Patent Publication Number: US-2023132819-A1

Title: System and method for electromagnetic interference mitigation for portable mri systems

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application Ser. No. 63/015,092, filed on Apr. 24, 2020, and entitled “SYSTEM AND METHOD FOR ELECTROMAGNETIC INTERFERENCE MITIGATION FOR PORTABLE MRI SYSTEMS,” which is herein incorporated by reference in its entirety. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH 
     This invention was made with government support under EB018976 awarded by the National Institutes of Health. The government has certain rights in the invention. 
    
    
     BACKGROUND 
     Electromagnetic interference (“EMI”) contaminates magnetic resonance signals and decreases the diagnostic quality of the image. The nuisance signals from EMI are detected the same way as the magnetic resonance signals originating from spins (i.e., through Faraday detection with primary MR imaging coils). Conventional MRI scanners use radio frequency (“RF”) shielded enclosures to reduce EMI. 
     Specialized low-field, portable point-of-care (“POC”) MRI systems have the advantage of being low-cost, lightweight, and mobile and could extend the use of MRI to unconventional locations. However, the necessity of an RF shielded room to mitigate EMI renders these portable systems no longer portable, and therefore precludes their use in a POC setting. The performance of a low-field POC MRI system operating outside an RF shielded room is adversely affected by the presence of EMI signals, which produce image artifacts, sometimes complicated enough to be confused with image noise. To enable truly portable MRI devices, alternative approaches to EMI suppression are needed. 
     SUMMARY OF THE DISCLOSURE 
     The present disclosure addresses the aforementioned drawbacks by providing a method for generating a magnetic resonance image. The method includes acquiring magnetic resonance data from a subject using a magnetic resonance imaging (“MRI”) system, and acquiring electromagnetic interference (“EMI”) signal data using at least one EMI detector positioned external to an imaging volume of the MRI system. Corrected magnetic resonance data are then generated by computing an EMI correction model using the magnetic resonance data and the EMI signal data; applying the EMI correction model to the EMI signal data, generating output as correction data; and subtracting the correction data from the magnetic resonance data, generating output as the corrected magnetic resonance data. Artifacts associated with electromagnetic interference are reduced in the corrected magnetic resonance data as compared to the originally acquired magnetic resonance data. An image is then reconstructed from the corrected magnetic resonance data, where artifacts associated with electromagnetic interference are mitigated in the image. 
     It is another aspect of the present disclosure to provide an EMI mitigation system that includes a plurality of EMI detectors arranged in a spaced relationship sufficient to allow for positioning of an MRI system therebetween, wherein at least some of the EMI detectors in the plurality of EMI detectors comprise coils with orthogonal directionality. 
     It is yet another aspect of the present disclosure to provide a portable MRI system that includes a magnet assembly and at least one EMI detector arranged external to an imaging volume of the magnet assembly. The magnet assembly includes a housing without radio frequency (“RF”) shielding, and houses a magnet, at least one gradient coil, and at least one RF coil. 
     The foregoing and other aspects and advantages of the present disclosure will appear from the following description. In the description, reference is made to the accompanying drawings that form a part hereof, and in which there is shown by way of illustration a preferred embodiment. This embodiment does not necessarily represent the full scope of the invention, however, and reference is therefore made to the claims and herein for interpreting the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of an example portable magnetic resonance imaging (“MRI”) system that can implement the systems and methods described in the present disclosure. 
         FIGS.  2 A and  2 B  show an example pick-up coil that can be used as an external electromagnetic interference (“EMI”) detector. 
         FIG.  3    shows an example of multiple pick-up coils that can be sued as external EMI detectors. 
         FIGS.  4 A and  4 B  show an example of multiple pick-up coils being arranged externally around an MRI system. 
       FIG. S shows an example of an external EMI detector that includes a plurality of coils coupled to a stand. 
         FIG.  6 A  illustrates an example of forming a convolutional model for correcting EMI. 
         FIG.  6 B  illustrates an example of forming temporal clusters of data for use in a convolutional EMI correction model. 
         FIG.  7    illustrates an example of a calibration-free external dynamic interference estimation and removal (“EDITER”) method based on a dynamic model that includes a kernel that relates the external detectors&#39; signal to the artifact&#39;s appearance in the recording of primary imaging coil. 
         FIG.  8    is a flowchart setting forth the steps of an example method for generating a magnetic resonance image in which EMI-related artifacts have been removed or otherwise reduced. 
         FIG.  9    is a block diagram of an example EMI mitigation system that can be used to generate magnetic resonance images in which EMI-related artifacts have been removed or otherwise reduced. 
         FIG.  10    is a block diagram of example components that can implement the system of  FIG.  9   . 
     
    
    
     DETAILED DESCRIPTION 
     Described here are systems and methods for mitigating electromagnetic interference (“EMI”) for portable magnetic resonance imaging (“MRI”) systems. In general, EMI signals are mitigated using postprocessing interference suppression techniques that make use of an external reference coil and/or electrode to detect the EMI signals and remove them from the magnetic resonance data. 
     In some embodiments, an EMI measurement detector that is external to the imaging volume is used to measure interference as EMI signals. The EMI measurement detector may be one or more pickup coils. Additionally or alternatively, the EMI measurement detector may be one or more electrodes. An EMI correction model, which may be static or dynamic, can then be used to estimate the EMI picked up by the primary imaging coil and remove it in postprocessing from the primary magnetic resonance data. For instance, the EMI correction model can include complex weighting variables or transfer functions that relate the EMI signal data to the primary data. The EMI correction model is then applied to the EMI signal data, which are then removed from the primary magnetic resonance data. As a result, EMI-related artifacts are removed, suppressed, or otherwise reduced in images reconstructed from the corrected magnetic resonance data. 
     The systems and methods described in the present disclosure improves the image quality and allows for operation of a portable MRI system outside of an RF-shielded room. Advantageously, the systems and methods described in the present disclosure can be implemented using portable, low-field (e.g., less than 0.2 T, such as 80 mT) MRI systems. 
     Referring first to  FIG.  1   , a schematic block diagram of an example portable MRI system  100  is shown. In  FIG.  1   , a schematic representation of the position of various elements in the MRI system  100  with respect to one another is shown using blocks rather than the specific shape described above. The MRI system  100  includes a magnet assembly having a magnet  102 , one or more gradient coils  104 , and an RF coil  106 , which in some configurations may be collectively housed in a housing  108 . The magnet  102  may be a permanent magnet, an array of permanent magnets, an electromagnet, or a superconducting magnet. In some instances, the magnet  102  may be a single-sided magnet. As one non-limiting example, the magnet  102  can be a single-sided magnet designed from a plurality of permanent magnet blocks (e.g., NdFeB or other rare-earth element permanent magnet blocks) arranged on a former. 
     One or more gradient coils  104  can be configured to be positioned proximate the magnet  102 . In one non-limiting example, the magnet  102  can be designed to have a B 0  gradient for readout and slice selection, and a pair of gradient coils may be used to enable phase encoding on the other two directions that are orthogonal to the readout direction. In other instances, the magnet  102  may have a homogeneous field over an imaging field-of-view, in which case three gradient coils may be used to provide spatial encoding in three orthogonal directions. 
     The RF coil  106  is also configured to be positioned proximate the magnet  102 . The RF coil  106  may be used to provide excitation and RF signal detection. In other embodiments, separate RF coils can be provided for excitation and signal detection. 
     The MRI system  100  also includes an external EMI detector  110 . As described above, the external EMI detector  110  is located external to the imaging volume of the magnet  102 . As one example, the external EMI detector  110  can include one or more coils. For instance, the external EMI detector  110  can include a single channel coil, such as a spiral coil tuned to the Larmor frequency of the spins being imaged, with 30 turns and a Mini-circuits ZHL-500LN+ pre-amplifier, as shown in  FIGS.  2 A and  2 B . 
     As another example, the external EMI detector  110  can include multiple coils, such as the coils shown in  FIG.  3   . These coils may include, for instance, 10 turns each and are each tuned to the Larmor frequency of the spins being imaged. Although four coils are shown in  FIG.  3   , more generally multiple coil implementations can include two or more coils, or alternatively a single coil can be used as described above. As a non-limiting example, five coils may be used and arranged around the MRI system as shown in  FIGS.  4 A and  4 B . In this example, five identical EMI detectors  410  were built from 10-turn coils wound on 3D printed formers (OD=8 cm). These EMI detectors  410  were tuned and matched to the Larmor frequency (3.38 MHz) of the MRI scanner  400  with a bandwidth of 25 kHz to achieve a similar frequency response to the primary RF coil  406 . Two detectors  410  were used with 50 Ohm 37 dB gain pre-amplifiers (MITEQ P/N AU  1583 , NY, USA) and three detectors were used with 50 Ohm 24 dB gain preamplifiers (Mini-circuits ZHL-500LN+, NY, USA). Two of the EMI detectors  410   a ,  410   b  were placed behind the sides of the MRI scanner  400 . One EMI detector  410   c  was placed underneath the MRI scanner  400 , and the last two EMI detectors  410   d ,  410   e  were placed by the sides of the MRI scanner  410 . EMI detectors  410   a ,  410   c ,  410   d , and  410   e  were oriented along the B1 direction, and EMI detector  410   b  was oriented orthogonal to B1 to acquire EMI characteristics in an additional direction. 
     In still other examples, multiple coils can be arranged about the MRI scanner in other arrangements. For instance, the coils can be distributed 360 degrees around the MRI scanner. The multiple coils may also include coils with orthogonal directionality for increased detection capability for a variety of EMI sources 
       FIG.  5    shows an example EMI detector assembly  550  that includes multiple coils as EMI detectors  110 . The EMI detectors  110  are coupled to a stand  552 , which can be in some instances coupled to a cart or a wheeled base to facilitate moving the EMI detector assembly  550  between rooms and/or to different locations within a room. The EMI detector assembly  550  can include coils with orthogonal directionality for increased detection capability for a variety of EMI sources. In the example shown in  FIG.  5   , the coils can be distributed 360 degrees about a center of the stand  552 . As in other examples, the EMI detector assembly  550  can be used in combination with other EMI detector types, such as electrodes and/or surface coils placed on the patient&#39;s body, but located external to the MRI system&#39;s imaging volume. 
     As another example, the external EMI detector  110  can include one or more electrodes on the body, but outside of the magnet  102 . As still another example, the external EMI detector  110  can include one or more surface coils on the body, but outside of the magnet  102 . Advantageously, using electrodes and/or surface coils positioned on the subject&#39;s body can allow for more direct measurement of nuisance EMI signals that may be amplified or otherwise “piped” by the body, which may act like an antenna. Additionally or alternatively, a combination of pickup coil(s), electrode(s), or surface coil(s) can also be used. 
     A controller  112  is coupled to the magnet  102 , gradient coil(s)  104 , RF coil  106 , and external EMI detector  110 , and is configured to control the operation of the magnet  102 , gradient coil(s)  104 , and RF coil  106  to acquire magnetic resonance data from a subject. For example, controller  112  is configured to drive the gradient coil(s)  104  and RF coil  106  for gradient waveform generation and RF waveform generation, respectively, using known hardware and methods. In addition, the controller  112  is configured to record magnetic resonance signals received by the RF coil  106  as magnetic resonance data acquired from the subject. 
     The controller  112  is also configured to acquire EMI signal data with the EMI detector  110 , and to postprocess the magnetic resonance data using the EMI signal data in order to remove or otherwise reduce EMI-related artifacts from the magnetic resonance data. For instance, the controller  112  is configured to record EMI signals received by the external EMI detector  110  as EMI signal data and to postprocess the magnetic resonance data using the EMI signal data as described in the present disclosure. The controller  112  may also be configured to generate images based on the magnetic resonance data, whether before or after the magnetic resonance data have been postprocessed to remove or otherwise reduce EMI-related artifacts using the EMI signal data. 
     In one aspect of the present disclosure, calibration data can be acquired by the primary coil and external EMI detector(s) shot-to-shot during the dead-time in an imaging pulse sequence to enable a dynamic transfer function calculation. This dynamic model can more accurately correct for time-varying EMI sources. 
     As a non-limiting example, the imaging pulse sequence may be a RARE pulse sequence. In each acquisition window of the RARE sequence, the external EMI detector(s) samples “noise data” (i.e., the EMI signal data) simultaneously with the primary coil&#39;s acquisition of magnetic resonance data (e.g., echo signals). In addition, “noise” can be sampled from both the EMI detector(s) and the primary imaging coil at the end of each echo train. As an example, 2.56 ms of “noise” can be sampled from both the EMI detector(s) and the primary imaging coil at the end of each echo train. 
     The noise data acquired from the end of the echo train dynamically models the relationship (i.e., transfer function) between signals measured by the EMI detector(s) and the primary imaging coil. A new transfer function can thus be generated for each repetition time (“TR”) period in the pulse sequence to account for environmental changes during the sequence. 
     Assuming C pri,N  to be the Fourier transform (e.g., fast Fourier transform (“FFT”)) of the primary coil calibration data, where N is the number of acquisition points, and C ext,N  to be the FFT of the external EMI detector calibration data, the transfer function is, 
     
       
         
           
             
               
                 
                   
                     
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     for each TR, providing the gain and phase relationship for every frequency bin in the bandwidth. Defining S pri,N  and S ext,N  as the primary coil magnetic resonance data and the “noise data” acquired from the external EMI detector (i.e., the EMI signal data) during the echo train, the EMI mitigated imaging data can be computed as, 
         S   pri,N −( H   N   ×S   ext,N )  (2),
 
     In another aspect of the present disclosure, calibration data is not needed and that the model can instead be fit directly using data acquired during signal reception. When signals are simultaneously acquired by both the EMI detector(s) and the primary MR coil, EMI correction can be implemented without additional acquisition periods beyond what is needed for imaging. In these instances, the EMI correction method can be added to any sequence without modifications. 
     As a non-limiting example, detector acquisition lines with similar signal properties are grouped together to build a time-varying model that is more robust to noise compared to using single acquisition lines. This approach provides larger convolution windows to correlate the EMI signal data, which yields more accurate impulse response functions and better EMI removal. 
     A generalized model that can dynamically adjust to time-varying external noise sources can be used for EMI correction. The model allows for simultaneously acquired data from multiple EMI detector(s) to be regressed from the primary MR coil data. In an example, a linear relationship can be assumed between the k-space signal measured by the primary receive coil, s(k x ,k y ), and the unwanted EMI on the imaging coil, e′(k x ,k y ), and the desired EMI-free k-space data, s′(k x ,k y ): 
         s ( k   x   ,k   y )= e ′( k   x   ,k   y )+ s ′( k   x   ,k   y )  (3).
 
     To allow for accurate estimation and removal of the EMI, it can be assumed that data are available from N c  external detectors, e i (k x ,k y ) with i=1, . . . , N c . A linear convolution model along the readout (k x ) and phase encoding (k y ) directions relates the EMI observed by the primary imaging coil to that observed by the external EMI detector detector(s): 
     
       
         
           
             
               
                 
                   
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     In this example implementation, each impulse response function is assumed to have limited spectral support; that is, h i (k x ,k y )=0, |k x |&gt;Δk x  or |k y |&gt;Δk y . In the most restrictive case, when Δk x =1 and Δk y =1, then Eqn. (4) represents a scalar combination of the detector coils: 
     
       
         
           
             
               
                 
                   
                     
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     The linear convolutional form described in Eqn. (4) can be incorporated into Eqn. (3) and written in matrix form as: 
         {right arrow over (s)}={right arrow over (e)}′+{right arrow over (s)}′=E{right arrow over (h)}+{right arrow over (s)}′   (6).
 
     The impulse response vector {right arrow over (h)}∈   N     F     ×N     C     Δk     x     Δk     y    is a concatenation of the spectral components for the impulse response functions, and the EMI convolution matrix E∈   N     F     ×N     C     Δk     x     Δk     y    is a block-Toeplitz matrix mapping the EMI detector coil data to N F , observations from the primary receive coil. An illustration of fitting impulse response functions is shown in  FIG.  6 A . In the case of temporally static EMI, a single set of impulse response functions can be valid across the full extent of k-space and all available data could be used during the fit (i.e., N F =N k     x   ·N k     y   ). While k x  and k y  coordinates are used in the present description for familiarity and convenience to index data points within the readouts (k x ) and between readouts (k y ), it will be appreciated that the methods described in the present disclosure do not depend on the specific k-space sampling trajectory used. 
     As a non-limiting example, a least squares solution, {right arrow over (h)}=E † {right arrow over (s)}, can be used to fit the model and the EMI-mitigated data can be produced as {right arrow over (s)}′={right arrow over (s)}−E{right arrow over (h)}. It is important to note that this method assumes low correlation between the spectral content of the image and noise sources. If such a correlation does exist between the spectral content of the image and noise sources this would merely lead to smoothly varying sensitivity loss across the image due to the limited support assumed for each h i (k x ,k y ). 
     The methods described in the present disclosure can be generalized to scenarios where EMI sources are time-varying by assuming limited temporal windows (e.g., a single phase encode) and fitting different impulse response functions for each temporal instance. As the number of observations in each temporal window becomes small the estimation robustness may become degraded. To minimize this effect, the data can be dynamically binned into larger temporal windows that have consistent EMI patterns. This can be accomplished by first constructing a matrix, H, that contains the {right arrow over (h)} vectors generated for the different temporal windows, N w . The Δk y  used to construct the H matrix is restricted by N w . As a non-limiting example, N w =1 such that Δk y =1. A matrix, C, can then be constructed by autocorrelating the normalized matrix, H. Consistent EMI sources will lead to similar impulse response functions, which in turn will produce high levels of correlation. The binning locations can be determined with standard clustering approaches, such as thresholding the correlation matrix, C, to form a thresholded correlation matrix, C threshold  as shown in  FIG.  6 B . A final pass of the method can then be performed to estimate and remove EMI from each dynamically determined temporal window. Here, there is no restriction on Δk y . 
     A general workflow for this generalized algorithm is shown in  FIG.  7   . First, phase encode lines are distributed in N w  small temporal windows of size W=N PE /N w , as indicated at step  702 . Then, for each temporal window 1≤k≤N w , the following sub-steps are performed. A convolution matrix, E (k) , is formed, as indicated at step  704 . The convolution matrix can be formed, for example, as shown in  FIG.  6 A  and described above. The primary receive signal is then arranged into a vector, {right arrow over (s)} (k) , as indicated at step  706 . Then, the impulse response functions, are calculated, as indicated at step  708 . The impulse response functions can be calculated as {right arrow over (h)} (k) =E (k)   † {right arrow over (s)} (k) . As a non-limiting example, the impulse response vectors can be determined using a convolution window size of Δk x =7 and Δk y =1. Increasing the convolution window size to Δk x &gt;1 aids the correction, but can require longer processing time. Thus, there is a tradeoff between the amount of correction and time utilized. As a more general example, the convolution window size can be selected from the range 3&lt;Δk x &lt;8. 
     After the impulse response functions have been computed for each temporal window, the impulse response correlation matrix is computed, as indicated at step  710 . For instance, the impulse response correlation matrix can be computed as C i,j = {right arrow over (h)} (i) ,{right arrow over (h)} (j)   , 1≤i, j≤N w . The temporal windows are then clustered into N G  groups, as indicated at step  712 . For instance, the temporal windows can be clustered as described above and shown in  FIG.  6 B . 
     Then, for each temporal cluster, 1≤g≤N G , the following sub-steps are performed. A convolution matrix, E (g)  is formed, as indicated at step  714 . The primary receive signal is arranged into a vector, {right arrow over (s)} (g) , as indicated at step  716 . Impulse response functions are then computed, as indicated at step  718 . For instance, the impulse response functions can be computed as {right arrow over (h)} (g) =E (g)   † {right arrow over (s)} (g) . The EMI-mitigated signal is then determined, as indicated at step  720 . For instance, the EMI-mitigated signal can be determined as {right arrow over (s)}′ (g) ={right arrow over (s)} (g) −E (g) {right arrow over (h)} (g) . 
     In the embodiment described above with respect to  FIG.  6   , phase encode lines are grouped into correlated temporal windows and separate impulse responses are created for each group. Alternatively, instead of PE grouping, separate impulse responses can be determined for each PE line. 
     In other aspects of the present disclosure, complex cancellation weights for each external EMI detector can be calculated without calibration data using a masked version of the primary coil&#39;s image. In still another aspect of the present disclosure, parallel imaging models can be used to describe the relationship between the artifacts observed at the external EMI detector(s) and those seen by the primary imaging coil. In one such instance, a GRAPPA kernel approach can be used to form the EMI correction model. In this case, shifted versions of the EMI signal data form a larger “EMI GRAPPA matrix” and the shifted versions of the primary coil data form a larger “primary GRAPPA matrix”. The GRAPPA kernel can then be linearly calculated from the matrices and used for the EMI correction. If the frequency content of the EMI signal data and the magnetic resonance data are sufficiently different, masking of the primary magnetic resonance data&#39;s image may not be necessary. 
     In yet another aspect of the present disclosure, a dynamic EMI correction model can also be formed with the addition of low-rank temporal elements to the correction model. 
     As noted above, in some implementations, electrodes can be used as the external EMI detector(s) instead of external pick-up coils, as described above. The small, portable MRI magnet can be electromagnetically shielded, but EMI can be “piped” into the primary MRI coil through the external parts of the patient&#39;s body. Therefore, a more direct measure of the EMI data can be made via electrodes on the patient&#39;s body. This can, advantageously, improve the accuracy of the EMI suppression. 
     Additionally or alternatively, MRI surface coils could be used as EMI detectors on the external parts of the patient&#39;s body. This could also allow for a direct measurement of the EMI that is “piped” through the patient&#39;s body, while maintaining similar measurement characteristics to the primary MRI coil (compared to electrode measurements). 
     Referring now to  FIG.  8   , a flowchart is illustrated as setting forth the steps of an example method for removing or otherwise reducing EMI-related artifacts in magnetic resonance images. The method includes acquiring magnetic resonance data with an MRI system, as indicated at step  802 . The method also includes acquired EMI signal data with one or more EMI detectors that are located external to the imaging volume of the MRI system, as indicated at step  804 . As described above, the EMI signal data are acquired contemporaneously with the magnetic resonance data. As also described above, in some instances calibration data can be acquired using the MRI system, the EMI detector(s), or both. These calibration data can be acquired at the end of the TR of the pulse sequence, as described above. 
     The magnetic resonance data are then corrected to remove or otherwise reduce EMI-related artifacts using the EMI signal data, as indicated at step  806 . As described above, the correction process can implement a number of different techniques. 
     In one example, a transfer function is computed from calibration data contained in both the magnetic resonance data and the EMI signal data. The transfer function may be computed statically (e.g., after all of the data have been acquired), or dynamically (e.g., while data are still being acquired, such as shot-to-shot). The transfer function can then be applied to the EMI signal data and the result subtracted from the magnetic resonance data in order to generate the corrected magnetic resonance data. 
     In another example, complex-valued cancellation weights for each external EMI detector can be calculated without the magnetic resonance data or EMI signal data requiring calibration data. For instance, an image can be reconstructed from the magnetic resonance data and masked, from which the complex-valued cancellation weights can be computed. The complex-valued cancellation weights can then be applied to the EMI signal data and the result subtracted from the magnetic resonance data in order to generate the corrected magnetic resonance data. 
     In still another example, parallel imaging models can be used to describe the relationship between the artifacts observed at the external EMI detector(s) and those seen by the primary imaging coil. In one such instance, a GRAPPA kernel approach can be used to form the EMI correction model. In this case, shifted versions of the EMI signal data form a larger “EMI GRAPPA matrix” and the shifted versions of the primary coil data form a larger “primary GRAPPA matrix”. The GRAPPA kernel can then be linearly calculated from the matrices and used for the EMI correction. 
     In one or more of the preceding methods, a dynamic EMI correction model can also be formed with the addition of low-rank temporal elements to the correction model. 
     One or more images of the subject are then reconstructed from the corrected magnetic resonance data, as indicated at step  808 . By way of the corrections performed on the magnetic resonance data, the reconstructed images will have EMI-related artifacts removed or otherwise reduced. 
     Referring now to  FIG.  9   , an example of a system  900  for generating magnetic resonance images with reduced EMI artifacts in accordance with some embodiments of the systems and methods described in the present disclosure is shown. As shown in  FIG.  9   , a computing device  950  can receive one or more types of data (e.g., magnetic resonance data, calibration data) from image source  902 , which may be an MRI system, and also receive one or more types of data (e.g., EMI signal data, calibration data) from external EMI detector(s)  960 . In some embodiments, computing device  950  can execute at least a portion of a electromagnetic interference (“EMI”) mitigation system  904  to generate magnetic resonance images with reduced EMI-related artifacts from magnetic resonance data received from the image source  902 . 
     Additionally or alternatively, in some embodiments, the computing device  950  can communicate information about data received from the image source  902  and/or external EMI detector(s)  960  to a server  952  over a communication network  954 , which can execute at least a portion of the EMI mitigation system  904 . In such embodiments, the server  952  can return information to the computing device  950  (and/or any other suitable computing device) indicative of an output of the EMI mitigation system  904 . 
     In some embodiments, computing device  950  and/or server  952  can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device  950  and/or server  952  can also perform postprocessing corrections and reconstruct images from the data. 
     In some embodiments, image source  902  can be any suitable source of image data (e.g., magnetic resonance data, images reconstructed from magnetic resonance data), such as an MRI system, another computing device (e.g., a server storing image data), and so on. In some embodiments, image source  902  can be local to computing device  950 . For example, image source  902  can be incorporated with computing device  950  (e.g., computing device  950  can be configured as part of a device for capturing, scanning, and/or storing images). As another example, image source  902  can be connected to computing device  950  by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, image source  902  can be located locally and/or remotely from computing device  950 , and can communicate data to computing device  950  (and/or server  952 ) via a communication network (e.g., communication network  954 ). 
     As described above, the external EMI detector(s)  960  can include one or more pick-up coils, electrodes, surface coils, or combinations thereof. The external EMI detector(s)  960  can be connected to computing device  950  by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, the external EMI detector(s)  960  can be located locally and/or remotely from computing device  950 , and can communicate data to computing device  950  (and/or server  952 ) via a communication network (e.g., communication network  954 ). 
     In some embodiments, communication network  954  can be any suitable communication network or combination of communication networks. For example, communication network  954  can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, and so on. In some embodiments, communication network  108  can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in  FIG.  9    can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on. 
     Referring now to  FIG.  10   , an example of hardware  1000  that can be used to implement image source  902 , computing device  950 , and server  954  in accordance with some embodiments of the systems and methods described in the present disclosure is shown. As shown in  FIG.  10   , in some embodiments, computing device  950  can include a processor  1002 , a display  1004 , one or more inputs  1006 , one or more communication systems  1008 , and/or memory  1010 . In some embodiments, processor  1002  can be any suitable hardware processor or combination of processors, such as a central processing unit (“CPU”), a graphics processing unit (“GPU”), and so on. In some embodiments, display  1004  can include any suitable display devices, such as a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs  1006  can include any suitable input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on. 
     In some embodiments, communications systems  1008  can include any suitable hardware, firmware, and/or software for communicating information over communication network  954  and/or any other suitable communication networks. For example, communications systems  1008  can include one or more transceivers, one or more communication chips and/or chip sets, and so on. In a more particular example, communications systems  1008  can include hardware, firmware and/or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on. 
     In some embodiments, memory  1010  can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor  1002  to present content using display  1004 , to communicate with server  952  via communications system(s)  1008 , and so on. Memory  1010  can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory  1010  can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory  1010  can have encoded thereon, or otherwise stored therein, a computer program for controlling operation of computing device  950 . In such embodiments, processor  1002  can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server  952 , transmit information to server  952 , and so on. 
     In some embodiments, server  952  can include a processor  1012 , a display  1014 , one or more inputs  1016 , one or more communications systems  1018 , and/or memory  1020 . In some embodiments, processor  1012  can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, display  1014  can include any suitable display devices, such as a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs  1016  can include any suitable input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on. 
     In some embodiments, communications systems  1018  can include any suitable hardware, firmware, and/or software for communicating information over communication network  954  and/or any other suitable communication networks. For example, communications systems  1018  can include one or more transceivers, one or more communication chips and/or chip sets, and so on. In a more particular example, communications systems  1018  can include hardware, firmware and/or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on. 
     In some embodiments, memory  1020  can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor  1012  to present content using display  1014 , to communicate with one or more computing devices  950 , and so on. Memory  1020  can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory  1020  can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory  1020  can have encoded thereon a server program for controlling operation of server  952 . In such embodiments, processor  1012  can execute at least a portion of the server program to transmit information and/or content (e.g., data, images, a user interface) to one or more computing devices  950 , receive information and/or content from one or more computing devices  950 , receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on. 
     In some embodiments, image source  902  can include a processor  1022 , one or more image acquisition systems  1024 , one or more communications systems  1026 , and/or memory  1028 . In some embodiments, processor  1022  can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, the one or more image acquisition systems  1024  are generally configured to acquire data, images, or both, and can include an MRI system, which may be a portable MRI system as described above. Additionally or alternatively, in some embodiments, one or more image acquisition systems  1024  can include any suitable hardware, firmware, and/or software for coupling to and/or controlling operations of an MRI system. In some embodiments, one or more portions of the one or more image acquisition systems  1024  can be removable and/or replaceable. 
     Note that, although not shown, image source  902  can include any suitable inputs and/or outputs. For example, image source  902  can include input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, and so on. As another example, image source  902  can include any suitable display devices, such as a computer monitor, a touchscreen, a television, etc., one or more speakers, and so on. 
     In some embodiments, communications systems  1026  can include any suitable hardware, firmware, and/or software for communicating information to computing device  950  (and, in some embodiments, over communication network  954  and/or any other suitable communication networks). For example, communications systems  1026  can include one or more transceivers, one or more communication chips and/or chip sets, and so on. In a more particular example, communications systems  1026  can include hardware, firmware and/or software that can be used to establish a wired connection using any suitable port and/or communication standard (e.g., VGA, DVI video, USB, RS-232, etc.), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on. 
     In some embodiments, memory  1028  can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor  1022  to control the one or more image acquisition systems  1024 , and/or receive data from the one or more image acquisition systems  1024 ; to images from data; present content (e.g., images, a user interface) using a display; communicate with one or more computing devices  950 ; and so on. Memory  1028  can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory  1028  can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory  1028  can have encoded thereon, or otherwise stored therein, a program for controlling operation of image source  902 . In such embodiments, processor  1022  can execute at least a portion of the program to generate images, transmit information and/or content (e.g., data, images) to one or more computing devices  950 , receive information and/or content from one or more computing devices  950 , receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), and so on. 
     In some embodiments, any suitable computer readable media can be used for storing instructions for performing the functions and/or processes described herein. For example, in some embodiments, computer readable media can be transitory or non-transitory. For example, non-transitory computer readable media can include media such as magnetic media (e.g., hard disks, floppy disks), optical media (e.g., compact discs, digital video discs, Blu-ray discs), semiconductor media (e.g., random access memory (“RAM”), flash memory, electrically programmable read only memory (“EPROM”), electrically erasable programmable read only memory (“EEPROM”)), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and/or any suitable tangible media. As another example, transitory computer readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and/or any suitable intangible media. 
     The present disclosure has described one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.