An MRI acoustic-vibration control system and a method thereof

Abstract

An acoustic-vibration control system for controlling the mechano-acoustic behavior of a Magnetic Resonance Imaging (MRI) system includes a Predictive Modeling Unit, a Parameter Optimization Unit, and a Sequence Control Unit. The Predictive Modeling Unit generates an improved prediction of the mechano-acoustic behavior for an imaging sequence by combining a model-based prediction of acoustic or vibration frequencies with a frequency response function (FRF) of the MRI system. The Parameter Optimization Unit analyzes the improved prediction to determine the mechano-acoustic behavior as a function of at least one timing parameter of the imaging sequence, and selects a value for the timing parameter that corresponds to a desired behavior. The Sequence Control Unit generates control signals to operate the MRI system using the imaging sequence adjusted with the selected value for the timing parameter. The predictive control reduces acoustic noise and mechanical vibrations by optimizing sequence timing parameters to avoid mechanical resonances.

Description

P-25267-PCAN MRI ACOUSTIC-VIBRATION CONTROL SYSTEM AND A METHOD THEREOFCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority from US provisional patent applications 63,777,780, filed March 26, 2025, and 63,841,430, filed July 10, 2025, both of which are incorporated herein by reference.FIELD OF THE INVENTION

[0002] Embodiments of the present disclosure relate generally to magnetic resonance imaging and to controlling mechano-acoustic behavior in MRI imaging acquisitions that acquire several to multiple readouts per excitation in particular.BACKGROUND OF THE INVENTION

[0003] Modern Magnetic Resonance Imaging (MRI) relies on a variety of imaging sequences, including pulse sequences designed for rapid data acquisition, acquiring several to multiple readouts per excitation. While Echo Planar Imaging (EPI) is a common example used in Functional Magnetic Resonance Imaging (fMRI) and dynamic clinical imaging, the technical challenges described herein apply broadly to many spatial encoding schemes.

[0004] MRI imaging sequences with rapid data acquisition, acquiring several to multiple readouts per excitation - including Echo Planar Imaging (EPI) Spin-Echo (SE) based sequences (e.g., Turbo Spin Echo (TSE) or Rapid Acquisition with Relaxation Enhancement (RARE)), Gradient-Echo (GRE) based sequences (e.g., Fast Low Angle Shot (FLASH) or Steady-State Free Precession (SSFP)), and ultra-fast or hybrid sequences (e.g., Spiral imaging, Radial imaging, orP-25267-PCGradient and Spin Echo (GRASE))— utilize rapid switching of electrical currents through gradient coil conductors.

[0005] The interaction of these alternating currents with the strong static magnetic field of the MRI scanner induces alternating Lorentz forces upon the gradient system as well as on other system parts, e.g., the cryostat (holding the liquid Helium), and the shielding of the RF coil. These forces trigger mechano-acoustic behavior, characterized by mechanical vibrations and resultant acoustic noise. This acoustic emission is an inherent byproduct of the spatial encoding process across various pulse sequences, often necessitating acoustic damping or auditory protection for the subject.

[0006] Apart from undesired mechano-acoustic behavior, MRI imaging sequences with rapid data acquisition are also known to be susceptible to image artifacts, commonly referred to as "ghost" artifacts.

[0007] To protect the MRI system hardware from excessive vibrations, which can occur when the gradient switching frequency coincides with a mechanical resonance of the system, conventional computer-implemented solutions often block the use of certain scan parameters (based on frequencies), creating "forbidden" parameter ranges for the operator such as forbidden echo-spacing (ESP) values in EPI. Additionally, alternative methods exist that involve modifying the gradient waveform— for example, utilizing sinusoidal (sine-shape) profiles rather than standard trapezoidal profiles— to reduce the high-frequency components of the gradient switching. However, such waveform-smoothing techniques are not widely adopted in high-throughput clinical settings because they typically prolong the acquisition time, thereby reducing the overall efficiency of the imaging protocol.P-25267-PCSUMMARY OF THE PRESENT INVENTION

[0008] There is therefore provided, in accordance with an embodiment of the present disclosure, an acoustic-vibration control system for controlling mechano-acoustic behavior associated with a set of gradient coils of a Magnetic Resonance Imaging (MRI) system during an imaging sequence. The system includes a Predictive Modeling Unit, a Parameter Optimization Unit, and a Sequence Control Unit. The Predictive Modeling Unit is configured to generate an improved prediction of the mechano-acoustic behavior for the imaging sequence by combining a model-based prediction of at least one of (a) acoustic frequencies and (b) vibration frequencies with a frequency response function (FRF) of the MRI system. The Parameter Optimization Unit is configured to analyze the improved prediction to determine the mechano-acoustic behavior as a function of at least one timing parameter of the imaging sequence, and to select a value for the at least one timing parameter that corresponds to a desired mechano-acoustic behavior. The Sequence Control Unit is configured to generate control signals to operate the MRI system using the imaging sequence adjusted with the selected value for the at least one timing parameter.

[0009] Moreover, in accordance with an embodiment of the present disclosure, the imaging sequence is one from among a sequence group including an echo planar imaging (EPI), Three-Dimensional (3D) EPI, Echo Planar Spectroscopic Imaging (EPSI), Echo Planar Time-resolved Imaging (EPTI), Spin Echo (SE), Fast Spin Echo (FSE), Turbo Spin Echo (TSE), and Rapid Acquisition with Relaxation Enhancement (RARE), Gradient-echo based sequences, Gradient Recalled Echo (GRE), Fast Low Angle Shot (FLASH), Steady-State Free Precession (SSFP), Balanced SSFP (bSSFP), Magnetization Prepared Rapid Gradient Echo (MP-RAGE), Spiral imaging, Radial imaging, Gradient and Spin Echo (GRASE) and derivative thereof.P-25267-PC

[0010] Further, in accordance with an embodiment of the present disclosure, at least one timing parameter is selected from a group including a time between consecutive echoes (ATEcho), a time between consecutive slices (ATslice), and a time between consecutive partitions (At_partition).

[0011] Still further, in accordance with an embodiment of the present disclosure, the Parameter Optimization Unit is configured to select a value for the time between consecutive slices (ATslice).

[0012] Additionally, in accordance with an embodiment of the present disclosure, the imaging sequence is a multi-echo sequence, and the Parameter Optimization Unit is configured to select a value for the time between consecutive echoes (ATEcho).

[0013] Moreover, in accordance with an embodiment of the present disclosure, the imaging sequence further includes an additional block which is a navigator gradient train, and the Parameter Optimization Unit is further configured to use the improved prediction to determine a timing for the additional block that minimizes interference from mechanical vibrations caused by a preceding gradient train, thereby reducing ghosting artifacts.

[0014] Further, in accordance with an embodiment of the present disclosure, the Parameter Optimization Unit is further configured to generate and provide for display a GUI component of a display interface, enabling an MRI operator to adjust the at least one timing parameter.

[0015] Still further, in accordance with an embodiment of the present disclosure, the system is further in data communication with one or more sensors configured to acquire an acoustic signal or a vibration signal generated by the MRI system to provide a basis for the measurement of the FRF.P-25267-PC

[0016] There is therefore provided, in accordance with an embodiment of the present disclosure, a Magnetic Resonance Imaging (MRI) system. The system includes an acousticvibration control system according to any of the preceding descriptions, where the frequency response function (FRF) is determined based on a signal indicative of mechanical vibrations or acoustic signals associated with a set of gradient coils of the MRI system acquired by at least one sensor, and the at least one sensor is an acoustic sensor or a vibration sensor.

[0017] Additionally, in accordance with an embodiment of the present disclosure, the signal is acquired during at least one of (i) a manufacturing stage, (ii) a calibration stage upon installation, and (iii) a calibration stage upon maintenance operation.

[0018] Moreover, in accordance with an embodiment of the present disclosure, the MRI system further includes at least one sensor configured to acquire the signal, and the acousticvibration control system is in data communication with the at least one sensor.

[0019] Further, in accordance with an embodiment of the present disclosure, the MRI system further includes a display interface configured to display a GUI component enabling an MRI operator to adjust the at least one timing parameter.

[0020] There is therefore provided, in accordance with an embodiment of the present disclosure, a computer-implemented acoustic-vibration control method for controlling mechano-acoustic behavior associated with a set of gradient coils of a Magnetic Resonance Imaging (MRI) system during an imaging sequence. The method includes generating an improved prediction of the mechano-acoustic behavior for the imaging sequence by combining a modelbased prediction of at least one of (a) acoustic frequencies and (b) vibration frequencies with a frequency response function (FRF) of the MRI system, analyzing the improved prediction to determine the mechano-acoustic behavior as a function of at least one timing parameter of theP-25267-PCimaging sequence, selecting a value for at least one timing parameter that corresponds to a desired mechano-acoustic behavior, and operating the MRI system to acquire an image using the imaging sequence adjusted with the selected value for the at least one timing parameter.

[0021] Still further, in accordance with an embodiment of the present disclosure, the imaging sequence is one from among a sequence group including an echo planar imaging (EPI), Three-Dimensional (3D) EPI, Echo Planar Spectroscopic Imaging (EPSI), Echo Planar Time-resolved Imaging (EPTI), Spin Echo (SE), Fast Spin Echo (FSE), Turbo Spin Echo (TSE), and Rapid Acquisition with Relaxation Enhancement (RARE), Gradient-echo based sequences, Gradient Recalled Echo (GRE), Fast Low Angle Shot (FLASH), Steady-State Free Precession (SSFP), Balanced SSFP (bSSFP), Magnetization Prepared Rapid Gradient Echo (MP-RAGE), Spiral imaging, Radial imaging, Gradient and Spin Echo (GRASE) and derivative thereof.

[0022] Additionally, in accordance with an embodiment of the present disclosure, at least one timing parameter is selected from a group including a time between consecutive echoes (ATEcho), a time between consecutive slices (ATslice), and a time between consecutive partitions (At_partition).

[0023] Moreover, in accordance with an embodiment of the present disclosure, the imaging sequence is a multi-slice sequence, and selecting the value includes selecting a value for the time between consecutive slices (ATslice).

[0024] Further, in accordance with an embodiment of the present disclosure, the imaging sequence is a multi-echo sequence, and selecting the value includes selecting a value for the time between consecutive echoes (ATEcho).

[0025] Still further, in accordance with an embodiment of the present disclosure, the imaging sequence further includes an additional block, which is a navigator gradient train, and theP-25267-PCmethod further includes using the improved prediction to determine a timing for the additional block that minimizes interference from mechanical vibrations caused by a preceding gradient train, thereby reducing ghosting artifacts.

[0026] Additionally, in accordance with an embodiment of the present disclosure, the method further includes generating and providing for display a graphical user interface (GUI) component of a display interface, enabling an MRI operator to view the determined mechanoacoustic behavior as a function of the at least one timing parameter and to adjust the at least one timing parameter.

[0027] Further, in accordance with an embodiment of the present disclosure, the method further includes determining the measured FRF of the MRI system. The determining includes operating the MRI system with one or more predefined gradient sequences, acquiring, via at least one sensor, a signal indicative of mechanical vibrations or acoustic signals generated during the operation, generating a measured acoustic power spectrum based on the acquired signal, and determining the FRF based on a ratio between the measured acoustic power spectrum and a model-based power spectrum of the one or more predefined gradient sequences.

[0028] Still further, in accordance with an embodiment of the present disclosure, determining the FRF is performed as a one-time calibration step for the MRI system.

[0029] Additionally, in accordance with an embodiment of the present disclosure, determining the FRF is performed as part of a periodic maintenance or quality assurance protocol for the MRI system.

[0030] There is therefore provided, in accordance with an embodiment of the present disclosure, a computer program product including instructions which, when the program isP-25267-PCexecuted by a computer, cause the computer to carry out the method of any of the preceding method descriptions.

[0031] There is therefore provided, in accordance with an embodiment of the present disclosure, a computer-readable storage medium including instructions which, when executed by a computer, cause the computer to carry out the method of any of the preceding method descriptions.

[0032] In accordance with yet another aspect of the present disclosure, there is provided a computer-implemented method for characterizing an acoustic-vibration Frequency Response Function (FRF) of a Magnetic Resonance Imaging (MRI) system, the method comprising: generating a model-based power spectrum for one or more predefined gradient sequences; operating the MRI system to run the one or more predefined gradient sequences; during said operation, acquiring a signal from at least one sensor indicative of mechanical vibrations or acoustic signals generated by the MRI system; generating a measured acoustic power spectrum based on the acquired signal; and determining the acoustic-vibration FRF by calculating a ratio between the measured acoustic power spectrum and the model-based power spectrum, wherein the determined FRF is stored for use in predicting mechano-acoustic behavior of the MRI system during subsequent imaging sequences.BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof,P-25267-PCmay best be understood by reference to the following detailed description when read with the accompanying drawings in which:

[0034] Figs. 1A, IB, and 1C are block diagram illustrations of an MRI system that comprises an acoustic-vibration control system, the acoustic-vibration control system itself, and an MRI operator console, respectively, constructed and operative in accordance with an embodiment of the present disclosure;

[0035] Figs. ID, IE and IF are flow chart illustrations of acoustic-vibration control methods, operative in accordance with an embodiment of the present disclosure;

[0036] Fig. 2 is a series of graph illustrations demonstrating the fundamental principle of a predictive model for an acoustic spectrum of a multi-echo multi-slice EPI sequence, showing the effect of adjusting the time between consecutive slices (ATslices) and consecutive echoes (ATE) on the acoustic power spectrum ;

[0037] Fig. 3 is a series of graph illustrations showing the effect of adjusting the time between consecutive slices (ATslice) on a gradient waveform and its resulting acoustic power spectrum;

[0038] Figs.4Aand 4B are image and graph illustrations providing proof of ghosting reduction in phantom and in-vivo MRI scans and showing the corresponding measured and modeled acoustic power spectra;

[0039] Fig. 5 is a series of graph illustrations demonstrating that the principles of acousticvibration control apply across different RF coils of an MRI system;

[0040] Fig. 6 is a pair of graph illustrations showing the difference between a modeled acoustic power spectrum and a measured acoustic power spectrum, introducing the concept of scanner-specific mechanical resonances;P-25267-PC

[0041] Fig. 7 is a series of graph illustrations demonstrating the solution of applying a measured frequency response function (FRF) to a predictive model, and showing the cyclical variation of acoustic power as a function of slice timing (ATslice);

[0042] Fig. 8 is a series of map illustrations showing measured and predicted two-dimensional acoustic power maps as a function of both echo timing (ATEcho) and slice timing (ATslice) for dual-echo and single-echo multi-slice EPI sequences;

[0043] Fig. 9 is a series of graph illustrations providing a technical explanation of the acoustic power maps by showing the factorization of spectral components and their interaction with a measured frequency response function (FRF);

[0044] Figs. 10A, 10B, and 10C are a series of map and graph illustrations presenting quantitative proof of the direct correlation between acoustic power and ghosting artifacts, showing they share the same cyclical period as a function of navigator and slice timing for different MRI systems and scan parameters;

[0045] Fig. 11 is a set of image illustrations providing a real-world validation with in-vivo human MRI scans, showing the reduction of ghosting artifacts by selecting timing parameters for minimal ghost levels;

[0046] Fig. 12 is a set of graph and image illustrations showing the application of the acousticvibration control concept to a 3D-EPI sequence;

[0047] Fig. 13 is a pair of "before and after" graph illustrations summarizing how a subtle timing change affects a gradient pulse train and its resulting acoustic spectrum for a multi-echo sequence;P-25267-PC

[0048] Figs. 14A, 14B, and 14C are schematic illustrations of a general hierarchical representation of repeated gradient blocks in various MRI sequences, including multi-echo multi-slice EPI and 3D EPI; and

[0049] Fig. 14D is a schematic illustration applying the hierarchical representation to multiecho multi-slice EPI with navigators and multi-echo 3D EPI with navigators.

[0050] It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements.DETAILED DESCRIPTION OF THE PRESENT INVENTION

[0051] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be understood by those skilled in the art that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present disclosure.

[0052] Applicant has realized that the conventional approach to mitigating the undesired mechano-acoustic behavior in Magnetic Resonance Imaging (MRI) sequences with rapid data acquisition is fundamentally limited and overly restrictive. The prior art method typically involves creating "forbidden" ranges of echo-spacing (ESP) values in EPI or of repetition times in other sequences to avoid exciting known mechanical resonances of the scanner hardware. ThisP-25267-PCapproach is based on the simplistic assumption that the acoustic noise and vibration disturbance are driven solely by a single frequency related to the ESP.

[0053] Applicant has further realized that this assumption is incorrect and fails to capture the true complexity of the mechano-acoustic phenomena. The actual acoustic and vibration spectrum is not determined by the ESP alone, but is rather a complex interference pattern created by the interplay and relative timing of all the repeated gradient pulse trains within a full acquisition sequence, including the timing between consecutive slices (ATslice) and consecutive echoes (ATEcho) in multi-echo scans. The prior art approaches lack a unified framework that addresses the mechano-acoustic behavior of MRI systems.

[0054] The present disclosure provides a system and method that overcome these limitations by implementing a predictive control framework. The present disclosure is based on the insight that the mechano-acoustic behavior, represented by acoustic and vibration spectrums, reflects complex interference patterns resulting from the interplay of all repeated gradient pulse trains within a sequence. The system actively controls this interference by precisely adjusting the subtle timing parameters between these trains, such as the time between consecutive slices (ATslice) and the time between consecutive echoes (ATEcho).

[0055] The present disclosure provides for an improved predictive model that accurately forecasts the mechano-acoustic behavior for a given set of imaging sequence parameters. This is achieved through a two-stage process. First, a model (e.g., an analytical model, a numerical mode) is generated that accounts for the timing structure of the gradient pulse trains. Second, the analytical model is combined with a system-specific Frequency Response Function (FRF) (also referred to as a Transfer Function) of the MRI scanner. The FRF captures the unique mechanical resonance characteristics of the specific hardware, allowing the combined model to generate aP-25267-PChighly accurate prediction of the actual mechano-acoustic behavior that will be produced. In some embodiments, the FRF reflects the response of the MRI system as a whole to the induced alternating Lorentz forces. In other embodiments, the FRF reflects the response of specific components, e.g., the response of the gradient coils.

[0056] The improved predictive model is then used to analyze the improved prediction to determine acoustic power as a function of timing parameters of the imaging sequence. For example, the improved predictive model is used to generate multi-dimensional "acoustic power maps," which visualize how the acoustic power changes as a function of the key timing parameters (ATslice and ATEcho). These maps can be used, automatically or manually (e.g., by an operator or an automated system) to identify, select and implement optimal timing configurations that fall within "quiet valleys," thereby steering the system's acoustic energy away from resonant peaks. The inventors have further investigated the correlation between acoustic power cycles with image quality. The inventors have realized that the same frequencies affect both image quality (e.g., ghosts) and the mechano-acoustic behavior (e.g., sound). This provides a unified solution that enable optimizing mechano-acoustic behavior and / or image quality.

[0057] As used herein, the term 'mechano-acoustic behavior' refers to the physical phenomena resulting from Lorentz forces on MRI hardware, encompassing both the mechanical vibrations of the system components and the resultant acoustic noise propagated therefrom. The control system and method of the present disclosure are configured to predict and control this behavior by analyzing and adjusting parameters that influence its underlying frequency spectrum.P-25267-PC

[0058] The system and method of the present disclosure provide significant benefits over conventional approaches, enhancing the entire MRI acquisition process for patients, clinicians, and equipment operators.

[0059] The present disclosure provides for an improvement in patient comfort and safety. By actively predicting and minimizing mechano-acoustic output, the present disclosure facilitates reducing the loud, often distressing noise levels characteristic of M Rl scans. This creates a quieter and more comfortable scanning environment, which can lead to less movement and a higher likelihood of a successful scan on the first attempt.

[0060] Furthermore, the present disclosure directly enhances the diagnostic value of the images produced by unlocking new clinical and research possibilities with safely using previously "forbidden" echo-spacing (ESP) ranges, enabling novel acquisition strategies for higher spatial or temporal resolution.

[0061] Further, the present disclosure contributes to the longevity and reliability of the MRI system itself. The intense acoustic noise is a direct manifestation of strong mechanical vibrations that place significant stress on the expensive gradient coils and their supporting structures. By actively steering the system away from its mechanical resonances and minimizing vibrational energy, the present disclosure reduces the cumulative wear and tear on the hardware. This leads to an increased operational lifetime for critical components, a lower risk of premature hardware failure, and reduced maintenance costs over the long term.

[0062] Magnetic Resonance Imaging (MRI) creates images by manipulating nuclear spins within a strong, static magnetic field, B0. Spatial information is encoded by applying weaker, time-varying magnetic fields known as gradients, which are generated by a set of gradient coils. These gradients superimpose a linear variation onto the main magnetic field, allowing theP-25267-PCsystem to distinguish signals originating from different locations within the MRI coordinate system.

[0063] Aspects of the present disclosure will be discussed mainly with reference to multi slice, multi echo EPI, but are not limited thereto.

[0064] Echo-Planar Imaging (EPI) is a particularly fast acquisition technique that is the workhorse for applications like functional MRI (fMRI) and other dynamic imaging methods. Its speed is achieved by rapidly and repeatedly switching the polarityof the gradient coils to acquire many lines of imaging data after a single radiofrequency excitation. This rapid switching of electric current through the gradient coils, which are situated within the strong BO field, induces powerful and alternating Lorentz forces. These forces cause physical vibrations in the coil hardware, which manifest as the loud, characteristic acoustic noise of an EPI scan.

[0065] A complete imaging volume is typically built up from multiple individual acquisitions. In a multi-slice EPI sequence, the acquisition process, known as an echo-train, is repeated for numerous different slice positions to cover a three-dimensional volume. The time interval between the start of the acquisition for one slice and the next is a critical timing parameter, referred to herein as the time between consecutive slices, ATslice. Furthermore, sequences can be designed as multi-echo EPI, where for a single slice, several echo-trains are acquired at different echo times (TEs) to capture different image contrasts (e.g., T2* decay). The time interval between the start of these consecutive echo-trains is another critical timing parameter, the time between consecutive echoes, ATEcho. The entire process of acquiring all slices and echoes for a single volume is completed within a Repetition Time (TR), after which the process may be repeated for dynamic imaging.P-25267-PC

[0066] Reference is now made to Fig. 1A, which is a block diagram illustration of a Magnetic Resonance Imaging (MRI) system 10, constructed and operative in accordance with an embodiment of the present disclosure. System 10 comprises an MRI machine 100, an MRI computer system 120, and an operator console 108. The MRI machine 100 includes a set of gradient coils 102 for generating magnetic field gradients for spatial encoding of signals within an MRI coordinate system 104, and a patient table 106. A patient (not shown) is placed on patient table 106 during examination. The rapid operation of the gradient coils 102 during an echo planar imaging (EPI) sequence causes mechanical vibrations and associated acoustic noise.

[0067] According to an embodiment of the present disclosure, the system 10 includes at least one or more acoustic or vibration sensor / s 110,. One such sensor 110 is shown positioned on the wall of the bore of MRI machine 100. Another sensor 110 is shown positioned on or near the ear of a patient (not shown). For example, the sensor 110 may be attached to the patient table 106 or carried by the patient.

[0068] The sensor 110 is configured to acquire a signal indicative of the mechanical vibrations or acoustic signals generated by the gradient coils 102. This signal provides the basis for determining an acoustic and / or vibration frequencies system-specific frequency response function (FRF). The present disclosure is not limited by the type of acoustic and vibration sensors that can be used, and their placement.

[0069] Further, the present disclosure is not limited by the manner of acquiring the acoustic / vibration signal. In some embodiments of the present disclosure, the signal that provide the basis for determining a system-specific frequency response function (FRF) is acquired during manufacturing of the MRI system. The FRF may be the same for all system of a specific type, or measured for each and every system. For example, the signal is acquired aspart of system testingP-25267-PCand calibration, e.g., upon shipment or installation. In yet other embodiments, the signal is acquired as part of system maintenance operations (e.g., after replacing system components or periodically). In some embodiments, the acoustic / vibration signal is acquired in real time.

[0070] The MRI computer system 120, which generally controls the operation of the MRI machine 100, includes an acoustic-vibration control system 122. The acoustic-vibration control system 122 is configured to control the mechano-acoustic behavior of the MRI system by precisely adjusting timing parameters of the EPI sequence, as will be described in further detail below. In some embodiments of the present disclosure, the operator console 108 provides a user interface for an MRI operator to interact with and control the MRI computer system 120.

[0071] Reference is now made to Fig. IB, which is a more detailed block diagram illustration of the acoustic-vibration control system 122 from Fig. 1A. In this embodiment of the present disclosure, the acoustic-vibration control system 122 comprises a predictive modeling unit 124, a parameter optimization unit 126, and a sequence control unit 128. The predictive modeling unit 124 is configured to generate an improved prediction of the mechano-acoustic behavior (e.g., an acoustic power spectrum) for an EPI sequence by combining a model-based prediction of acoustic frequencies (and / or vibration frequencies) with a frequency response function (FRF) of the MRI system. The parameter optimization unit 126 is configured to analyze the improved prediction to determine acoustic power as a function of key timing parameters, such as the time between consecutive echoes (ATEcho) and the time between consecutive slices (ATslice), and to select values for these parameters that correspond to a reduced acoustic power. The sequence control unit 128 is configured to generate control signals to operate the MRI system 10 using the EPI sequence adjusted with the optimal values selected by the parameter optimization unit 126 to yield a desired mechano-acoustic behavior.P-25267-PC

[0072] Reference is now made to Fig. 1C, which is a detailed illustration of the operator console 108 from Fig. 1A. The operator console 108 includes a sequence parameter control operator interface 132, which may be a component of a graphical user interface (GUI). In some embodiments of the present disclosure, the interface 132 enables an MRI operator to view and adjust the timing parameters, such as ATEcho and / or ATslice, to control the acoustic noise and mechanical vibrations. In an embodiment of the present disclosure, the interface 132 may display visual representations (e.g., the acoustic power maps generated by the parameter optimization unit 126), allowing the operator to visually identify and select optimal timing configurations for desired mechano-acoustic behavior. For illustration, Fig. 1C shows an Acoustic Power Map display 132A. The present disclosure is not limited to this example and other visual or numerical representations of the timing parameters can be employed.

[0073] It is to be understood that the representation of the acoustic-vibration control system 122 as a distinct block within the MRI computer system 120 in Fig. 1A is illustrative. The acousticvibration control system 122 may be implemented in various architectural configurations. For instance, it may be a dedicated hardware component or a separate computer in data communication with the main MRI computer system 120. Alternatively, it may be embodied as a software module, a set of executable instructions, or a virtual machine hosted and executed by the main processor(s) of the MRI computer system 120. In other embodiments, its functionalities may be fully integrated as a native subsystem of the MRI computer system 120. The present disclosure is not limited by any specific system architecture, as the inventive concept resides in the functional capabilities of the control system rather than its particular physical instantiation.P-25267-PC

[0074] In embodiments of the present disclosure involving in-situ determination of system's frequency response function (FRF), at least one sensor 110 is in data communication with the acoustic-vibration control system 122 to provide the acquired acoustic and / or vibration signals necessary for determining the frequency response function (FRF). This data communication pathway can be established through any suitable means known in the art. For example, the sensor 110 may be connected via a physical wired link, such as a fiber optic cable to prevent RF interference, or via a wireless communication link (e.g., Bluetooth, Wi-Fi). The connection may be made directly to the acoustic-vibration control system 122, or it may be routed indirectly through the existing data acquisition infrastructure of the main MRI computer system 120. The present disclosure is not limited by the specific means of data transmission between the sensor 110 and the acoustic-vibration control system 122.

[0075] In general operation, the acoustic-vibration control system 122 receives operator inputs and initial EPI sequence definitions, for example from the operator console 108, and receives measurement data from the sensor 110 to establish the FRF. Based on these inputs, the acoustic-vibration control system 122 performs its predictive modeling and parameter optimization functions. It subsequently provides the adjusted, optimized EPI sequence parameters and corresponding control signals to the MRI computer system 120, which in turn operates the gradient coils 102 of the MRI machine 100 to acquire images with reduced acoustic noise and vibrations.

[0076] Reference is now made to Fig. ID, which is a flow chart illustration of an acousticvibration control method 14, operative in accordance with an embodiment of the present disclosure. Method 14 outlines the core processing operations performed by the acousticvibration control system 122 shown in Figs. 1A and IB. The method begins at operation 140 withP-25267-PCgenerating an improved prediction of an acoustic power spectrum by combining a model-based prediction with a frequency response function (FRF) of the MRI system. The FRF maybe received from an external source (e.g., FRF characterized in association with the MRI system type, or FRF characterized at preliminary operations such as during manufacturing and / or calibration) or measured in-situ (As detailed in conjunction with Fig. IF, this operation may involve a characterization phase 18A to determine the FRF and a prediction phasel8B to generate the improved prediction for the specific imaging sequence). At operation 142, the improved prediction is analyzed to determine acoustic power as a function of at least one timing parameter, such as ATEcho and / or ATslice, and a value corresponding to the desired mechanoacoustic behavior (e.g., a reduced acoustic power) is selected. At operation 144, the value for the at least one timing parameter that corresponds to the reduced acoustic power is formally selected for use in adjusting the EPI sequence.

[0077] Reference is now made to Fig. IE, which is a flow chart illustration of a method 16 of operating an MRI machine incorporating acoustic-vibration control according to embodiments of the present disclosure. In embodiments involving in-situ FRF determination, method 16 begins at operation 160 with acquiring, via the sensor 110, a signal indicative of the system's mechanical vibrations and / or sound. At operation 162, this signal is used to determine the FRF. In other embodiments, the FRF is obtained from an external source. The FRF is then used to perform the predictive and optimization operations of generating an improved prediction and selecting an optimal timing parameter, similar to the process shown in method 14 of Fig. ID.

[0078] Following the optimization, method 16 proceeds to the implementation phase. The implementation phase may be automatic or manual. For example, at optional operation 164, a GUI component is provided for display on the operator console 108, enabling an operator toP-25267-PCinteract with and adjust the timing parameters via the interface 132. At operation 166, the MRI system 10 is operated to acquire an image using the EPI sequence that has been adjusted with the selected optimal timing parameter, thereby achieving a scan with reduced acoustic noise or improved image quality.

[0079] In accordance with an embodiment of the present disclosure, the acoustic-vibration control system 122 utilizes a two-stage predictive framework to characterize and control the acoustic energy generated during an EPI sequence. The first stage generates a model-based prediction of the acoustic frequencies and / or vibration frequencies based on the fundamental physics of the gradient sequence itself. The second stage refines this prediction by incorporating the system-specific Frequency Response Function (FRF) to account for the physical hardware's mechanical resonances.

[0080] The first stage of the predictive framework involves generating a model of the acoustic frequencies based on the complete timing structure of the gradient sequence. This process begins by defining the gradient waveform G(t) for the acquisition. In an EPI sequence, the core component is a train of rapidly alternating gradient 'lobes'. The time between the center of one gradient lobe and the next, oppositely signed lobe is a fundamental parameter known as the echo-spacing (ESP). A single, finite echo-train of a given Echo-Train Length (ETL) can be mathematically modeled, for instance, as a series of trapezoidal pulses.

[0081] The gradient sequence for a multi-echo, multi-slice acquisition is then constructed by mathematically repeating this single echo-train according to the sequence's timing structure. This structure is defined by key timing parameters including the time between consecutive echoes (ATEcho), the time between consecutive slices (ATslice), and the overall Repetition Time (TR). Performing a Fourier transform on this complete gradient waveform G(t) yields its complexP-25267-PCfrequency spectrum g(co). The magnitude squared of this spectrum, |g(co) |2, represents the predicted input power spectrum of the vibrational forces that cause the acoustic noise.

[0082] A key insight of the present disclosure, derivable from this analytical model, is that the final predicted spectrum is a multiplicative product of a broad spectral envelope from the single echo-train and a series of "sinc-like" interference functions that arise from the periodic repetitions defined by ATEcho, ATslice, and TR. The peaks of these interference functions are highly sensitive to the chosen timing parameters. In some scenarios, the sensitivity for acousticvibration control can be leverage through a principle referred to herein as the "2ESP-raster". An "on 2ESP raster" condition is achieved when the timing parameters, particularly ATslice and ATEcho, are adjusted to be integer multiples of twice the echo-spacing (2*ESP). This precise timing causes the peaks of the various interference factors to align constructively, concentrating their energy into a single, dominant main peak (e.g., at a frequency of 1 / 2ESP) while other side peaks are suppressed by destructive interference.

[0083] Commonly, EPI scans also include an additional, shorter train of alternating gradients per slice, known as a navigator. Navigators are used to measure and correct for inconsistencies between signals acquired during positive and negative gradient lobes, which helps to reduce ghosting artifacts. In an embodiment of the present disclosure, the analytical model is extended to include the acoustic effects of these navigators. This is achieved by constructing a model for the main gradient train and a separate model for the navigator train, and then performing a complex-valued sum of their respective frequency spectra before calculating the final power spectrum. This allows the model to accurately account for the acoustic interference between the main acquisition and the navigator acquisitions.P-25267-PC

[0084] It should be noted that the present disclosure is not limited to the described navigator scenario and is applicable to any scenario involving the presence of an additional block other than the main block.

[0085] The second stage of the predictive framework refines the model-based prediction to account for the specific physical characteristics of the MRI machine 100. The model-based prediction from the first stage is incomplete because it assumes the scanner hardware responds uniformly to all frequencies. In reality, the hardware (including the gradient coils, cryostat, and supporting structures) has inherent mechanical resonances that will cause it to vibrate much more strongly and produce more sound at certain frequencies. To account for this, the present disclosure introduces the concept of employing a system-specific Frequency Response Function (FRF), also referred to as a transfer function. The FRF acts as a frequency-dependent amplification profile, or an "acoustic fingerprint," unique to each MRI scanner. This FRF is, e.g., empirically derived by comparing the system's actual acoustic output to the predicted vibrational input from the analytical model. The derivation process comprises several steps. First, one or more predefined gradient sequences are run on the MRI system 100. To best match the analytical model, these sequences may be modified to include only the gradient trains of interest. During the operation of these sequences, at least one sensor 110 records the actual acoustic or vibrational signal, which is then processed (e.g., via Fourier transform) to obtain a measured acoustic power spectrum. Concurrently, the analytical model from the first stage is used to generate a corresponding model-based power spectrum for the exact same predefined gradient sequences. The FRF is then calculated as the ratio between these two spectra, for example, by dividing the measured spectrum by the model spectrum on a frequency-by-frequency basis. To produce a robust FRF, this process may be repeated for multiple differentP-25267-PCtiming configurations, and the resulting ratios can be combined, for instance through a weighted average. Once derived, this FRF is stored and used by the Predictive Modeling Unit 124 to generate the final "improved prediction". This is accomplished by multiplying the model-based power spectrum of any desired future EPI sequence by the stored FRF. This "improved prediction" is therefore a highly accurate, scanner-specific forecast of the actual acoustic power spectrum that will be produced, and it is this final prediction that is analyzed by the Parameter Optimization Unit 126 to identify the optimal, quiet timing parameters for the EPI sequence.

[0086] Thus, in accordance with embodiment of the present disclosure, Predictive Modeling Unit 124 carrying out operation 140 of Fig. ID, may implement operation 140 following method 18 of Fig. IF.

[0087] Method 18 outlines the two-stage process of first characterizing the MRI system to determine its FRF, and then using that FRF to predict the acoustic output of a subsequent imaging sequence.

[0088] The method begins with a characterization phase (operations 180-186). At operation 180, a model-based power spectrum is generated for one or more predefined gradient sequences. These predefined sequences are designed specifically for system characterization. In some embodiments, these predefined sequences may be simplified to include only the main gradient echo-trains, deliberately excluding other components like navigator trains. This allows for a more direct measurement of the system's response to the primary source of acoustic energy.

[0089] At operation 182, the MRI system 100 is operated to run these exact predefined sequences, and during their execution, the at least one sensor 110 acquires a signal indicative ofP-25267-PCthe actual mechanical vibrations or acoustic sounds produced. At operation 184, this acquired signal is processed to generate a measured acoustic power spectrum.

[0090] At operation 186, the Frequency Response Function (FRF) (also referred to as Transfer Function) is determined. This is achieved by calculating the ratio between the measured power spectrum (from operation 184) and the model-based power spectrum (from operation 180) on a frequency-by-frequency basis. The resulting FRF represents the unique, frequency-dependent amplification profile, or "acoustic fingerprint," of the specific MRI machine 100, and is stored by the acoustic-vibration control system 122 for later use.

[0091] Method 18 proceeds to the prediction phase for a clinical or research scan (operation 188-190). At operation 188, the acoustic-vibration control system 122 receives the parameters for a subsequent EPI sequence that an operator intends to use for image acquisition and generates its corresponding model-based power spectrum using the model. At operation 190, the system generates the final "improved prediction" of the acoustic power spectrum. This is accomplished by multiplying the model-based spectrum of the subsequent EPI sequence (from operation 188) by the previously determined and stored FRF (from operation 186). This improved prediction provides a highly accurate forecast of the acoustic power that the subsequent EPI sequence will actually produce. This improved prediction is then provided as the input for the analysis and optimization operations of method 14, as shown at operation 142 of Fig. ID.

[0092] The effectiveness of the predictive framework outlined in Figs. ID, IE, and IF stems from a formal understanding of modern imaging sequences as a hierarchical structure of repeated gradient patterns, a concept illustrated in Figs. 14A-14D. As shown abstractly in Fig.14A, a fundamental gradient block is repeated at a first interval to form a composite block, whichP-25267-PCcan itself be repeated at a second interval and so on. This nested repetition is the mathematical origin of the complex acoustic interference patterns seen in MRI.

[0093] This hierarchical model provides a unified understanding of widely used sequences like EPI. As illustrated in Figs. 14B and 14C, the key timing parameters controlled by the invention— such as the time between echoes (ATecho), slices (ATslice), and partitions (Atpartition)— are simply the repetition intervals at different levels of this sequence hierarchy. The framework is also extensible to include other repeated elements like navigator blocks (Fig.14D). This confirms that by predictively manipulating these repetition intervals, the disclosed system can control the mechano-acoustic behavior of any imaging sequence built from such nested gradient patterns.

[0094] Reference is now made to Figs. 2-13, which are illustrations based on figures and data presented in Applicant's scientific publications, including "Timing is everything: How subtle timing changes in M Rl echo planar imaging can significantly alter mechanical vibration and sound level" and related materials, which are attached to U.S. Provisional Application No. 63 / 841,430 and U.S. Provisional Application No. 63 / 777,780, respectively. The entire contents of these publications and provisional applications are hereby incorporated by reference in their entirety. These figures are used to illustrate the problems in the art, the principles of the present disclosure, and the validation of the inventive concepts.

[0095] Reference is now made again to Fig. 2, which is a series of graph illustrations providing a detailed demonstration of the fundamental principle of the predictive acoustic model in accordance with an embodiment of the present disclosure. The figure shows results for a specific operational scenario where the Echo-Spacing (ESP) is 0.53 ms and the Echo-Train Length (ETL) is 54. This figure illustrates the effect of adjusting the time between consecutive slices (ATslices)P-25267-PCand consecutive echoes (ATE) on the acoustic power spectrum: how the final predicted acoustic power spectrum is factorized into its constituent parts and how subtle changes in sequence timing can dramatically alter this spectrum.

[0096] The figure is illustratively organized into three rows of increasing sequence complexity. The top row represents a simple sequence with a single slice and a single echo-train (#TEs = 1, #Slices = 1). The middle row represents a multi-echo sequence with three echo-trains (#TEs = 3, #Slices = 1). The bottom row represents a full multi-echo, multi-slice sequence with three echoes and six slices (#TEs = 3, #Slices = 6), resulting in a total of 18 echo-trains. The left column shows the gradient waveform G(t) for each of these scenarios, illustrating the basic gradient building block 200, the introduction of a time delay between echoes, ATE 202, and the time delay between slices, ATslices 204.

[0097] The middle and right columns show the corresponding predicted acoustic power spectrum for two distinct timing configurations. Each power spectrum plot illustrates how the final predicted spectrum 210 is a multiplicative product of a broad single echo-train spectral envelope 212, a multi-echo interference factor 214 (visible in the middle and bottom rows), and a densely packed multi-slice interference factor 216 (visible in the bottom row).

[0098] The core inventive principle is highlighted by comparing the "Not on 2ESP raster" case (middle column) with the "On 2ESP raster" case (right column). In the "off-raster" scenario, the timing parameters are set to arbitrary values (ATE = 31.35 ms and ATslices = 100 ms). As shown, this results in the peaks of the interference factors 214 and 216 being spread out, creating a "collection of substantial peaks" 211 when multiplied by the main envelope 212. In some cases, the undesirable distribution of acoustic energy can excite hardware resonances within theP-25267-PCforbidden frequency range 218. In other cases, the transition to the forbidden range is not a hard transition.

[0099] The "on-raster" scenario (right column) illustrates aspects of the solution provided by the present disclosure. Here, the timing parameters are adjusted to be integer multiples of twice the echo-spacing (i.e., multiples of 2 * ESP = 1.06 ms), specifically ATE = 31.8 ms and ATslices = 100.7 ms. This timing causes the peaks of the interference factors 214 and 216 to align constructively, concentrating their energy into a single, dominant main peak 211. Simultaneously, destructive interference suppresses the other side peaks, resulting in "almost negligible peaks in other frequencies." As demonstrated, this subtle adjustment— a change of only 0.45 ms for ATE and 0.7 ms for ATslice— is sufficient to transform a louder, multi-peaked acoustic spectrum into a clean, single-peaked spectrum, thereby providing a powerful method for controlling and minimizing acoustic noise.

[0100] Reference is now made to Fig. 3, which is a series of graph illustrations showing the effect of adjusting the time between consecutive slices (ATslice), a key timing parameter, in accordance with an embodiment of the present disclosure. This figure provides a clear demonstration of how manipulating ATslice can be used to control the acoustic power spectrum of an EPI sequence.

[0101] The figure is organized into three rows, each representing a different timing scenario within a single Repetition Time (TR). The left column shows the gradient waveform G[a.u.], which is composed of a series of individual gradient echo-trains 300. The key variable between the rows is the time interval ATslice 302. The top row shows a scenario with ATslices = 100 ms, the middle row with ATslices = 34.70 ms, and the bottom row with a value of ATslices = 34.98 ms. This valueP-25267-PCcorresponds to an "on-raster" condition 304, where ATslice is an integer multiple of twice the echo-spacing (2*ESP).

[0102] The right column shows the corresponding predicted acoustic power spectrum for each waveform. As previously explained, the final power spectrum 310 is a product of the single echo-train's spectral envelope 312 and the multi-slice interference factor 314. In the "off-raster" scenarios (top and middle rows), the slice factor 314 produces a series of prominent peaks whose spacing 316 is inversely proportional to ATslice. This results in a final spectrum 310 with multiple significant acoustic peaks.

[0103] The bottom row illustrates the impact of timing parameter adjustment according to embodimnets of the present disclosure. When the timing is set e.g., to the "on-raster" condition 304, the interference pattern generated by the slice factor 314 changes. Destructive interference causes most of the acoustic side peaks to be greatly diminished, as shown in the suppressed side peak region 318. Concurrently, constructive interference concentrates the acoustic energy into a few strong, consolidated main peaks 320. This demonstrates that ATslice is a highly effective control parameter for tailoring the acoustic spectrum.

[0104] Reference is now made to Figs. 4A and 4B, which together provide initial proof of the inventive concept, demonstrating a direct correlation between the acoustic spectrum of an EPI sequence and the quality of the resulting image. Fig. 4A provides a simple, visual proof of ghosting artifact reduction, while Fig. 4B shows the corresponding measured and modeled acoustic spectra. .

[0105] Fig. 4A illustrates three experimental blocks. Block (1) shows a multi-echo EPI sequence performed on a phantom 410, with parameters ESP=0.53 ms, three echoes (#TE=3), and six slices. Block (2) shows a multi-slice EPI sequence on the same phantom, with parametersP-25267-PCESP=1 ms, one echo, and 150 slices. Block (3) validates the principle in a real-world scenario, showing an in-vivo scan of a human brain 412 using the same parameters as block (1). In each block, the top row shows images acquired with a sub-optimal "Off 2ESP raster" timing 406, while the bottom row shows images acquired with an optimized "On 2ESP raster" timing 408, in accordance with the present disclosure.

[0106] As is clearly visible, the images from the "off-raster" condition 406 are corrupted by significant ghosting artifacts 402. In contrast, by making only a subtle adjustment to the timing to operate on the 2ESP-raster, the resulting images 400 are substantially cleaner. The difference images 404 visually confirm that the artifact signal has been effectively removed. This demonstrates that precise control of sequence timing directly impacts image quality.

[0107] Fig. 4B provides the corresponding acoustic data that explains the results seen in Fig.4A. Plots (1) and (2) show the measured acoustic power spectra 418 for the scenarios in blocks (1) and (2) of Fig. 4A, respectively. In both plots, the "Off 2ESP raster" spectrum 422 exhibits multiple, strong side peaks 424, many of which fall within the scanner's "forbidden range" 426. This multi-peaked, high-energy acoustic profile corresponds directly to the high-artifact images. Conversely, the "On 2ESP raster" spectrum 420 shows that the acoustic energy has been consolidated with the unwanted side peaks dramatically suppressed. This quiet acoustic profile corresponds to the clean, low-artifact images.

[0108] Plots (3) and (4) show the corresponding acoustic spectra 430 as predicted by the model-based prediction. The strong agreement between the model and the measured data validates the underlying predictive framework of the present disclosure. Notably, the annotation 428 in plot (1) points to a peak in the measured spectrum that is significantly larger than predicted by the basic model. This discrepancy highlights the influence of the scanner's physicalP-25267-PCstructure and mechanical resonances, and it demonstrates the need for the next stage of the predictive framework: incorporating a measured Frequency Response Function (FRF) to create the final, highly accurate "improved prediction."

[0109] Reference is now made to Fig. 5, which is a series of graph illustrations demonstrating the hardware robustness of the present disclosure. This figure shows that the principles of acoustic-vibration control by adjusting sequence timing are effective across different types of Radio Frequency (RF) coils. It is important to distinguish these RF coils, which are used to transmit RF pulses and / or receive the MRI signal, from the gradient coils 102 that are the primary source of the acoustic noise. By demonstrating the principle's validity with different RF coils, this experiment confirms that the system and method of the present disclosure are not dependent on a specific hardware configuration within the scanner bore.

[0110] Fig. 5 is arranged as a matrix of six plots, labeled as plots (1) through (6). The top row, containing blocks (1) and (2), shows measured acoustic spectra for an operational scenario using a "Nova Coil", which is a 32-channel (32Rx / lTx) receive head coil designed to fit closely around a subject's head. The middle row, containing blocks (3) and (4), shows corresponding spectra using a physically distinct "Flex Coil", which is a smaller, single-channel (IRx) flexible surface coil. For validation, the bottom row, containing blocks (5) and (6), shows the theoretical spectra as predicted by the model-based prediction of the present disclosure (scaled to the Flex coil). The left column corresponds to an operational scenario with an Echo-Spacing (ESP) of 0.53 ms, while the right column corresponds to an ESP of 0.64 ms.

[0111] Within each plot, the different lines represent the measured or predicted acoustic spectrum for different settings of the slice timing parameter, ATslice. The plots clearly demonstrate the principle of acoustic-vibration control previously illustrated in Figs. 2 and 3. ForP-25267-PC"off-raster" ATslice values, the spectrum, such as the off-raster spectrum 512, exhibits multiple significant side peaks that arise from the interference of the multi-slice factor with the spectrum of the single echo-train model 514.

[0112] Comparing plots (1) and (3) shows the consistency across different hardware (Nova vs. Flex coil) for the first ESP. Comparing plots (2) and (4) shows the consistency across different hardware for the second ESP. Comparing plots (2) and (6) shows the theoretical validation also holds for a different ESP (0.64 ms).

[0113] The conclusion drawn from Fig. 5 is the consistency of this behavior across these two very different hardware configurations, each with its own FRF. The result of spectral changes in response to timing adjustments observed with the large, form-fitting Nova head coil is highly similar to that observed with the small, flexible Flex coil. Furthermore, the theoretical model accurately predicts the peak locations for both cases. This demonstrates that the principles of acoustic-vibration control by manipulating the gradient sequence timing are robust and that the system and method of the present disclosure are effective for different MRI systems.

[0114] Reference is now made to Fig. 6, which is a pair of graph illustrations that introduces an insight of the present disclosure by highlighting the limitationsofa basic predictive model and demonstrating the necessity of accounting for the scanner's physical mechanical resonances. The figure shows data for an operational scenario with ESP=0.53 ms, three echoes (#TE=3), and six slices, corresponding directly to the scenario shown in Fig. 4A block (1) and Fig. 4B plots (1) and (3).

[0115] Plot (1), labeled "Model (scaled)", shows the acoustic power spectrum as predicted by the model-based prediction alone, before the incorporation of any system-specific hardware characteristics. This plot includes the "Model envelope" 618, which represents the underlyingP-25267-PCspectral shape. The model predicts that for an "Off 2-ESP raster" timing, the resulting spectrum 606 will have multiple side peaks under its envelope. In contrast, for an "On 2-ESP raster" timing, the model predicts that the energy will be consolidated into a single dominant peak, shown as spectrum 604.

[0116] Plot (2), labeled "Measured", shows the actual, empirically measured acoustic spectrum for the same two timing conditions. It plots the measured "On 2-ESP raster" spectrum 608 and the measured "Off 2-ESP raster" spectrum 610. A discrepancy is apparent when comparing the model to the measurement. While the peak locations are generally consistent, the peak amplitudes are not. The measured "off-raster" spectrum 610 contains one or more amplified resonance peaks 612 that are dramatically larger than what the basic model predicted in plot (1). In fact, these amplified peaks, which fall within the forbidden range 616 and are queried by the "resonances?" annotation 614, are significantly louder than the main peak of the measured "on-raster" spectrum 608.

[0117] This discrepancy proves that the basic model-based prediction is incomplete because it fails to account for the physical hardware's tendency to resonate at and amplify certain frequencies. This figure powerfully demonstrates the problem that the "improved prediction" of the present disclosure— which incorporates the Frequency Response Function (FRF) to account for these very resonances— is designed to solve. It sets the stage for the subsequent figures, which will show the application of this FRF to create a highly accurate predictive model for acoustic-vibration control.

[0118] Reference is now made to Fig. 7, which is a series of graph illustrations demonstrating the solution of applying a Frequency Response Function (FRF) to the analytical model, and showing the resulting variation of acoustic power as a function of slice timing (ATslice). ThisP-25267-PCfigure validates the core principle that an "improved prediction," which combines the model with the FRF, can accurately forecast and be used to control the acoustic output of an EPI sequence.

[0119] Block (A) of Fig. 7 illustrates the two-stage predictive framework of the present disclosure, showing its application to four distinct ATslice timing values for a MR I system with a 7 Tesla magnetic field. Plot 700, labeled "Model," shows the acoustic power spectrum as predicted by the first-stage analytical model alone. This model, based purely on the gradient sequence timing, predicts multiple acoustic peaks whose positions shift as ATslice is varied. Plot 702, labeled "Model xTrans. func.," demonstrates the second stage of the framework. Here, the model-based spectra from plot 700 are multiplied by a previously determined, system-specific FRF. This operation selectively amplifies the model peaks that coincide with the scanner's inherent mechanical resonances, thereby generating the final, highly accurate "improved prediction." Plot 704, labeled "Measured," shows the actual, empirically measured acoustic spectrum for the same four ATslice values. The striking similarity between the improved prediction shown in plot 702 and the measured reality shown in plot 704 provides a clear validation of the effectiveness of the two-stage predictive framework.

[0120] Block (B) demonstrates the cyclical nature of acoustic power as a function of ATslice for two operational scenarios with a dominant first harmonic. The top row of block (b) (plots 708 and 710) corresponds to a sequence with an ESP of 0.53 ms, while the bottom row (plots 712 and 714) corresponds to an ESP of 0.74 ms. In each row, the left plot (708, 712) shows the measured acoustic spectra for the minimum ("Emin") and maximum ("Emax") power points found in the corresponding right plot. The right plots (710, 714) visualize the acoustic power as a function of ATslice, showing an agreement between the relative behavior of the measuredP-25267-PCpower curve 716 and the predicted power curve 718. These plots reveal that acoustic power cycles up and down as ATslice is varied. As indicated by the period annotation 715, the period of this variation is approximately twice the echo-spacing (2ESP). This confirms that ATslice is a control parameter that can be adjusted to place the sequence timing in a "quiet valley" of minimal acoustic power by avoiding hardware resonances.

[0121] Block (C) illustrates a more complex but equally important case where the dominant acoustic energy is in the 3rd harmonic of the sequence. Plot 730 shows a wideband spectrum where the 1st harmonic region 731 is weak, but the 3rd harmonic region 733 is strong. Plots 732 and 734 show the spectral details for the 1st and 3rd harmonics, respectively. The key insight is shown in the power vs. ATslice plot 736. Here, both the measured curve 738 and predicted curve 739 again show a distinct cyclical variation. However, as indicated by the cyclical period annotation 737, the period is now approximately two-thirds of the echo-spacing (2ESP / 3). This proves that the period of acoustic power variation is determined by the dominant acoustic harmonic of the sequence, not necessarily the first. The plots in the bottom row (740, 742, 744) further validate this principle on a different MRI system (with a 10.5T magnetic field), showing the same 2ESP / 3 cyclical period 745 and demonstrating the achievable power reduction factor 746 . This confirms that the predictive control framework of the present disclosure is robust and adaptable, allowing for the minimization of acoustic noise even in complex, multi-harmonic scenarios.

[0122] Reference is now made to Fig. 8, which presents a series of map illustrations showing measured and predicted two-dimensional acoustic power maps as a function of both echo timing (ATEcho) and slice timing (ATslice). This figure demonstrates the full, two-dimensionalP-25267-PCnature of the solution space and the multi-parameter control capabilities of the present disclosure, moving beyond the single-parameter analysis of Fig. 7.

[0123] Plot (A) of Fig. 8 shows the measured Frequency Response Function (FRF) 800 for the MRI system, which highlights the system's inherent mechanical resonances within the "forbidden range" 802. The vertical dashed lines 804, 806, 808, and 810 indicate the primary acoustic frequency (1 / 2ESP) for the four different ESP values used in the subsequent scenarios shown in columns (i) through (iv). This plot provides the context for the acoustic maps, explaining why certain ESP values, such as the one corresponding to line 808, fall within a region of high resonance.

[0124] Block (B) of Fig. 8 illustrates the acoustic power for a dual-echo multi-slice EPI sequence. The top row shows the simulated acoustic power maps 820 generated by the improved predictive model, while the bottom row shows the corresponding empirically measured acoustic power maps 822. Each map visualizes acoustic power as a function of the time between consecutive slices (ATslice, x-axis) and the time between consecutive echoes (ATEcho, y-axis). The color scale represents acoustic power, where dark areas correspond to optimal "quiet valleys" 824 and bright areas correspond to high-power peaks 826. The strong agreement between the simulated maps 820 and the measured maps 822 provides further validation for the predictive model. The acoustic power maps demonstrate that by precisely coadjusting both ATslice and ATEcho, it is possible to navigate the system's operation into a quiet valley 824. This is illustrated e.g., in column (iii), where even within a "forbidden" region 828, a quiet operating point is identified. The power reduction factor annotations 830 quantify the dramatic reduction in acoustic power— up to 70-fold in simulation— achievable by moving fromP-25267-PCa power peak to a nearby valley. This capability to safely use previously forbidden ESP values unlocks new potential for high-resolution imaging.

[0125] Block (C) of Fig. 8 shows, for comparison, the acoustic power for a single-echo multislice EPI sequence that includes navigators. Here, the maps are plotted as a function of ATslice (x-axis) and the time between the navigator and the slice acquisition (ATnav-slice, y-axis). The simulated maps 840 and measured maps 842 again confirm that the principle of timing-based acoustic-vibration control is effective. However, a key finding is revealed by the minimal power ratio 850 at the bottom of the figure. This ratio compares the lowest measured acoustic power from the dual-echo case (Block B) to that of the single-echo case (Block C). In scenario (iv), this ratio is 0.5, indicating that the optimized dual-echo sequence, despite having nearly double the gradient activity, can be operated at half the minimal acoustic power of its single-echo counterpart. This non-obvious result demonstrates the superior control and acoustic performance achievable through the multi-parameter optimization framework of the present disclosure.

[0126] Reference is now made to Fig. 9, which provides a series of graph illustrations offering a technical explanation for the acoustic power maps of Fig. 8 (ESP = 0.4 ms). This figure deconstructs the predictive framework to show how the factorization of spectral components and their interaction with a measured FRF allows for precise control of acoustic power. The figure compares a high-power case (left column titled 'Max. power case'), plots (1), (3), (5)) with a low-power case (right column titled 'Min. power case'), plots (2), (4), (6)).

[0127] Plot (1) in the high-power case illustrates the factorization of the analytical model. The final model spectrum, represented by the solid line 918, is the product of a broad single echotrain envelope 916, a multi-echo interference factor 912, and a multi-slice interference factorP-25267-PC914. In this timing configuration, the peaks of the echoes factor 912 and slices factor 914 align constructively, creating a strong peak in the model spectrum 918. Plot (3) shows what happens when this model spectrum is multiplied by the system's measured FRF 924 (dotted line). The constructively aligned peak from the model falls directly on a resonance of the FRF, resulting in a predicted final spectrum 922 (solid line) with a very large, amplified peak. Plot (5) confirms this prediction with the empirically measured spectrum, which shows a correspondingly large measured power peak 932 located within the scanner's "forbidden freq." range 934.

[0128] The two cases (high-power and low-power) differ in timing by an echo timing difference of 0.4 ms and slice timing difference of 0.23 ms.

[0129] The low-power case, plot (2) shows that this timing shift alters the interference pattern, causing the peaks of the echoes factor 912 and slices factor 914 to become misaligned. This results in a new model spectrum, represented by the solid line 958, where the energy is dispersed and the peak amplitudes are low apart for a single dominant peak. In plot (4), when this new model spectrum is multiplied by the same FRF 924, its dispersed peaks now miss the FRF's primary resonance. This results in a predicted final spectrum 962 (solid line) with a reduced peak, as highlighted by the scaling annotation 964 ("spectrum xl5"), indicating its amplitude is magnified for visibility. Plot (6) provides the definitive validation, showing that the measured power spectrum now has only a very small peak 972 (solid line). This demonstrates that by predictively manipulating the interference of the spectral components, the acoustic energy has been successfully steered away from the scanner's mechanical resonances, transforming a loud, high-power scan into a quieter, lower-power one.

[0130] Reference is now made to Figs. 10A, 10B, and 10C, which are a series of map and graph illustrations presenting quantitative proof of the direct correlation between acoustic power andP-25267-PCghosting artifacts. These figures collectively demonstrate that acoustic power and ghosting artifacts share the same cyclical period as a function of sequence timing parameters, and that this period is determined by the dominant acoustic harmonic of the sequence. The principle is shown to be robust across different systems and parameters: Fig. 10A shows results from a 7T MRI with an ESP of 0.53 ms; Fig. 10B shows results from a 10.5T MRI, also with an ESP of 0.53 ms; and Fig. 10C shows results from a 7T MRI with an ESP of 1.26 ms, where the 3rd harmonic is dominant.

[0131] In each of Figs. 10A-10C, plot (1) provides a direct visual confirmation of the impact of timing optimization on image quality. It compares a "Max. Ghosts" image 1002, acquired with sub-optimal timing, to a "Min. Ghosts" image 1004, acquired with optimized timing. The accompanying Ghost Ratio map 1006 and ratio value annotation 1008 quantify the significant reduction in artifact intensity.

[0132] The core evidence for the correlation is presented by comparing plot (2) and plot (3) in each figure. Plot (2) shows the 2D acoustic power map 1010, while plot (3) shows the 2D ghost level map 1020. Both maps are plotted as a function of the same timing parameters: slice timing (ATslice) on the x-axis and navigator-to-train time on the y-axis. The plots show the correlation between the timing parameters cycles - where the acoustic power have maxima and minima as function of slice timing (ATslice) and the ghosts have maxima and minima changing in diagonal as function of the slice timing (ATslice) and the navigator-to-train timing, while the distance between the peaks in both cases is 2ESP in Fig.lOA-B and 2ESP / 3 in Fig.lO.C.

[0133] The periodicity of this effect is further clarified in plots (4) and (5). Plot (4) displays a ghost level map 1030 where the y-axis is redefined as the time from the end of the prior echotrain to the start of the navigator. This representation reveals a clear, predictable cyclical pattern.he period of this cycle is explicitly annotated as 1032. For the first harmonic cases (Figs. 10A and 10B), this period is approximately twice the echo-spacing (~2ESP). For the third harmonic case (Fig. IOC), the period is approximately two-thirds of the echo-spacing (~2ESP / 3). Plot (5) presents this data as a ID graph 1040, showing the averaged ghost level as a function of the train-to-navigator time, again highlighting the cyclical period 1032. The fact that the ghost level oscillates with the exact same period as the acoustic power, and that this period is determined by the dominant acoustic harmonic of the EPI sequence, provides definitive proof of the physical link between the system's mechanical vibrations and the resulting image artifacts.

[0134] This demonstration of a shared periodicity provides a unique and novel insight into the role of the navigator in image artifact formation. While the temporal placement of navigator acquisitions relative to the main echo-train is known to influence ghosting artifacts, the present disclosure reveals the underlying physical mechanism: this effect is directly driven by the acoustic interference from the preceding echo-train. As demonstrated in plot (4) of each figure, the critical timing parameter is the interval between the end of a main acquisition train and the start of the subsequent navigator. The mechanical vibrations from the main train persist and physically interfere with the navigator's own acquisition. Since the navigator's purpose is to provide a correction for the next main echo-train, a corrupted navigator measurement leads to an inaccurate correction, which manifests as the observed ghosting artifacts.Thus, in some embodiments of the present disclosure, the timing parameters that are adjusted comprises slice timing and / or echo timing, as well as navigator timing. It should be noted that the present disclosure is not limited to the described navigator scenario and is applicable to any scenario involving the presence of an additional block other than the main block.P-25267-PC

[0135] Reference is now made to Fig. 11, which is a set of image illustrations providing a real-world validation of the present disclosure's efficacy with in-vivo human MRI scans. This figure demonstrates the significant reduction of ghosting artifacts achieved by selecting sequence timing parameters that correspond to minimal ghost levels, in accordance with the principles of the present disclosure.

[0136] The top row compares the imagequalityfor a sequence with an ESP of 0.53 ms. Image (A), labeled "Max Ghost" 1102, was acquired using sub-optimal timing parameters that correspond to a predicted peak in ghosting and acoustic power. It is corrupted by significant ghosting artifacts 1120, which manifest as structured, coherent noise appearing outside the anatomical structure of the brain, as indicated by the arrows. In stark, image (B), labeled "Min Ghost" 1104, was acquired using other timing parameters. In this image, the ghosting artifacts are almost entirely eliminated, resulting in a substantially cleaner and more diagnostically useful image.

[0137] The bottom row shows a similar comparison for a different sequence with a higher spatial resolution and an ESP of 1.26 ms. In this scenario, the artifacts manifest differently. Image (C), the "Max Ghost" image 1112, exhibits prominent artifacts 1120 that appear as local signal dropoutsand blurring within the anatomical structure of the brain, degrading the visibility of fine details. Image (D), the "Min Ghost" image 1114, was acquired with optimized timing. Here, the intra-brain artifacts are visibly reduced, restoring image integrity and sharpness. Together, these comparisons provide compelling, real-world validation that the control framework of the present disclosure can be used for improving diagnostic image quality by minimizing various types of ghosting artifacts across different imaging sequence parameters and field strengths.P-25267-PC

[0138] Reference is now made to Fig. 12, which is a set of graph and image illustrations showing the application of the acoustic-vibration control concept to a 3D Echo-Planar Imaging (3D-EPI) sequence. This section demonstrates that the fundamental principles of the predictive model are not limited to the 2D multi-slice acquisitions previously discussed, but are broadly applicable to other important classes of EPI-like sequences (e.g., EPSI spectroscopic imaging, SPEN and more).

[0139] While both 2D multi-slice EPI and 3D-EPI are used for rapid volumetric imaging and rely on the repetition of gradient echo-trains, they differ in their volume encoding strategy. As previously described, a 2D multi-slice sequence acquires a volume slice-by-slice, where each echo-train encodes a single 2D slice, and the repetition of these slice acquisitions is governed by the timing parameter ATslice. In contrast, a 3D-EPI sequence encodes the entire volume at once using phase-encoding in the third dimension (the slice direction). In this approach, each echotrain encodes a "k-space partition" of the 3D volume, and the full volume is acquired by repeating the acquisition for multiple different partitions. From the perspective of the acoustic model, this represents the same hierarchical structure of repeated gradient blocks (as illustrated in Fig. 14B). The repetition of partitions in 3D-EPI is conceptually analogous to the repetition of slices in 2D multi-slice EPI, with the time between partitions (At_partition) serving a similar role to ATslice. Because the underlying physics of acoustic interference arises from this repetition of building blocks, the predictive control framework of the present disclosure is equally applicable to 3D-EPI.

[0140] Fig. 12 validates this principle by comparing two different timing configurations for a 3D-EPI sequence with an ESP of 0.53 ms at different timing configurations. An 'Even ESP raster' and an 'Odd ESP raster' refer to timing configurations where a key repetition interval of theP-25267-PCsequence, such as the TR in 3D-EPI, is set to be an approximately even or odd integer multiple of the echo-spacing (ESP), respectively.

[0141] Plot (1) shows the results for an "Even ESP raster" timing configuration. It includes the measured acoustic spectrum 1200 and the corresponding resulting image 1202. The spectrum 1200 is characterized by a dominant frequency peak 1220 at a frequency of f = 1 / 2ESP, but it also contains several noticeable side-peaks 1222.

[0142] Plot (2) shows the results for an "Odd ESP raster" timing configuration. Its acoustic spectrum 1210 does not feature a dominant peak at the same f = 1 / 2ESP frequency, the overall profile is significantly cleaner. The side-peaks 1224 are higher compared to those in the "Even" raster case. The corresponding image 1212 shows visibly reduced ghosting artifacts compared to image 1202. This demonstrates a direct correlation between the cleaner, single-peaked acoustic spectrum and improved image quality in the 3D-EPI context. This confirms that the principles of manipulating timing parameters to concentrate acoustic energy and simultaneously reduce artifacts are broadly applicable across different types of EPI sequences, not being limited to any single acquisition strategy.

[0143] Reference is now made to Fig. 13, which provides a pair of "before and after" graph illustrations. The figure demonstrates how a subtle, predictive adjustment to sequence timing can dramatically transform a noisy, multi-peaked acoustic spectrum into a clean, single-peaked acoustic profile.

[0144] The "Before" state 1300, shown in section (1), represents an "off-raster" timing configuration. The gradient waveform 1302 is defined by initial timing parameters 1320, such as Techo before and Tslice before. The corresponding acoustic spectrum analysis, shown in plot 1304, reveals the outcome of this timing.P-25267-PC

[0145] The "After" state 1310, shown in section (2), shows the gradient waveform 1312 that has been modified by applying a subtle timing change (AT) to the initial parameters, resulting in new"on-raster" timing parameters 1322, where Techo after = Techo before + Aiecho and Tslice after = Tslice before + Aisiice- For example, in the specific case illustrated, an initial Techo before of 29.23 ms and Tslice before of 100 ms are adjusted by only a few hundred microseconds to an optimized Techo after of 29.68 ms and Tslice after of 100.70 ms. This figure illustrates the ability of a system and method according to embodiments of the present disclosure to adjust the mechano-acoustic behavior through the control of imaging sequence timing parameters.

[0146] Reference is now made to Figs. 14A, 14B, 14C, and 14D, which are schematic illustrations that establish a general hierarchical framework for representing repeated gradient blocks in various MRI sequences. These figures provide the formal, unified terminology and description of embodiments of the present disclosure, demonstrating that the inventive concept is not limited to a single type of sequence but can be generalized to any sequence built from repeated gradient patterns.

[0147] Fig. 14A illustrates the abstract principle of this hierarchical framework. A fundamental gradient block 1402, denoted Gl(t), serves as the basic building block. This block is repeated multiple times at a defined first repetition interval 1406 (Atl) to form a first-level composite block 1404, G2(t). This composite block can, in turn, be treated as a new building block and be repeated at a second repetition interval 1410 (At2) to form a second-level composite block 1408, G3(t). This nested repetition structure can continue and is the mathematical origin of the complex acoustic interference patterns seen in EPI, as each level of repetition introduces a corresponding periodic interference factor into the final acoustic spectrum.P-25267-PC

[0148] Figs. 14B and 14C apply this general principle to specific, widely used EPI sequences. Fig. 14B illustrates the structure for multi-echo sequences. In a multi-echo multi-slice EPI sequence (top), the fundamental block is the single echo-train 1420, Gecho(t). These are repeated at the echo interval 1422 (Atecho) to form a complete slice acquisition block 1424, Gslice(t). These slice blocks are then repeated at the slice interval 1426 (Atslice) to form the final volume acquisition block 1432, Gvolume(t). In a multi-echo 3D EPI sequence (bottom), the structure is analogous: the same fundamental Gecho(t) block 1420 is repeated to form a partition acquisition block 1428, Gpartition(t), which is then repeated at the partition interval 1430 (Atpartition) to build the final Gvolume(t) block 1432. Fig. 14C shows how this framework simplifies for single-echo versions of multi-slice and 3D EPI, where the Gecho(t) level is trivial. This unified representation confirms that the key timing parameters (Atecho, Atslice, Atpartition) are simply the repetition intervals at different levels of the sequence hierarchy.

[0149] Finally, Fig. 14D illustrates how the framework is extended to incorporate navigator acquisitions, which are themselves a form of repeated gradient block. A navigator block 1440, Gnav(t), is introduced into the sequence. In a multi-echo multi-slice EPI with navigators (top), the Gnav(t) block is repeated before each Gslice(t) block. The timing between them is defined by the navigator interval 1442 (AtNav), which corresponds to the "train-to-navigator" time shown in Fig. 10 to be directly correlated with ghosting artifacts. The same principle applies to a multiecho 3D EPI with navigators (bottom), where Gnav(t) is repeated before each Gpartition(t) block.

[0150] By describing all EPI variants using this consistent, hierarchical structure, these figures establish the formal basis for the generalized analytical model. The ability of systems and methods of the present disclosure to predict and control acoustic energy by manipulating the repetition intervals (At) at any level of the hierarchy is thus shown to be broadly applicable toP-25267-PCany imaging sequence that can be deconstructed into such a pattern of nested, repeated gradient blocks.

[0151] Thus, according to a broad aspect of the present disclosure, there is provided An acoustic-vibration control system for controlling acoustic performance of an MRI system, the system comprising: a modeling unit configured to characterize an MRI sequence as comprising at least one fundamental gradient block repeated at a first repetition interval to form a first-level composite block; an optimization unit configured to predictively select a value for the first repetition interval to yield a desired mechano-acoustic behavior (e.g., reduce acoustic power) generated during operation of the MRI sequence, wherein the selection is based on a model that accounts for an acoustic / vibration interference pattern arising from the repetition of the at least one fundamental gradient block combined with a frequency response function (FRF) of the MRI system; and a control unit configured to operate the MRI system using the MRI sequence adjusted with the selected value for the first repetition interval.

[0152] According to another broad aspect of the present disclosure, there is provided a -computer-implemented method for controlling mechano-acoustic behavior of an MRI system, the method comprising: characterizing an MRI sequence as comprising at least one fundamental gradient block repeated at a first repetition interval to form a first-level composite block; predictively selecting a value for the first repetition interval to yield a desired mechano-acoustic behavior generated during operation of the MRI sequence, wherein the selection is based on a model that accounts for an acoustic / vibration interference pattern arising from the repetition of the at least one fundamental gradient block combined with a frequency response function (FRF) of the MRI system; and operating the MRI system using the MRI sequence adjusted with theP-25267-PCselected value for the first repetition interval. The frequency response function (FRF) may be obtained from an external source or measured.

[0153] The term "imaging sequence" as used in the present disclosure is to be understood in its broadest sense and includes any sequence of radio-frequency (RF) pulses and magnetic field gradients used in Magnetic Resonance Imaging (MRI). While the systems and methods of the present disclosure have been described mainly with reference to optimizing specific sequences such as 2D multi-slice and 3D Echo-Planar Imaging (EPI), the inventive framework is not limited thereto.

[0154] The principle of modeling and mitigating acoustic behavior is broadly applicable to any MRI sequence that utilizes rapid switching of electrical currents through gradient coil conductors, particularly those with rapid data acquisition schemes that acquire several to multiple readouts per excitation. More specifically, the invention is relevant to any magnetic resonance sequence that exhibits a hierarchical, repetitive structure of gradient blocks, as the timing parameters governing the repetition intervals of these blocks can be adjusted.

[0155] Examples of such imaging sequences include, but are not limited to: Spin-echo based sequences, such as Spin Echo (SE), Fast Spin Echo (FSE), Turbo Spin Echo (TSE), and Rapid Acquisition with Relaxation Enhancement (RARE). Gradient-echo based sequences, such as Gradient Recalled Echo (GRE), Fast Low Angle Shot (FLASH), Steady-State Free Precession (SSFP), including Balanced SSFP (bSSFP), and Magnetization Prepared Rapid Gradient Echo (MP-RAGE). Ultra-fast or hybrid sequences, such as Echo Planar Imaging (EPI) and its variants like 3D-EPI, Echo Planar Spectroscopic Imaging (EPSI), Echo Planar Time-resolved Imaging (EPTI), as well as Spiral imaging, Radial imaging, and Gradient and Spin Echo (GRASE). This list is illustrative andP-25267-PCnot exhaustive, as the inventive framework can be applied to these sequences, their variants, and any other sequence that possesses a similar repetitive structural and temporal nature.

[0156] Furthermore, several aspects of the invention can be generalized without deviating from its teachings. The model is not limited to a specific gradient waveform shape; while described with respect to trapezoidal pulses, it can be adapted to use sinusoidal waveforms or any other arbitrary gradient shape. The optimization goal itself is also flexible; while the description has focused on minimizing total acoustic power, the target could alternatively be the minimization of mechanical vibrations. The present disclosure is not limited by the type and characteristics of the desired mechano-acoustic behavior that can be achieved. For example, in embodiments that involve the improvement of image quality (ghost reduction) in response to timing parameter adjustment, various settings for the desired mechano-acoustic behavior may apply, depending on the specifics of the MRI system (e.g., its FRF), the imaging sequence and additional operational factors.

[0157] The implementation of the acoustic-vibration control system 122 is also not limited; it may be embodied as a software module running on the main MRI operator console, as a dedicated hardware component, or even as a remote or cloud-based service that provides optimized sequence parameters. The user interface may be a graphical one as described, or the entire optimization process may be fully automated, requiring no direct operator input.

[0158] It will also be appreciated that the operation of determining the Frequency Response Function (FRF), as detailed in method 18 (Fig. IF), can be implemented with varying frequency and specificity depending on the desired accuracy and use case. In one embodiment, a generic FRF may be determined once for an entire product line of MRI systems (e.g., for all scanners of a specific model), with this generic FRF being installed at the factory. In another embodiment, a more accurate, machine-specific FRF is determined for each individual MRI machine. ThisP-25267-PCcharacterization can be performed as a one-time calibration step, either at the factory during final quality assurance testing or on-site by a service engineer upon installation of the system. In yet another embodiment, recognizing that a scanner's mechanical properties may change over time due to hardware aging, environmental temperature shifts, or maintenance, the FRF measurement process can be integrated into a periodic calibration or quality assurance (QA) protocol. For example, the FRF could be re-measured on a scheduled basis (e.g., monthly or annually) or performed as needed after any significant hardware service or replacement, ensuring the predictive model remains highly accurate throughout the operational lifetime of the MRI system.

[0159] Unless specifically stated otherwise, as apparent from the preceding discussions, it is appreciated that, throughout the specification, discussions utilizing terms such as "analyzing," "generating," "processing," "computing," "calculating," "determining," or the like, refer to the action and / or processes of a general purpose computer of any type, such as a client / server system, mobile computing devices, smart appliances, cloud computing units or similar electronic computing devices that manipulate and / or transform data within the computing system's registers and / or memories into other data within the computing system's memories, registers or other such information storage, transmission or display devices.

[0160] The inventive elements discussed herein above may be implemented on a suitable apparatus. This apparatus may be specially constructed for the desired purposes, or it may comprise a computing device or system typically having at least one processor and at least one memory, selectively activated or reconfigured by a computer program, code or prompt. The resultant apparatus when instructed by program, code or prompt may turn the general purpose computer into inventive elements as discussed herein. The program, code or prompt may defineP-25267-PCthe inventive device in operation with the computer platform for which it is desired. Such program, code or prompt may be stored in a computer readable storage medium, such as, but not limited to, any type of disk, including optical disks, magnetic-optical disks, read-only memories (ROMs), volatile and non-volatile memories, random access memories (RAMs), electrically programmable read-only memories (EPROMs), electrically erasable and programmable read only memories (EEPROMs), magnetic or optical cards, Flash memory, disk-on-key or any other type of media suitable for storing programs, code or prompts . The computer readable storage medium may also be implemented in cloud storage.

[0161] Some general purpose computers may comprise at least one communication element to enable communication with a data network and / or a mobile communications network.

[0162] The processes and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the desired method. The desired structure for a variety of these systems will appear from the description below. In addition, embodiments of the present invention 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 the teachings of the invention as described herein.

[0163] While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.

Claims

P-25267-PCCLAIMSWhat is claimed is:

1. An acoustic-vibration control system for controlling mechano-acoustic behavior associated with a set of gradient coils of a Magnetic Resonance Imaging (MRI) system during am imaging sequence, the system comprising:a Predictive Modeling Unit configured to generate an improved prediction of the mechano-acoustic behavior for the imaging sequence by combining a model-based prediction of at least one of (a) acoustic frequencies and (b) vibration frequencies with a frequency response function (FRF) of the MRI system;a Parameter Optimization Unit configured to analyze the improved prediction to determine the mechano-acoustic behavior as a function of at least one timing parameter of the imaging sequence, and to select a value for the at least one timing parameter that corresponds to a desired mechano-acoustic behavior; and a Sequence Control Unit configured to generate control signals to operate the MRI system using the imaging sequence adjusted with the selected value for the at least one timing parameter.

2. The system of claim 1, wherein the imaging sequence is one from among a sequence group consisting of a echo planar imaging (EPI), Three-Dimensional (3D) EPI, Echo Planar Spectroscopic Imaging (EPSI), Echo Planar Time-resolved Imaging (EPTI), Spin Echo (SE), Fast Spin Echo (FSE), Turbo Spin Echo (TSE), and Rapid Acquisition with Relaxation Enhancement (RARE), Gradient-echo based sequences, Gradient Recalled Echo (GRE), Fast Low Angle Shot (FLASH), Steady-State Free Precession (SSFP), Balanced SSFP (bSSFP), Magnetization PreparedP-25267-PCRapid Gradient Echo (MP-RAGE), Spiral imaging, Radial imaging, Gradient and Spin Echo (GRASE) and derivative thereof.

3. The system of claim 1, wherein the at least one timing parameter is selected from a group comprising a time between consecutive echoes (ATEcho), a time between consecutive slices (ATslice), and a time between consecutive partitions (ATpartition).

4. The system of claim 3, wherein the Parameter Optimization Unit is configured to select a value for the time between consecutive slices (ATslice).

5. The system of claim 3, wherein the imaging sequence is a multi-echo sequence, and wherein the Parameter Optimization Unit is configured to select a value for the time between consecutive echoes (ATEcho).

6. The system according to any of claims 1 to 6, wherein the imaging sequence further comprises an additional block, and wherein the Parameter Optimization Unit is further configured to use the improved prediction to determine a timing for the additional block that minimizes interference from mechanical vibrations caused by a preceding gradient train , thereby reducing ghosting artifacts.

7. The system according to any of claims 1 to 6, wherein the Parameter Optimization Unit is further configured to generate and provide for display a GUI component of a display interface, enabling an MRI operator to adjust the at least one timing parameter.

8. The system according to any of claims 1 to 8, further being in data communication with one or more sensors configured to acquire an acoustic signal or a vibration signal generated by the MRI system to provide a basis for the measurement of the FRF.

9. A Magnetic Resonance Imaging (MRI) system, comprising:P-25267-PCan acoustic-vibration control system according to any of claims 1 to 8, wherein the frequency response function ( FRF), is determined based on a signal indicative of mechanical vibrations or acoustic signals associated with a set of gradient coils of the MRI system acquired by at least one sensor, where the at least one sensor is an acoustic sensor or a vibration sensor.

10. The MRI system of claim 9, wherein the signal is acquired during at least one of (i) a manufacturing stage, (ii) a calibration stage upon installation, and (iii) a calibration stage upon maintenance operation.

11. The MRI system of claim 9, further comprising at least one sensor configured to acquire said signal, and wherein the acoustic-vibration control system is in data communication with the at least one sensor.

12. The MRI system of claim 9, further comprising a display interface configured to display a GUI component enabling an MRI operator to adjust the at least one timing parameter.

13. A computer-implemented acoustic-vibration control method for controlling mechanoacoustic behavior associated with a set of gradient coils of a Magnetic Resonance Imaging (MRI) system during an imaging sequence, the method comprising:generating an improved prediction of the mechano-acoustic behavior for the imaging sequence by combining a model-based prediction of at least one of (a) acoustic frequencies and (b) vibration frequencies with a frequency response function (FRF) of the MRI system;analyzing the improved prediction to determine the mechano-acoustic behavior as a function of at least one timing parameter of the imaging sequence;P-25267-PCselecting a value for at least one timing parameter that corresponds to a desired mechano-acoustic behavior; andoperating the MRI system to acquire an image using the imaging sequence adjusted with the selected value for the at least one timing parameter.

14. The method of claim 13, wherein the imaging sequence is one from among a sequence group consisting of a echo planar imaging (EPI), Three-Dimensional (3D) EPI, Echo Planar Spectroscopic Imaging (EPSI), Echo Planar Time-resolved Imaging (EPTI), Spin Echo (SE), Fast Spin Echo (FSE), Turbo Spin Echo (TSE), and Rapid Acquisition with Relaxation Enhancement (RARE), Gradient-echo based sequences, Gradient Recalled Echo (GRE), Fast Low Angle Shot (FLASH), Steady-State Free Precession (SSFP), Balanced SSFP (bSSFP), Magnetization Prepared Rapid Gradient Echo (MP-RAGE), Spiral imaging, Radial imaging, Gradient and Spin Echo (GRASE) and derivative thereof.

15. The method of claim 13 or 14, wherein the at least one timing parameter is selected from a group comprising a time between consecutive echoes (ATEcho), a time between consecutive slices (ATslice), and a time between consecutive partitions (ATpartition).

16. The method of claim 13, wherein the imaging sequence is a multi-slice sequence, and wherein selecting the value comprises selecting a value for the time between consecutive slices (ATslice).

17. The method of claim 13, wherein the imaging sequence is a multi-echo sequence, and wherein selecting the value comprises selecting a value for the time between consecutive echoes (ATEcho).

18. The method according to any of claims 13 to 17, wherein the imaging sequence further comprises an additional block, and wherein the method further comprises using the improvedP-25267-PCprediction to determine a timing for the additional block that minimizes interference from mechanical vibrations caused by a preceding gradient train , thereby reducing ghosting artifacts.

19. The method according to any of claims 13 to 18, further comprising generating and providing for display a graphical user interface (GUI) component of a display interface, enabling an MRI operator to view the determined mechano-acoustic behavior as a function of the at least one timing parameter and to adjust said at least one timing parameter.

20. The method according to any of claims 13 to 19, further comprising determining the measured FRF of the MRI system, said determining comprising: operating the MRI system with one or more predefined gradient sequences; acquiring, via at least one sensor, a signal indicative of mechanical vibrations or acoustic signals generated during said operation; generating a measured acoustic power spectrum based on the acquired signal; and determining the FRF based on a ratio between the measured acoustic power spectrum and a model-based power spectrum of the one or more predefined gradient sequences.

21. The method of claim 20, wherein determining the FRF is performed as a one-time calibration step for the MRI system.

22. The method of claim 20, wherein determining the FRF is performed as part of a periodic maintenance or quality assurance protocol for the MRI system.

23. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of any of claims 13 to 22.

24. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of any of claims 13 to 22.

Images