Methods and systems for optimizing magnetization transfer magnetic resonance imaging

Abstract

The disclosed systems and methods use the developed Flexible Relaxation and Saturation Time (FIRST) approach to provide customization of parameters for a Magnetization Transfer (MT) sequences to maximize sensitivity and efficiency. The methods may include generating one or more customized parameters for one or more iterations of a Magnetization Transfer Magnetic Resonance (MR) sequence that is executed on a Magnetic Resonance Imaging (MRI) System. The generating may include receiving input parameters for each iteration of the sequence. The one or more input parameters may include radiofrequency (RF) offset and / or saturation power. The generating may further include determining a set of one or more customized parameters for each iteration using the input parameters for each RF offset and / or saturation power. The one or more customized parameters may include repetition time (TR).

Description

METHODS AND SYSTEMS FOR OPTIMIZING MAGNETIZATION TRANSFERMAGNETIC RESONANCE IMAGINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 725,795 filed November 27, 2024. The entirety of this application is hereby incorporated by reference for all purposes.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under NS083654 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND

[0003] Magnetization Transfer Magnetic Resonance Imaging (MRI), such as Chemical Exchange Saturation Transfer (CEST) Magnetic Resonance Imaging (MRI), is an advanced imaging technique that allows for the detection of specific molecules or metabolites in biological tissues. CEST MRI, specifically, has shown promise in various applications, including tumor detection, stroke assessment, and neurodegenerative disease diagnosis. The technique involves selective radiofrequency saturation of exchangeable protons, followed by transfer of this saturation to water protons through exchange processes, resulting in a measurable decrease in the water signal.

[0004] Traditional CEST imaging methods face significant technical limitations that restrict their clinical utility. Conventional approaches typically employ fixed acquisition parameters across all radiofrequency offset frequencies, leading to suboptimal sensitivity and prolonged scan times. The inherently weak CEST effects, often only a few percent in magnitude, necessitate long continuous wave radiofrequency irradiation periods to achieve steady-state conditions for quantitative measurements. These extended saturation times, combined with the need for multiple frequency offsets to characterize the complete CEST spectrum, result in acquisition protocols that are impractical for routine clinical applications.SUMMARY

[0005] Thus, there is a need for accurate and efficient techniques to optimize Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging through comprehensive parameter customization and post-processing reconstruction techniques. The disclosed methods and systems can address limitations in conventional CEST imaging approaches that employ fixed acquisition parameters and lack systematic optimization frameworks.

[0006] Additional advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure. The advantages of the disclosure will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure, as claimed.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The disclosure can be better understood with the reference to the following drawings and description. The components in the figures are not necessarily to scale, the emphasis being placed upon illustrating the principles of the disclosure.

[0008] Figure 1 shows a flow diagram illustrating an example of a method of customized parameters for acquiring Magnetic Resonance (MR) image data and processing the acquired MR image data according to embodiments.

[0009] Figure 2 shows a flow diagram illustrating an example of a method for generating customized parameters for a MR sequence according to embodiments.

[0010] Figure 3 shows an example of a pulse sequence with customized parameters generated according to embodiments.

[0011] Figure 4 illustrates another example of a pulse sequence with customized parameters generated according to embodiments.

[0012] Figure 5 shows a flow diagram illustrating an example of processing MR image data acquired using a customized multi-iteration pulse sequence according to embodiments.

[0013] Figure 6 shows an illustrative example of customized TR for peak delta signal per unit of time as a function of RF duty cycle according to embodiments.

[0014] Figure 7 shows an illustrative example of customized TR generated according to embodiments.

[0015] Figure 8 shows illustrative examples of CEST signal as a function of saturation time, under the high RF duty cycle.

[0016] Figure 9 shows illustrative examples of simulations of customized saturation time according to embodiments.

[0017] Figure 10 shows illustrative examples of simulated routine CEST Z-spectra for labile amide proton transfer according to embodiments.

[0018] Figure 11 shows illustrative examples of comparisons of Z-spectra generated using different methods according to embodiments.

[0019] Figure 12A shows an illustrative example of a three-compartment phantom with two Creatine concentrations (25 and 50 mM) with three MnC12 concentrations (0, 15, and 30 pM). Figure 12B shows an illustrative comparison of routine CEST scan with fixed Td and Ts and the FIRST approach with customized Td and Ts according to embodiments.

[0020] Figure 13 shows an illustrative comparison of apparent and QUASS Z-spectra from conventional and disclosed CEST MRI sequences.

[0021] Figure 14 shows a block diagram illustrating an example of a system according to embodiments.DETAILED DESCRIPTION

[0022] In the following description and Appendix, numerous specific details are set forth such as examples of specific components, devices, methods, etc., in order to provide a thorough understanding of embodiments of the disclosure. It will be apparent, however, to one skilled in the art that these specific details need not be employed to practice embodiments of the disclosure. In other instances, well-known materials or methods have not been described in detail in order to avoid unnecessarily obscuring embodiments of the disclosure. While the disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that there is no intent to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure.

[0023] The disclosed systems and methods use the developed Flexible Relaxation and Saturation Time (FIRST) approach to provide customization of parameters for a Magnetization Transfer (MT) sequences to maximize sensitivity and efficiency. In some examples, one or more customized parameters may be determined on a per-offset basis. For example, for CEST MRI, a range of values for customized parameter(s), such as repetition time (TR) and / or RF duty cycle, can be determined on a per-offset basis, This customization can enhance CEST contrast while also minimizing total scan time.

[0024] In some examples, the customization of parameter(s) may provide improvements in sensitivity and efficiency relative to conventional approaches. For instance, standard CEST MRI protocols often use fixed saturation time, relaxation delay, and flip angle values across multiple offsets. The disclosed techniques enable these parameters to be flexibly customized. This can facilitate reduced scan time, improved motion correction, and registration to concurrent relaxation and field inhomogeneity determinations. At the same time, the QUASS CEST images and quantitative CEST (qCEST) analysis can be accomplished to utilize customized acquisition that enhances CEST sensitivity efficiency and motion correction.

[0025] For example, optimizing value(s) of one or more customizable parameters, such as saturation time and relaxation delay, individually for each offset can enable a reduction in the total scan time while maintaining or improving image quality. Higher spatial resolution may also be achieved for the same scan time by leveraging the increased signal-to-noise ratio enabled by the customization.

[0026] For optimization of customized parameters, such as saturation time and relaxation delay, also may reduce RF energy deposition (SAR) in tissues, which may be advantageous for ultrahigh field applications. Likewise, customized post-saturation delay (PSD) can support motion correction and registration, particularly in spectral regions near water resonance, without prolonging PSD unnecessarily for scans with sufficient signal intensity.

[0027] The disclosed techniques further support the simultaneous determination of relaxation and field inhomogeneity using generalized FIRST QUASS procedures, advancing the capabilities of quantitative CEST imaging.

[0028] The systems and methods described herein may be implemented using an MRI system capable of Magnetization Transfer MR sequences. In some examples, the MRI system may include a magnet, gradient coils, RF coils, and a controller. The controller may include one or moreprocessors configured to receive the input parameters, perform the parameter customized, and control the MRI hardware to execute the customized pulse sequence.

[0029] In some examples, the MRI system may be configured for any imaging protocol employing offset saturation to affect magnetization, including but not limited to CEST imaging. In some examples, the one or more customizable parameters may include but is not limited to RF duty cycle, Saturation Time (Tsat), Repetition Time (TR), excitation flip angle (FA), average saturation power (Blavg), peak Bl (Bl, peak), saturation Duty Cycle (DC), saturation amplitude (for continuous wave RF saturation approach), among others, or any combination thereof.

[0030] In some embodiments, the parameter customization may be performed in real-time or near real-time during a scan session. This can allow for values of parameter(s) to be dynamically adjusted based on initial scout scans or updated tissue property estimates.

[0031] In some examples, the method and systems of the disclosure may generate one or more customized parameters for each pulse sequence iteration (also referred to as a “iteration”) of a pulse sequence performed in a scan. In some examples, the pulse sequence may include a series of one or more iterations (e.g., CEST RF saturation offset) that can be performed in each acquisition. Each pulse sequence iteration may include one saturation pulse and one signal acquisition signal. In some examples, each iteration may have its own set of one or more customized parameters. In some examples, the MR system may be instructed to sequentially acquire a series of MRI signals for the one or more iterations in one scan.

[0032] In some examples, the customized MRI acquisition may be combined with postprocessing approaches. For example, the customized CEST acquisition may be combined with post-processing techniques, such as quasi -steady- state (QUASS) reconstruction (for example, as described in P.Z. Sun, Quasi-steady state chemical exchange saturation transfer (QUASS CEST) analysis-correction of the finite relaxation delay and saturation time for robust CEST measurement, Magn Reson Med, 85 (2021) 3281-3289, which is incorporated by reference in its entirety), to further improve quantification accuracy. By optimizing sensitivity during acquisition and reconstructing the full CEST effect, both imaging efficiency and quantitative reliability may be improved.

[0033] Exemplary applications of the customized Magnetization Transfer MR sequences can include but is not limited to tumor imaging, cardiac assessment (e.g., cardiac CEST MRI), stroke assessment, metabolite mapping, among others, or any combination thereof. The increasedsensitivity can enable detection of subtle changes in labile proton concentrations and exchange rates.

[0034] The customization approach described herein may be applied to various types of Magnetization Transfer MR sequences. For example, CEST MRI sequences may include but not limited to gradient echo, spin echo readouts, among others, or any combination thereof. The flexibility to customize timing parameters and flip angles on a per-offset basis can enhance the capabilities of CEST MRI for detecting subtle metabolic changes in tissues.

[0035] Various embodiments are described herein, including systems, methods, devices, modules, models, algorithms, networks, structures, processes, computer-program products, and the like.

[0036] Figures 1, 2, and 5 show examples of flow diagrams 100, 200, and 500 illustrating methods of generating one or more customized parameters for CEST MRI sequence and processing the acquired MR image data according to embodiments. Operations described in diagrams 100, 200, and 500 may be performed by a computing system, such as a computing system described below with respect to Figure 14.

[0037] Although the flow diagrams 100, 200, and 500 may describe the operations as a sequential process, in various embodiments, some of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. An operation may have additional steps not shown in the figure. In some embodiments, some operations may be optional. Embodiments of the method may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the associated tasks may be stored in a computer-readable medium such as a storage medium.

[0038] Figure 1 show a flow diagram 100 for optimized CEST MRI acquisition by generating customized parameters and associated processing according to embodiments of the present disclosure. Operations in flow diagram 100 may begin at block 110 when one or more input parameters are received for a subject. In some examples, the input parameters may depend on the sequence, the area to be imaged, among others, or any combination thereof. For example, the one or more input parameters may include but is not limited to one or more tissue relaxation properties (e.g., tissue relaxation times T1 and / or T2), representative magnetization transfer (MT) properties, one or more saturation power levels (e.g., a Bl RF field level), an RF duty cycle, among others,or any combination thereof. In some examples, the input parameter(s) may also include one or more selected target functions (also referred to as “optimization function”) for customizing one or more parameters.

[0039] In some examples, the target function may include but is not limited to a target function sensitivity. In some examples, the target function sensitivity may include but is not limited to CEST signal sensitivity (function (1)), CEST contrast sensitivity, among others, or any combination thereof. For example, contrast sensitivity per unit of time may include but is not limited to Rlrho (i.e., Rip), CEST, among others, or any combination thereof. For example, the target function sensitivity may also include sensitivity per unit of RF energy deposition. In this example, the corresponding target function may take into account RF power. The specific functions and calculations may differ depending on the chosen optimization target and purpose.

[0040] In some examples, these input parameters may be provided by an operator through a user interface or may be automatically determined based on the specific imaging application and target tissue characteristics. For example, RF duty cycle may be preset by the MRI system. In other examples, Tl, T2, B0 (field strength), CEST Rlrho spectra, and / or MT function may be provided / preset by the MRI scanner, such as based on the tissue of interest.

[0041] At block 120, one or more customized parameters for one or more pulse sequence iterations may be determined using the received input parameters. The customized parameters may include but are not limited to repetition time (TR), saturation time (Ts), relaxation delay (Td), flip angle (FA), among others, or any combination thereof. In some examples, the one or more customized parameters may be determined for each RF offset frequency. The determination process may involve calculating spinlock relaxation rates for each RF offset and applying optimization algorithms to maximize target function(s) (also referred to as “sensitivity efficiency functions”). The optimization of the one or more customized parameters may be performed using physics-based models that account for the interdependency between RF duty cycle and optimal repetition time values.

[0042] In some examples, the optimization of the one or more customized parameters may be generated using one or more target functions, including but not limited to contrast sensitivity per unit of time. For example, contrast sensitivity per unit of time may include but is not limited to Rlrho, CEST, among others, or any combination thereof. The specific functions and calculations may differ depending on the chosen optimization target and purpose.

[0043] Figure 2 shows an example of a flow diagram 200 illustrating a method of generating one or more customized parameters for CEST MRI sequence using a target function according to embodiments. The method may be implemented for both spin-echo and gradient-echo CEST sequences. For gradient-echo sequences, additional customization of the flip angle for each offset may be performed.

[0044] Using the input parameters received at block 110, one or more customized parameters may be determined at block 210. For example, the TR, RF duty cycle, and / or RF Flip angle using the input parameters (block 110) may be determined for each offset.

[0045] In some examples, the effective rotation angle (0) may first be determined for each RF offset. For example, the effective rotation angle may be determined based on 6 = tan-1with 1' being the gyromagnetic ratio constant.

[0046] Next, the spinlock rate (Rip) may be determined for each RF offset. In some examples, the spinlock relaxation rate may be determined based on Rlp= Rlwcos26 + R2wsin26 +Rex,, where Rex is approximated by the fraction / cxchange / R.2 of MT based on conventional equation for R ex.

[0047] Next, repetition time TR (also referred to as “optimal TR” or “customized TR”) for each RF offset frequency, based on the determined spinlock relaxation rate and the selected target function. In some embodiments, the optimal TR may be determined by solving an equation that maximizes the selected target function. For example, optimizing sensitivity per unit time may involve maximizing a target function related to MTRasym / TR.

[0048] For example, for optimizing CEST sensitivity per unit time, the customized TR may be determined by solving the following target function (1):

[0050] In this example, the customized TR may be determined that maximizes the CEST sensitivity per unit of time per offset for any given RF duty cycle under a chosen Bl. In thisexample, a derivative of CEST sensitivity efficiency with respect to TR may be determined and solved for TR when the derivative equals zero.

[0051] In some examples, if CEST and MT are known / estimated for different offsets, the customized TR could be different for offsets (e.g., +1.9 ppm from -1.9 ppm).

[0052] In other examples, a different target function may be solved to determine the customized TR using MTRasym. For example, the customized TR may be determined by solving the following target function (2):

[0054] In these examples, multivariable cost minimization of either target function may be performed to determine values for TR and q that may be maxima for target function sensitivity. In some examples, the target function sensitivity may include but is not limited to CEST signal sensitivity, CEST contrast sensitivity, among others, or any combination thereof.

[0055] For example, the target function sensitivity may also include sensitivity per unit of RF energy disposition. In this example, the corresponding target function may take into account RF power.

[0056] Next, at block 230, one or more additional customized parameters may be determined. For example, the RF saturation time Ts and / or relaxation delay time Td (also referred to as “optimal Ts”, “optimal Td”, “customized Ts,” or “customized Td”) for each RF offset frequency may be determined based on the customized TR and the RF duty cycle determined at block 230.

[0057] For example, the saturation time (Ts) may be determined Ts= T] ■ TR. Relaxation delay (Ta) may be determined as Td= (1 — ?]) ■ TR.

[0058] In some examples, the RF flip angle (FA) may be optionally determined. For example, the RF flip angle (FA) be approximated based on the Ernst angle equation, inputting both TR and Tiw.

[0059] This can result in customized parameters (e.g., Ts, Td, and / or FA per RF offset) that can optimize the contrast sensitivity per unit of time for each offset so the total scan time can be reduced. This flexible relaxation and irradiation time (FIRST) CEST MRI approach can allowparameters to be varied across different offsets, in contrast to conventional CEST sequences that use fixed timing for all offsets.

[0060] Alternatively, the same scan time can be used to drive up the resolution, which demands more Signal-to-Noise Ratio (SNR) that is conferred by adopting the FIRST CEST MRI approach.

[0061] In some examples, the customized parameters may be generated for different input parameters, for example, using the one or more target functions (e.g., using the method illustrated in the flow diagram 200), using other optimization techniques (e.g., described in the Examples), among others, or any combination thereof. By way of example, the customized parameters may be stored in a database so that the customized parameters may be determined automatically from the input parameters at block 120.

[0062] Next, at block 130, instructions for a chemical exchange saturation transfer (CEST) MRI pulse sequence may be generated using the customized parameters and the input parameters for each RF offset frequency.

[0063] For example, the instructions for the CEST MRI pulse sequence may then be generated using the one or more customized parameter (TR, Ts, Td, and / or flip angle) values for each RF offset. This flexible relaxation and irradiation time (FIRST) CEST MRI approach can allow parameters to be varied across different offsets, in contrast to conventional CEST sequences that use fixed timing for multiple offsets.

[0064] The pulse sequence instructions, including the customized timing and flip angle parameters, may be sent to the MRI scanner for execution. The scanner may then perform the CEST MRI acquisition using the customized, offset-dependent parameters.

[0065] In some examples, the instructions for the sequence may include instructions for a series of one or more pulse sequence iterations within the sequence. In this example, each iteration may have a different set of customized parameters, such as a different offset, saturation time, relaxation delay, and / or flip angle with a single RF saturation amplitude (Bi).

[0066] For example, Figures 3 and 4 illustrate examples of a CEST MRI pulse sequence generated according to embodiments. Figure 3 shows an example 300 of a pulse sequence with customized Td, Ts, PSD, and FA that can be customized at different RF offsets and amplitudes. Such a modified sequence can provide substantial flexibility with advantages in scan time, SNR, gated acquisition, and motion correction.

[0067] Figure 4 illustrates an example 400 of a pulse sequence with multiple iterations with varied customized parameters as provided in the example 300. In this example, the example 400 may include two iterations of CEST scans of representative B 1 (i and m) and offset (j and n), with customized Td, Ts, alpha, and PSD but it will be understood that the pulse sequence may include more or less iterations and different customization of parameter(s).

[0068] As shown in Figure 4, a series of MRI signals may be acquired sequentially in one scan with two or more offset(s) (Aco) with two or more sets of saturation times (Ts), relaxation delays (Ta), and RF flip angles (FA) with a single RF saturation amplitude (Bi) using the instructions.

[0069] Using the pulse sequence 400, the series of MRI signals may be acquired by an instruction to the MRI system to enact a pulse sequence including at least one iteration that includes a saturation pulse and one signal acquisition embedded in a looping structure with one or more additional iterations that simultaneously increments a prestored list of saturation offsets (Aco) for the saturation pulse in concurrence with the corresponding customized parameters (e g., saturation times (Ts), relaxation delays (Td), and RF flip angles (FA)) for the signal acquisition while maintaining the same value of RF saturation amplitude (Bi).

[0070] In this example, the sequence may include a series of at least a first pulse iteration 410 and a second pulse iteration 420. In this example, the first pulse iteration 410 may be performed at one offset, and the second pulse iteration 420 may be performed at a different offset. In this example, the signal recovery time (Td), the saturation time (Ts), and the RF flip angle have been customized, for example, according to Figure 2, according to embodiments. It will also be understood that the pulse sequence may include a series of any number of iterations and is not limited to the two iterations illustrated. For example, the pulse sequence may include more than two iterations (e.g., three, four, five, more than five, etc.) and may have more or less customized parameters. In some examples, the post-saturation delay (PSD) and / or flip angle may not be varied as shown in Figure 4.

[0071] Next, at block 140, the MRI system may be controlled to execute the CEST MRI pulse sequence according to the generated instructions. The MRI system may include a magnet for generating a static magnetic field, gradient coils for generating magnetic field gradients, RF coils for transmitting RF pulses and receiving MR signals, and a controller for controlling the various components and executing the pulse sequence.

[0072] Next, at block 150, the apparent MR data for each iteration of the sequence may be acquired.

[0073] Next, at blocks 160 and / or 170, post-processing may optionally be performed on the MRI data acquired using the generated CEST MRI sequence. For example, the MRI data may be optionally reconstructed and quantified using quasi-steady-state (QUASS) reconstruction techniques, such as those described in PZ. Sun, Quasi-steady state chemical exchange saturation transfer (QUASS CEST) analysis-correction of the finite relaxation delay and saturation time for robust CEST measurement, Magn Reson Med, 85 (2021) 3281-3289, which is incorporated by reference in its entirety).

[0074] For example, at block 160, the acquired MR data may be reconstructed. For example, the reconstruction process may involve applying quasi-steady-state (QUASS) algorithms to recover equilibrium CEST effects from the optimized, customized acquisitions. The reconstruction may use one or more parameter maps, including Tl, AB 1, and / or ABO mapping data, to correct for field inhomogeneities and relaxation effects. In some examples, for example, for a series of MR volumes with distinct offsets and different customized parameters, reconstruction may be performed where each subsequent image volume may be reconstructed using previously reconstructed volumes and their associated parameters.

[0075] Figure 5 shows an example of a flow diagram 500 illustrating a method of processing a series of MR volumes with distinct offsets and different customized parameters sequentially acquired in a scan, for example, using the generated pulse sequence according to embodiments.

[0076] At block 510, a series of apparent MR image volumes acquired during the scan using the customized parameters associated with the respective iteration of the series, along with the corresponding parameter sets and calculated parameter map(s) for the scan may be received. The parameter map(s) may include but is not limited to Tl, AB1, ABO mapping data, among others, or any combination thereof.

[0077] For example, the series of apparent image volumes may include an apparent image volume for each pulse sequence iteration of the pulse sequence used to acquire MR image data. For example, pulse sequence may include a series of a first pulse iteration and a second pulse iteration, for example, as described in the example 400. In this example, the series of apparent image volumes may include a first apparent image volume and a second apparent image volume.

[0078] In some examples, the pulse sequence may include additional pulse iterations (N), and the series of apparent image volumes may include additional apparent volumes for each additional pulse iteration.

[0079] At block 520, a first corrected MR image volume for the first pulse sequence iteration may be reconstructed. This reconstruction may utilize the standard QUASS algorithm with the first apparent MR image volume, the first set of customized parameters, and the parameter map(s) for the scan. The QUASS algorithm may correct for incomplete saturation transfer effects to recover the equilibrium CEST signal magnitude.

[0080] At block 530, a second corrected MR image volume may be reconstructed for the second pulse sequence iteration using an enhanced FIRST QUASS algorithm. For example, the reconstruction may incorporate the previously reconstructed first corrected MR volume, the second apparent MR image volume, the second set of customized parameters, and the parameter map(s) for the scan. The FIRST QUASS approach may leverage information from previous reconstructions to improve the accuracy of subsequent volume reconstructions.

[0081] Next, the flow diagram 500 may optionally include operations at block 540 if there are additional pulse sequence iterations (N). For example, block 540 may include repeating the reconstruction process (block 530) for each additional pulse sequence iteration (N). For example, to correct the apparent Nth MRI volume for the Nth pulse sequence iteration, the reconstruction may utilize the apparent Nth MRI image volume, the corrected preceding (N-l) image volume, the customized parameters for the proceeding iteration (N-l), the customized parameters for the Nth iteration, and the parameter map(s) for the scan. This iterative approach may progressively improve the reconstruction accuracy as more data becomes available.

[0082] After each the apparent image volume for each iteration is reconstructed to generate corrected MRI volume, the process may conclude at block 550 when the series of corrected MRI image volumes may be outputted. For example, the series of corrected MRI image volumes may be outputted for further analysis or clinical interpretation, such as for quantitative analysis performed at block 170. These corrected MRI volumes may represent synthetic image series with equivalent theoretical parameters that enable direct comparison and quantitative analysis of CEST effects across different acquisition conditions.

[0083] At block 170, quantitative analysis may be performed on the corrected MRI volume (e g., from block 550). For example, quantitative information, such as molecular informationmetabolic information, etc., may be determined using the corrected MRI volume. For example, the quantitative information may include but is not limited to protein concentrations, metabolite concentrations, protein content, tissue pH, neurotransmitter levels, among others, or any combination thereof.

[0084] By combining the disclosed methods with quasi-steady-state (QUASS) reconstruction techniques in post-processing, QUASS reconstruction can allow recovery of the full equilibrium CEST effect from the optimized acquisitions that may use not sufficiently long saturation times. This combination of customized acquisition and QUASS reconstruction can enable both improved sensitivity during data collection and accurate quantification of the CEST effect.

[0085] In some embodiments, the methods may be implemented as part of the Flexible Relaxation and Irradiation Time (FIRST) CEST approach. The FIRST CEST approach allows varying of the repetition time (TR), saturation time (Ts), relaxation delay time (Td), and flip angle for each RF offset. This provides greater flexibility to customize the acquisition parameters on a per-offset basis compared to conventional CEST sequences that use fixed timing parameters across multiple offsets.

[0086] In some examples, the methods may include performing real-time customization of the acquisition parameters during a scan session. As data is acquired, the customization algorithm(s) may refine the parameter estimates and update the acquisition scheme to further improve sensitivity and efficiency.

[0087] The customization framework described herein may be applied to various CEST MRI applications, including but not limited to tumor imaging for detection and characterization of cancerous tissue, stroke assessment to identify ischemic regions, neurodegenerative disease evaluation, cartilage imaging for osteoarthritis assessment, and metabolite mapping in the brain or other organs.

[0088] While the method has been primarily described for chemical exchange saturation transfer (CEST) imaging, the optimization framework may be extended to other Magnetization Transfer type MR sequences, such as chemical exchange-sensitive spin-lock (CESL) imaging. The principles of optimizing sensitivity per unit time can be applied to CESL acquisitions as well.

[0089] The disclosed methods can provide a comprehensive framework for customizing Magnetization Transfer MR acquisition parameters to maximize sensitivity (e.g., contrast sensitivity per unit of time) and quantification accuracy. By leveraging physical models of theexchange process along with advanced reconstruction techniques, the disclosed approach can enable more efficient and sensitive molecular imaging within clinically feasible scan times. The flexibility of the customization strategy allows it to be adapted for a wide range of Magnetization Transfer MR applications and experimental conditions.

[0090] In some implementations, machine learning techniques may be incorporated to further refine the customization process. For example, a neural network could be trained on a large dataset of CEST acquisitions to learn customized parameter settings for different tissue types and applications. This could potentially enable adaptive optimization that accounts for inter-subject variability.

[0091] The disclosed methods can enable more efficient and sensitive molecular imaging within clinically feasible scan times by jointly customization acquisition parameters and leveraging advanced reconstruction techniques. By tailoring the CEST MRI protocol to the specific tissue properties, field strength, and experimental goals, researchers and clinicians can maximize the information obtained from CEST experiments while minimizing scan time and SAR.

[0092] Examples

[0093] Now, having described the embodiments of the disclosure, in general, the examples describe some additional embodiments. While embodiments of the present disclosure are described in connection with the examples and the corresponding text and figures, there is no intent to limit embodiments of the disclosure to these descriptions. On the contrary, the intent is to cover all alternatives, modifications, and equivalents included within the spirit and scope of embodiments of the present disclosure. Figures 6-13 show examples of customized parameters generated using different methods according to embodiments and Figure 14 illustrates an example of a system architecture for implementing the disclosed methods.

[0094] Figures 6 and 7 show examples of customized TR generated according to disclosed methods, for example, as described in the target function (1). Figure 6 shows an illustrative example of customized TR for peak delta signal per unit of time as a function of RF duty cycle. For example, Figure 6 shows the interaction between the RF duty cycle and the customized TR under which the delta signal per unit of time peaks. As shown, when the RF duty cycle is small, it can take a longer TR to provide sufficient RF saturation time and, hence, the delta signal. However, when the RF duty cycle is above 50%, the customized TR can seem to plateau at about twice Tl.

[0095] As described, the customized TR and r| can depend on the effective spinlock relaxation rate, including Rlw, R2w, CEST exchange properties, and chemical shifts, as well as field strength and B 1 irradiation amplitude, etc. Therefore, the customized TR that can maximize the CEST signal per unit of time also depends on the RF offset.

[0096] Figure 7 shows illustrative examples of simulations of the customized TR generated according to embodiments. For example, simulation 710 shows customized TR for delta signal per unit of time as a function of offsets and eta; and simulation 720 shows customized TR under a typical eta of 50%.

[0097] In the examples shown in Figure 7, the range for customized TR can be set to be between 0.5 and 20 times Tl. As shown in the chemical shift approaches 0 ppm, the spinlock relaxation rate increases, and therefore, the customized TR can drop (710). For a representative RF duty cycle of 50%, the customized TR can be about 3*T1 at large offsets and drops to the minimum of 0.5 *T1 close to 0. Such a rational design of adaptive scan parameters can not only improve the contrast sensitivity per unit of time (e.g., Rlrho or CEST), but also can reduce the total scan time than fixing the TR across the Z-spectrum.

[0098] Figures 8-13 show additional examples of generating customized parameter(s) according to embodiments.

[0099] Figures 8 and 9 show an example of determining customized parameter(s), according to embodiments. In this example, signal recovery can be described by an expression in which both the CEST signal intensity and effective spinlock relaxation rate depend on the RF amplitude and offset. Additionally, the rate can also depend on intrinsic bulk water R1 and R2, MT, and CEST properties. In these examples, readout time can be ignored when RF duty cycle is 100 % (i.e., the RF saturation is continuous across offsets (i.e., TR=Ts).

[0100] Figure 8 shows simulations of CEST signal as a function of saturation time, under the condition of high RF duty cycle. Simulation 810 shows CEST si naVsqrt(TR) as a function of saturation time under the assumption Ts=TR. This simulation 810 can illustrate that the CEST signal increases with TR due to signal recovery under RF saturation. However, the gain per unit time peaks at an intermediate TR. This can be understood as the signal initially increasing and quickly reaching the CEST equilibrium, but when the saturation time becomes too long, additional gains in the CEST signal are no longer efficient. Therefore, a balance can exist between signal gain per unit time and the total duration. The mean and standard deviation for the optimalsaturation time (under 100% RF duty cycle) can be about 1 .26 ± 0.02 times T1 p. In practice, some readout delay or relaxation recovery delay may slightly affect the relationship between the optimal saturation time and Tip. Simulation 820 shows customized saturation time as a function of Rlrho.

[0101] Assuming the simple condition that Ts=TR using spin echo EPI readout, the customized Ts can be determined as a function of a model CEST system. For example, assuming T1 = 1.5 s, T2 = 100 ms as typical relaxation properties of the brain at 3T, along with semisolid macromolecules at 15% and an exchange rate of 20 Hz, with Tlmt= 1 s and T2mt=10 ps. Additionally, representative amide proton concentration and exchange rate of 1 / 1000 and 100 s-1 were included.

[0102] Figure 9 shows illustrative examples of simulation of optimal saturation time. Simulation 910 shows equilibrium CEST Z spectra under the assumption of 100% RF duty cycle with minimal relaxation recovery and readout time (e.g., Ts = TR) under two Bl levels of 1 and 2 pT. In the simulation 910, equilibrium Z spectra at two typical Bl levels of 1 and 2 pT was demonstrated. Simulation 920 shows the estimated spinlock relaxation rate as a function of RF offsets and Bl levels. The spinlock relaxation rate, shown in simulation 920, increases as the RF offset nears the bulk water resonance. In this simulation, the MT (semisolid macromolecules) effect was centered around 0 ppm. Based on the results from 820, simulation 930 illustrates the customized saturation time as a function of RF offsets and Bl levels. This highlights the core idea of the FIRST concept — that the saturation time that maximizes the CEST signal per unit time varies with RF offset, Bl level, field strength, as well as bulk water, MT, and CEST properties. These properties can be estimated from the scanner, tissue of interest, and prior knowledge. For example, comparing the scan time using the optimal Ts determined by the FIRST approach versus a fixed saturation time (e.g., 2s), results in a reduction of 45% and 69% in scan time for 1 and 2 pT, respectively. This corresponds to acceleration factors of 1.8 and 3.2 times, respectively.

[0103] Figure 10 shows examples of simulation results demonstrating the relationship between customized parameters and CEST signal characteristics. In this example, FIRST CEST MRI at 3T was simulated. In the simulation, a labile amide proton ratio and exchange rate of 1 / 1000 and 100 s'1at 3.5 ppm for amide protons, with Bi= 2 pT, recommended for tumor APTw MRI was assumed. Representative tissue T i and T2 of 1.4 s and 75 ms were assumed, respectively, with the RF offsets between ±6ppm in 100 steps. Long Td and Ts of 10 s (full relaxation and equilibrium CEST state)for the gold standard reference equilibrium CEST MRI was also assumed. For a representative Z- spectrum, a fixed Ts of 2 s (Td=10 s) was assumed.

[0104] Simulation results 1010 shows two Z-spectra: routine Z with a fixed Ts of 2 s (blue dash-dotted) and equilibrium Z (black). Simulation 820 shows 7?lp(Aa>), which varied substantially along the Z-spectral offsets. If we set / ?lp(Am) ■ Ts = 2, which translates to about 86% completeness of the spinlock relaxation 1 — eA / rs, a comfortable level without exceedingly long saturation time. The variable Ts can appear somewhat a similar pattern to the Z- spectrum (Simulation 1030). This observation can be expected because a Z spectrum close to 0 ppm is of low intensity on the Z-spectrum, higher ?lp(Am); hence, it takes a reduced Ts to reach the same level of spinlock relaxation completeness. The variable Ts can be range-bound for practical implementation to stay within a pre-determined range. In this example, we set the maximal variable Ts to be 2 s and the minimal variable Ts to be 0.5 s (Simulation 1040).

[0105] Figure 11 shows illustrative examples of comparisons of Z-spectra generated using different methods according to embodiments. In this example, the apparent FIRST Z-spectrum, assuming a long relaxation delay and flexible Ts to demonstrate the QUASS FIRST CEST MRI. There are two notable features. First, the apparent FIRST Z-spectrum (without range bound Ts) close to 0 ppm has non-negligible signal intensity due to a relatively short flexible Ts (Graph 1110), making it more amenable for motion correction. Second, there is a notable difference between the apparent FIRST and the equilibrium Z-spectrum (dash-dotted). Fortunately, the QUASS algorithm can correct the apparent FIRST Z-spectrum (bold dashed), which overlaps perfectly with the gold-standard equilibrium Z-spectrum (dash-dotted). To avoid the extremely short Ts close to 0 ppm, the FIRST CEST with range-bound Ts was tested (Graph 1120). As expected, the QUASS reconstruction also applied to the FIRST Z spectrum with range-bound Ts, which overlapped well with the equilibrium Z-spectrum. The FIRST QUASS approach can be generalized to concurrent flexible relaxation and saturation time simultaneously (Graph 1130). This simulation can also be generalized to the one or more customizable parameters, such as Ts, Td, FA, and / or PSD with either spin echo or gradient echo readout. Furthermore, the QUASS solution can be extended to CEST MRI sequences with unevenly-segmented RF saturation approach and 3D CEST MRI sequences, which can work seamlessly with the FIRST CEST MRI approach.

[0106] Figure 12A shows an illustrative example 1210 of a three-compartment CEST phantom with two Creatine concentrations (25 and 50 mM) dope with three MnC12 concentrations (0, 15, and 30 pM) and Figure 12B shows an illustrative comparison 1220 of two different pulse sequences: representative fixed Td and Ts across offsets vs the proposed FIRST CEST approach with customized Td and Ts at different RF offsets with a minimal delay bound. In these examples, it was demonstrated that the generalized FIRST QUASS CEST MRI could expedite the acquisition, reconstruct an accurate QUASS CEST MRI, and simultaneously determine the T1 map. The conventional CEST Z-spectral spin echo MRI was acquired as the reference, with fixed Ts and Td being 3 s and 3 s (TR= Ts+Td= 6 s, Bl=l pT). The total scan time was 11 min 12 s. For the FIRST CEST MRI, the flexible Ts and TR were calculated from the estimated spinlock relaxation rate according to Bi (1 pT) at 7 T (Figure 12B). The level of spinlock relaxation completeness was 63% (Rlp■ Ts FIRST=1). In this example, a representative T1 and T2 of 2 s and 100 ms was used, respectively, and 7?lp(Am) was estimated from Rlw and R2w (e.g.Ao>) = Rlw■ cos26 -I- R2W■ sln29). The Ts and Td had a range bound between 0.5 and 3 s. The total scan time for FIRST MRI was 5 min 27 s, more than a 50% reduction from the conventional CEST MRI

[0107] Figure 13 shows illustrative examples of the comparison of FIRST, equilibrium, and QUASS Z-spectra.

[0108] In this example, apparent and QUASS phantom CEST MRI from conventional (fixed Ts and Td) and FIRST CEST MRI (adaptive Ts and Td) were compared. Graph 1310 shows a comparison of the apparent conventional Z-spectra (light gray, gray, and black dashed) from three compartments (exterior, left, and right compartments (cmpts)) and their QUASS reconstruction (light gray, gray, and black solid lines). This comparison shows that QUASS CEST Z-spectra showed more attenuation from the apparent Z-spectra, as expected. Graph 1320 shows the apparent FIRST Z-spectra (light gray, gray, and black dashed) from three compartments (exterior, left, and right cmpts) and their QUASS reconstruction (light gray, gray, and black solid lines). Unlike the conventional CEST MRI, the apparent FIRST Z-spectra were more attenuated than QUASS Z- spectra. Such a difference is because the short TR (Ts + Td) caused a reduction in the MRI signal. Graph 1330 shows an overlaid apparent Z-spectra conventional (solid) and FIRST (dashed) CEST MRI, showing large differences. Graph 1340 shows the QUASS CEST Z-spectra from conventional (solid) and FIRST (dashed) CEST MRI, which overlapped well, suggesting thegeneralized QUASS FIRST CEST MRT fully reconstructed the equilibrium Z-spectra, despite the substantially accelerated CEST acquisition.

[0109] Figure 14 illustrates an exemplary system architecture 1400 for implementing the disclosed methods. The system for carrying out the embodiments of the methods disclosed herein is not limited to the system shown in Figure 14. Other systems may also be used. It is also to be understood that the system 1400 may omit any of the modules illustrated and / or may include additional modules not shown. By way of example, the system 1400 may include an imaging apparatus 1410. The imaging apparatus 1410 may include an MRI scanner with capabilities for executing CEST pulse sequences with variable timing parameters. In some examples, the imaging apparatus 1410 may include a magnet for generating a static magnetic field, gradient coils for generating magnetic field gradients, RF coils for transmitting RF pulses and receiving MR signals, and a controller for controlling the various components and executing the pulse sequence, such as the processors 1422.

[0110] In some examples, the system 1400 may include a processing device 1420 configured to determine the customizable parameters, control the imaging apparatus 1410, and / or perform post-processing, such as reconstruction and quantitative analysis, of the acquired MR data (e.g., QUASS reconstruction algorithms and quantitative analysis). In some examples, the processing device 1420 may include dedicated computing hardware optimized for image processing and reconstruction tasks, and / or quantitative analysis.

[0111] In some examples, the processing device 1420 may include one or more processor 1422. The one or more processors 1422 may include one or more processing units, which may be any known processor or a microprocessor. For example, the processor(s) may include any known central processing unit (CPU), imaging processing unit, graphical processing unit (GPU) (e.g., capable of efficient arithmetic on large matrices encountered in deep learning models), among others, or any combination thereof. The processor(s) 1422 may be coupled directly or indirectly to one or more computer-readable storage media (e.g., memory) 1426. In some examples, the processor(s) 1422 may be configured to perform the mathematical calculations required for parameter customization, including solving the FIRST equations and determining customized TR and duty cycle values for each RF offset.

[0112] In some examples, the device 1420 may include a display 1424. The display 1424 may be configured to provide user interface capabilities for parameter input and image display. Thedisplay 1424 may present graphical user interfaces showing customization results, parameter recommendations, and reconstructed images.[001131 The device 1420 may include the memory 1426. The memory 1426 may be configured to store computer-executable instructions that, when executed by the processors 1422, cause the system to perform the customization and reconstruction methods described herein. For example, the memory 1426 may store the program instructions, customized parameters, acquired image data, and reconstruction results. The memory 1426 may include volatile memory, such as randomaccess memory for temporary data storage, and non-volatile memory, such as solid-state drives or hard disk drives for persistent storage of programs and data.

[0114] The system may include a user input device 1428. The user input device 1428 may include but is not limited to keyboards, pointing devices, touchscreens, or other input mechanisms that allow operators to configure scan parameters and initiate customization procedures. The user input device 1428 may be configured to allow operators to specify imaging parameters, select target function(s) to optimize one or more customized parameters, and control the acquisition process.

[0115] The system architecture may support both integrated and distributed implementations, where the parameter customization, image acquisition, and reconstruction processing may be performed on the same system or distributed across multiple connected devices. The flexible architecture may accommodate various clinical and research MRI environments while maintaining the customization and reconstruction capabilities essential for enhanced CEST imaging performance.

[0116] The disclosed systems and methods may be implemented using various hardware configurations and software architectures.

[0117] The system components may communicate through various interfaces and data connections. The processing device 1420 may receive input parameters and generate one or more customized parameters that are transmitted to the imaging apparatus 1410. Acquired image data may be transferred from the imaging apparatus 1410 to the processing device 1420 for postprocessing.

[0118] In some embodiments, the functionality of the processing device 1420 may be integrated with the imaging apparatus 1410 as part of the MRI scanner control system.

[0119] The system 1400 may be configured to operate in real-time or near real-time during scan sessions, allowing for dynamic parameter adjustment based on initial measurements or updated tissue property estimates. The parameter customization may be performed rapidly to minimize delays in the imaging workflow.

[0120] In some implementations, the system 1400 may include network connectivity to allow remote access to customization and / or reconstruction algorithms and / or cloud-based processing resources. This may enable the use of more computationally intensive customization procedures or access to updated customized parameter databases.

[0121] The processing device 1420 may implement various post-processing algorithms, including motion correction, noise reduction, image registration, and reconstruction (e.g., QUASS reconstruction).

[0122] The system architecture may be scalable to accommodate different MRI scanner configurations and clinical workflows. The modular design allows individual components to be upgraded or replaced without affecting the entire system operation.

[0123] In some embodiments, the disclosed methods (e.g., Figures 1, 2, and 5) may be implemented using software applications that are stored in a memory and executed by the one or more processors (e.g., CPU and / or GPU). In some embodiments, the disclosed methods may be implemented using software applications that are stored in memories and executed by one or more processors distributed across the system.

[0124] As such, any of the modules of the system 1400 may be a general -purpose computer system that becomes a specific-purpose computer system when executing the routines and methods of the disclosure. The systems and / or modules of the system 1400 may also include an operating system and micro instruction code. The various processes and functions described herein may either be part of the micro instruction code or part of the application program or routine (or any combination thereof) that is executed via the operating system.

[0125] If written in a programming language conforming to a recognized standard, sequences of instructions designed to implement the methods may be compiled for execution on a variety of hardware systems and for interface to a variety of operating systems. In addition, embodiments are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement embodiments of the disclosure. An example of hardware for performing the described functions is shown in Figure 14.

[0126] It is to be further understood that because some of the constituent system components and method steps depicted in the accompanying figures can be implemented in software, the actual connections between the system components (or the process steps) may differ depending upon the manner in which the disclosure is programmed. Given the teachings of the disclosure provided herein, one of ordinary skill in the related art will be able to contemplate these and similar implementations or configurations of the disclosure.

[0127] The disclosures of each and every publication cited herein are hereby incorporated herein by reference in their entirety.

[0128] Further examples

[0129] In some examples, the present disclosure may relate to a method for customizing one or more parameters for magnetic resonance (MR) imaging. In some examples, the method may include generating one or more customized parameters for one or more iterations of a Magnetization Transfer Magnetic Resonance (MR) sequence that is executed on a Magnetic Resonance Imaging (MRI) System. The generating may include receiving input parameters for each iteration of the sequence. The input parameters may include radiofrequency (RF) offset and / or saturation power. The method may further include determining a set of one or more customized parameters for each iteration using the input parameters for each RF offset and / or saturation power. The one or more customized parameters may include repetition time (TR).

[0130] In some examples, the one or more customized parameters for each iteration may further include saturation time (Ts) and / or relaxation delay (Td). The saturation time (Ts) and / or the relaxation delay (Td) may be determined using the repetition time (TR).

[0131] In some examples, the one or more customized parameters may further include the flip angle (FA). The flip angle is determined using the repetition time (TR).

[0132] In some examples, the MRI system may be a Chemical Exchange Saturation Transfer (CEST) system. In some examples, the repetition time (TR) may be determined to maximize CEST effect a CEST effect (e.g., Rip) and / or contrast (e.g., MTRasym) sensitivity per unit of time.

[0133] In some examples, the method may further include generating a scan sequence for series of the one or more iterations for execution on the MRI system using the one or more customized parameters for each iteration.

[0134] In some examples, the one or more iterations may include a first iteration and a second iteration. The first iteration may include a first saturation pulse and a first signal acquisition. The second iteration may include a second saturation pulse and a second signal acquisition. The first saturation pulse may include a first RF offset and a first set of the one or more customized parameters. The second saturation pulse may include a second RF offset and a second set of the one or more customized parameters. In some examples, the first offset and the second offset may be different. The first set of the one or more customized parameters and the second set of the one or more customized parameters may be different.

[0135] For example, the first set of the one or more customized parameters and the second set of the one or more customized parameters differ in the saturation time (Ts) and / or the relaxation delay (Td).

[0136] In some examples, the method may further include performing the generated scan sequence on the MRI system to acquire a series of MRI signals sequentially in one scan.

[0137] In some examples, the method may further include receiving a series of apparent MR image volumes for each iteration. The series of apparent MR image volumes may include a first apparent MR image volume for the first iteration and a second apparent MR image volume for the second iteration. The method may further include receiving parameter map for the series. The parameter map may include Tl, AB1, and / or ABO mapping data.

[0138] In some examples, the method may further include reconstructing a first corrected MR image volume for the first iteration using the first set of customized parameters, the first apparent MR image volume, and the parameter map. The method may also include reconstructing a second corrected MR image volume for the second iteration using the first corrected MR image volume, the first set of customized parameters, the parameter map, the second apparent MR image volume, and the second set of customized parameters.

[0139] In some examples, the method may further include outputting a series of corrected MR image volumes. The series of corrected MR image volumes may include the first corrected MR image volume and the second corrected MR image volume.

[0140] In some examples, The method may also include determining one or more quantitative measurements from the series of corrected MR image volumes.

[0141] In some examples, the one or more customized parameters may be determined by first determining a spinlock relaxation rate for each RF offset frequency based on the input parameters.Next, the method may include determining repetition time and RF duty cycle values for each RF offset frequency by maximizing a target function that represents sensitivity per unit time. Next, the one or more customized parameters for using the determined repetition time and RF duty cycle may be determined. In some examples, the one or more customized parameters may include saturation time and relaxation delay for each RF offset frequency.

[0142] In some examples, the repetition time and RF duty cycle values may be determined by first calculating a derivative of the target function with respect to repetition time and solving for repetition time values where the derivative equals zero.

[0143] In some examples, the spinlock relaxation rate may be determined using the target function:Rip = Rlw cos29 + R2w sin20 + ERexwhere 0 = tan’VBi / Acu), Bi is the RF saturation amplitude, and Acu is the RF offset frequency.

[0144] In some examples, the sensitivity efficiency function may be proportional to MTRasYmN'TR. where MTRasYm is magnetization transfer ratio asymmetry and TR is repetition time. In some examples, the saturation time may be determined as Ts = r| TR and the relaxation delay may be determined as Td = (l-q) TR, where q is the RF duty cycle.

[0145] In some examples, the customized flip angle for each RF offset frequency may be determined based on the determined repetition time using Ernst angle calculations.

[0146] In some examples, the present disclosure may relate to a system for customizing one or more parameters for magnetic resonance (MR) imaging. The system may further include one or more processors. The system may further include one or more hardware storage devices having stored thereon computer-executable instructions which are executable by the one or more processors to cause the computing system to perform any combination of the methods above.

[0147] In some examples, the present disclosure may relate to a non-transitory computer- readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of any combination of the methods above.

[0148] References to “one embodiment”, “an embodiment”, “one example”, and “an example” indicate that the embodiment s) or example(s) so described may include a particular feature, structure, characteristic, property, element, or limitation, but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element orlimitation. Furthermore, repeated use of the phrase “in one embodiment” does not necessarily refer to the same embodiment, though it may.

[0149] “Computer-readable storage device” or “hardware storage device” as used herein, refers to a device that stores instructions or data. “Computer-readable storage device” or “hardware storage device” does not refer to propagated signals. A computer-readable storage device or hardware storage device may take forms, including, but not limited to, non-volatile media, and volatile media. Non-volatile media may include, for example, optical disks, magnetic disks, tapes, and other media. Volatile media may include, for example, semiconductor memories, dynamic memory, and other media. Common forms of a computer-readable storage device may include but are not limited to a floppy disk, a flexible disk, a hard disk, a magnetic tape, other magnetic medium, an application specific integrated circuit (ASIC), a compact disk (CD), other optical medium, a random access memory (RAM), a read only memory (ROM), a memory chip or card, a memory stick, and other media from which a computer, a processor or other electronic device can read.

[0150] To the extent that the term “includes” or “including” is employed in the detailed description or the claims, it is intended to be inclusive in a manner similar to the term “comprising” as that term is interpreted when employed as a transitional word in a claim.

[0151] Throughout this specification and the claims that follow, unless the context requires otherwise, the words 'comprise' and 'include' and variations such as 'comprising' and 'including' will be understood to be terms of inclusion and not exclusion. For example, when such terms are used to refer to a stated integer or group of integers, such terms do not imply the exclusion of any other integer or group of integers.

[0152] To the extent that the term “or” is employed in the detailed description or claims (e.g., A or B) it is intended to mean “A or B or both”. When the applicants intend to indicate “only A or B but not both” then the term “only A or B but not both” will be employed. Thus, use of the term “or” herein is the inclusive, and not the exclusive use. See, Bryan A. Garner, A Dictionary of Modem Legal Usage 624 (2d. Ed. 1995).

[0153] While example systems, methods, and other embodiments have been illustrated by describing examples, and while the examples have been described in considerable detail, it is not the intention of the applicants to restrict or in any way limit the scope of the appended claims to such detail. It is, of course, not possible to describe every conceivable combination of componentsor methodologies for purposes of describing the systems, methods, and other embodiments described herein. Therefore, the invention is not limited to the specific details, the representative apparatus, and illustrative examples shown and described. Thus, this application is intended to embrace alterations, modifications, and variations that fall within the scope of the appended claims.

Claims

1. CLAIMSWhat is claimed:

1. A method for customizing one or more parameters for magnetic resonance (MR) imaging, the method comprising: generating one or more customized parameters for one or more iterations of a Magnetization Transfer Magnetic Resonance (MR) sequence that is executed on a Magnetic Resonance Imaging (MRI) System, the generating including: receiving input parameters for each iteration of the sequence, the input parameters including radiofrequency (RF) offset and / or saturation power; and determining a set of one or more customized parameters for each iteration using the input parameters for each RF offset and / or saturation power, the one or more customized parameters including repetition time (TR).

2. The method according to claim 1, wherein: the one or more customized parameters for each iteration further include saturation time (Ts) and / or relaxation delay (Td); and the saturation time (Ts) and / or the relaxation delay (Td) are determined using the repetition time (TR).

3. The method according to claim 2, wherein: the one or more customized parameters further include flip angle (FA); and the flip angle is determined using the repetition time (TR).

4. The method according to any of claims 1-3, wherein: the MRI system is a Chemical Exchange Saturation Transfer (CEST) system; and the repetition time (TR) is determined to maximize CEST effect or contrast sensitivity per unit of time.

5. The method according to claim 4, further comprising:generating a scan sequence for series of the one or more iterations for execution on the MRI system using the one or more customized parameters for each iteration.

6. The method according to claim 4, wherein: the one or more iterations includes a first iteration and a second iteration, the first iteration includes a first saturation pulse and a first signal acquisition; the second iteration includes a second saturation pulse and a second signal acquisition; the first saturation pulse includes a first RF offset and a first set of the one or more customized parameters; the second saturation pulse includes a second RF offset and a second set of the one or more customized parameters; the first offset and the second offset are different; and the first set of the one or more customized parameters and the second set of the one or more customized parameters are different.

7. The method according to claim 6, wherein the first set of the one or more customized parameters and the second set of the one or more customized parameters differ in the saturation time (Ts) and / or the relaxation delay (Td).

8. The method according to claim 7, further comprising: performing the generated scan sequence on the MRI system to acquire a series of MRI signals sequentially in one scan.

9. The method according to claim 8, further comprising: receiving a series of apparent MR image volumes for each iteration, the series of apparent MR image volumes including a first apparent MR image volume for the first iteration and a second apparent MR image volume for the second iteration; receiving a parameter map for the series; reconstructing a first corrected MR image volume for the first iteration using the first set of customized parameters, the first apparent MR image volume, and the parameter map; andreconstructing a second corrected MR image volume for the second iteration using the first corrected MR image volume, the first set of customized parameters, the parameter map, the second apparent MR image volume, and the second set of customized parameters.

10. The method according to claim 9, further comprising: outputting a series of corrected MR image volumes, the series of corrected MR image volumes including the first corrected MR image volume and the second corrected MR image volume.

11. The method according to claim 10, further comprising: determining one or more quantitative measurements from the series of corrected MR image volumes.

12. A system for customizing one or more parameters for magnetic resonance (MR) imaging, the system comprising: one or more processors; and one or more hardware storage devices having stored thereon computer-executable instructions which are executable by the one or more processors to cause the computing system to perform at least the following: generating one or more customized parameters for one or more iterations of a Magnetization Transfer Magnetic Resonance (MR) sequence that is executed on a Magnetic Resonance Imaging (MRI) System, the generating including: receiving input parameters for each iteration of the sequence, the input parameters including radiofrequency (RF) offset and / or saturation power; and determining a set of one or more customized parameters for each iteration using the input parameters for each RF offset and / or saturation power, the one or more customized parameters including repetition time (TR).

13. The system according to claim 12, wherein: the one or more customized parameters for each iteration further include saturation time (Ts) and / or relaxation delay (Td); andthe saturation time (Ts) and / or the relaxation delay (Td) are determined using the repetition time (TR).

14. The system according to claim 13, wherein: the one or more customized parameters further include flip angle (FA); and the flip angle is determined using the repetition time (TR).

15. The system according to any of claims 12-14, wherein: the MRI system is a Chemical Exchange Saturation Transfer (CEST) system; and the repetition time (TR) is determined to maximize the CEST effect or contrast sensitivity per unit of time.

16. The system according to claim 15, wherein the instructions, which, when executed on the one or more data processors, further cause the one or more data processors to perform: generating a scan sequence for series of the one or more iterations for execution on the MRI system using the one or more customized parameters for each iteration.

17. The system according to claim 15, wherein: the one or more iterations includes a first iteration and a second iteration, the first iteration includes a first saturation pulse and a first signal acquisition; the second iteration includes a second saturation pulse and a second signal acquisition; the first saturation pulse includes a first RF offset and a first set of the one or more customized parameters; the second saturation pulse includes a second RF offset and a second set of the one or more customized parameters; the first offset and the second offset are different; and the first set of the one or more customized parameters and the second set of the one or more customized parameters are different.

18. The system according to claim 17, wherein the first set of the one or more customized parameters and the second set of the one or more customized parameters differ in the saturation time (Ts) and / or the relaxation delay (Td).

19. The system according to claim 18, wherein the instructions, which, when executed on the one or more data processors, further cause the one or more data processors to perform: performing the generated scan sequence on the MRI system to acquire a series of MRI signals sequentially in one scan.

20. The system according to claim 19, wherein the instructions, which, when executed on the one or more data processors, further cause the one or more data processors to perform: receiving a series of apparent MR image volumes for each iteration, the series of apparent MR image volumes including a first apparent MR image volume for the first iteration and a second apparent MR image volume for the second iteration; receiving a parameter map for the series; reconstructing a first corrected MR image volume for the first iteration using the first set of customized parameters, the first apparent MR image volume, and the parameter map; and reconstructing a second corrected MR image volume for the second iteration using the first corrected MR image volume, the first set of customized parameters, the parameter map, the second apparent MR image volume, and the second set of customized parameters.

21. The system according to claim 20, wherein the instructions, which, when executed on the one or more data processors, further cause the one or more data processors to perform: outputting a series of corrected MR image volumes, the series of corrected MR image volumes including the first corrected MR image volume and the second corrected MR image volume.

22. The system according to claim 21, wherein the instructions, which, when executed on the one or more data processors, further cause the one or more data processors to perform: determining one or more quantitative measurements from the series of corrected MR image volumes.

23. A method for customizing one or more parameters for magnetic resonance (MR) imaging, the method comprising: generating one or more customized parameters for one or more iterations of a Magnetization Transfer Magnetic Resonance (MR) sequence that is executed on a Magnetic Resonance Imaging (MRI) System, the generating including: receiving input parameters for each iteration of the sequence, the input parameters including radiofrequency (RF) offset and / or saturation power; and determining a set of one or more customized parameters for each iteration using the input parameters for each RF offset and / or saturation power, the one or more customized parameters including repetition time (TR).

24. The method according to claim 23, wherein the MRI system is a Chemical Exchange Saturation Transfer (CEST) system.

25. The method according to claims 23 or 24, wherein: the one or more customized parameters for each iteration further include saturation time (Ts) and / or relaxation delay (Td); and the saturation time (Ts) and / or the relaxation delay (Td) are determined using the repetition time (TR).

26. The method according to any of claims 23-25, wherein: the one or more customized parameters further include flip angle (FA); and the flip angle is determined using the repetition time (TR).

27. The method according to any of claims 23-26, wherein: the repetition time (TR) is determined to maximize CEST effect or contrast sensitivity per unit of time.

28. The method according to any of claims 23-27, further comprising: generating a scan sequence for series of the one or more iterations for execution on the MRI system using the one or more customized parameters for each iteration.

29. The method according to any of claims 23-28, wherein: the one or more iterations includes a first iteration and a second iteration, the first iteration includes a first saturation pulse and a first signal acquisition; the second iteration includes a second saturation pulse and a second signal acquisition; the first saturation pulse includes a first RF offset and a first set of the one or more customized parameters; the second saturation pulse includes a second RF offset and a second set of the one or more customized parameters; the first offset and the second offset are different; and the first set of the one or more customized parameters and the second set of the one or more customized parameters are different.

30. The method according to any of claims 23-29, wherein the first set of the one or more customized parameters and the second set of the one or more customized parameters differ in the saturation time (Ts) and / or the relaxation delay (Td).

31. The method according to any of claims 23-30, further comprising: performing the generated scan sequence on the MRI system to acquire a series of MRI signals sequentially in one scan.

32. The method according to any of claims 23-31, further comprising: receiving a series of apparent MR image volumes for each iteration, the series of apparent MR image volumes including a first apparent MR image volume for the first iteration and a second apparent MR image volume for the second iteration; receiving a parameter map for the series; reconstructing a first corrected MR image volume for the first iteration using the first set of customized parameters, the first apparent MR image volume, and the parameter map; and reconstructing a second corrected MR image volume for the second iteration using the first corrected MR image volume, the first set of customized parameters, the parameter map, the second apparent MR image volume, and the second set of customized parameters.

33. The method according to any of claims 23-32, further comprising: outputting a series of corrected MR image volumes, the series of corrected MR image volumes including the first corrected MR image volume and the second corrected MR image volume.

34. The method according to any of claims 23-33, further comprising: determining one or more quantitative measurements from the series of corrected MR image volumes.

35. A system for customizing one or more parameters for magnetic resonance (MR) imaging, the system comprising: one or more processors; and one or more hardware storage devices having stored thereon computer-executable instructions which are executable by the one or more processors to cause the computing system to perform at least the following: the method according to any of claims 23-34.

36. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of any of claims 1-11 or claims 23-34.

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