CN112309362A

CN112309362A - Active acoustic noise reduction method and device of MR system and computer equipment

Info

Publication number: CN112309362A
Application number: CN202011188137.6A
Authority: CN
Inventors: 张双悦; 夏新源; 胡凌志; 曹拓宇; 张馨月
Original assignee: Shanghai United Imaging Healthcare Co Ltd
Current assignee: Shanghai United Imaging Healthcare Co Ltd
Priority date: 2020-10-30
Filing date: 2020-10-30
Publication date: 2021-02-02

Abstract

The application relates to an active acoustic noise reduction method, an apparatus, a computer device and a readable storage medium of an MR system, wherein the method comprises acquiring a reference signal, the reference signal comprising a gradient excitation signal; updating the filtering parameters of the filter in real time according to the reference signal and the error signal; and filtering the reference signal according to the filter with the updated filter parameters, and driving the noise reduction loudspeaker to output a cancellation signal through the filtered reference signal so as to cancel the noise signal generated by the MR system, thereby improving the noise reduction effect. According to the method, the gradient excitation signal is used as the reference signal of the active acoustic noise reduction system, so that a filtering reference signal which is highly correlated with noise and highly causal can be obtained. The filter parameters of the filter are updated in real time according to the filter reference signal, so that the filter can output a reverse cancellation signal which is closer to the noise to be eliminated, and the noise reduction effect is improved.

Description

Active acoustic noise reduction method and device of MR system and computer equipment

Technical Field

The present application relates to the field of medical technology, and in particular, to an active acoustic noise reduction method and apparatus for an MR system, and a computer device.

Background

During the scanning process of the MR and PET/MR devices, the vibration of the gradient coil of the MR system causes great acoustic noise, which can reach 100dB to 130dB, and may affect the hearing of the patient, and also adversely affect the information exchange during the scanning process, the scanning experience of the patient, and the like.

Noise reduction in MR systems can generally be achieved by several methods: 1. adding sound-absorbing and vibration-reducing materials in the system; 2. a special mute MR sequence is adopted for scanning, but the mute sequence usually limits the scanning time and the image quality and cannot effectively work under all scanning requirements; 3. passive noise reduction measures such as earplugs and earmuffs are adopted to prevent noise from entering the auditory meatus of a patient; 4. an active noise reduction system is adopted, and backward sound waves are emitted through an additionally arranged loudspeaker and offset with noise, so that the noise intensity near the head of a patient is reduced.

Because the MR system has high noise frequency, the noise frequency is generally concentrated in the range from 1kHz to more than 5kHz, the stability is poor, and the noise reduction effect of the existing noise reduction system is poor.

Disclosure of Invention

The application provides an active acoustic noise reduction method and device of an MR system and computer equipment, and aims to at least solve the problem of poor noise reduction effect in the related art.

In a first aspect, an embodiment of the present application provides an active acoustic noise reduction method for an MR system, where the method includes:

acquiring a reference signal, wherein the reference signal comprises a gradient excitation signal, and the gradient excitation signal is an excitation signal of an MR gradient system;

updating the filtering parameters of the filter in real time according to the reference signal and the error signal; the error signal is a cancelled noise signal;

and filtering the reference signal according to the filter with the updated filter parameters, and driving a noise reduction loudspeaker to output a cancellation signal through the filtered reference signal so as to cancel the noise signal generated by the MR system.

In some embodiments, after the acquiring the reference signal, the method further comprises:

and preprocessing the reference signal, wherein the preprocessing mode comprises but is not limited to filtering the reference signal through a linear filter with fixed parameters.

In some embodiments, the updating, in real time, the filter parameters of the filter according to the reference signal and the error signal includes:

and iteratively updating through a self-adaptive algorithm to obtain the filtering parameters of the filter.

In some embodiments, the iteratively updating the filter parameters of the filter by the adaptive algorithm includes:

inputting the gradient excitation signal and the error signal into a self-adaptive algorithm to obtain an updated quantity of filter parameters;

and updating the filtering parameters of the filter in real time according to the updating amount of the filter parameters.

In some of these embodiments, the adaptive algorithm includes, but is not limited to, any of LMS, NLMS, APA, RLS, and artificial intelligence algorithms.

In a second aspect, an embodiment of the present application provides an active noise reduction apparatus for an MR system, the apparatus including:

an acquisition module, configured to acquire a reference signal, where the reference signal includes a gradient excitation signal, and the gradient excitation signal is an excitation signal of an MR gradient system;

the updating module is used for updating the filtering parameters of the filter in real time according to the reference signal and the error signal; the error signal is a cancelled noise signal;

and the output module is used for carrying out filtering processing on the reference signal according to the filter after the filtering parameters are updated, and driving the noise reduction loudspeaker to output a cancellation signal through the filtered reference signal so as to cancel the noise signal generated by the MR system.

In some embodiments, the apparatus further comprises a preprocessing module for preprocessing the reference signal, where the preprocessing includes, but is not limited to, filtering the reference signal by a fixed-parameter linear filter.

In a third aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the active acoustic noise reduction method of the MR system according to the first aspect.

In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the active acoustic noise reduction method of the MR system according to the first aspect.

Compared with the related art, the active noise reduction method, the active noise reduction device, the computer device and the readable storage medium of the MR system provided by the embodiment of the application have the advantages that the reference signals are obtained, the reference signals comprise gradient excitation signals, and the gradient excitation signals are excitation signals of the MR gradient system; updating the filtering parameters of the filter in real time according to the reference signal and the error signal; the error signal is a cancelled noise signal; and filtering the reference signal according to the filter with updated filter parameters, and driving a noise reduction loudspeaker to output a cancellation signal through the filtered reference signal so as to cancel the noise signal generated by the MR system, so that the problem of poor noise reduction effect in the related technology is solved.

The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a schematic diagram of an active acoustic noise reduction system according to an embodiment;

FIG. 2 is a flowchart of an active acoustic noise reduction method of an MR system according to an embodiment;

FIG. 3 is a flow chart of an active acoustic noise reduction method of an MR system according to a preferred embodiment of the present application;

FIG. 4 is a block diagram of an active acoustic noise reduction device of an MR system in one embodiment;

FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.

It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.

Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.

Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.

Various techniques described herein may be applied in an active acoustic noise reduction system. The active acoustic noise reduction system can be applied to the fields of automobiles, medical equipment and the like. The present application is preferably applied to an MR system or a PET/MR system, and the following embodiments are described by taking an active acoustic noise reduction system as an example for actively reducing noise generated by an MR system. An active acoustic noise reduction system is shown in fig. 1, where u is a reference signal; d is the target signal, i.e. the noise signal to be cancelled; e is the error signal, i.e. the residual noise signal after cancellation. S is a Secondary channel (Secondary path) of the acoustic system, defined as the transfer function from the output of the adaptive algorithm, i.e. the input y of the noise reduction loudspeaker, to the error signal e; w is a filter; and A is the adaptive updating algorithm of the filter. An active noise reduction system in this application may also be understood as a feed-forward adaptive filter.

It should be noted that in MR active acoustic noise reduction applications, the secondary channel needs to be modeled off-line or on-line, where the off-line modeling may be performed by advanced system parameter measurement, and low-frequency (e.g. daily, weekly, monthly, etc.) update algorithm, and the on-line modeling is performed by updating the secondary channel model in real time during the operation of the noise reduction system. The modeling of the secondary channel is described in an off-line manner below.

The active acoustic noise reduction is to generate reverse sound waves which are equal to external noise as much as possible through an active acoustic noise reduction system, and neutralize the noise, so that the noise reduction effect is realized.

Fig. 2 is a flowchart of an active acoustic noise reduction method of an MR system according to an embodiment, where, as shown in fig. 2, the active noise reduction method of the MR system includes steps 210 to 230; wherein:

step 210, obtaining a reference signal, wherein the reference signal comprises a gradient excitation signal, and the gradient excitation signal is an excitation signal of an MR gradient system.

When a patient is scanned by MR, after the scanning parameters are determined, the time waveform of the gradient excitation signal is calculated according to the scanning parameters, and the gradient excitation signal is input to the gradient control system and simultaneously input to the input end of the active noise reduction system to be used as a reference signal of a filtering algorithm in the active acoustic noise reduction system.

Step 220, updating the filtering parameters of the filter in real time according to the reference signal and the error signal; the error signal is a cancelled noise signal.

During the scanning process, the adaptive updating algorithm A of the filter calculates the latest filter parameters of the filter W according to the input gradient excitation signal and the error signal, and the latest filter parameters are used as the current filter parameters of the filter.

The error signal is a noise signal output by the active acoustic noise reduction system after being cancelled, and it can be understood that the current filtering parameter of the filter is updated in real time according to an input reference signal and an error signal output after noise reduction at the last moment.

In some embodiments, the updating, in real time, the filter parameters of the filter according to the reference signal and the error signal includes: and iteratively updating through a self-adaptive algorithm to obtain the filtering parameters of the filter.

In some embodiments, a filter with preset filtering parameters may also be used.

Since the energy of the noise generated by the MR system varies greatly, in order to ensure that the filter parameters of the filter converge quickly and stably, the embodiment achieves the results of quick convergence and minimum convergence value by using an effective adaptive algorithm, such as the RLS algorithm. In addition, a highly causal reference signal (which can be understood as a time advance) is combined as a filtering reference signal, so that the accuracy of updating the filter parameters can be improved, and the noise reduction effect can be further improved.

In some of these embodiments, the adaptive algorithm includes, but is not limited to, any of Least Mean Square (LMS), Normalized Least Mean Square (NLMS), APA, RLS, or variants thereof, and artificial intelligence algorithms. The LMS algorithm and the NLMS algorithm are simple in structure, but are low in convergence speed and poor in convergence ratio, namely the difference between a result after convergence and an optimal result is large, and the effect is poor; the RLS algorithm has high convergence speed, but the algorithm is complex, and the performance and the operand of the APA are a choice between the NLMS and the RLS algorithm. Which of the adaptive algorithms is specifically used can be selected according to actual conditions.

Preferably, the filter parameters are updated by adopting a high-stability, high-following and high-convergence adaptive algorithm, so that the filter parameters can be updated more efficiently and accurately, and the noise reduction effect and efficiency are improved.

And step 230, performing filtering processing on the gradient excitation signal according to the filter with updated filtering parameters, and driving a noise reduction loudspeaker to output a cancellation signal through the filtered gradient excitation signal so as to cancel a noise signal generated by the MR system.

Specifically, according to the reference signal and the error signal, the current filtering parameter of the filter W is updated through the adaptive update algorithm of the filter, then the filter W outputs a driving signal after filtering the input gradient excitation signal, the driving signal drives the noise reduction speaker, and the noise reduction speaker outputs a cancellation signal to cancel the noise signal d generated by the MR system.

It is understood that

steps

220 and 230 are loop iteration processes, i.e. the filter parameters of the filter are updated in real time according to the reference signal and the error signal obtained after processing in step 330, so as to form a closed loop.

It should be noted that, in practical application, updating the parameter of the filter and filtering the reference signal are cyclic processes of step 220 and step 230, that is, the updating, filtering, updating, and filtering are repeated continuously, where step 220 and step 230 are only one ring of the cyclic process, and when the method is applied to an actual scene, the sequence of updating the parameter of the filter and filtering the reference signal is not particularly limited.

The active acoustic noise reduction method of the MR system is applied to the active acoustic noise reduction system and comprises the steps of obtaining a reference signal, wherein the reference signal comprises a gradient excitation signal, and the gradient excitation signal is an excitation signal of the MR gradient system; updating the filtering parameters of the filter in real time according to the reference signal and the error signal; the error signal is a cancelled noise signal; and filtering the reference signal according to the filter with the updated filter parameters, and driving a noise reduction loudspeaker to output a cancellation signal through the filtered reference signal so as to cancel the noise signal generated by the MR system. According to the method, the excitation signal of the gradient coil is used as the reference signal of the active acoustic noise reduction system, so that a filtering reference signal which is highly related to noise and highly causal (namely, the filtering reference signal can be earlier than the noise generation time) can be obtained, and the filtering parameter of the filter is updated according to the filtering reference signal, so that the noise can be better offset, and the active acoustic noise reduction effect is improved.

It is noted that in the above-described active acoustic noise reduction system all signals u, y, d may be multi-channel, i.e. there are a plurality of different signals. The reference signal, the collection position of the noise signal, i.e. the position of the noise reduction speaker, can be determined during system design according to the requirements of the noise reduction target.

In some of these embodiments, after acquiring the reference signal, the active acoustic noise reduction method of the MR system further includes: and preprocessing the reference signal, wherein the preprocessing mode comprises but is not limited to filtering the reference signal through a linear filter with fixed parameters.

After the gradient excitation signal is obtained, a fixed pre-filtering process is performed on the gradient excitation signal through a linear filter with fixed parameters, and then the gradient excitation signal subjected to the fixed filtering process is used as a reference input of the active acoustic noise reduction system.

The parameters of the linear filter with fixed parameters may be obtained by other methods such as system modeling or system self-calibration of low-frequency secondary response, and the specific obtaining method is not specifically limited in this embodiment and may be set according to actual situations. It should be noted that the reference signal may also be preprocessed by using any form of mathematical relationship or a fully trained artificial neural network, and the specific preprocessing manner is not limited in this embodiment.

In some of these embodiments, the update frequency of the filtering algorithm is equal to the sampling frequency of the error signal or a multiple of the sampling frequency.

The embodiments of the present application are described and illustrated below by means of preferred embodiments.

FIG. 3 is a flowchart of a preferred embodiment of an active acoustic noise reduction method of an MR system according to the preferred embodiment of the present application, as shown in FIG. 3, the active acoustic noise reduction method of an MR system includes steps 310 through 330; wherein:

step 310, acquiring a reference signal, wherein the reference signal comprises a gradient excitation signal and an acoustic reference signal, and the gradient excitation signal is an excitation signal of an MR gradient system;

and picking up an acoustic reference signal in real time by arranging a reference microphone inside the space while adopting the gradient excitation signal as the reference signal, and taking the gradient excitation signal and the acoustic reference signal as the reference signal together.

Step 320, updating the filtering parameters of the filter in real time according to the gradient excitation signal, the acoustic reference signal and the error signal; the error signal is a cancelled noise signal.

And 330, filtering the reference signal according to the filter with the updated filter parameters, and driving a noise reduction loudspeaker to output a cancellation signal through the filtered reference signal so as to cancel the noise signal generated by the MR system.

Step 320 is similar to step 220, and step 330 is similar to step 230, and will not be described again.

In the embodiment, the gradient excitation signal and the acoustic reference signal are used as the reference signal together, so that the filter parameters of the filter can be set more accurately, and therefore, the reverse cancellation signal closer to the noise to be eliminated is output, and the active noise reduction effect can be further improved.

It should be noted that, the active acoustic noise reduction system may additionally introduce one or more acoustic reference signals, and the specific number of paths is not limited in this embodiment.

It should be understood that although the steps in the flowcharts of fig. 2 and 3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2 and 3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the sub-steps or stages of other steps.

The present embodiment further provides an active acoustic noise reduction device of an MR system, which is used to implement the foregoing embodiments and preferred embodiments, and the description of the active acoustic noise reduction device is omitted for brevity. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.

In one embodiment, as shown in fig. 4, there is provided an active acoustic noise reduction apparatus of an MR system, including: an obtaining module 410, an updating module 420, and an outputting module 430, wherein:

an obtaining module 410, configured to obtain a reference signal, where the reference signal includes a gradient excitation signal, and the gradient excitation signal is an excitation signal of an MR gradient system;

an updating module 420, configured to update a filtering parameter of the filter in real time according to the reference signal and the error signal; the error signal is a cancelled noise signal;

and an output module 430, configured to perform filtering processing on the reference signal according to the filter with updated filtering parameters, and drive the noise reduction speaker to output a cancellation signal through the filtered reference signal, so as to cancel the noise signal generated by the MR system.

The active acoustic noise reduction device of the MR system provided by the embodiment includes an obtaining module 410, an updating module 420 and a driving module 430; acquiring, by an acquisition module 410, a reference signal, which includes a gradient excitation signal, the gradient excitation signal being an excitation signal of an MR gradient system; the updating module 420 updates the filtering parameters of the filter in real time according to the reference signal and the error signal; the error signal is a cancelled noise signal; the driving module 430 performs filtering processing on the reference signal according to the filter with updated filtering parameters, and drives the noise reduction speaker to output a cancellation signal through the filtered reference signal, so as to cancel the noise signal generated by the MR system. According to the device, the gradient excitation signal is used as the reference signal of the active acoustic noise reduction system, so that a filtering reference signal which is highly related to noise and highly causal (namely, the filtering reference signal can be earlier than the noise in time) can be obtained, and the filtering parameter of the filter is updated according to the filtering reference signal, so that the noise can be better offset, and the active acoustic noise reduction effect is improved.

In some embodiments, the active acoustic noise reduction apparatus of the MR system further includes a pre-processing module (not shown) for pre-processing the reference signal, including but not limited to filtering the reference signal through a fixed-parameter linear filter.

In some embodiments, the update module 420 is further configured to: and iteratively updating through a self-adaptive algorithm to obtain the filtering parameters of the filter.

In some embodiments, the update module 420 is further configured to: inputting the gradient excitation signal and the error signal into a self-adaptive algorithm to obtain an updated quantity of filter parameters; and updating the filtering parameters of the filter in real time according to the updating amount of the filter parameters.

In some of these embodiments, the reference signal further comprises an acoustic reference signal.

For specific definition of the active acoustic noise reduction device of the MR system, reference may be made to the above definition of the active acoustic noise reduction method of the MR system, which is not described herein again. The various modules in the active acoustic noise reduction apparatus of the MR system described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In addition, the active acoustic noise reduction method of the MR system of the embodiment of the present application described in conjunction with fig. 2 can be implemented by a computer device. Fig. 5 is a hardware structure diagram of a computer device according to an embodiment of the present application.

The computer device may comprise a processor 51 and a memory 52 in which computer program instructions are stored.

Specifically, the processor 51 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 52 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 52 may include a Hard Disk Drive (Hard Disk Drive, abbreviated to HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, magnetic tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 52 may include removable or non-removable (or fixed) media, where appropriate. The memory 52 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 52 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, Memory 52 includes Read-Only Memory (ROM) and Random Access Memory (RAM). The ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), Electrically rewritable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these, where appropriate. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended data output Dynamic Random-Access Memory (EDODRAM), a Synchronous Dynamic Random-Access Memory (SDRAM), and the like.

The memory 52 may be used to store or cache various data files for processing and/or communication use, as well as possibly computer program instructions for execution by the processor 52.

The processor 51 realizes the active acoustic noise reduction method of the MR system in any of the above embodiments by reading and executing computer program instructions stored in the memory 52.

In some of these embodiments, the computer device may also include a communication interface 53 and a bus 50. As shown in fig. 5, the processor 51, the memory 52, and the communication interface 53 are connected via the bus 50 to complete mutual communication.

The communication interface 53 is used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application. The communication port 53 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.

Bus 50 comprises hardware, software, or both coupling the components of the computer device to each other. Bus 50 includes, but is not limited to, at least one of the following: data Bus (Data Bus), Address Bus (Address Bus), Control Bus (Control Bus), Expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example, and not limitation, Bus 50 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (Front Side Bus), an FSB (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a Video Electronics Bus (audio Association) Bus, abbreviated VLB) bus or other suitable bus or a combination of two or more of these. Bus 50 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

The computer device may execute the active acoustic noise reduction method of the MR system in the embodiment of the present application based on the acquired program instructions, so as to implement the active acoustic noise reduction method of the MR system described in conjunction with fig. 2.

In addition, in combination with the active acoustic noise reduction method of the MR system in the above embodiments, the present application embodiment may be implemented by providing a computer readable storage medium. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement the method of active acoustic noise reduction for an MR system of any of the above embodiments.

The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the claims. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. An active acoustic noise reduction method of an MR system, applied to the active acoustic noise reduction system, is characterized in that the method comprises the following steps:

and filtering the reference signal according to the filter with updated filter parameters, and driving a noise reduction loudspeaker to output a cancellation signal through the filtered reference signal so as to cancel the noise signal generated by the MR system.

2. The method of claim 1, wherein after the acquiring the reference signal, the method further comprises:

3. The method of claim 1, wherein updating the filter parameters of the filter in real time according to the reference signal and the error signal comprises:

4. The method of claim 3, wherein iteratively updating the filter parameters of the filter by an adaptive algorithm comprises:

5. The method of claim 1, wherein the reference signal further comprises an acoustic reference signal.

6. The method of claim 3, wherein the adaptive algorithm includes but is not limited to any one of LMS, NLMS, APA, RLS, and artificial intelligence algorithm.

7. An active acoustic noise reduction apparatus of an MR system, the apparatus comprising:

8. The active acoustic noise reduction apparatus of an MR system of claim 7, further comprising a pre-processing module for pre-processing the reference signal, including but not limited to filtering the reference signal through a fixed parameter linear filter.

9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.