything contained in the Specification.

[0121] The present disclosure has described one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention. QB\920171.00647\95776343.4

Claims

Client Ref. UMN 2024 118CLAIMS 1. A method for monitoring a magnetic field of a magnetic resonance imaging (MRI) system, the method comprising: a) determining a pulse sequence of the MRI system, the pulse sequence having a repetition time and a readout window repeated for a plurality of repetitions with the repetition time, the readout window having a readout start time and readout duration; b) determining a plurality of field measurement segments, wherein each of the plurality of field measurement segments is associated with one of the plurality of repetitions, and wherein each of the plurality of field measurement segments has a trigger time and a field measurement duration, thereby defining a measurement window, and wherein the measurement windows of the plurality of field measurement segments collectively span the readout window; c) using the MRI system to perform the pulse sequence, wherein performing the pulse sequence comprises measuring MRI data during the readout window for the plurality of repetitions while detecting field data using an NMR probe system during the associated field measurement segment; and d) stitching together the field data based on each trigger time of the plurality of field measurement segments, generating a characterization of the magnetic field of the MRI system through the readout window.

2. The method of claim 1, further comprising: e) correcting the MRI data based on the characterization of the magnetic field of the MRI system.

3. The method of claim 1, wherein the field measurement duration of each of the plurality of field measurement segments is equal.

4. The method of claim 1, wherein stitching together the field data comprises discarding a part of the field data associated with at least one of the plurality of field measurement segments prior to stitching together the field data. QB\920171.00647\95776343.4Client Ref. UMN 2024 1185. The method of claim 1, wherein the NMR probe system has a T2*decay time, and wherein each field measurement duration does not exceed the T2*decay time.

6. The method of claim 1, wherein each field measurement duration does not exceed 25 ms and the readout duration exceeds 25 ms.

7. The method of claim 1, wherein the field measurement duration of a first of the plurality of field measurement segments is not equal to the field measurement duration of a second of the plurality of field measurement segments.

8. The method of claim 1, wherein the field measurement duration of each of the plurality of field measurement segments is determined based on an intra-voxel signal dephasing model, the intra-voxel signal dephasing model characterizing a signal loss of the NMR probe system based on a maximum gradient moment of the pulse sequence.

9. The method of claim 8, wherein the intra-voxel signal dephasing model further characterizes a signal loss of the NMR probe system based on T2*decay.

10. The method of claim 1, wherein the field measurement duration of each of the plurality of field measurement segments is determined based on a signal threshold, the signal threshold being relative to a signal level of the NMR probe system measured at a start of the readout window, wherein a signal level of the NMR probe system within each of the plurality of field measurement segments equal to or greater than the signal threshold.

11. The method of claim 10, wherein the signal threshold is selected to retain residual signals from intra-probe dephasing to compensate for additional signal decay due to T2*relaxation.

12. The method of claim 1, wherein the pulse sequence comprises a 2D spiral readout. QB\920171.00647\95776343.4Client Ref. UMN 2024 11813. The method of claim 1, wherein the pulse sequence comprises at least one of an echo planar imaging readout, a spiral readout, a stack of stars readout, a radial readout, or a multishot readout.

14. The method of claim 1, wherein the characterization of the magnetic field of the MRI system through the readout window comprises 0th-2ndorder dynamic field measurements.

15. A system for monitoring a magnetic field of a magnetic resonance imaging (MRI) system, the system comprising: an MRI system; a nuclear magnetic resonance (NMR) probe system; and a control system configured to: a) determine a pulse sequence of the MRI system, the pulse sequence having a repetition time and a readout window repeated for a plurality of repetitions with the repetition time, the readout window having a readout start time and readout duration; b) determine a plurality of field measurement segments, wherein each of the plurality of field measurement segments is associated with one of the plurality of repetitions, and wherein each of the plurality of field measurement segments has a trigger time and a field measurement duration, thereby defining a measurement window, and wherein the measurement windows of the plurality of field measurement segments collectively span the readout window; c) control the MRI system to perform the pulse sequence, wherein performing the pulse sequence comprises measuring MRI data during the readout window for the plurality of repetitions while detecting field data using the NMR probe system during the associated field measurement segment; and d) stitch together the field data based on each trigger time of the plurality of field measurement segments, generating a QB\920171.00647\95776343.4Client Ref. UMN 2024 118characterization of the magnetic field of the MRI system through the readout window.

16. The system of claim 15, wherein the control system is further configured to: e) correct the MRI data based on the characterization of the magnetic field of the MRI system.

17. The system of claim 15, wherein the field measurement duration of each of the plurality of field measurement segments is equal.

18. The system of claim 15, wherein stitching together the field data comprises discarding a part of the field data associated with at least one of the plurality of field measurement segments prior to stitching together the field data.

19. The system of claim 15, wherein the NMR probe system has a T2*decay time, and wherein each field measurement duration does not exceed the T2*decay time.

20. The system of claim 15, wherein each field measurement duration does not exceed 25 ms and the readout duration exceeds 25 ms.

21. The system of claim 15, wherein the field measurement duration a first of the plurality of field measurement segments is not equal to the field measurement duration of a second of the plurality of field measurement segments.

22. The system of claim 15, wherein the field measurement duration of each of the plurality of field measurement segments is determined based on at least one of an intra-voxel signal dephasing model or a T2*decay model, wherein the intra-voxel signal dephasing model characterizes a signal loss of the NMR probe system based on a maximum gradient moment of the pulse sequence; and wherein the T2*decay model characterizes a signal loss of the NMR probe system based on a T2*of the NMR probe system. QB\920171.00647\95776343.4Client Ref. UMN 2024 11823. The system of claim 15, wherein the field measurement duration of each of the plurality of field measurement segments is determined based on a signal threshold, the signal threshold being relative to a signal level of the NMR probe system measured at a start of the readout window, wherein a signal level of the NMR probe system within each of the plurality of field measurement segments equal to or greater than the signal threshold.

24. The system of claim 23, wherein the signal threshold is selected to retain residual signals from intra-probe dephasing to compensate for additional signal decay due to T2*relaxation.

25. The system of claim 15, wherein the pulse sequence comprises a 2D spiral readout.

26. The system of claim 15, wherein the pulse sequence comprises at least one of an echo planar imaging readout, a spiral readout, a stack of stars readout, a radial readout, or a multishot readout.

27. The system of claim 15, wherein the characterization of the magnetic field of the MRI system through the readout window comprises 0th-2ndorder dynamic field measurements. QB\920171.00647\95776343.4

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