Magnetic resonance imaging system and method for suppressing electromagnetic interference in magnetic resonance data obtained by magnetic resonance imaging system

Abstract

The invention relates to a magnetic resonance imaging system and a method for suppressing electromagnetic interference in magnetic resonance data obtained by the magnetic resonance imaging system. Systems and methods for suppressing electromagnetic interference (EMI) in magnetic resonance (MR) data are also provided. These systems and methods include identifying a first subset of MR data that is affected by EMI and suppressing EMI in the first subset to obtain a second subset of MR data. Suppression of EMI in the first subset is performed by applying filtering to the first subset of MR data to suppress contributions of MR spin echo signals in the first subset of MR data, thereby obtaining signal suppressed MR data; suppressing EMI in the signal suppressed MR data to obtain EMI suppressed MR data; and applying the filtered inverse to the EMI suppressed MR data to obtain a second subset of MR data. These systems and methods include generating MR images using the second subset of MR data and outputting the generated MR images.

Description

[0001] (This application is a divisional application of the application with the application date of June 12, 2023, application number 202380059016.7, and invention name "Electromagnetic interference suppression technology for magnetic resonance imaging".) Technical Field

[0002] The present invention relates to electromagnetic interference suppression technology for magnetic resonance imaging. Background Art

[0003] Magnetic resonance imaging (MRI) provides an important imaging modality for many applications and is widely used in clinical and research settings to generate images of the interior of the human body. Generally, MRI is based on the detection of magnetic resonance (MR) signals, which are electromagnetic waves emitted by atoms in response to a change in state caused by an applied electromagnetic field. For example, nuclear magnetic resonance (NMR) technology involves detecting MR signals emitted from the nuclei of excited atoms when the nuclear spins of atoms (e.g., atoms in human tissue) in the object being imaged are realigned or relaxed. The detected MR signals can be processed to generate images that enable the investigation of internal structures and / or biological processes within the body for diagnostic, therapeutic, and / or research purposes in the context of medical applications. Summary of the Invention

[0004] Some embodiments provide a method for suppressing electromagnetic interference (EMI) in magnetic resonance data (MR data) obtained by a magnetic resonance imaging system, i.e., an MRI system. The method includes: using at least one computer hardware processor to: identify a first subset of the MR data that is affected by EMI; suppress the EMI in the first subset of the MR data to obtain a second subset of the MR data by: applying a filter to the first subset of the MR data to suppress the contribution of MR spin echo signals in the first subset of the MR data, thereby obtaining signal-suppressed MR data; suppressing the EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; and applying the inverse of the filter to the EMI-suppressed MR data to obtain the second subset of the MR data; using the second subset of the MR data to generate an MR image; and outputting the generated MR image.

[0005] In some embodiments, suppressing the EMI in the signal-suppressed MR data includes: using component decomposition to obtain the EMI-suppressed MR data.

[0006] In some embodiments, the first subset of the MR data includes a plurality of data portions for respective ones of a plurality of frequency or time intervals, and wherein suppressing EMI in the first subset of the MR data includes: for each particular data portion of the plurality of data portions, applying the filtering to the particular data portion to obtain a corresponding signal-suppressed MR data portion; suppressing EMI in the corresponding signal-suppressed MR data portion to obtain a corresponding EMI-suppressed MR data portion; and applying an inverse of the filtering to the EMI-suppressed MR data portion.

[0007] In some embodiments, suppressing EMI in the corresponding signal-suppressed MR data portion includes: determining a component decomposition of the corresponding signal-suppressed MR data portion; modifying the component decomposition by setting at least one component value of the component decomposition to a predetermined value or by multiplying at least one component value of the component decomposition by a predetermined weight; and using the modified component decomposition to obtain the corresponding EMI-suppressed MR data portion.

[0008] In some embodiments, the MRI system includes a plurality of radiofrequency coils, i.e., a plurality of RF coils, and each data portion of the plurality of data portions includes measurements made by each of the plurality of RF coils for a corresponding one of the plurality of frequency or time intervals.

[0009] In some embodiments, the data in the corresponding signal-suppressed MR data portion is organized as an N c ×M matrix, where N c is an integer representing the number of RF coils and M is one or more further dimensions representing the number of measurements made by each of the RF coils, and determining the component decomposition of the corresponding signal-suppressed MR data portion includes: determining a singular value decomposition of the N c ×M matrix.

[0010] In some embodiments, the data in the corresponding signal-suppressed MR data portion is organized as an N c ×M matrix, where N c is an integer representing the number of RF coils and M represents a set of a plurality of dimensions of the number of measurements made by each of the RF coils, and determining the component decomposition of the corresponding signal-suppressed MR data portion includes: determining a higher-order singular value decomposition of the N c ×M matrix.

[0011] In some embodiments, the decomposition is performed using one of a plurality of component decomposition methods, the component decomposition methods including but not limited to principal component analysis (PCA), independent component analysis (ICA), or sparse principal component analysis (SPCA).

[0012] In some embodiments, the method further includes: generating the MR data by operating the MRI system according to a spin echo or gradient echo pulse sequence before identifying the first subset of the MR data.

[0013] In some embodiments, the spin echo pulse sequence is selected from the group consisting of a T1 pulse sequence, a T2 pulse sequence, a fluid-attenuated inversion recovery (FLAIR) pulse sequence, and a diffusion-weighted imaging (DWI) pulse sequence.

[0014] In some embodiments, the method further includes: suppressing EMI in the MR data detected by an auxiliary coil of the MRI system after generating the MR data and before identifying the first subset.

[0015] In some embodiments, the EMI is narrowband EMI, and wherein the MR data includes sensor domain data in a plurality of frequency intervals, and the EMI is present in a certain frequency or time interval among the plurality of frequencies or time intervals.

[0016] In some embodiments, the EMI is present in no more than a threshold number of adjacent frequency or time intervals among the plurality of frequency or time intervals.

[0017] In some embodiments, the EMI is present in the nth frequency or time interval among the plurality of frequency or time intervals, and applying the filtering to the first subset of the MR data includes: calculating a weighted linear combination of data in a plurality of frequency or time intervals including the nth frequency or time interval using weights determined by coefficients of the filtering.

[0018] In some embodiments, the MR data includes data in a plurality of frequency or time intervals, the EMI is present in a plurality of adjacent frequency or time intervals, and applying the filtering to the first subset of the MR data includes: applying a convolutional filter having a length at least equal to the number of adjacent frequency or time intervals among the plurality of adjacent frequency or time intervals.

[0019] In some embodiments, identifying the first subset of the MR data includes: identifying data in a single frequency or time interval or a set of adjacent frequency or time intervals of the MR data affected by EMI.

[0020] In some embodiments, identifying the first subset of the MR data is performed by analyzing a portion of the MR data acquired during at least one predetermined echo signal of the pulse sequence used to acquire the MR data.

[0021] In some embodiments, identifying the first subset of the MR data includes determining whether the data in the single frequency or time interval or in the set of adjacent frequency or time intervals has an amplitude greater than a threshold.

[0022] In some embodiments, generating the MR image includes modifying the MR data to obtain modified MR data by replacing the first subset of the MR data with the second subset of the MR data; and generating the MR image using the modified MR data.

[0023] In some embodiments, the MRI system includes an RF coil configured to transmit radiofrequency pulses, i.e., RF pulses, and identifying the first subset of the MR data includes transforming the MR data into the reference frame of the transmitted RF pulses.

[0024] Some embodiments provide a magnetic resonance imaging system, i.e., an MRI system, including: a magnetic system having a plurality of magnetic components to generate a magnetic field for performing MRI by acquiring MR data; and at least one processor configured to: identify a first subset of the MR data affected by electromagnetic interference, i.e., EMI; suppress the EMI in the first subset of the MR data to obtain a second subset of the MR data by: applying a filter to the first subset of the MR data to suppress the contribution of the MR spin echo signal in the first subset of the MR data, thereby obtaining signal-suppressed MR data; suppressing the EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; and applying the inverse of the filter to the EMI-suppressed MR data to obtain the second subset of the MR data; generating an MR image using the second subset of the MR data; and outputting the generated MR image.

[0025] Some embodiments provide at least one tangible computer-readable storage medium storing instructions executable by a processor, which, when executed by the at least one processor, cause the at least one processor to perform a method for suppressing electromagnetic interference (EMI) in magnetic resonance (MR) data obtained by a magnetic resonance imaging system, i.e., an MRI system, the method comprising: identifying a first subset of the MR data affected by EMI; suppressing EMI in the first subset of the MR data to obtain a second subset of the MR data by: applying a filter to the first subset of the MR data to suppress contributions of MR spin echo signals in the first subset of the MR data, thereby obtaining signal-suppressed MR data; suppressing EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; and applying an inverse of the filter to the EMI-suppressed MR data to obtain the second subset of the MR data; using the second subset of the MR data to generate an MR image; and outputting the generated MR image. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Aspects and embodiments will be illustrated with reference to the following figures. It should be understood that the figures are not drawn to scale. In the drawings, components that are identical or almost identical in each figure are represented by like numerals. For clarity, not every component may be labeled in every figure.

[0027] Figure 1 Shows a magnetic resonance (MR) image of a patient's brain including narrowband electromagnetic interference (EMI) according to some embodiments.

[0028] Figure 2 Illustrates a processing pipeline for generating an MR image according to some embodiments.

[0029] Figure 3 Illustrates a processing pipeline for performing EMI suppression according to some embodiments.

[0030] Figure 4 Illustrates pseudocode for performing EMI suppression according to some embodiments.

[0031] Figure 5 Illustrates MR data obtained using multiple radio frequency (RF) coils and including narrowband EMI according to some embodiments.

[0032] Figure 6 Illustrates a schematic diagram of a pulse sequence including a portion for acquiring noise during a pulse repetition period according to some embodiments.

[0033] Figure 7Schematic illustration of a noise acquisition block configured to capture EMI caused by internal EMI sources and external EMI sources, according to some embodiments.

[0034] Figure 8 Alternative schematic illustration of a noise acquisition block configured to capture EMI (including EMI caused by operating gradient coils and RF coils) caused by internal EMI sources and external EMI sources, according to some embodiments.

[0035] Figure 9A and Figure 9B Graphical plot of an MR echo signal in the time domain and the frequency domain, according to some embodiments.

[0036] Figure 10A and Figure 10B Graphical plot of a narrowband EMI signal in the time domain and the frequency domain, according to some embodiments.

[0037] Figure 11A MR images of a subject's brain, according to some embodiments (these MR images include narrowband EMI).

[0038] Figure 11B Shows, according to some embodiments, after suppression of narrowband EMI Figure 11A of the MR images.

[0039] Figure 12A MR images of a phantom, according to some embodiments (these MR images include narrowband EMI).

[0040] Figure 12B Shows, according to some embodiments, after suppression of narrowband EMI Figure 12A of the MR images.

[0041] Figure 13A MR images of a phantom, according to some embodiments (these MR images include narrowband EMI that affects an adjacent set of intervals in MR data).

[0042] Figure 13B Shows, according to some embodiments, after suppression of narrowband EMI Figure 13A of the MR images.

[0043] Figure 14A MR images of a subject's brain, according to some embodiments (these MR images include narrowband EMI caused by the magnetostrictive effect).

[0044] Figure 14B Shows, according to some embodiments, after suppression of narrowband EMI caused by the magnetostrictive effect Figure 14A of the MR images.

[0045] Figure 15 Flowchart illustrating a process for suppressing EMI in an MR image according to some embodiments.

[0046] Figure 16 Illustration of components of an MRI system according to some embodiments.

[0047] Figure 17 Illustration of components of an MRI system for noise suppression according to some embodiments.

[0048] Figure 18 Illustration of an MRI system according to some embodiments.

[0049] Figure 19 Illustration of a portable MRI system according to some embodiments.

[0050] Figure 20 Illustration of an alternative MRI system according to some embodiments.

[0051] Figure 21 Diagram of an exemplary computer system according to some embodiments. Detailed Description

[0052] Conventional clinical magnetic resonance imaging (MRI) systems are typically located in rooms that are specifically shielded to prevent electromagnetic interference (EMI) from interfering with their proper operation. In addition to protecting personnel and equipment from the magnetic fields generated by the MRI system, the shielded room can also prevent artifacts such as RF interference generated by various external electronic devices (e.g., other medical devices) from affecting the operation of the MRI system and the quality of the resulting images. For operation outside of a specifically shielded room and, more particularly, to allow for general portability of an MRI, an MRI system is capable of operating in a relatively uncontrolled electromagnetic environment (e.g., in an unshielded or partially shielded room) and generally capable of accounting for the presence of sources of interference and / or noise that may exist in such an environment and that can introduce artifacts into the acquired MR images and / or compensating for the presence of sources of interference and / or noise.

[0053] An MRI system may be affected by various sources of interference when not shielded from EMI, and each of these sources of interference may require a different mitigation strategy. Some sources of interference may be external to the MRI system. These sources include patient monitoring devices (e.g., electrocardiogram (ECG) devices), patient medical devices (e.g., intracranial electroencephalogram (EEG) devices), and other active electronic devices placed near the MRI system (e.g., computers, tablets, phones, mobile and / or smart phones, wearable electronic devices, etc.). These external sources of interference may generate incoherent noise that may not be synchronized with the radio frequency (RF) transmissions of the MRI system.

[0054] Some sources of interference can be within the MRI system and can generate coherent noise that may be synchronous with the RF transmissions of the MRI system. As a first example, in the case where the MRI system includes a permanent magnet (e.g., as a primary B0 field magnet, as a shim magnet, etc.), the permanent magnet may be affected by magnetostrictive effects during operation of the MRI system. Magnetostriction is a change in the shape of a magnetic material during magnetization processing, and the applied magnetic fields (e.g., gradient magnetic fields, transmitted RF signals) can cause the permanent magnets of the MRI system to change shape or dimensions and thereby affect the magnitude and / or uniformity of the B0 magnetic field, which can be detected as artifacts in the acquired MR images. Magnetostrictive noise can create low-amplitude noise lines and can appear as "zipper" line noise artifacts perpendicular to the imaging readout direction. As another example of an internal source of EMI, the RF transmission electronics of the MRI system can also generate coherent line noise synchronous with the RF transmissions of the MRI system.

[0055] In addition to being affected by EMI generated by various sources of interference, an unshielded MRI system can also be affected by different types of EMI, including broadband EMI and narrowband EMI. Broadband (sometimes referred to as "wideband") EMI can exist over a wide range of (e.g., all or substantially all) frequencies and can span the entire bandwidth of the acquired signal (e.g., 64 kHz). The acquired MR data can include multiple spatial frequencies organized in multiple frequency bins, and broadband EMI can exist in all or substantially all of these multiple frequency bins. Thus, since broadband EMI affects most or all of the frequencies detected during MR imaging, MR images affected by broadband EMI generally include artifacts that affect the entire MR image.

[0056] In contrast, narrowband EMI can exist within a single frequency bin or within a threshold number of adjacent frequency bins such that the spectral content of the EMI can exist in a narrow frequency range. For example, narrowband EMI can exist within a single frequency bin, within two adjacent frequency bins, within five adjacent frequency bins, within ten adjacent frequency bins, or within 25 adjacent frequency bins. As an example, if each frequency bin corresponds to a bandwidth of approximately 670 Hz, narrowband EMI with a 1 kHz bandwidth can exist within two adjacent frequency bins. As described herein, narrowband EMI typically appears as "zipper" line noise artifacts perpendicular to the imaging readout direction. These zipper line noise artifacts can affect a single line or several adjacent lines of the MR image. An example of narrowband EMI artifacts can be seen in Figure 1 (which includes an MR image of the subject's brain). The arrow points to a horizontal band of zipper line noise that can be caused by narrowband EMI.

[0057] In a non-limiting example, EMI can be characterized as narrowband in a domain other than the spectral frequency. For example, if the signal-suppressed data portion is organized as an N c × N t × M matrix (where: N c is an integer representing the number of RF coils, N t is an integer representing the number of time points used for each data acquisition, and M represents a set of one or more dimensions of the number of measurements made by each RF coil), then a Fourier or other wavelet transform can be applied along the N t or M dimensions or both. In the transformed basis, the noise source can also be narrowband. A non-limiting example of a narrowband noise source in this transform domain would be a noise source that is broadband in the spectral frequency domain but constant in time across the number of acquisitions. Such a noise source would be narrowband in the frequency of occurrence domain over M measurements.

[0058] In some embodiments, the domain transform can be applied along the acquisition time domain dimension and / or along the dimension including the number of acquisitions. The domain transform can be a Fourier transform or a wavelet transform. Thus, if a noise source occupies a narrow range of the frequency spectrum, the noise source can be classified as narrowband. Or the noise source can occupy a broadband frequency spectrum but be classified as narrowband based on the frequency of occurrence. Or the noise source can be narrow in both the frequency of occurrence band and the spectral band.

[0059] In some MRI systems, conventional noise suppression techniques can be applied to the entire dataset and may not adequately suppress narrowband EMI artifacts, which potentially interferes with the clinical utility of MR images. New techniques are described herein that can reduce the impact of narrowband EMI artifacts on MR images. These techniques can allow an MRI system to operate in the presence of one or more sources of narrowband EMI. Additionally, although the techniques described herein can allow an MRI system to operate in the presence of narrowband EMI, these techniques can also be used to mitigate the impact of narrowband interference on the operation of an MRI system in a shielded or semi-shielded environment, as aspects of the techniques described herein are not limited in this regard.

[0060] The techniques described herein can enable suppression of EMI in MR images by targeting EMI-affected portions of MR data. These techniques can include: first identifying which portions of the MR data (e.g., “lines” or “frequency bins”) are likely to be affected by EMI, and then suppressing the EMI in these identified portions. In the identified portions, EMI can be suppressed by first applying a filter to the MR data. The filter can be designed to suppress the contribution of MR spin echo signals to the MR data such that the EMI is the primary contributing factor to the filtered data (i.e., the data generated as a result of applying the filter to the MR data). Then, EMI can be suppressed in the filtered data (e.g., by computing the singular value decomposition of the filtered data and setting at least one singular value (e.g., the first singular value) to zero) to obtain EMI-suppressed data, and the inverse of the filter can be applied to obtain a version of the identified portion of the MR data with the EMI contribution suppressed. This process can be repeated until no further MR data portions (e.g., no additional lines) can be identified as having EMI. Thereafter, these EMI-suppressed portions of the MR data can be used to generate and output an MR image. In non-limiting examples, the filter can be or can include a convolution filter. In other non-limiting example implementations, the filter can be or can include a tapering filter. In still other non-limiting examples, the filtering can involve applying singular value decomposition (SVD) or higher-order singular value decomposition (HOSVD). In still other non-limiting examples, the filter can be or can include a component decomposition such as independent component analysis (ICA) or principal component analysis (PCA). In still other non-limiting examples, the filter can be or can include a combination of two or more filters or other operations.

[0061] In some embodiments, the MR data can include multiple frequency bins, each frequency bin including a portion of the MR data (e.g., a portion of the MR data acquired at a particular frequency). In some embodiments, identifying EMI-affected portions of the MR data can include identifying the MR data in a single frequency bin or multiple frequency bins. The multiple frequency bins can include a set of adjacent frequency bins or multiple sets of adjacent frequency bins, where the sets are not adjacent to each other. Adjacent frequency bins can be frequency bins whose corresponding frequency ranges are no more than a threshold Hz (e.g., 0 Hz) apart. A set of adjacent frequency bins can include no more than a threshold number (e.g., 2, 5, 10, 25, 50, etc.) of adjacent frequency bins. In some embodiments, the threshold number of frequency bins can depend on the sampling frequency of the MRI device (e.g., when there is a potentially large sampling frequency, the threshold number can be set higher). In some embodiments, the length of the filter applied to the MR data can be set as a function of the number of adjacent frequency bins in which EMI can be suppressed.

[0062] In some embodiments, suppressing EMI in an identification portion of MR data that may be affected by EMI may include, for each identification portion of the MR data: applying a filter to a particular data portion to obtain a corresponding signal-suppressed MR data portion; suppressing EMI in the corresponding signal-suppressed MR data portion to obtain a corresponding EMI-suppressed MR data portion; and applying an inverse of the filter to the EMI-suppressed MR data portion.

[0063] In some embodiments, suppressing EMI in the signal-suppressed MR data may be implemented using a component decomposition process, which may include singular value decomposition (SVD), independent component analysis (ICA), or principal component analysis (PCA). In one or more non-limiting example implementations, the SVD of the filtered data may be determined and the SVD may be modified to suppress the contribution of EMI. In some embodiments, the SVD may be modified by setting at least one singular value of the SVD to zero (e.g., by setting the first singular value of the SVD to zero, by setting the first two, first three, or a threshold number of singular values of the SVD to zero). In other embodiments, the SVD may be modified by multiplying one or more singular values of the SVD by a number between 0 and 1 (e.g., by multiplying the first singular value of the SVD by a number between 0 and 1, by multiplying the first two, first three, or a threshold number of singular values of the SVD by a number between 0 and 1). Then, the modified SVD may be used to determine the EMI-suppressed data. However, in other embodiments, one or more other techniques may be used instead of the SVD method to suppress noise in the filtered data. In some embodiments, principal component analysis (PCA) may be used to suppress noise in the filtered data. In some embodiments, independent component analysis (ICA) may be used to suppress noise in the filtered data.

[0064] In some embodiments, an MRI system may include multiple (e.g., 2, 4, 8, 16, 32, etc.) RF coils, and each data portion among the multiple data portions includes measurements for a corresponding frequency interval among multiple frequency intervals performed by each of the multiple RF coils. In some embodiments, the data in the corresponding signal-suppressed data portion may be organized as an N c ×M matrix, where N c is an integer representing the number of RF coils and M is an integer representing the number of measurements performed by each RF coil, and where determining the SVD of the corresponding signal-suppressed MR data portion includes determining the singular value decomposition of the N c ×M matrix. The singular value decomposition may be calculated in any suitable manner (including by using any suitable software library for performing numerical linear algebra algorithms).

[0065] In some embodiments, before identifying a first subset of MR data, MR data can be generated by operating an MRI system in accordance with a spin echo pulse sequence. The spin echo MR signal can have a characteristic linear phase that produces an echo at the center of acquisition, and as a result, the spin echo MR signal can cause the phase to alternate between even and odd points. In contrast, EMI noise may not always match this signal pattern throughout the duration of image acquisition. For this reason, filtering can be used to suppress the contribution of the spin echo MR signal, such that EMI is the primary contributing factor to the signal from which the convolution filter is applied to the MR data. In a non-limiting example, the filtering can be convolution filtering.

[0066] The techniques described herein can be applied to any of a plurality of types of spin echo pulse sequences and / or gradient echo pulse sequences. For example, the techniques described herein can be applied to a T1 pulse sequence, a T2 pulse sequence, a fluid attenuated inversion recovery (FLAIR) pulse sequence, a diffusion weighted imaging (DWI) pulse sequence, and / or other spin echo pulse sequences and / or gradient echo pulse sequences currently in use or to be developed.

[0067] In some embodiments, after generating the MR data and before identifying the first subset, a portion of the EMI can be detected by an auxiliary coil of the MRI system and suppressed in the MR data.

[0068] In some embodiments, the EMI can be narrowband EMI. In some embodiments, the MR data can include sensor domain data having a plurality of frequency bins, and the EMI can be present in a certain frequency bin among the plurality of frequency bins. Alternatively, the EMI can be present in a set of adjacent frequency bins having a threshold number (e.g., 2, 5, 10, 25, 50, etc.) among the plurality of frequency bins. Alternatively, the EMI can be present in each set of adjacent frequency bins among a plurality of sets of adjacent frequency bins, where these sets are not adjacent to each other.

[0069] In some embodiments, the EMI can be present in the nth frequency bin among the plurality of frequency bins, and wherein applying the filtering to the first subset of the MR data includes: calculating a weighted linear combination of data in the plurality of frequency bins (including the nth frequency bin) using weights determined by the coefficients of the convolution filter.

[0070] In some embodiments, the MR data may include multiple frequency bands, EMI may be present in multiple adjacent frequency bands, and applying a filter to a first subset of the MR data may include applying a filter having a certain length. The length of the filter may be at least equal to the number of adjacent frequency bands in which EMI may be present. In some embodiments, if the number of frequency bands in a band is even, the size of the filter may be selected to be equal to the number of frequency bands in the band. If the number of frequency bands in a band is odd, the size of the filter may be selected to be equal to a number that is 1 greater than the number of frequency bands in the band.

[0071] In some embodiments, identifying a first subset of the MR data may include identifying data in a single frequency band or a set of adjacent frequency bands of the MR data that may be affected by EMI.

[0072] In some embodiments, identifying a first subset of the MR data may be performed by analyzing a portion of the MR data obtained by probing the edges of k-space. For example, identifying a first subset of the MR data may be performed by analyzing one or more echo signals obtained from the edges of k-space. In some embodiments, identifying a first subset of the MR data may be performed by analyzing a portion of the MR data obtained during two echo signals at the end of the repetition period of the pulse sequence used to acquire the MR data. In some embodiments, identifying a first subset of the MR data includes identifying any frequency band in which a portion of the data being analyzed has an amplitude greater than a threshold.

[0073] In some embodiments, generating an MR image may include: modifying the MR data by replacing the first subset of the MR data with a second subset of the MR data to obtain modified MR data; and using the modified MR data to generate an MR image.

[0074] The techniques described herein can be used to suppress (e.g., reduce and / or eliminate) artifacts from MR data obtained by any suitable type of MRI system. In some embodiments, low-field MRI systems may be more susceptible to EMI. In some systems, EMI may be present regardless of the field strength of the MRI system. The techniques described herein can be used to reduce and / or eliminate artifacts from MR data obtained by any suitable type of MRI system. The techniques described herein are not limited to use with low-field MRI systems or any particular type of MRI system and can be used with high-field and / or any other suitable type of MRI system.

[0075] "High field" generally refers to MRI systems currently used in clinical settings, and more particularly to MRI systems operating at a main magnetic field (i.e., B0 field) at or above 1.5 T, although clinical systems operating between 0.5 T and 1.5 T are also commonly characterized as "high field". Field strengths between 0.2 T and 0.5 T have been characterized as "mid field", and as the field strengths in the high field regime have continued to increase, field strengths in the range between 0.5 T and 1 T have also been characterized as mid field. In contrast, "low field" generally refers to MRI systems operating at a B0 field strength between 0.02 T and 0.2 T, although systems with a B0 field between 0.2 T and 0.3 T are sometimes also characterized as low field due to the increased field intensity at the upper end of the high field regime.

[0076] Techniques for processing medical data to generate MR images of a subject routinely involve applying different computational tools to perform different tasks that can be part of a data processing pipeline. For example, and as Figure 2 shown, an MRI system 202 can be used to acquire MR data by imaging a subject (e.g., a human patient, an animal patient, an inanimate object, etc.). As Figure 2 shown, the MRI system 202 can be a portable or non-portable MRI system. The MRI system 202 can be any type of MRI system, as aspects of the techniques described herein are generally not limited to the physical size or portability of the MRI system.

[0077] In some embodiments, MR data 204 can be acquired in multiple instances. For example, and as Figure 2 shown, multiple acquisitions of data can be made to enable averaging of the MR data 204 (e.g., to improve the signal-to-noise ratio and / or improve the quality of the resulting MR image). Additionally, in some embodiments, the MRI system 202 can include multiple sensors that can be configured to acquire portions of the medical data. The MRI system 202 can include multiple RF coils located at different positions relative to the subject, and the multiple RF coils can be used to acquire different portions ("data channels") of the MR data. In some embodiments, the multiple RF coils can include 2 or 4 or 6 or 16 or 32 or more or any other suitable number of RF coils.

[0078] In Figure 5 is shown an example of MR data acquired by an MRI system having multiple RF coils and affected by narrowband EMI. Figure 5The MR data shown includes data obtained from 8 RF coils and is shown in the "sensor domain", where the real component of the acquired complex data is shown in the top row and the imaginary component of the acquired complex data is shown in the bottom row. The data in the sensor domain may include the raw sensor measurements obtained by the MRI system. The sensor domain data may include measurements acquired line by line for a set of coordinates specified by the sampling pattern. The measurement lines may be referred to as "readout" lines. Each measurement may be a spatial frequency. Thus, the sensor domain data may include multiple readout lines. For example, if measurements are made for n readout lines and each readout line includes M samples, the sensor domain data may be organized into an M×n matrix. Given the k-space coordinates associated with each of the M×n samples, the sensor domain data may be reorganized into the corresponding k-space data and may then be considered spatial frequency domain data. Image domain data may be obtained by reconstructing the spatial frequency domain data.

[0079] After the MR data 204 is acquired, various computational tools may be applied to the MR data 204 as part of a processing pipeline. For example, as Figure 2 shown, the processing pipeline may involve performing various preprocessing tasks 206, which may include basebanding 206a, filtering 206b, and / or broadband noise suppression 206c. It should be understood that the preprocessing tasks 206 may include other processing procedures not shown in the Figure 2 example, as aspects of the techniques described herein are not limited in this regard.

[0080] In some embodiments, basebanding 206a may include transforming the raw voltage signals from one or more RF coils into one or more baseband signals. The baseband signals may be complex signals, which may describe the complex envelope of the received voltage signals and may represent the integral of the MR spin echo signals received from the subject being imaged. Basebanding 206a may be performed, for example, by multiplying the input data received from the analog-to-digital converter (ADC) of the MRI system by the baseband frequency. In some embodiments, basebanding 206a may also include decimation, thereby reducing the sampling of the signals received from the ADC. As a non-limiting example, in an embodiment, the reduction in sampling may reduce the sampling from 50 MHz sampling of a 2.7 MHz signal to 200 kHz sampling of a signal centered at 0 Hz.

[0081] In some embodiments, filtering 206b may include one or more filtering steps for removing or suppressing spurious signals that fall outside the baseband signal. In some embodiments, filtering 206b may include one or more low-pass filtering steps for removing or suppressing signals that fall outside the baseband signal. Filtering 206b may also include "trimming" the baseband signal to a desired acquisition bandwidth (e.g., to 64 kHz).

[0082] In some embodiments, the broadband EMI suppression 206c can include any suitable noise suppression technique that is applied to the MR data as a whole and is arranged to suppress noise that affects the entire MR data. In some embodiments, the broadband EMI suppression 206c can be based on noise measurements obtained from the environment of the MRI system. The noise measurements can be made by one or more auxiliary sensors as described herein and can be used to suppress the noise present in the MR signals detected by the MRI system during operation.

[0083] In some embodiments, the differences (e.g., different physical characteristics, different positions relative to the field of view, etc.) between the (one or more) auxiliary sensors configured to receive MR signals from the subject and the RF coil can result in differences in the characteristics of the noise signals received by the (one or more) auxiliary sensors and the RF coil. As a result, subtracting the noise signals measured by the (one or more) auxiliary sensors may not sufficiently suppress the noise detected by the RF coil. Thus, in some embodiments, a transfer function can be estimated and the transfer function can be used to transform the noise signals received via the one or more auxiliary sensors into an estimate of the noise received by the one or more RF coils. In some embodiments, the broadband EMI suppression can include: obtaining samples of the noise by using one or more auxiliary sensors; obtaining samples of the MR data by using a primary RF coil; obtaining the transfer function; transforming the noise samples using the transfer function; and subtracting the transformed noise samples from the obtained MR data to suppress and / or eliminate the noise.

[0084] After the preprocessing 206, EMI artifacts may still be present in the MR data. More specifically, narrowband EMI artifacts may still be present in the MR data. Thus, after the MR data has been preprocessed 206, the processing pipeline can include narrowband EMI suppression 208. The processing for performing the narrowband EMI suppression 208 is further described herein. According to some embodiments, Figure 3 illustrate embodiments of a processing pipeline for performing narrowband EMI suppression, and Figure 4 illustrate embodiments of pseudocode for performing EMI suppression.

[0085] In some embodiments, the processing pipeline 300 can start with a first action 302 (represented by the EMI_index line 402 of the pseudocode 400) that identifies portions of the MR data that include EMI. As indicated in line 404 of the pseudocode 400, the processing pipeline 300 can be iteratively implemented until no remaining EMI is identified in the MR data.

[0086] In some embodiments, the EMI-affected portion of the MR data can be identified based on which frequency intervals of the MR data are likely to be affected by EMI. Let y be the complete set of MR data and n be the readout index. The MR data at index n is y n = s n + EMI n , where y n is a two-dimensional signal cross-section across the nth readout index or frequency interval, s n is the desired MR data, and EMI n is the EMI interference. The MR data collected from the end of the spin echo train y e can be used to identify the frequency intervals that include the EMI-affected MR data. In some embodiments, a portion of the MR data obtained from a subset of the echoes of the spin echo train can be used to identify which portions of the MR data are likely to be affected by EMI. The subset of echoes used to identify which portions of the MR data are likely to be affected by EMI can be the echoes that probe the edges of k-space. Compared to the contribution of the MR data from the (one or more) sources of EMI, the MR data obtained from the outer edges of k-space can include a small or vanishing contribution from the spin echo signal to the MR data. In some pulse sequences, the echoes at the end of the spin echo train (e.g., the last echo, the last two echoes, some of the last echoes) can be used to identify which portions of the MR data are affected by EMI. Collectively, the last echoes from all the repetition periods of the pulse sequence used to acquire the MR data can form a k-space edge data set for detecting the frequency intervals affected by EMI.

[0087] In some embodiments, the absolute value of the data set y e can be used to detect the frequency intervals affected by EMI. If the magnitude of the data is greater than a threshold, the nth frequency interval can be identified as being affected by EMI, where can be the MR data collected from the end of the spin echo train for the nth frequency interval. In some embodiments, if the magnitude of the data satisfies the following, the nth frequency interval can be labeled as being affected by EMI:

[0088]

[0089] where, is the signal at the nth frequency of the data set y e , and are the mean and standard deviation of y e respectively, and θ is a predefined threshold.

[0090] In some embodiments, one or more methods for measuring external and / or internal EMI may be used to identify EMI-affected portions of the MR data. In some embodiments, noise acquisition may be performed during one or more pulse repetition periods in which little or no MR signal is expected to be detected. In some embodiments, EMI may be acquired before and / or after the MR acquisition block during at least some (e.g., at least one, at least half, all) pulse repetition periods of a spin echo pulse sequence. In Figure 6 it, EMI may be acquired during noise acquisition block 602a before the MR signal acquisition block 604 and during noise acquisition block 602b after the MR signal acquisition block 604.

[0091] To this end, during noise acquisition blocks 602a and 602b, with no encoding gradients (which may be achieved by turning off the gradient coils) or RF excitation pulses present, the RF receiver of the MRI system may be turned on such that the primary RF coil and / or one or more auxiliary RF sensors may acquire noise data that includes EMI (to whatever extent it is present) but may not include any spin echo MR signals from the subject. Subsequently, the data collected may be used to determine the frequencies at which EMI may affect the MR data collected during block 604. In this way, the data collected may be used to identify portions (e.g., frequency ranges) of the MR data where the amplitude and / or power of the EMI exceeds a threshold. In some embodiments, identifying EMI-affected portions of the MR data may be performed (e.g., using noise acquisition blocks 602a, 602b within respective repetition periods) for each repetition period of the pulse sequence to account for variations in EMI over acquisition periods (e.g., due to drift of the center frequency of the MRI system, due to physical movement of the EMI-emitting device relative to the MRI system). In some embodiments, identifying EMI-affected portions of the MR data may be performed for the first and last repetition periods of the pulse sequence, for a subset of the repetition periods of the pulse sequence, and / or for at least half of the repetition periods of the pulse sequence.

[0092] As described herein, noise acquisition blocks 602a and 602b may be configured to detect noise generated by external sources of EMI. However, in some embodiments, it may also be desirable to detect EMI generated by sources of EMI within the MRI system. In some embodiments, the electronic components of the MRI system (e.g., gradient and / or RF coils and / or electronics) may generate or otherwise induce EMI during operation of the MRI system. The noise acquisition block may be configured such that the gradient coils and / or RF transmit coils operate during the noise acquisition block to detect EMI generated by internal sources. In Figure 7 andFigure 8 An example of a noise acquisition block for detecting EMI generated by both internal and external sources of EMI is shown.

[0093] In some embodiments, the noise acquisition block may include an alternating series of RF pulses 702, 802 and gradient pulses 704, 804, where acquisitions 706, 806 are performed while the MRI system generates gradient pulses 704, 804. Acquisitions 706, 806 may be performed by turning on the RF receiver of the MRI system and turning off the RF transmitter. The RF pulses 702, 802 may be refocusing pulses configured to flip the direction of the magnetization vector of the spins in the field of view (e.g., flip 180°). The RF pulses 702, 802 may preferably not be 90° pulses, which may cause the net magnetization of the spins in the field of view to tilt into the transverse plane and emit a spin echo MR signal as the spins relax back to the longitudinal plane.

[0094] In some embodiments, the gradient pulses may be configured to suppress MR spin echo signals from the subject and may not include phase encoding gradient pulses. In some embodiments, correction gradients ("spoiler" gradients) may be included in the noise acquisition block to dephase and / or otherwise suppress MR spin echo signals generated by spins within the field of view of the MRI system. In this way, the RF receiver of the MRI system may acquire noise data including EMI caused by external and internal EMI sources.

[0095] In some embodiments, the noise acquisition block may be configured based on the type and / or parameters of the pulse sequence used to acquire the MR signal. The noise acquisition block may be configured to mimic the parameters of the pulse sequence used to acquire the MR signal such that the acquired EMI caused by internal EMI sources may be similar to the internal EMI generated during the pulse sequence used to acquire the MR signal. In some embodiments, the noise acquisition block may include an RF excitation pulse 808. The RF excitation pulse 808 may be included when the pulse sequence used to acquire the MR signal is a fast spin echo (FSE) pulse sequence.

[0096] In some embodiments, one or more advanced peak extraction algorithms may be used to identify portions of the MR data affected by EMI. In some embodiments, Bayesian peak extraction, non-negative matrix factorization, and / or undecimated discrete wavelet transform (UDWT) techniques may be used to identify portions of the MR data affected by EMI.

[0097] In some embodiments, a portion of the MR data acquisition (e.g., MR data acquired in (one or more) subsets of a repetition period) can be used to identify the EMI-affected portions of the MR data. In some embodiments, it may be desirable to identify the temporal variation of the effect of EMI on the MR data and then apply the EMI suppression techniques described herein to the subset of the MR data based on the EMI identified in (one or more) subsets of the MR data acquisition. In some embodiments, an external device emitting EMI may have been present near the MRI system during the first half of the MR data acquisition period and may have been repositioned thereafter. It may be desirable to identify a subset of the MR data acquisition that includes the first half (which may have been affected by EMI from the external device) and which (one or more) frequency bins within this first half of the MR data may include EMI signals. It may also be desirable to apply EMI suppression techniques to the first half of the MR data acquisition. In some embodiments, the (one or more) subsets of the MR data can be not just the first half of the MR data acquisition period, but can be smaller or larger (one or more) subsets, more than one subset, and / or (one or more) subsets that are not at the start or end of the MR data acquisition period.

[0098] In some embodiments, a sliding window can be used to identify the EMI-affected portions of the MR data. In some embodiments, a first subset of the MR data acquisition can be identified as being affected by EMI and, as described herein, noise suppression can be applied to the first subset of the MR data. Thereafter, a second subset of the MR data (e.g., an adjacent subset, a partially overlapping subset) can be identified as being affected by EMI and noise suppression can be applied to the second subset of the MR data. The identification and noise suppression can be performed iteratively by continuing to "slide" the window across the MR data.

[0099] In some embodiments, after identifying the frequency bins of the MR data that contain EMI-affected MR data, for each identified frequency bin as indicated by the for loop line 406 of the pseudocode 400, as exemplified by Figure 3 the second action 304 and the fifth line 406a of the pseudocode 400, filtering can be applied to the MR data. In this non-limiting example, the convolution filter can be arranged to suppress the MR signal contribution s n , and can be designed based on the characteristics of the spin echo MR signal.

[0100] The spin echo MR signal s n can have a characteristic linear phase that produces an echo at the center of the acquisition. The MR signal can accordingly be described by the Fourier-time shift relationship:

[0101]

[0102] Among them, the Fourier transform can be represented by multiplying the frequency X(ω) by a function with a linear phase For the spin echo MR signal centered at the obtained middle at time (where N can be the total number of sampling points within the readout and δt can be the duration between sampling points), the phase of the spin echo MR signal alternates between even and odd points:

[0103]

[0104] Figure 9A and Figure 9B respectively illustrate non-limiting embodiments of the MR echo signal plotted in the time domain and the frequency domain. In Figure 9A , the real part 902 of the exemplary spin echo MR signal and the imaginary part 904 of the same exemplary spin echo MR signal are superimposed in the time domain. In Figure 9B , the real part 906, the imaginary part 908, and the amplitude 910 of the spin echo MR signal are superimposed in the frequency domain. As described herein, the real part 906 and the imaginary part 908 of the spin echo MR signal exhibit an alternating phase.

[0105] In some embodiments, the signal contribution due to EMI is unlikely to match this alternating phase signal pattern exhibited by the spin echo MR signal. This feature is illustrated by the curve diagrams of Figure 10A and Figure 10B , where Figure 10A and Figure 10B respectively illustrate the EMI signals plotted in the time domain and the frequency domain according to some embodiments. In Figure 10A , the real part 1002 and the imaginary part 1004 of the exemplary EMI signal are superimposed in the time domain. In Figure 10B , the real part 1006, the imaginary part 1008, and the amplitude 1010 of the EMI signal are superimposed in the frequency domain. The real part 1006 and the imaginary part 1008 do not exhibit an alternating phase as would be expected from the spin echo MR signal.

[0106] In some embodiments, a convolutional filter can be designed based on the difference in the phase behavior between the spin echo MR signal and the EMI signal. In some embodiments, if EMI can be identified in a single frequency interval n, the following convolutional filter can be applied:

[0107] [-1, 1, 4, 1, -1].

[0108] The convolution filtering may apply a broad apodization window that suppresses any signal that is approximately centered within the acquisition window. This will suppress most of the spin echo MR signal, and EMI noise may dominate in enhancing signal a n in which the enhanced signal a n can be rewritten as the following expression:

[0109]

[0110] In some embodiments, since the position of the spin echo MR signal (e.g., due to eddy currents and / or stray magnetic fields) may drift during MR data acquisition, the assumption that the spin echo MR signal is centered at the middle of the acquisition may be inaccurate. In some embodiments, the convolution filtering may be adapted to compensate for spin echo MR signals that may not be centered at the middle of the acquisition window. For a spin echo MR signal that has drifted by Δt, its Fourier transform may obtain a linear phase across the frequency interval given by:

[0111]

[0112] where TE may be the echo duration. The linear phase may be compensated for the linear phase by phase modulating the frequency spectrum for all n:

[0113]

[0114] Then, the convolution filtering may be applied to the phase compensated signal z n . In some embodiments, the enhanced signal a n can then be written as:

[0115]

[0116] In some embodiments, EMI may be present in consecutive adjacent frequency intervals in the MR data. In such embodiments, the width of the convolution filtering may be adapted based on the width of the noise band. The width of the noise band may be the number of consecutive frequency intervals of the MR data affected by EMI. In some embodiments, if interference is detected at indices [1, 2, 3, 16, 17, 48, 61, 62, 63, 64], these indices may be grouped into four bands as follows:

[0117] [[1, 2, 3], [16, 17],

[48] , [61, 62, 63, 64]].

[0118] For each grouped band having indices of MR data affected by EMI, the size k of the convolutional filter can be selected to be at least equal to the number of indices in the band. If the number of indices in the band is even, the size k of the convolutional filter can be selected to be equal to the number of indices in the band. If the number of indices in the band is odd, the size k of the convolutional filter can be selected to be equal to a number that is 1 greater than the number of indices in the band. For example, for the first band [1,2,3], the size of the convolutional filter will be selected to be k = 4, and for the second band [16,17], the size of the convolutional filter will be selected to be k = 2. Then, the filtered signal a n can be expressed as:

[0119]

[0120] In some embodiments, after applying the convolutional filter to suppress the spin echo MR signal, as illustrated by Figure 3 the third action 306 and line 406b of the pseudocode 400, the singular value decomposition (SVD) of a n can be determined. Using SVD, the filtered signal a n can be written as:

[0121] a n = USV T

[0122] where: and

[0123] In some embodiments, the convolutional filter has suppressed the spin echo MR signal in a n , and the EMI signal can remain to dominate at least the first singular value of the SVD of a n . Then the EMI signal can be suppressed, and a modified SVD, i.e., (USV T )', can be obtained. As illustrated by Figure 3 the fourth action 308 and line 406c of the pseudocode 400, the modified SVD can be obtained by setting at least the first singular value of the SVD to be equal to zero. In some embodiments, the first singular value of the SVD can be set to be equal to zero to suppress the EMI signal. In other embodiments, the first two, first several, or some singular values of the SVD can be set to be equal to zero to suppress the EMI signal.

[0124] Alternatively, a modified SVD can be obtained by multiplying at least the first singular value of the SVD by a number between 0 and 1 ( “scaling down”). In some embodiments, the first singular value can be multiplied by a number between 0 and 1 to reduce the contribution of EMI to the MR data. In other embodiments, the first two, first several, or some of the singular values of the SVD can be multiplied by a number between 0 and 1 to modify the SVD. Modifying the SVD by multiplying at least the first singular value of the SVD by a number between 0 and 1 can suppress a portion of the EMI in the MR data such that a small portion of the EMI remains in the MR data. Iteratively performing the actions of identifying the portions of the MR data affected by the remaining EMI and suppressing the EMI can sufficiently suppress the EMI such that the EMI approaches a negligible contribution to the MR data.

[0125] In some embodiments, after obtaining the modified SVD by suppressing the EMI signal, as illustrated by Figure 3 the fifth action 310 and line 406d of the pseudocode 400, the modified SVD can be used to obtain a modified signal a n '(where the contribution of the EMI is suppressed). For example, a n ' can be obtained according to the following:

[0126] a ′ n = (USV T ) ′ .

[0127] In some embodiments, the modified signal a n ' can then be used to obtain MR data with the contribution of the EMI suppressed. As illustrated by Figure 3 the sixth action 312 and line 406e of the pseudocode 400, the inverse of the convolution filter can be applied to the modified signal a n ' to produce the desired EMI-suppressed MR data:

[0128]

[0129] Return Figure 2 , in some embodiments, after narrowband EMI suppression 208, image reconstruction 210 can be used to generate an MR image. In some embodiments, the EMI-suppressed MR data can then be used to generate an MR image. In some embodiments, the original MR data y n affected by the EMI can be replaced by the EMI-suppressed MR data y n '. The modified MR data including the EMI-suppressed MR data y n ' can then be used to generate an MR image.

[0130] In some embodiments, image reconstruction techniques can be used to generate MR images to reconstruct the images from the spatial frequency domain to the image domain. Any suitable image reconstruction technique (including but not limited to linear reconstruction techniques and non-linear reconstruction techniques) can be used for image reconstruction 210. Non-limiting examples of linear reconstruction techniques include gridding, principal component analysis (PCA), generalized autocalibrating partially parallel acquisition (GRAPPA), sensitivity encoding (SENSE), and conjugate gradient sensitivity encoding (CG-SENSE). Aspects related to PCA, GRAPPA, SENSE, and CG-SENSE are described in the literature.

[0131] In some embodiments, after generating the MR image using image reconstruction 210, additional post-processing 212 can be performed to further refine the MR image. In some embodiments, post-processing 212 can further include denoising 212a, image registration 212b, distortion correction 212c, and / or coil intensity correction 212d.

[0132] In some embodiments, post-processing 212 can further include denoising 212a. Denoising 212a can be arranged to suppress the remaining EMI artifacts in the MR image that are not sufficiently suppressed by the wideband noise suppression 206c or the narrowband EMI suppression 208. In some embodiments, denoising 212a can include using any suitable noise suppression technique (including deep learning techniques) to suppress the remaining noise in the generated MR image.

[0133] In some embodiments, post-processing 212 can include image registration 212b. Image registration 212b can include techniques for aligning multiple MR images acquired during the operation of the MRI system. Accurately aligning the multiple MR images before combining them can improve the MR image contrast and quality. In some embodiments, image registration 212b can include using any suitable image registration technique (including deep learning techniques) to align the MR images acquired by the MRI system.

[0134] In some embodiments, post-processing 212 can include distortion correction 212c. Distortion correction 212c can correct the distortion between multiple MR images acquired during the operation of the MRI system. Correcting the distortion can improve the MR image contrast and quality and provide a clinically more accurate MR image. In some embodiments, distortion correction 212c can include any suitable distortion correction technique (including deep learning techniques).

[0135] In some embodiments, post - processing 212 may include coil intensity correction 212d. Coil intensity correction 212d may address differences in contrast caused by different RF coils outputting different signal intensities due to different physical characteristics (e.g., being located at different positions relative to the field of view of the MRI system). In some embodiments, coil intensity correction 212d may include any suitable coil intensity correction technique (including deep - learning techniques).

[0136] In some embodiments, post - processing 212 may include Figure 2 other processing procedures not shown because research on the present disclosure may reveal a wide range of similar, equivalent, or alternative methods covered by the spirit of the present disclosure.

[0137] In Figures 11A to 14B Additional embodiments showing narrow - band EMI suppression are shown. Figure 11A MR images of a subject's brain are shown, which include narrow - band EMI present in horizontal bands extending across the images. Figure 11B Shows the Figure 11A MR images after suppression of narrow - band EMI according to some embodiments described herein.

[0138] Figure 12A And Figure 13A MR images of an MR phantom are shown, which include narrow - band EMI present in vertical bands extending through the images. Figure 12B And Figure 13B Respectively show the Figure 12A And Figure 13A MR images after suppression of narrow - band EMI according to some embodiments of the techniques described herein.

[0139] Figure 14A MR images of a subject's brain are shown, which include narrow - band EMI caused by the magnetostrictive effect. As described herein, magnetostriction can cause phase - line noise that can be synchronized with the time and phase of the RF pulses transmitted by the MRI system. Since this noise is coherent with the RF pulse transmission and is not a spin - echo MR signal as described in connection with Figures 2 to 4 a transform can be applied to amplify the EMI signal and suppress the spin - echo MR signal. The transform can transform the data into frames of RF transmit phase. In some embodiments, for a multi - spin - echo sequence, the frame of RF transmit phase may be different from the frame of RF receive phase. The transform can be described as follows:

[0140] S tx = e -i(tx-adc) S aq ,

[0141] where tx and adc are the phases of the transmitted RF pulse and the RF receiver, respectively, and S aq and S tx are the observed data and the transformed data, respectively.

[0142] Since magnetostrictive noise may be coherent across the echo train over the repetition period in S tx it is possible to most coherently collect magnetostrictive noise by applying a fast Fourier transform (FFT) along the repetition period. In this domain, the magnetostrictive EMI signal may be coherent and accumulate at the zero frequency bin along the repetition period FFT. The selection criterion for this EMI can be set much higher than that for the incoherent EMI because the coherent EMI can be consistent throughout the acquisition. Thus, coherent EMI can be identified based on the following threshold:

[0143]

[0144] where is the S data at index n tx and tx and and are the corresponding mean and standard deviation, respectively, and θ2 is a predefined threshold. Once the frequency bin including the MR data affected by the coherent EMI is identified, the coherent EMI can be suppressed as described herein. Figure 14B illustrates an MR image of Figure 14A after suppressing narrowband EMI due to the magnetostrictive effect according to some embodiments.

[0145] Figure 15 FIG. 1500 is a flow chart of a process for suppressing EMI in an MR image according to some embodiments of the techniques described herein. Process 1500 can be performed using any suitable computing device. For example, in some embodiments, process 1500 can be performed by a computing device co-located (e.g., in the same room) with an MRI system that has obtained MR data by imaging a subject. As another example, in some embodiments, process 1500 can be performed by one or more processors located on the MRI system that has obtained the MR data. Alternatively, in some embodiments, process 1500 can be performed by one or more processors located at a location remote from the MRI system that has obtained the MR data (e.g., as part of a cloud computing environment).

[0146] In some embodiments, processing 1500 may optionally include: generating MR data before identifying a first subset of the MR data that is affected by EMI. Generating the MR data may include: operating an MRI system according to a spin echo pulse sequence. For example, the MRI system may operate according to a spin echo pulse sequence selected from the group consisting of a T1 pulse sequence, a T2 pulse sequence, a fluid-attenuated inversion recovery (FLAIR) pulse sequence, and a diffusion-weighted imaging (DWI) pulse sequence.

[0147] In some embodiments, processing 1500 may optionally include: suppressing EMI in the MR data detected by the auxiliary sensors and / or coils of the MRI system after generating the MR data and before identifying a first subset of the MR data. As described herein, the (one or more) auxiliary sensors may be any suitable (one or more) auxiliary sensors. The (one or more) auxiliary sensors may be arranged to detect EMI generated by an EMI source located near or within the field of view of the MRI system.

[0148] In some embodiments, suppressing EMI in the MR data may include estimating the transfer function of the (one or more) auxiliary sensors to more accurately estimate the noise detected by the RF coil. As a result, subtracting the noise signal measured by the (one or more) auxiliary sensors may not sufficiently suppress the noise detected by the RF coil. In some embodiments, the transfer function of the (one or more) auxiliary sensors may be estimated, and the transfer function may be used to transform the noise signal received via the (one or more) auxiliary sensors into an estimate of the noise received by the RF coil. In some embodiments, wideband EMI suppression may include: obtaining samples of the noise by using one or more auxiliary sensors; obtaining samples of the MR data by using a primary RF coil; obtaining the transfer function; using the transfer function to transform the noise samples; and subtracting the transformed noise samples from the obtained MR data to suppress and / or eliminate the noise.

[0149] In some embodiments, processing 1500 may begin with action 1502, which is for identifying a first subset of the MR data that may be affected by EMI. Identifying the first subset of the MR data may include identifying data in a single frequency interval or a set of adjacent frequency intervals of the MR data that may be affected by EMI. In some embodiments, identifying the first subset of the MR data may include determining whether data in a frequency interval or a set of adjacent frequency intervals may have an amplitude greater than a threshold. In some embodiments, the threshold may be determined based on the mean and / or standard deviation of the amplitude of the MR data.

[0150] In some embodiments, one or more methods for measuring external EMI and / or internal EMI can be used to identify EMI-affected portions of MR data. In some embodiments, noise acquisition blocks placed before and / or after the MR signal acquisition block of a pulse sequence can be used for noise data acquisition. The noise acquisition blocks can be configured to acquire noise data including EMI caused by external sources and / or internal sources of EMI.

[0151] In some embodiments, one or more advanced peak extraction algorithms can be used to identify EMI-affected portions of MR data. In some embodiments, Bayesian peak extraction, non-negative matrix factorization, and / or undecimated discrete wavelet transform (UDWT) techniques can be used to identify EMI-affected portions of MR data.

[0152] In some embodiments, only a portion of the MR data can be used to identify EMI-affected portions of the MR data. In some embodiments, one or more subsets of the repetition period of MR data acquisition can be used to identify EMI-affected portions of the MR data.

[0153] In some embodiments, identifying a first subset of the MR data can be performed by analyzing a portion of the MR data acquired during an echo signal at the end of the repetition period of the pulse sequence used to acquire the MR data. The echo at the end of a spin echo train can acquire MR data from the outer edge of k-space, where the contribution of the spin echo signal generated by the subject to the MR data may be small or vanishing. In some embodiments, identifying a first subset of the MR data can be performed by analyzing the last two echo signals at the end of the repetition period of the pulse sequence used to acquire the MR data.

[0154] In some embodiments, in the case where the EMI includes coherent EMI (e.g., due to magnetostriction, RF transmission electronics, etc.), determining a first subset of the MR data can include transforming the MR data into the reference frame of the transmitted pulse. Thereafter, determining a first subset of the MR data can include identifying whether the MR data in a frequency interval or a set of adjacent frequency intervals can have an amplitude greater than a threshold. Additional aspects for identifying coherent EMI are described herein.

[0155] In some embodiments, after identifying a first subset of MR data, processing 1500 may proceed to operation 1504, which is for suppressing EMI in the first subset of MR data to obtain a second subset of MR data. In some embodiments, suppressing EMI in the first subset of MR data may begin with sub-operation 1504a, which is for applying a convolutional filter to the first subset of MR data. The convolutional filter may be configured to suppress the contribution of MR spin echo signals in the first subset of MR data and thereby obtain signal-suppressed MR data. In some embodiments, as described herein, the convolutional filter may be adapted to compensate for spin echo MR signals that are not centered within an acquisition window.

[0156] In some embodiments, EMI may be present in the nth frequency bin among a plurality of frequency bins containing MR data. In some embodiments, applying a convolutional filter to the first subset of MR data may include calculating a weighted linear combination of data in a plurality of frequency bins including the nth frequency bin, where the weights are determined by the coefficients of the convolutional filter. In some embodiments, calculating the weighted linear combination of data in the plurality of frequency bins may include calculating the weighted linear combination of data in the nth frequency bin and adjacent frequency bins (e.g., the (n±1) bins, the (n±2) bins, etc.).

[0157] In some embodiments, EMI may be present in a plurality of adjacent frequency bins (e.g., EMI may have a width greater than that of a single frequency bin). In some embodiments, applying a convolutional filter to the first subset of MR data may include applying a convolutional filter that has a length equal to the number of adjacent frequency bins in the plurality of adjacent frequency bins when the number of adjacent frequency bins is even, or equal to a number that is 1 greater than the number of adjacent frequency bins in the plurality of adjacent frequency bins when the number of adjacent frequency bins is odd.

[0158] After applying convolutional filtering to a first subset of MR data, processing 1000 can continue: suppressing EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data 1504b. In some embodiments, suppressing EMI in the signal-suppressed MR data can include using SVD to obtain the EMI-suppressed MR data. In some embodiments, suppressing EMI in the signal-suppressed MR data can include: determining the SVD of the signal-suppressed MR data, modifying the SVD by setting at least one singular value of the SVD to zero, and using the modified SVD to obtain the corresponding EMI-suppressed MR data. In some embodiments, modifying the SVD can include: setting the first singular value of the SVD to zero, setting the first several singular values of the SVD to zero, and / or setting some singular values of the SVD to zero. After suppressing EMI in the signal-suppressed MR data, processing 1500 can continue: applying the inverse of the convolutional filtering to the EMI-suppressed MR data to obtain a second subset of MR data 1504c.

[0159] In some embodiments, the first subset of MR data can include multiple data portions for corresponding multiple frequency intervals. In some embodiments, in the case where the MRI system includes multiple RF coils, each data portion can include measurements made by each respective RF coil in each corresponding frequency interval. In some embodiments, the data in the corresponding signal-suppressed data portions can be organized as an N c ×M matrix, where N c can be an integer representing the number of RF coils and M can be an integer representing the number of measurements made by each RF coil. Then, determining the SVD of the corresponding signal-suppressed MR data portion can include determining the singular value decomposition of the N c ×M matrix.

[0160] In some embodiments, the first subset of MR data can include multiple data portions for corresponding multiple frequency intervals, and suppressing EMI in the first subset of MR data can include performing the described actions 1504a to 1504c for each specific data portion among the multiple data portions. In some embodiments, suppressing EMI in the first subset of MR data can include: applying convolutional filtering to the specific data portion to obtain a corresponding signal-suppressed MR data portion, suppressing EMI in the corresponding signal-suppressed MR data portion to obtain a corresponding EMI-suppressed MR data portion, and applying the inverse of the convolutional filtering to the EMI-suppressed MR data portion. In some embodiments, suppressing EMI in the corresponding signal-suppressed MR data portion can include: determining the SVD of the corresponding signal-suppressed MR data portion, modifying the SVD by setting at least one singular value of the SVD to zero, and using the modified SVD to obtain the corresponding EMI-suppressed MR data portion.

[0161] After obtaining the second subset of the MR data, processing 1500 can continue: determining whether there is any remaining EMI in the MR data 1005. Determining whether there is any remaining EMI in the MR data 1005 can be performed in substantially the same manner as identifying the first subset of the MR data affected by EMI in the first action 1502 of processing 1500. If it is determined that there may still be EMI in the MR data, processing 1500 can return to perform additional EMI suppression. In this way, processing 1500 can iteratively suppress the EMI in the MR data until all the EMI has an amplitude below a threshold. In some embodiments, as described herein, processing 1500 can iteratively suppress the EMI in the MR data until all the EMI has an amplitude below a threshold determined based on the standard deviation of the MR data.

[0162] In some embodiments, once the EMI is sufficiently suppressed, processing 1500 can continue with action 1506, which is to generate an MR image using the second subset of the MR data. In some embodiments, generating an MR image using the modified MR data can include modifying the MR data by replacing the first subset of the MR data with the second subset of the MR data to obtain the modified MR data. Then the modified MR data can be used to generate an MR image.

[0163] In some embodiments, generating an MR image can be implemented using any suitable image reconstruction technique arranged to reconstruct the image from the spatial frequency domain to the image domain. Any suitable image reconstruction technique (including but not limited to linear and / or non-linear reconstruction techniques) can be used for image reconstruction. In some embodiments, one or more of fast Fourier transform (FFT), non-uniform Fourier transform, gridding, principal component analysis (PCA), sensitivity encoding (SENSE), conjugate gradient sensitivity encoding (CG-SENSE), generalized autocalibrating partially parallel acquisition (GRAPPA), compressed sensing (CS), and / or deep learning techniques can be used for image reconstruction.

[0164] In some embodiments, after generating the MR image, processing 1500 may continue by outputting the generated MR image 1008. In some embodiments, outputting the generated image may include displaying the generated MR image. In some embodiments, the generated MR image may be displayed on a screen associated with a desktop or laptop computer, a television monitor, a tablet computer, a smart phone, or any other suitable electronic device. In some embodiments, outputting the generated image may include transmitting the generated MR image to another computing device. For example, the generated MR image may be transmitted over a network (e.g., a local area network (LAN) or a wide area network (WAN)) or over the Internet (e.g., in an electronic message such as an email and / or SMS). In some embodiments, outputting the generated MR message may include storing the generated MR image in a computer memory. In some embodiments, the generated MR message may be stored locally on the computer memory of the computer that generated the MR image or remotely on a computer memory located at a location remote from the computer that generated the MR image. In some embodiments, the generated MR image may be stored on an image server (such as a DICOM server or a patient database server).

[0165] Figure 16 is a block diagram of exemplary components of an MRI system 1600. In Figure 16 the illustrative example of, the MRI system 1600 includes a computing device 1604, a controller 1606, a pulse sequence storage unit 1608, a power management system 1610, and a magnetic assembly 1620. It should be understood that the MRI system 1600 is illustrative, and in addition to Figure 16 the components illustrated in or instead of Figure 16 the components illustrated in, the MRI system may have any suitable type of one or more other components. However, the MRI system will generally include these high-level components, although the implementation of these components used in a particular MRI system may vary. It can be understood that the techniques described herein for suppressing EMI can be used with any suitable type of MRI system, including high-field MRI systems, low-field MRI systems, and ultra-low-field MRI systems. In some implementations, a magnetic shielding paint for reducing magnetic noise may be applied to one or more parts of the MRI system 1600 to reduce magnetic noise from one or more external sources.

[0166] As Figure 16As illustrated, the magnetic assembly 1620 includes a B0 magnet 1622, a shim magnet 1624, a radio frequency (RF) transmit and receive coil 1626, and a gradient coil 1628. The B0 magnet 1622 can be used to generate a main magnetic field B0. The B0 magnet 1622 can be any suitable type or combination of magnetic assemblies that can generate a desired main magnetic field B0. In some embodiments, the B0 magnet 1622 can be a permanent magnet, an electromagnet, a superconducting magnet, or a hybrid magnet that includes one or more permanent magnets and one or more electromagnets and / or one or more superconducting magnets. In some embodiments, the B0 magnet 1622 can be configured to generate a B0 magnetic field having a field strength that is less than or equal to 0.2 T and greater than or equal to 20 mT, less than or equal to 0.2 T and greater than or equal to 50 mT, less than or equal to 0.15 T and greater than or equal to 40 mT, less than or equal to 0.1 T and greater than or equal to 20 mT, or less than or equal to 0.1 T and greater than or equal to 50 mT.

[0167] In some embodiments, the B0 magnet 1622 can include a first B0 magnet and a second B0 magnet, each of which includes permanent magnet blocks arranged in concentric rings around a common center. The first B0 magnet and the second B0 magnet can be arranged in a biplanar configuration such that the imaging region is located between the first B0 magnet and the second B0 magnet. In some embodiments, the first B0 magnet and the second B0 magnet can each be coupled to and supported by a ferromagnetic yoke that is configured to capture and direct the magnetic flux from the first B0 magnet and the second B0 magnet.

[0168] The gradient coil 1628 can be arranged to provide a gradient field and can, for example, be arranged to generate gradients in three substantially orthogonal directions (X, Y, Z) in the B0 field. The gradient coil 1628 can be configured to encode the transmitted MR signal by systematically varying the B0 field (the B0 field generated by the magnet 1622 and / or the shim magnet 1624) to encode the spatial position of the received MR signal as a function of frequency or phase. In some embodiments, the gradient coil 1628 can be configured to vary the frequency or phase as a linear function of the spatial position along a particular direction, although more complex spatial encoding distributions can also be provided by using non-linear gradient coils. In some embodiments, a laminate (e.g., a printed circuit board) can be used to implement the gradient coil 1628.

[0169] MRI is performed by separately using transmit and receive coils (commonly referred to as radiofrequency (RF) coils) to excite and detect the transmitted MR signals. The transmit / receive coil 1626 can include separate coils for transmission and reception, multiple coils for transmission and / or reception, or the same coil for transmission and reception. Thus, the transmit / receive assembly can include one or more coils for transmission, one or more coils for reception, and / or one or more coils for transmission and reception. The transmit / receive coil 1626 is also commonly referred to as a Tx / Rx or Tx / Rx coil to generally refer to the various configurations of the transmit and receive magnetic components of an MRI system. These terms are used interchangeably herein. In Figure 16 which, the RF transmit and receive coil 1626 includes one or more transmit coils that can be used to generate RF pulses to induce an oscillating magnetic field B1. The (one or more) transmit coils can be configured to generate any suitable type of RF pulse. The transmit / receive coil 1626 can include additional electronic components of the transmit and receive chain.

[0170] The power management system 1610 can include electronics for providing operating power to one or more components of the MRI system 1600. In some embodiments, the power management system 1610 can include one or more power supplies, gradient power components, transmit coil components, and / or any other suitable power electronics required to provide suitable operating power to power on and operate the components of the MRI system 1600. The power management system 1610 can include a power supply 1612, (one or more) amplifiers 1614, transmit / receive circuitry 1616, and a thermal management component 1618 (e.g., a cryogenic cooling device for a superconducting magnet, or a fluid cooling device for an electromagnet and / or circuitry). The power supply 1612 can include electronics for providing operating power to the magnetic component 1620 of the MRI system 1600. In some embodiments, the power supply 1612 can include electronics for providing operating power to one or more B0 coils (e.g., the B0 magnet 1622) to generate the main magnetic field used in a low-field MRI system. In a non-limiting example, the power management system 1610 can receive power from a standard wall outlet to provide power to the MRI system 1600.

[0171] (One or more) amplifiers 1614 may include: one or more RF receive (Rx) preamplifiers that amplify MR signals detected by one or more RF receive coils (e.g., coil 1626); one or more RF transmit (Tx) power components configured to provide power to one or more RF transmit coils (e.g., coil 1626); one or more gradient power components configured to provide power to one or more gradient coils (e.g., gradient coil 1628); and one or more shim power components configured to provide power to one or more shim coils (e.g., shim magnet 1624). The transmit / receive circuitry 1616 may be configured to select (e.g., using one or more switches) whether an RF transmit coil or an RF receive coil is being operated.

[0172] As Figure 16 Illustrated, the MRI system 1600 includes a controller 1606 (also referred to as a console) having control electronics that send instructions to and receive information from the power management system 1610. The controller 1606 may be configured to implement one or more pulse sequences that determine instructions sent to the power management system 1610 to operate the magnetic components 1620 in a desired sequence (e.g., parameters for operating the RF transmit and receive coils 1626, parameters for operating the gradient coil 1628, etc.). In some embodiments, the controller 1606 also interacts with a computing device 1604 programmed to process received MR data. In some embodiments, the computing device 1604 may process the received MR data using any suitable (one or more) image reconstruction processes to generate one or more MR images. The controller 1606 may provide information related to the one or more pulse sequences to the computing device 1604 for processing the data by the computing device. In some embodiments, the controller 1606 may provide information related to the one or more pulse sequences to the computing device 1604, and the computing device may perform image reconstruction processing at least in part based on the provided information.

[0173] Figure 17 Illustrative components of a portion of an MRI system that may be used for broadband EMI suppression according to some embodiments described herein are shown. In some embodiments, the transmit / receive system 1700 may form at least a part of the transmit / receive apparatus of the MRI system. As discussed herein, the transmit / receive system 1700 may be configured to detect MR signals emitted from excited atoms of a subject 1704 being imaged and to characterize noise in the environment to suppress or remove the characterized noise from the detected MR signals.

[0174] AsFigure 17 As shown, the transmit / receive system 1700 can include a primary RF receive coil 1702 configured to measure an MR signal emitted by a subject 1704 in response to an excitation pulse sequence. The excitation pulse sequence can be generated by the primary RF receive coil 1702 and / or by one or more other transmit RF coils arranged near the subject 1704 and configured to generate a suitable MR pulse sequence during operation. The primary receive coil 1702 can be a single coil or can be multiple coils, and in the latter case, the primary receive coil 1702 can be used for parallel MRI. The tuning circuitry 1708 can facilitate the operation of the primary receive coil 1702 and can provide the signal(s) detected by the RF coil(s) 1702 to an acquisition system 1710, which can amplify the detected signal, digitize the detected signal, and / or perform any other suitable type of processing.

[0175] The transmit / receive system 1700 can also include one or more auxiliary sensors 1706, which can include any number or type of sensors configured to detect or otherwise measure noise sources in the environment and / or environmental noise generated by the MRI system itself. The noise measured by the auxiliary sensor(s) 1706 can be characterized and used to suppress noise in the MR signal detected by the primary RF coil(s) 1702 using the techniques described herein. In some embodiments, after the acquisition system 1710 processes the signals detected by the RF coil(s) 1702 and the auxiliary sensor(s) 1706, the acquisition system 1710 can provide the processed signal(s) to one or more other components of the MRI system for further processing (e.g., for use in forming one or more MR images of the subject 1704). The acquisition system 1710 can include any suitable circuitry and can include, for example, one or more controllers and / or processors configured to control the MRI system to perform noise suppression in accordance with the embodiments described herein.

[0176] In some embodiments, one or more auxiliary sensors 1706 may include one or more auxiliary coils configured to measure noise from one or more noise sources in the environment in which the MRI system is operating. In some embodiments, one or more primary RF coils 1702 may include eight primary RF coils, and one or more auxiliary sensors 1706 may include eight auxiliary coils, although it should be understood that the number of one or more primary RF coils 1702 and the number of one or more auxiliary sensors 1706 may be less than or greater than eight (e.g., 2, 4, 6, 10, 12, 14, and / or 16), and the number of one or more primary RF coils 1702 and the number of one or more auxiliary sensors 1706 need not be equal, as the techniques described herein are not limited in this regard.

[0177] In some embodiments, one or more auxiliary RF coils may be constructed to be more sensitive to ambient noise than to any noise generated by the coil itself. For example, the auxiliary RF coil may have a sufficiently large aperture and / or number of turns such that the auxiliary coil is more sensitive to noise from the environment than to noise generated by the auxiliary coil itself. In some embodiments, one or more auxiliary RF coils may have a larger aperture and / or more turns than one or more primary RF coils 1702. However, one or more auxiliary RF coils may be the same as the primary RF coil in this regard and / or may be different from one or more primary RF coils 1702 in other respects.

[0178] In some embodiments, one or more auxiliary RF coils may be located at a distance from the primary RF coils 1702. This distance may be selected such that one or more auxiliary coils are sufficiently far from the sample 1704 to avoid sensing MR signals emitted by the sample during imaging, but otherwise are arranged as close as possible to the primary RF coils 1702 such that one or more auxiliary coils detect noise similar to the noise detected by one or more primary coils 1702. In this way, the noise from one or more noise sources measured by one or more auxiliary coils 1706 and characterized using the techniques discussed herein (e.g., by using the detected noise to at least partially calculate a transfer function that can be used to suppress and / or eliminate noise present on the detected MR signal) may represent the noise detected by one or more primary coils 1702. In some embodiments, one or more auxiliary coils may not be RF coils, but rather may be any type of sensor capable of detecting or measuring noise in the environment that may affect the performance of the MRI system.

[0179] According to some embodiments, the (one or more) auxiliary sensors 1706 may include the (one or more) primary coils themselves, where the (one or more) primary RF coils are labeled as both the primary receive coil 1702 used by the system and the auxiliary sensor 1706, because the (one or more) primary RF coils can perform both roles in some cases. As discussed herein, certain pulse sequences can facilitate the use of signals acquired from the (one or more) primary coils to also suppress noise thereon. Pulse sequences generally refer to operating the (one or more) transmit coils and the (one or more) gradient coils in a prescribed sequence to induce an MR response. By repeating the same pulse sequence with the same spatial encoding, "redundant" MR signals can be obtained and these "redundant" MR signals can be used to estimate the noise present in the MR signal.

[0180] Figure 18 Illustrate an MRI system 1800 that can be used to acquire MR images of a subject. The MRI system 1800 may include a B0 magnet 1810 partially formed by an upper magnet 1810a and a lower magnet 1810b, and the B0 magnet 1810 is coupled with a yoke 1820 to increase the flux density within the imaging region. The B0 magnet 1810 may be housed in a magnet housing 1812 together with gradient coils 1815. According to some embodiments, the B0 magnet 1810 may include an electromagnet. According to some embodiments, the B0 magnet 1810 may include a permanent magnet.

[0181] The MRI system 1800 may further include a base 1850 for housing the electronics required to operate the MRI system. In some embodiments, the base 1850 may house electronics including power components configured to operate the MRI system 1800 using mains power (e.g., via connection to a standard wall outlet and / or a large appliance outlet). In some embodiments, the MRI system 1800 may be brought to the patient's side and plugged into a nearby wall outlet.

[0182] The base 1850 may be supported by a transport mechanism 1880. As Figure 18 shown, the transport mechanism 1880 may include wheels that enable the movement of the MRI system 1800. In some embodiments, the transport mechanism 1880 may include electric wheels configured to assist the user in transporting the MRI system 1800. In this way, the MRI system 1800 can be transported to the patient and maneuvered to the bedside for imaging. Figure 19 An MRI system 1900 is shown that has been transported to the patient's bedside for a brain scan.

[0183] Figure 20Exemplify an alternative MRI system 2000 that can be used to acquire MR images of a subject according to some embodiments. The MRI system 2000 can include a housing 2010 that can accommodate a B0 magnet having a bore 2020. The imaging region can be located within the bore 2020. In some embodiments, the subject can be placed on a subject support 2030 and slid into the bore 2020 to be placed within the imaging region of the MRI system 2000. In some embodiments, the B0 magnet can include a superconducting magnet. In some embodiments, the B0 magnet can include an electromagnet. In some embodiments, the B0 magnet can generate a magnetic field having an intensity in the range of 0.2T to 0.5T, 0.5T to 1.5T, 1.5T to 3T, 2T to 4T, or greater than 4T. In some embodiments, the MRI system 2000 can be a high-field system.

[0184] Figure 21 is a diagram of an exemplary computer system on which the embodiments described herein can be implemented. In Figure 21 illustrates an exemplary implementation of a computer system 2100 that can be used in conjunction with any of the embodiments provided herein. In some embodiments, any of the processes described herein can be implemented on and / or using the computer system 2100. The computer system 2100 can include one or more than one processor 2110 and one or more than one article of manufacture that includes a tangible (e.g., non-transitory) computer-readable storage medium (e.g., a memory 2120 and one or more than one non-volatile storage medium 2130). The processor 2110 can control writing data to and reading data from the memory 2120 and the non-volatile storage device 2130 in any suitable manner. To perform any of the functionality described herein, the processor 2110 can execute one or more than one processor-executable instructions stored in one or more than one non-transitory computer-readable storage medium (e.g., the memory 2120), and the one or more than one non-transitory computer-readable storage medium can serve as a non-transitory computer-readable storage medium for storing the processor-executable instructions to be executed by the processor 2110.

[0185] Example 1

[0186] In an exemplary embodiment, EMI suppression can be implemented in conjunction with a low-field MRI system. The low-field MRI system can include a B0 magnet configured to generate a B0 magnetic field having a magnetic field strength in the range from 0.05 T to 0.2 T. The B0 magnet can include permanent magnets arranged in one or more concentric rings. The low-field MRI system can have an open configuration, wherein the permanent magnets can be arranged in a biplanar configuration such that the imaging region is configured therebetween. The low-field MRI system can further include one or more gradient coils. The low-field MRI system can further include one or more RF coils. In addition to one or more RF coils, the low-field MRI system can include one or more auxiliary sensors configured to detect EMI. The auxiliary sensors can be positioned outside the field of view of the low-field MRI system such that they primarily detect noise outside the low-field MRI system rather than MR signals generated during imaging using the low-field MRI system. The low-field MRI system can be located outside a shielded room, inside a partially shielded room, or inside a fully shielded room. The low-field MRI system can be portable and can be transported to different locations where it can be used.

[0187] EMI suppression can be performed by acquiring MR data using the low-field MRI system. A first subset of the MR data in one or more frequency intervals of the MR data can be identified as including EMI based on a portion of the data acquired near or at the edge of k-space. Convolution filtering can be applied to the first subset of the MR data to suppress spin echo MR signals in the first subset of the MR data and obtain signal-suppressed MR data. The SVD of the signal-suppressed MR data can be determined, and the first singular value of the SVD can be set to zero to obtain a modified SVD. The modified SVD can be used to obtain EMI-suppressed MR data, and the inverse of the convolution filtering can be applied to the EMI-suppressed MR data to obtain a second subset of the MR data. The first subset of the MR data can be replaced with the second subset of the MR data, and the modified MR data can be used to generate an MR image. The MR image can be output (e.g., displayed, transmitted, and / or stored in a computer memory) after being generated.

[0188] Example 2

[0189] In another exemplary embodiment, EMI suppression can be implemented in conjunction with a high-field MRI system. The high-field MRI system can include a B0 magnet configured to generate a B0 magnetic field having a magnetic field strength in the range from 1.0 T to 13.0 T. The B0 magnet can include magnets arranged to form a bore such that the imaging region is configured within the bore. The magnets can be electromagnets, superconducting magnets, or a combination of electromagnets and superconducting magnets. The high-field MRI system can be located in a shielded room or a partially shielded room.

[0190] EMI suppression can be performed by acquiring MR data using a high - field MRI system. A first subset of MR data in one or more than one frequency interval of the MR data can be identified as including EMI based on a portion of the data acquired near or at the edge of k - space. Convolution filtering can be applied to the first subset of the MR data to suppress the spin - echo MR signals in the first subset of the MR data and obtain signal - suppressed MR data. The SVD of the signal - suppressed MR data can be determined, and the first singular value of the SVD can be set to zero to obtain a modified SVD. The modified SVD can be used to obtain EMI - suppressed MR data, and the inverse of the convolution filtering can be applied to the EMI - suppressed MR data to obtain a second subset of the MR data. The first subset of the MR data can be replaced by the second subset of the MR data, and the modified MR data can be used to generate an MR image. The MR image can be output (e.g., displayed, transmitted, and / or stored in a computer memory) after being generated.

[0191] Example 3

[0192] In another illustrative embodiment, EMI suppression can be implemented in conjunction with a low - field, mid - field, or high - field MRI system. The MRI system can include a B0 magnet configured to generate a B0 magnetic field having a magnetic field strength in the range from 0.02 T to 13 T. The B0 magnet can include a permanent magnet, an electromagnet, and / or a superconducting magnet arranged to form an imaging region. The MRI system can be located outside a shielded room, inside a partially shielded room, or inside a fully shielded room.

[0193] EMI suppression can be performed by acquiring MR data using an MRI system. A first subset of MR data in one or more than one frequency interval of the MR data can be identified as including EMI based on a portion of the data acquired near or at the edge of k - space. Convolution filtering can be applied to the first subset of the MR data to suppress the spin - echo MR signals in the first subset of the MR data and obtain signal - suppressed MR data. The SVD of the signal - suppressed MR data can be determined, and the first singular value of the SVD can be multiplied by a number between 0 and 1 to obtain a modified SVD. The modified SVD can be used to obtain EMI - suppressed MR data, and the inverse of the convolution filtering can be applied to the EMI - suppressed MR data to obtain a second subset of the MR data. The first subset of the MR data can be replaced by the second subset of the MR data, and the modified MR data can be used to generate an MR image. The MR image can be output (e.g., displayed, transmitted, and / or stored in a computer memory) after being generated.

[0194] In addition, the present technology can be embodied in any of the following structures:

[0195] (1) A method for suppressing electromagnetic interference (EMI) in magnetic resonance (MR) data obtained by a magnetic resonance imaging (MRI) system, the method comprising: using at least one computer hardware processor to: identify a first subset of the MR data affected by EMI; suppress the EMI in the first subset of the MR data to obtain a second subset of the MR data by: applying a filter to the first subset of the MR data to suppress the contribution of MR spin echo signals in the first subset of the MR data, thereby obtaining signal-suppressed MR data; suppressing the EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; and applying the inverse of the filter to the EMI-suppressed MR data to obtain the second subset of the MR data; using the second subset of the MR data to generate an MR image; and outputting the generated MR image.

[0196] (2a) The method according to (1), wherein suppressing the EMI in the signal-suppressed MR data comprises: using component decomposition to obtain the EMI-suppressed MR data.

[0197] (2b) The method according to (1), wherein suppressing the EMI in the signal-suppressed MR data comprises: applying a domain transform to the signal-suppressed MR data, using component decomposition to obtain the EMI-suppressed MR data, and applying the inverse of the domain transform, wherein the domain transform and the EMI signal suppression can be applied iteratively.

[0198] (3) The method according to (1) or (2a) or (2b), wherein the first subset of the MR data comprises a plurality of data portions for corresponding multiple frequency or time intervals, and wherein suppressing the EMI in the first subset of the MR data comprises: for each specific data portion of the plurality of data portions, applying the filter to the specific data portion to obtain a corresponding signal-suppressed MR data portion; suppressing the EMI in the corresponding signal-suppressed MR data portion to obtain a corresponding EMI-suppressed MR data portion; and applying the inverse of the filter to the EMI-suppressed MR data portion.

[0199] (4) The method according to any one of (1) to (3), wherein suppressing the EMI in the corresponding signal-suppressed MR data portion comprises: determining the component decomposition of the corresponding signal-suppressed MR data portion; modifying the singular value decomposition (SVD) by setting at least one singular value of the component decomposition to a predetermined value or by multiplying at least one singular value of the component decomposition by a predetermined weight; and using the modified component decomposition to obtain the corresponding EMI-suppressed MR data portion.

[0200] (5) The method according to any one of (1) to (4), wherein the MRI system includes a plurality of radio frequency coils, i.e., a plurality of RF coils, and wherein each of the plurality of data portions includes measurements performed by each of the plurality of RF coils for a corresponding one of the plurality of frequencies or time intervals.

[0201] (6) The method according to any one of (1) to (5), wherein the data in the corresponding signal-suppressed data portion is organized as an N c ×M matrix, where N c is an integer representing the number of RF coils and M is an integer representing the number of measurements performed by each of the RF coils, and wherein determining the component decomposition of the corresponding signal-suppressed MR data portion includes: determining the singular value decomposition of the N c ×M matrix.

[0202] (7) The method according to any one of (1) to (6), further comprising: generating the MR data by operating the MRI system according to a spin echo pulse sequence or a gradient echo pulse sequence before identifying the first subset of the MR data.

[0203] (8) The method according to any one of (1) to (7), wherein the spin echo pulse sequence is selected from the group consisting of a T1 pulse sequence, a T2 pulse sequence, a fluid-attenuated inversion recovery (FLAIR) pulse sequence, and a diffusion-weighted imaging (DWI) pulse sequence.

[0204] (9) The method according to any one of (1) to (8), further comprising: suppressing EMI in the MR data detected by an auxiliary coil of the MRI system after generating the MR data and before identifying the first subset.

[0205] (10) The method according to any one of (1) to (9), wherein the EMI is narrowband EMI, and wherein the MR data includes sensor domain data in a plurality of frequencies or time intervals, and wherein the EMI exists in a certain frequency or time interval among the plurality of frequencies or time intervals.

[0206] (11) The method according to any one of (1) to (10), wherein the EMI exists in no more than a threshold number of adjacent frequencies or time intervals among the plurality of frequencies or time intervals.

[0207] (12) The method according to any one of (1) to (11), wherein the EMI is present in the nth frequency or time interval among the plurality of frequencies or time intervals, and wherein applying the filtering to the first subset of the MR data includes: calculating a weighted linear combination of data in a plurality of frequencies or time intervals including the nth frequency or time interval, using weights determined by the coefficients of the filtering.

[0208] (13) The method according to any one of (1) to (12), wherein the MR data includes data in a plurality of frequencies or time intervals, the EMI is present in a plurality of adjacent frequencies or time intervals, and applying the filtering to the first subset of the MR data includes: applying a filtering having a length at least equal to the number of adjacent frequencies or time intervals among the plurality of adjacent frequencies or time intervals.

[0209] (14) The method according to any one of (1) to (13), wherein identifying the first subset of the MR data includes: identifying data in a single frequency or time interval or a set of adjacent frequencies or time intervals of the MR data that is affected by EMI.

[0210] (15) The method according to any one of (1) to (14), wherein identifying the first subset of the MR data is performed by analyzing a portion of the MR data acquired during at least one predetermined echo signal of a pulse sequence used to acquire the MR data.

[0211] (16) The method according to any one of (1) to (15), wherein identifying the first subset of the MR data includes: determining whether data in a frequency or time interval or in the set of adjacent frequencies or time intervals has an amplitude greater than a threshold.

[0212] (17) The method according to any one of (1) to (16), wherein generating the MR image includes: modifying the MR data by replacing the first subset of the MR data with the second subset of the MR data to obtain modified MR data; and using the modified MR data to generate the MR image.

[0213] (18) The method according to any one of (1) to (17), wherein the MRI system includes an RF coil configured to transmit radiofrequency pulses, i.e., RF pulses, and determining the first subset of the MR data includes: transforming the MR data into the reference frame of the transmitted RF pulses.

[0214] (19) A magnetic resonance imaging system, i.e., an MRI system, includes: a magnetic system having a plurality of magnetic components to generate a magnetic field for performing MRI by acquiring MR data; and at least one processor configured to perform: identifying a first subset of the MR data affected by electromagnetic interference, i.e., EMI; suppressing the EMI in the first subset of the MR data to obtain a second subset of the MR data by: applying a filter to the first subset of the MR data to suppress the contribution of MR spin echo signals in the first subset of the MR data, thereby obtaining signal-suppressed MR data; suppressing the EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; and applying the inverse of the filter to the EMI-suppressed MR data to obtain the second subset of the MR data; generating an MR image using the second subset of the MR data; and outputting the generated MR image.

[0215] (20) At least one tangible computer-readable storage medium storing instructions executable by a processor, the instructions when executed by the at least one processor cause the at least one processor to perform a method for suppressing electromagnetic interference, i.e., EMI, in magnetic resonance data, i.e., MR data, obtained by a magnetic resonance imaging system, i.e., an MRI system, the method including: identifying a first subset of the MR data affected by EMI; suppressing the EMI in the first subset of the MR data to obtain a second subset of the MR data by: applying a filter to the first subset of the MR data to suppress the contribution of MR spin echo signals in the first subset of the MR data, thereby obtaining signal-suppressed MR data; suppressing the EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; and applying the inverse of the filter to the EMI-suppressed MR data to obtain the second subset of the MR data; generating an MR image using the second subset of the MR data; and outputting the generated MR image.

[0216] (21) A method for generating an MR image by suppressing electromagnetic interference (EMI) in magnetic resonance data, i.e., MR data, which is obtained using at least one radio frequency receiving coil unit of an MRI system, i.e., at least one RF receiving coil unit. The suppression is for removing zipper line noise artifacts that would otherwise be visible in the MR image. The MRI system communicates with a computer including a processor. The method includes: using the processor, identifying a noisy subset of the MR data in which the EMI is to be suppressed; using the processor, applying a filter to the noisy subset of the MR data to suppress the contribution of one or more MR signals to the noisy subset to obtain signal-suppressed MR data; using the processor, suppressing the EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; using the processor, applying the inverse of the filter to the EMI-suppressed MR data to obtain a denoised subset in which the EMI of the MR data has been at least partially suppressed; and using the processor, using the denoised subset of the MR data to generate the MR image.

[0217] (22) The method according to (21), further including: obtaining the MR data using the at least one RF receiving coil unit of the MRI system.

[0218] (23) The method according to (21) or (22), wherein applying the filter to the noisy subset of the MR data includes: suppressing the contribution of one or more spin echo MR signals to the noisy subset of the MR data.

[0219] (24) The method according to any one of (21) to (23), wherein suppressing the EMI in the signal-suppressed MR data includes: using component decomposition to obtain the EMI-suppressed MR data.

[0220] (25) The method according to any one of (21) to (24), wherein the noisy subset of the MR data includes a plurality of data portions for corresponding multiple frequency or time intervals, and wherein suppressing the EMI in the noisy subset of the MR data includes: for each specific data portion among the plurality of data portions, applying the filter to the specific data portion to obtain a corresponding signal-suppressed MR data portion; suppressing the EMI in the corresponding signal-suppressed MR data portion to obtain a corresponding EMI-suppressed MR data portion; and applying the inverse of the filter to the EMI-suppressed MR data portion.

[0221] (26) The method according to any one of (21) to (25), wherein suppressing EMI in the corresponding signal-suppressed MR data portion includes: determining a component decomposition of the corresponding signal-suppressed MR data portion; modifying the component decomposition by setting at least one singular value of the component decomposition to a predetermined value or by multiplying at least one singular value of the component decomposition by a predetermined weight; and using the modified component decomposition to obtain the corresponding EMI-suppressed MR data portion.

[0222] (27) The method according to any one of (21) to (26), wherein at least one RF coil includes a plurality of RF coils, and wherein each data portion of the plurality of data portions includes measurements made by each of the plurality of RF coils for a corresponding one of the plurality of frequencies or time intervals.

[0223] (28) The method according to any one of (21) to (27), wherein the data in the corresponding signal-suppressed data portion is organized as an N c ×M matrix, where N c is an integer representing the number of RF coils and M is an integer representing the number of measurements made by each of the RF coils, and wherein determining a component decomposition of the corresponding signal-suppressed MR data portion includes: determining a singular value decomposition of the N c ×M matrix.

[0224] (29) The method according to any one of (21) to (28), further comprising: obtaining the MR data using the at least one RF receive coil portion of the MRI system during operation of the MRI system according to a spin echo pulse sequence or a gradient echo pulse sequence.

[0225] (30) The method according to any one of (21) to (29), wherein the spin echo pulse sequence is selected from the group consisting of a T1 pulse sequence, a T2 pulse sequence, a fluid-attenuated inversion recovery (FLAIR) pulse sequence, and a diffusion-weighted imaging (DWI) pulse sequence.

[0226] (31) The method according to any one of (21) to (30), further comprising: suppressing EMI in the MR data detected by an auxiliary RF coil of the MRI system after generating the MR data and before identifying the noisy subset.

[0227] (32) The method according to any one of (21) to (31), wherein the EMI is narrowband EMI, and wherein the MR data includes sensor domain data organized in a plurality of frequency or time intervals, and wherein the EMI exists in a certain frequency or time interval among the plurality of frequency or time intervals.

[0228] (33) The method according to any one of (21) to (32), wherein the EMI exists in no more than a threshold number of adjacent frequency or time intervals among the plurality of frequency or time intervals.

[0229] (34) The method according to any one of (21) to (33), wherein the EMI exists in the nth frequency or time interval among the plurality of frequency or time intervals, and wherein applying the filtering to the noisy subset of the MR data includes: calculating a weighted linear combination of data in a plurality of frequency or time intervals including the nth frequency or time interval, using weights determined by the coefficients of the filtering.

[0230] (35) The method according to any one of (21) to (34), wherein identifying the first subset of the MR data includes: identifying data in a single frequency or time interval or a set of adjacent frequency or time intervals of the MR data that is affected by EMI.

[0231] (36) The method according to any one of (21) to (35), wherein the MR data includes data in a plurality of frequency or time intervals, the EMI exists in a plurality of adjacent frequency or time intervals, and applying the filtering to the noisy subset of the MR data includes: applying a convolutional filter having a length at least equal to the number of adjacent frequency or time intervals among the plurality of adjacent frequency or time intervals.

[0232] (37) The method according to any one of (21) to (36), wherein identifying the noisy subset of the MR data is performed by analyzing a part of the MR data acquired during at least one predetermined echo signal of a pulse sequence used to acquire the MR data.

[0233] (38) The method according to any one of (21) to (37), wherein identifying the noisy subset of the MR data includes: determining whether data in a frequency or time interval or a set of adjacent frequency or time intervals has an amplitude greater than a threshold.

[0234] (39)A method according to any one of (21) to (38), wherein generating the MR image includes: modifying the MR data by replacing the noisy subset of the MR data with the denoised subset of the MR data to obtain modified MR data; and using the modified MR data to generate the MR image.

[0235] (40)A magnetic resonance imaging system, i.e., an MRI system, includes: a magnetic system having a plurality of magnetic components for generating a magnetic field for performing MRI by acquiring magnetic resonance data, i.e., MR data; and a processor configured to perform a method for generating an MR image by suppressing electromagnetic interference, i.e., EMI, in the MR data, the suppression being for removing zipper line noise artifacts that would otherwise be visible in the MR image: using the processor, identifying a noisy subset of the MR data in which the EMI is to be suppressed; using the processor, applying a filter to the noisy subset of the MR data to suppress the contribution of one or more MR signals to the noisy subset to obtain signal-suppressed MR data; using the processor, suppressing the EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; using the processor, applying the inverse of the filter to the EMI-suppressed MR data to obtain a denoised subset of the MR data in which the EMI has been at least partially suppressed; and using the processor, using the denoised subset of the MR data to generate the MR image.

[0236] (41)At least one tangible computer-readable storage medium storing instructions executable by a processor, the instructions, when executed by the processor, cause the processor to perform a method for generating an MR image by suppressing electromagnetic interference, i.e., EMI, in magnetic resonance data, i.e., MR data, the MR data being obtained using at least one radio frequency receiving coil unit, i.e., at least one RF receiving coil unit, of an MRI system, the suppression being for removing zipper line noise artifacts that would otherwise be visible in the MR image, the MRI system communicating with a computer including the processor, the method including: using the processor, identifying a noisy subset of the MR data in which the EMI is to be suppressed; using the processor, applying a filter to the noisy subset of the MR data to suppress the contribution of one or more MR signals to the noisy subset to obtain signal-suppressed MR data; using the processor, suppressing the EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; using the processor, applying the inverse of the filter to the EMI-suppressed MR data to obtain a denoised subset of the MR data in which the EMI has been at least partially suppressed; and using the processor, using the denoised subset of the MR data to generate the MR image.

[0237] Accordingly, after describing several aspects and embodiments of the technology set forth in this disclosure, it should be understood that various changes, modifications, and improvements will readily occur to those skilled in the art. Such changes, modifications, and improvements are intended to be within the spirit and scope of the technology described herein. For example, those of ordinary skill in the art will readily envision various other ways and / or structures for performing the functions and / or obtaining the results and / or one or more of the advantages described herein, and such variations and / or modifications are each considered to be within the scope of the embodiments described herein. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation many equivalents to the specific embodiments described herein. Accordingly, it should be understood that the foregoing embodiments are presented by way of example only, and that, within the scope of the appended claims and their equivalents, embodiments of the invention may be practiced otherwise than as specifically described. Additionally, any combination of two or more of such features, systems, articles, materials, kits, and / or methods, if not mutually inconsistent, is included within the scope of this disclosure.

[0238] The above embodiments can be implemented in any of a number of ways. One or more aspects and embodiments of the present disclosure related to the performance of a process or method can be processed or the performance of the method controlled using program instructions executable by a device (e.g., a computer, a processor, or other device). In this regard, various inventive concepts can be embodied as a computer-readable storage medium (or multiple computer-readable storage media) (e.g., computer memory, one or more floppy disks, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in a field-programmable gate array or other semiconductor device, or other tangible computer storage media) encoded with one or more programs that, when executed on one or more computers or other processors, perform the method for implementing one or more of the various embodiments described above. One or more computer-readable media can be transportable, such that the one or more programs stored on the one or more computer-readable media can be loaded onto one or more different computers or other processors to implement various aspects of the above aspects. In some embodiments, the computer-readable medium can be a tangible (e.g., non-transitory) computer-readable medium. In some embodiments, the computer-readable medium can include persistent memory.

[0239] As used herein, the term "program" or "software" generally refers to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement the various aspects described above. Additionally, it should be understood that, according to one aspect, one or more computer programs that perform the methods of the present disclosure, when executed, need not reside on a single computer or processor, but can be distributed in a modular fashion among multiple different computers or processors to implement the various aspects of the present disclosure.

[0240] Computer-executable instructions can take many forms, such as program modules executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. for performing particular tasks or implementing particular abstract data types. Typically, the functionality of program modules can be combined or distributed as desired in various embodiments.

[0241] Additionally, data structures can be stored in a computer-readable medium in any suitable form. For simplicity of illustration, a data structure may be shown as having fields related by their positions in the data structure. Similarly, such a relationship can be implemented by allocating storage for the fields with positions in the computer-readable medium for conveying the relationship between the fields. However, any suitable mechanism can be used to establish the relationship between the information in the fields of a data structure, including by using pointers, tags, or other mechanisms for establishing the relationship between data elements.

[0242] When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether disposed in a single computer or distributed among multiple computers.

[0243] Furthermore, it should be understood that, by way of non-limiting example, a computer can be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer can be embedded in a device that is not generally considered a computer but has appropriate processing capabilities, including a personal digital assistant (PDA), a smart phone, or any other suitable portable or fixed electronic device.

[0244] In addition, a computer may have one or more than one input and output device. These devices can be used to present a user interface, etc. Examples of output devices that can be used to provide a user interface include: printers or displays for visual presentations for output and speakers or other sound generating devices for audible presentations for output. Examples of input devices that can be used for a user interface include: keyboards and pointing devices such as mice, touchpads, and digitizing tablets. As another example, a computer can receive input information by voice recognition or in other audible formats.

[0245] Such computers can be interconnected via one or more than one network in any suitable form, including local area networks or wide area networks such as enterprise networks and intelligent networks (IN) or the Internet, etc. Such networks can be based on any suitable technology and can operate according to any suitable protocol, and can include wireless networks, wired networks, or fiber optic networks.

[0246] In addition, as described, some aspects can be embodied as one or more than one method. The actions taken as part of a method can be ordered in any suitable way. Thus, even if shown as sequential actions in an exemplary embodiment, embodiments can be constructed that perform the actions in a different order than that illustrated, which can include performing some actions simultaneously.

[0247] All definitions as defined and used herein shall be understood to control dictionary definitions, definitions in incorporated by reference documents, and / or the ordinary meaning of the defined terms.

[0248] Unless explicitly indicated to the contrary, as used in the specification and claims, the indefinite articles "a" and "an" used herein shall be understood to mean "at least one".

[0249] As used herein in the specification and claims, the phrase "and / or" shall be understood to mean "either or both" of the elements so combined (i.e., elements presented together in some cases and separately in other cases). Multiple elements listed using "and / or" shall be interpreted in the same way, i.e., "one or more than one" of the elements so combined. There may optionally be other elements in addition to those specifically identified by the "and / or" clause, whether related or unrelated to those specifically identified elements. Thus, as a non-limiting example, a reference to "A and / or B" when used in conjunction with open-ended language such as "comprising", in one embodiment, may refer only to A (optionally including elements other than B); in another embodiment, may refer only to B (optionally including elements other than A); in yet another embodiment, may refer to both A and B (optionally including other elements); and so on.

[0250] As used herein in the specification and claims, the phrase "at least one," when referring to a list of one or more elements, shall be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements, and not excluding any combinations of elements in the list of elements. This definition also allows that there may optionally be additional elements other than those specifically identified within the list of elements referred to by the phrase "at least one," whether related or unrelated to the specifically identified elements. Thus, as a non-limiting example, "at least one of A and B" (or equivalently "at least one of A or B," or equivalently "at least one of A and / or B") may, in one embodiment, refer to optionally including at least one A more than one, without B (and optionally including elements other than B); in another embodiment, it may refer to optionally including at least one B more than one, without A (and optionally including elements other than A); in yet another embodiment, it may refer to optionally including at least one A more than one and optionally including at least one B more than one (and optionally including other elements); and so on.

[0251] In the claims and in the foregoing specification, all transitional phrases such as "comprising," "including," "carrying," "having," "containing," "involving," "holding," and "constituting" shall be understood to be open-ended, i.e., meaning including but not limited to. Only the transitional phrases "consisting of" and "consisting essentially of" shall be closed or semi-closed transitional phrases, respectively.

[0252] The terms "about" and "approximately" may, in some embodiments, be used to mean within ±20% of the target value, in some embodiments within ±10% of the target value, in some embodiments within ±5% of the target value, and in some embodiments within ±2% of the target value. The terms "about" and "approximately" may include the target value.

[0253] Cross-reference to Related Applications

[0254] This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 351,710, filed on June 13, 2022, the entire content of which is incorporated herein by reference.

Claims

1. A method for suppressing electromagnetic interference (EMI) in magnetic resonance data (MR data) obtained by a magnetic resonance imaging system, i.e., an MRI system, the method comprising: using at least one computer hardware processor to perform: identifying a first subset of the MR data affected by EMI; suppressing the EMI in the first subset of the MR data to obtain a second subset of the MR data by: applying a filter to the first subset of the MR data to suppress the contribution of MR spin echo signals in the first subset of the MR data, thereby obtaining signal-suppressed MR data; suppressing the EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; and applying the inverse of the filter to the EMI-suppressed MR data to obtain the second subset of the MR data; using the second subset of the MR data to generate an MR image; and outputting the generated MR image.

2. The method according to claim 1, wherein, Suppressing the EMI in the signal-suppressed MR data includes: using component decomposition to obtain the EMI-suppressed MR data; or applying a domain transform to the signal-suppressed MR data, using component decomposition to obtain the EMI-suppressed MR data; and applying the inverse of the domain transform.

3. The method according to claim 2, wherein The first subset of the MR data includes multiple data portions for corresponding multiple frequency or time intervals, and wherein suppressing the EMI in the first subset of the MR data includes: for each specific data portion among the multiple data portions, applying the filter to the specific data portion to obtain a corresponding signal-suppressed MR data portion; suppressing the EMI in the corresponding signal-suppressed MR data portion to obtain a corresponding EMI-suppressed MR data portion; and applying the inverse of the filter to the EMI-suppressed MR data portion.

4. The method according to claim 3, wherein Suppressing the EMI in the corresponding signal-suppressed MR data portion includes: determining the component decomposition of the corresponding signal-suppressed MR data portion; modifying the component decomposition by setting at least one component value of the component decomposition to a predetermined value or by multiplying at least one component value of the component decomposition by a predetermined weight; and using the modified component decomposition to obtain the corresponding EMI-suppressed MR data portion.

5. The method according to claim 4, Among them, the MRI system includes multiple radio frequency coils, i.e., multiple RF coils, and wherein each data portion among the multiple data portions includes measurements made by each of the multiple RF coils for a corresponding frequency or time interval among the multiple frequency or time intervals.

6. The method according to claim 5, Among them, Data in a corresponding MR data portion subject to signal suppression is organized as an N c × M matrix, where N c is an integer representing the number of RF coils, and M represents one or more further dimensions of the number of measurements made by each of the RF coils among the RF coils, and where determining the component decomposition of the corresponding signal-suppressed MR data portion includes: determining the singular values or higher-order singular value decomposition of the N c × M matrix.

7. The method according to claim 1, further comprising: before identifying the first subset of the MR data, generating the MR data by operating the MRI system according to a spin echo pulse sequence or a gradient echo pulse sequence.

8. The method according to claim 7, further comprising: after generating the MR data and before identifying the first subset, Suppress EMI in the MR data detected by the auxiliary coil of the MRI system.

9. The method according to claim 1, wherein The EMI is narrowband EMI, and wherein, the MR data includes sensor domain data in a plurality of frequency or time intervals, and wherein, the EMI exists in a certain frequency or time interval among the plurality of frequency or time intervals.

10. The method according to claim 9, wherein, The EMI exists in no more than a threshold number of adjacent frequency or time intervals among the plurality of frequency or time intervals.

11. The method according to claim 9, wherein, The EMI exists in the nth frequency or time interval among the plurality of frequency or time intervals, and wherein, applying the filtering to the first subset of the MR data includes: calculating a weighted linear combination of data in a plurality of frequency or time intervals including the nth frequency or time interval, using weights determined by the coefficients of the filtering.

12. The method according to claim 1, wherein, The MR data includes data in a plurality of frequency or time intervals, The EMI exists in a plurality of adjacent frequency or time intervals, and Applying the filtering to the first subset of the MR data includes: applying a convolutional filter having a length at least equal to the number of adjacent frequency or time intervals among the plurality of adjacent frequency or time intervals.

13. The method according to claim 1, wherein, Identifying the first subset of the MR data includes: Identifying data in a single frequency or time interval or a set of adjacent frequency or time intervals in the MR data that is affected by EMI.

14. The method according to claim 13, wherein, Identifying the first subset of the MR data is performed by: Analyzing a portion of the MR data acquired during at least one predetermined echo signal of the pulse sequence used to acquire the MR data, or Determining whether data in the single frequency or time interval or the set of adjacent frequency or time intervals has an amplitude greater than a threshold.

15. A magnetic resonance imaging system, i.e., an MRI system, comprising: A magnetic system having a plurality of magnetic components to generate a magnetic field for performing MRI by acquiring MR data; And At least one processor configured to perform: Identifying a first subset of the MR data affected by electromagnetic interference, i.e., EMI; Suppressing EMI in the first subset of the MR data to obtain a second subset of the MR data by: Applying a filtering to the first subset of the MR data to suppress the contribution of MR spin echo signals in the first subset of the MR data, thereby obtaining signal-suppressed MR data; Suppressing EMI in the signal-suppressed MR data to obtain EMI-suppressed MR data; And Applying the inverse of the filtering to the EMI-suppressed MR data to obtain the second subset of the MR data; Using the second subset of the MR data to generate an MR image; And Outputting the generated MR image.

Images