System and method for RF interference removal for optimal MRI signal to interference ratio

Abstract

A method comprising acquiring k-space data of a subject acquired during a scan from a plurality of receiving channels of a magnetic resonance imaging scanner (102), wherein at least some of the plurality of receiving channels are located at spatial locations different from the other receiving channels of the plurality of receiving channels. The method comprises acquiring channel sensitivity data of the plurality of receiving channels acquired using the magnetic resonance imaging scanner (102) prior to acquiring the k-space data. The method comprises acquiring noise data of the plurality of receiving channels acquired using the magnetic resonance imaging scanner (102) prior to acquiring the k-space data. The method comprises linearly combining the k-space data from the plurality of receiving channels based on both the channel sensitivity data and the noise data to estimate a virtual channel with virtual k-space data that maximizes the signal-to-radio frequency interference ratio while isolating zipper artifacts.

Description

Background Technology

[0001] The topics disclosed in this article relate to medical imaging, and more specifically to systems and methods for radio frequency interference removal to obtain optimal signal-to-interference ratios in magnetic resonance imaging.

[0002] Non-invasive imaging techniques allow for the acquisition of images of the internal structures or features of a patient / subject without the need for invasive procedures. Specifically, such non-invasive imaging techniques rely on various physical principles (such as differential transmission of X-rays through a target volume, sound wave reflection within the volume, paramagnetism of different tissues and materials within the volume, and the disintegration of the target radionuclide within the body) to acquire data and construct images or otherwise represent the observed internal features of a patient / subject.

[0003] During magnetic resonance imaging (MRI), when material such as human tissue is subjected to a uniform magnetic field (polarization field B0), the individual magnetic moments of the spins within the tissue attempt to align with that polarization field, but precess around it in a random order at their characteristic Larmor frequencies. If the material or tissue is subjected to a magnetic field (excitation field B1) in the xy-plane and close to the Larmor frequency, the net alignment magnetic moment, or "longitudinal magnetization" M, is determined. z It can be rotated or "tilted" into the xy plane to produce a net transverse magnetic moment M. t After the excitation signal B1 is terminated, a signal is emitted by the excitation spin, and this signal can be received and processed to form an image.

[0004] When these signals are used to generate images, the magnetic field gradient (G) is employed. x G y and G z Typically, the area to be imaged is scanned sequentially according to a measurement cycle, during which these gradient fields vary depending on the specific localization method used. The resulting set of received nuclear magnetic resonance (NMR) signals is digitized and processed to reconstruct an image using one of many well-known reconstruction techniques.

[0005] MR images are acquired using coils. Multiple coils and / or multiple channels of one or more coils simultaneously capture signals from different spatial locations. Cartesian k-space acquisition is the most common data acquisition method. In Cartesian k-space acquisition, a specific direction is called the frequency-encoded direction because each spatial location is encoded as a frequency in the MR image acquisition. In the presence of radio frequency interference, spatial locations corresponding to the radio frequency interference frequencies in the frequency encoding will be corrupted by the interfering signals. If the desired signal and the interfering signal occupy the same frequency band, they cannot be effectively separated. Radio frequency manifests as zipper artifacts and high noise levels in MRI and can significantly affect image quality and its diagnostic quality. These zipper artifacts and high noise levels are particularly amplified in high-value MRI systems such as low-field MRI systems and mobile MRI systems due to hardware degradation and use case environments. Summary of the Invention

[0006] The following provides an overview of some of the embodiments disclosed herein. It should be understood that these aspects are provided merely to give the reader a brief overview of these specific embodiments, and are not intended to limit the scope of this disclosure. In fact, this disclosure may cover various aspects that may not be set forth below.

[0007] In one embodiment, a computer-implemented method for removing radio frequency interference is provided. The computer-implemented method includes obtaining k-space data of a subject acquired during a scan from multiple receiver channels of a magnetic resonance imaging (MRI) scanner via a processing system including one or more processors, wherein at least some of the multiple receiver channels are located at spatial locations different from other receiver channels. The computer-implemented method further includes obtaining channel sensitivity data of the multiple receiver channels acquired using the MRI scanner prior to acquiring the k-space data via the processing system. The computer-implemented method further includes obtaining noise data of the multiple receiver channels acquired using the MRI scanner prior to acquiring the k-space data via the processing system. The computer-implemented method further includes estimating a virtual channel with virtual k-space data by linearly combining the k-space data from the multiple receiver channels based on both the channel sensitivity data and the noise data, while isolating zipper artifacts.

[0008] In another embodiment, a system for removing radio frequency interference is provided. The system includes a memory that encodes processor-executable routines. The system also includes a processing system comprising one or more processors configured to access the memory and execute the processor-executable routines, wherein the processor-executable routines, when executed by the processing system, cause the processing system to perform actions. These actions include obtaining k-space data of a subject acquired during a scan from multiple receiver channels of a magnetic resonance imaging (MRI) scanner, wherein at least some of the multiple receiver channels are located at spatial locations different from other receiver channels. These actions also include obtaining channel sensitivity data of the multiple receiver channels acquired using the MRI scanner prior to the acquisition of the k-space data. These actions further include obtaining noise data of the multiple receiver channels acquired using the MRI scanner prior to the acquisition of the k-space data. These actions also include linearly combining the k-space data from the multiple receiver channels based on both the channel sensitivity data and the noise data to estimate a virtual channel with virtual k-space data that maximizes the signal-to-RF interference ratio while isolating zipper artifacts.

[0009] In another embodiment, a non-transitory computer-readable medium is provided, comprising processor-executable code that, when executed by a processing system comprising one or more processors, causes the processing system to perform actions. These actions include obtaining k-space data of a subject acquired during a scan from multiple receiver channels of a magnetic resonance imaging (MRI) scanner, wherein at least some of the multiple receiver channels are located at spatial locations different from other receiver channels. These actions also include obtaining channel sensitivity data of the multiple receiver channels acquired using the MRI scanner prior to acquiring the k-space data. These actions further include obtaining noise data of the multiple receiver channels acquired using the MRI scanner prior to acquiring the k-space data. These actions also include linearly combining the k-space data from the multiple receiver channels based on both the channel sensitivity data and the noise data to estimate a virtual channel with virtual k-space data that maximizes the signal-to-radio frequency interference ratio while isolating zipper artifacts. Attached Figure Description

[0010] These and other features, aspects, and advantages of the invention will be better understood when reading the following detailed description with reference to the accompanying drawings, in which like reference numerals denote like parts throughout the drawings, wherein:

[0011] Figure 1 Magnetic resonance (MR) images depicting a portion of a subject with zipper artifacts;

[0012] Figure 2 A schematic diagram illustrating a magnetic resonance imaging (MRI) system suitable for use with the disclosed techniques is shown;

[0013] Figure 3 This is a schematic diagram of an image processing system according to various aspects of this disclosure;

[0014] Figure 4 This is a schematic diagram of a process for removing radio frequency interference and generating a zipper-free image according to various aspects of this disclosure;

[0015] Figure 5 An image depicting an input receiving channel according to various aspects of this disclosure is provided;

[0016] Figure 6 An image depicting a virtual channel generated according to various aspects of this disclosure is shown;

[0017] Figure 7 This is a flowchart of a method for removing radio frequency interference according to various aspects of this disclosure;

[0018] Figure 8 Images depicting aspects of this disclosure associated with methods for removing radio frequency interference;

[0019] Figure 9 Images depicting a comparison of the disclosed method with noise whitening according to various aspects of this disclosure; and

[0020] Figure 10 Images depicting a comparison of the disclosed methods with principal component analysis according to various aspects of this disclosure. Detailed Implementation

[0021] One or more specific implementations will be described below. To provide a concise description of these implementations, not all characteristics of the actual implementations are described in the specification. It should be understood that in the development of any such actual implementation, as in any engineering or design project, many implementation-specific decisions must be made to achieve the developer's specific objectives, such as complying with system-related and business-related constraints that may differ from implementation to implementation. Furthermore, it should be understood that such development work may be complex and time-consuming, but remains a routine task of design, fabrication, and manufacturing for those skilled in the art who benefit from this disclosure.

[0022] When describing elements of various embodiments of the subject matter of this invention, the articles “a,” “an,” “the,” and “the” are intended to indicate the presence of one or more elements among the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that additional elements may be present in addition to the listed elements. Furthermore, any numerical examples in the following discussion are intended to be non-limiting, and therefore the additional numerical values, ranges, and percentages are within the scope of the disclosed embodiments.

[0023] While the various aspects discussed below are provided within the context of medical imaging, it should be understood that the disclosed techniques are not limited to such medical settings. In fact, the examples and explanations provided in such medical settings are merely for illustrative purposes by offering real-world examples of implementation and application. However, the disclosed techniques can also be used in other settings, such as image reconstruction for non-destructive inspection of manufactured parts or goods (i.e., quality control or quality inspection applications) and / or non-invasive inspection of packages, boxes, luggage, etc. (i.e., security screening or screening applications). Generally, the disclosed techniques can be used in any imaging or screening setting or in the field of image processing or photography, where a set or class of acquired data undergoes a reconstruction process to generate an image or volume.

[0024] Low-cost MRI systems primarily achieve this through hardware degradation (such as reducing field strength), resulting in additional artifacts and image quality deterioration. This necessitates compensation measures within the software reconstruction pipeline to maintain image quality. One of the major artifacts in these systems is the zipper artifact, which arises due to radio frequency interference occurring within or outside the system. The zipper artifact manifests as distinct lines along the phase encoding direction (indicated by arrow 10), such as... Figure 1 The MR image 12 depicts this, and it can significantly reduce the diagnostic quality of the resulting image.

[0025] This disclosure provides systems and methods for MR image reconstruction solutions that address these problems without requiring additional sensors (e.g., external sensors or coils for detecting radio frequency interference). Specifically, the systems and methods are configured to remove radio frequency interference and, consequently, zipper artifacts. The disclosed techniques utilize redundancy in radio frequency interference observed across channels in multichannel MRI systems to detect and remove zipper artifacts. The desired signal and interference vary across channels. All channels receive radio frequency interference. However, the variation of the desired signal and radio frequency interference signal across channels is influenced by factors such as the signal source, the orientation and magnitude of the specific channel, and the path the signal traverses from the source to the channel. The spatial variation of the desired signal across different channels depends on the spatial sensitivity distribution of the receiver coils. This relationship means that the signal change across channels reflects the inherent sensitivity pattern of the coils / channels, resulting in variations in the spatial location specific to each coil / channel and its response to the input signal. The spatial variation of the desired signal across channels is smooth and captured in the channel / coil sensitivity matrix, while the spatial variation of the radio frequency interference depends on the Fourier spectrum of the radio frequency interference. Furthermore, the desired signal source is closer to the receiver channel than the radio frequency interference source. These different spatial variations are used to linearly combine (e.g., using beamforming) the received signal, and a set of virtual coils / channels tailored to the data is subsequently derived, thereby effectively separating RF interference from the desired data simultaneously across the virtual coils / channels. The virtual coils / channels maximize the signal-to-RF interference ratio. MR images can be generated, thereby separating and suppressing / eliminating RF interference through various combinations of the generated virtual channels.

[0026] The disclosed technology provides simultaneous channel compression and artifact / noise removal. By reducing artifacts, the disclosed technology enables radiologists and medical professionals to obtain / utilize higher-quality images, leading to more accurate diagnoses and better patient outcomes. The disclosed technology offers reduced hardware costs because the need for additional sensors or expensive components is minimized / eliminated. The disclosed technology provides a software-based solution that is easier to integrate into existing systems, allowing for seamless upgrades and enhancements without significant hardware changes.

[0027] The disclosed embodiments include a computer-implemented method and system for removing radio frequency interference. This computer-implemented method and system includes obtaining k-space data of a subject acquired during scanning from multiple receiver channels of a magnetic resonance imaging (MRI) scanner via a processing system including one or more processors, wherein at least some of the multiple receiver channels are located in spatial locations different from other receiver channels. The computer-implemented method and system also includes obtaining channel sensitivity data of the multiple receiver channels acquired using the MRI scanner prior to acquiring the k-space data via the processing system. The computer-implemented method and system further includes obtaining noise data of the multiple receiver channels acquired using the MRI scanner prior to acquiring the k-space data via the processing system. The computer-implemented method and system even includes estimating a virtual channel with virtual k-space data by linearly combining the k-space data from the multiple receiver channels based on both the channel sensitivity data and the noise data via the processing system. This virtual channel maximizes the signal-to-RF interference ratio while isolating zipper artifacts (e.g., through separation of the desired signal from the RF interference).

[0028] In some embodiments, the computer-implemented method and system includes calculating an inter-channel signal correlation matrix using channel sensitivity data via a processing system. In some embodiments, the computer-implemented method and system includes calculating an inter-channel radio frequency interference (RF interference) correlation matrix using noise data via a processing system. In some embodiments, the computer-implemented method and system includes determining, via a processing system, channel combination weights that maximize signal and minimize RF interference when calculating the signal-to-RF interference ratio for each virtual channel based on the inter-channel signal correlation matrix and the inter-channel RF interference correlation matrix, using generalized eigenvector decomposition. In some embodiments, the estimation of virtual channels is based on channel combination weights. In some embodiments, the computer-implemented method and system includes generating a reconstructed image free of zipper artifacts via a processing system using virtual k-space data from a subset of virtual channels. In some embodiments, the computer-implemented method and system includes determining, via a processing system, a subset of virtual channels to be used to generate the reconstructed image based on the percentile of the total variance retained from the eigenvalues ​​used in estimating each corresponding virtual channel in the virtual channel estimation.

[0029] In some implementations, multiple receive channels are part of multiple radio frequency coils. In some implementations, multiple receive channels are part of a single radio frequency coil. In some implementations, zipper artifacts are removed without utilizing external sensors to determine radio frequency interference.

[0030] Considering the above, Figure 2The magnetic resonance imaging (MRI) system 100 is schematically illustrated as including a scanner 102, a scanner control circuitry system 104, and a system control circuitry system 106. According to the embodiments described herein, the MRI system 100 is generally configured to perform MR imaging.

[0031] System 100 further includes: a remote access and storage system or device, such as a Picture Archiving and Communication System (PACS) 108; or other devices, such as remote radiology equipment, enabling on-site or remote access to data acquired by system 100. In this way, MR data can be acquired and then processed and evaluated on-site or remotely. While MRI system 100 may include any suitable scanner or detector, in the illustrated embodiment, system 100 includes a whole-body scanner 102 with a housing 120 through which an aperture 122 is formed. An examination table 124 is movable into the aperture 122 to allow a patient 126 (e.g., a subject) to be positioned therein for imaging of selected anatomical structures within the patient's body.

[0032] Scanner 102 includes a series of associated coils for generating a controlled magnetic field used to excite gyromagnetic material within the anatomical structures of the patient being imaged. Specifically, a primary magnetic coil 128 is provided to generate a primary magnetic field B0 generally aligned with an aperture 122. A series of gradient coils 130, 132, and 134 allow the generation of a controlled gradient magnetic field during the examination sequence for positional encoding of certain gyromagnetic nuclei within the patient 126. A radio frequency (RF) coil 136 (e.g., an RF transmit coil) is configured to generate radio frequency pulses for exciting certain gyromagnetic nuclei within the patient. In addition to the coils that may be located locally within scanner 102, system 100 also includes a set of receiving coils or RF receiving coils 138 (e.g., an array of coils) configured to be placed proximal to (e.g., against) the patient 126. For example, receiving coils 138 may include cervical / thoracic / lumbar (CTL) coils, head coils, single-sided spinal coils, etc. Generally, the receiving coil 138 is placed near or above the patient 126 in order to receive weak RF signals generated by certain magnetic nuclei in the patient's body when the patient 126 returns to its relaxed state (weak in relation to the transmit pulse generated by the scanner coil).

[0033] The various coils of system 100 are controlled by external circuitry to generate desired fields and pulses and to read out emissions from the gyromagnetic material in a controlled manner. In an illustrated embodiment, a main power supply 140 powers the primary field coil 128 to generate a primary magnetic field Bo. Power inputs (e.g., power from a utility or grid), a power distribution unit (PDU), a power supply (PS), and drive circuitry 150 may together provide power to cause gradient field coils 130, 132, and 134 to generate pulses. Drive circuitry 150 may include amplification and control circuitry for supplying current to the coils according to a sequence of digitized pulses output by scanner control circuitry 104.

[0034] Another control circuit 152 is provided for regulating the operation of the RF coil 136. Circuit 152 includes a switching device for alternating between an active operating mode and a passive operating mode, wherein the RF coil 136 transmits a signal and does not transmit a signal, respectively. Circuit 152 also includes an amplifier configured to generate RF pulses. Similarly, a receiving coil 138 is connected to a switch 154 capable of switching the receiving coil 138 between a receiving mode and a non-receiving mode. Thus, in receiving mode, the receiving coils 138 resonate with the RF signal generated by the release of the magnetic nucleus within the patient 126, and in non-receiving mode, they do not resonate with the RF energy from the transmitting coil (i.e., coil 136) to prevent undesirable operation. Additionally, the receiving circuit 156 is configured to receive data detected by the receiving coil 138 and may include one or more multiplexing and / or amplification circuits.

[0035] It should be noted that although the scanner 102 and the control / amplification circuit described above are illustrated as being coupled by a single wire, in practice, many such wires may exist. For example, separate wires may be used for control, data communication, power transmission, etc. Furthermore, appropriate hardware may be provided along each type of wire for proper handling of data and current / voltage. In practice, various filters, digitizers, and processors may be provided between the scanner and either or both of the scanner control circuit 104 and system control circuit 106.

[0036] As shown in the figure, the scanner control circuit 104 includes an interface circuit 158 ​​that outputs signals for driving the gradient field coil and the RF coil, and for receiving data representing magnetic resonance signals generated in the examination sequence. The interface circuit 158 ​​is coupled to a control and analysis circuit 160. Based on a defined scheme selected via the system control circuit 106, the control and analysis circuit 160 executes commands for driving circuits 150 and 152.

[0037] The control and analysis circuit 160 is also used to receive magnetic resonance signals and perform subsequent processing before sending the data to the system control circuit 106. The scanner control circuit 104 also includes one or more memory circuits 162 that store configuration parameters, pulse sequence descriptions, inspection results, etc. during operation.

[0038] Interface circuitry 164 is coupled to control and analysis circuitry 160 for exchanging data between scanner control circuitry 104 and system control circuitry 106. In some embodiments, control and analysis circuitry 160, while exemplified as a single unit, may include one or more hardware devices. System control circuitry 106 includes interface circuitry 166 that receives data from scanner control circuitry 104 and sends data and commands back to scanner control circuitry 104. Control and analysis circuitry 168 may include a CPU in a general-purpose or special-purpose computer or workstation. Control and analysis circuitry 168 is coupled to memory circuitry 170 to store programming code for operating the MRI system 100, and to store processed image data for subsequent reconstruction, display, and transmission. The programming code may execute one or more algorithms configured, when executed by a processor, to remove radio frequency interference as described below. In some embodiments, the disclosed techniques may occur on a separate computing device having processing and memory circuitry.

[0039] Additional interface circuitry 172 may be provided for exchanging image data, configuration parameters, etc., with external system components, such as remote access and storage device 108. Finally, system control and analysis circuitry 168 may be communicatively coupled to various peripheral devices for use in facilitating the operator interface and for generating hard copies of the reconstructed images. In the illustrated embodiment, these peripheral devices include a printer 174, a monitor 176, and a user interface 178, which includes devices such as a keyboard, mouse, and touchscreen (e.g., integrated with monitor 176).

[0040] refer to Figure 3 An image processing system 202 is shown, which is configured to receive and process k-space data. In some embodiments, the image processing system 202 is combined with... Figure 2In the MRI system 100, for example, an image processing system 202 may be provided as a data processing unit within the MRI system 100. In some embodiments, at least a portion of the image processing system 202 is located at a device (e.g., an edge device, server, etc.) communicatively coupled to the MRI system 100 via a wired and / or wireless connection. In some embodiments, at least a portion of the image processing system 202 is located at a separate device (e.g., a workstation) that can receive k-space data from the MRI system 100 or from a storage device storing image / k-space data generated by the MRI system 100. The image processing system 202 may be operatively / communically coupled to a user input device 232 and a display device 234. The user input device 232 may be integrated into the MRI system, such as at a user input device in the MRI system 100. Similarly, the display device 234 may be integrated into the MRI system, such as at a display device in the MRI system 100.

[0041] Image processing system 202 includes processor 204 configured to execute machine-readable instructions stored in non-transitory memory 206. Processor 204 may be single-core or multi-core, and programs executing on it may be configured for parallel or distributed processing. In some embodiments, processor 204 may optionally include individual components distributed across two or more devices, these individual components may be located remotely and / or configured for collaborative processing. In some embodiments, one or more aspects of processor 204 may be virtualized and executed by remotely accessible networked computing devices configured in a cloud computing configuration.

[0042] Then, the non-transitory memory 206 can store the image processing / reconstruction module 208 and the k-space / image database 214. The image processing / reconstruction module 208 can obtain k-space data from the k-space image database. The image processing / reconstruction module 208 is configured to remove radio frequency interference. The image processing / reconstruction module 208 is configured to obtain k-space data of the subject acquired during scanning from multiple receiving channels of a magnetic resonance imaging scanner (one or more coils), wherein at least some of the multiple receiving channels are located in spatial locations different from other receiving channels. The image processing / reconstruction module 208 is configured to obtain channel sensitivity data of the multiple receiving channels acquired using the magnetic resonance imaging scanner prior to acquiring the k-space data. The image processing / reconstruction module 208 is configured to obtain noise data of the multiple receiving channels acquired using the magnetic resonance imaging scanner prior to acquiring the k-space data. The image processing / reconstruction module 208 is configured to linearly combine the k-space data from the plurality of received channels based on both the channel sensitivity data and the noise data to estimate a virtual channel with virtual k-space data. This virtual channel maximizes the signal-to-RF interference ratio while isolating zipper artifacts (e.g., separation of the desired signal from RF interference). Optimizing the signal-to-RF interference avoids signal loss that would otherwise occur if only radio interference were minimized.

[0043] In some embodiments, the image processing / reconstruction module 208 is configured to compute an inter-channel signal correlation matrix using channel sensitivity data. In some embodiments, the image processing / reconstruction module 208 is configured to compute an inter-channel radio frequency interference (RF interference) correlation matrix using noise data. In some embodiments, the image processing / reconstruction module 208 is configured to determine channel combination weights that maximize signal and minimize RF interference when computing the signal-to-RF interference ratio for each virtual channel based on the inter-channel signal correlation matrix and the inter-channel RF interference correlation matrix. In some embodiments, the estimation of virtual channels is based on channel combination weights. In some embodiments, the image processing / reconstruction module 208 is configured to generate a reconstructed image free of zipper artifacts using virtual k-space data from a subset of virtual channels. In some embodiments, the image processing / reconstruction module 208 is configured to determine a subset of virtual channels to be used to generate the reconstructed image based on the percentile of the total variance retained from the eigenvalues ​​used in estimating each corresponding virtual channel in the virtual channel estimation.

[0044] In some implementations, multiple receive channels are part of multiple radio frequency coils. In some implementations, multiple receive channels are part of a single radio frequency coil. In some implementations, zipper artifacts are removed without utilizing external sensors to determine radio frequency interference.

[0045] The non-transitory memory 206 also stores a k-space / image database 214. The k-space / image database 214 may include, for example, k-space data acquired via an MRI system and images reconstructed from the k-space data. For example, the k-space / image database 214 may store k-space data acquired via MRI system 100 and / or received from other communicatively coupled MRI systems or image databases. In some examples, the k-space / image database 214 may store images reconstructed by the image processing / reconstruction module 208.

[0046] In some embodiments, the nontransitory memory 206 may include components located at two or more devices that are remotely located and / or configured for coordinated processing. In some embodiments, one or more aspects of the nontransitory memory 206 may include remotely accessible networked storage devices configured in a cloud computing configuration.

[0047] User input device 232 may include one or more of a touchscreen, keyboard, mouse, touchpad, motion-sensing camera, or other devices configured to enable a user to interact with and manipulate data within image processing system 202. Display device 234 may include one or more display devices utilizing virtually any type of technology. In some embodiments, display device 234 may include a computer monitor and be capable of displaying MR images. Display device 234 may be integrated into the same package as processor 204, nontransitory memory 206, and / or user input device 232, or may be a peripheral display device and may include a monitor, touchscreen, projector, or other display devices known in the art that enable a user to view MRI images generated by the MRI system and / or interact with various data stored in nontransitory memory 206.

[0048] It should be understood that Figure 3 The image processing system 202 shown is for illustration only, not for limitation. Another suitable image processing system may include more, fewer, or different components.

[0049] Figure 4This is a schematic diagram of process 236 for removing radio frequency interference (while optimizing the signal-to-interference ratio) and generating a zipper-free image. Process 236 includes estimating a virtual channel using a channel / coil sensitivity matrix and noise acquired before imaging data acquisition (i.e., the acquisition of the subject's data) in the presence of stationary radio frequency interference (i.e., unchanged between noise data acquisition and imaging data acquisition). Process 236 includes acquiring data, as indicated by reference numeral 238 in the attached figure. The data is k-space data of the subject acquired from multiple channels (e.g., receiving channels of one or more receiving radio frequency coils, such as body coils or surface coils) at different spatial locations of the magnetic resonance imaging scanner. Let c i Let k be the spatial signal acquired in the i-th receiver channel. It can be modeled as follows:

[0050]

[0051] For i=1, 2, …N c , where N c It is the number of channels, s i Represents a signal, and n i Represents noise, and r i This indicates radio interference. Figure 5 Image 240 depicts signals simultaneously captured by multiple receiving channels (e.g., 14 input channels) at different spatial locations.

[0052] Process 236 also includes obtaining coil / channel sensitivity data (e.g., a coil / channel sensitivity map) as part of the imaging session (e.g., during pre-scanning) (but before acquiring imaging data on the subject's imaging volume (i.e., the acquired k-space data 238)), as indicated by reference numeral 242 in the appendix. The coil / channel sensitivity map is used to obtain the inter-coil / inter-channel signal correlation matrix R. S Inter-coil / inter-channel signal correlation matrix R S It is the cross-correlation of the coil / channel sensitivity vectors across the slice, which is represented below: Data from each channel is converted into columns, forming a matrix used as the basis for analysis. The advantage of using sensitivity maps on direct imaging data is that it provides a more accurate approximation of noise-free / artifact-free signals.

[0053] Process 236 also includes acquiring noise data as part of the imaging session (e.g., during pre-scanning) (but before acquiring imaging data (i.e., acquired k-space data 238)) without exciting any imaging volumes within the imaging volume, as indicated by reference numeral 244 in the attached diagram. The noise data is used to obtain / estimate the inter-coil / inter-channel RF interference correlation matrix R. Z Inter-coil / inter-channel signal radio frequency interference correlation matrix RZ The cross-correlation of noise vectors within the desired frequency range of radio frequency interference is expressed as follows: In some embodiments, to more accurately approximate the zipper variance, peaks in the spectrum of the noise data are identified. A range of values ​​around these peaks is selected to calculate the radio frequency interference correlation matrix. In some embodiments, process 236 also includes obtaining imaging volume geometry data as part of the imaging session (e.g., from calibration during pre-scanning) (but prior to acquiring imaging data (i.e., acquired k-space data 238)), as indicated by reference numeral 246 in the accompanying drawings.

[0054] As noted above, these different spatial variations are used to linearly combine (e.g., using beamforming) the received signals, and a set of virtual coils / channels tailored to the data is subsequently derived, thereby effectively separating radio frequency interference from the desired data across the virtual coils / channels simultaneously. Process 236 enables simultaneous channel compression and radio frequency interference / zipper artifact removal. Specifically, one or more algorithms (as indicated by reference numeral 247 in the appendix) are used to remove radio frequency interference (and zipper artifacts) to maximize the MRI signal-to-interference ratio. The algorithm linearly combines (e.g., using beamforming) both channel sensitivity data and noise data from k-space data (via compression of the received channels) to estimate a virtual channel with virtual k-space data that maximizes the signal-to-interference ratio while isolating zipper artifacts (e.g., via separation of radio frequency interference from the desired signal). Optimizing the signal-to-interference ratio avoids signal loss that occurs when only radio interference is minimized. In some embodiments, imaging volume geometry data can also be used to estimate the virtual channel.

[0055] With channel combination weight w i The signal-to-interference ratio in virtual channel i is given by the following formula:

[0056]

[0057] The signal-to-interference ratio (SIR) can be used to find channel combination weights that maximize signal strength and minimize or increase the distance between signal and interference. Equation 4 has the form of a generalized Rayleigh quotient. The channel combination weights used to generate virtual channels are on the order of SIR, and the normalized orthogonal channel combination weights across channels can be generated using generalized eigenvalue decomposition as a standard eigenvalue problem of the matrix. Specifically, ordered eigenvectors W generate virtual coils / channels with ordered SIR values. In some implementations, Tikhonov regularization can be applied to improve... The stability of the estimation. When generating the channel combination weights, the final matrix (i.e., the final form of Equation 4) is used as the weight basis matrix in channel compression.

[0058] Collected data C i It can be linearly transformed into virtual channel data according to the following formula:

[0059]

[0060] For j=1, 2, …N c , where N c It is the number of channels, w ij This represents the channel combination weight, and v j (k) represents the j-th virtual channel. This can be represented in matrix form as follows:

[0061]

[0062] Where V, W, and C are vectors that are stacked to form column vectors, respectively. i w i and c i The resulting matrix.

[0063] Figure 6 Image 248 depicts the generated virtual channels (in order from maximum to minimum eigenvalues). The virtual channel corresponding to the maximum eigenvalue maximizes the signal while minimizing interference, thus effectively enhancing the desired signal. Conversely, the virtual channel corresponding to the minimum eigenvalue minimizes the signal and maximizes interference. In some embodiments, the virtual channels can be derived before scanning to acquire imaging data. Process 236 includes generating a reconstructed image 249 (e.g., a zipperless image) using virtual k-space data from a subset of the virtual channels. The number of virtual channels (i.e., the subset) is based on the percentile of the total variance retained from the eigenvalues ​​used when estimating each corresponding virtual channel. This ensures that only the most important components are used. In some embodiments, other virtual channel combination schemes can be utilized to improve SIR.

[0064] Figure 7 A flowchart illustrating method 250 for removing radio frequency interference (while optimizing the signal-to-interference ratio) is provided. One or more steps of method 1100 can be performed by... Figure 2 The processing circuitry of the magnetic resonance imaging system 100 or a remote computing device is used to execute the operation.

[0065] Method 250 includes acquiring k-space data of a subject during a scan from multiple receiving channels of a magnetic resonance imaging scanner (box 252). At least some of the multiple receiving channels are located in different spatial locations from the other receiving channels. In some embodiments, some receiving channels may overlap. In some embodiments, each of the receiving channels may be located in a different spatial location. In some embodiments, the multiple receiving channels are part of multiple radio frequency coils. In some embodiments, the multiple receiving channels are part of a single radio frequency coil.

[0066] Method 250 further includes obtaining channel sensitivity data (e.g., a channel sensitivity map) (on the imaging volume of the subject) for multiple receiving channels acquired using a magnetic resonance imaging scanner (box 254) prior to acquiring k-space data. The channel sensitivity data is acquired as part of the imaging session (e.g., during pre-scanning) but prior to acquiring imaging data.

[0067] Method 250 also includes obtaining noise data of the plurality of receiving channels acquired using a magnetic resonance imaging scanner prior to the acquisition of the k-space data (box 256). The noise data is acquired as part of the imaging session (e.g., during pre-scanning) but prior to the acquisition of imaging data (without exciting any imaging volume).

[0068] Method 250 even includes using channel sensitivity data to calculate the inter-channel signal correlation matrix (R0). S (Box 258). Data from each channel is converted into columns to form a matrix used as the basis for analysis. The advantage of using sensitivity maps on direct imaging data is that it provides a more accurate approximation of noise-free / artifact-free signals.

[0069] Method 250 also includes using noise data to calculate the inter-channel radio frequency interference correlation matrix (R0). Z (Box 260). In some implementations, to more accurately approximate the zipper variance, peaks in the spectrum of the noise data are identified. A range of values ​​around these peaks is selected to calculate the radio frequency interference correlation matrix.

[0070] Method 250 further includes using generalized eigenvector decomposition to determine channel combination weights that maximize signal and minimize RF interference when calculating the signal-to-RF interference ratio for each virtual channel based on the inter-channel signal correlation matrix and the inter-channel RF interference correlation matrix (box 262). Equation 4 can be used to determine the channel combination weights. Method 250 also includes estimating virtual channels with virtual k-space data that maximize the signal-to-RF interference ratio while isolating zipper artifacts (e.g., separation from the desired signal via RF interference) based on a linear combination (e.g., compression) of both the channel sensitivity data and the noise data from the plurality of received channels (box 264). The estimation of the virtual channels is based on the channel combination weights. Equation 3 can be used to estimate the virtual channels.

[0071] In some implementations, method 250 includes determining a subset of virtual channels to be used to generate the reconstructed image based on the percentile of the total variance retained from the eigenvalues ​​used in estimating each corresponding virtual channel in the virtual channels (box 266). Method 250 includes generating the reconstructed image using virtual k-space data from the subset of virtual channels (i.e., less than the sum of all virtual channels) (box 268). This ensures that only the most important components are used. In some implementations, other virtual channel combination schemes may be utilized to improve SIR. The reconstructed image is free of zipper artifacts.

[0072] Figure 8 The method for removing radio frequency interference (e.g., Figure 7 Image 270 is an input MR image of a part of the subject (e.g., the brain). Image 270 is a channel combination image of the input channels (i.e., the receiving channels of one or more coils) utilized in conjunction with the algorithm discussed above. Image 272 is an output MR image obtained by removing radio frequency interference using method 250. Image 272 is a reconstructed image from a subset of the estimated virtual channels. Image 272 is free of zipper artifacts. Image 274 is residual data discarded using method 250.

[0073] Figure 9 Images depicting a comparison between the disclosed method and noise whitening are presented. Image 276 is an input MR image of a portion of a subject (e.g., the brain). Image 276 is a channel combination image of the input channels (i.e., the receive channels of one or more coils), which is utilized in conjunction with different techniques for channel compression and noise removal. Image 278 is an output MR image generated using standard noise whitening. Image 280 is an output MR image generated using custom noise whitening for zipper artifact removal. Image 282 is an image generated using... Figure 7The output MR image generated by method 250 is shown in Figure 278. As depicted, standard noise whitening techniques fail to completely remove zipper artifacts, leaving residual distortion in the image. On the other hand, a custom noise whitening technique effectively removes zipper artifacts, but at the cost of image quality, resulting in a significant degradation as depicted in Figure 280. This trade-off highlights the challenge of achieving both artifact removal and image preservation simultaneously. Figure 282 removes zipper artifacts while providing a high-quality image.

[0074] Figure 10 Images depicting a comparison between the disclosed method and Principal Component Analysis (PCA) are shown. Image 284 is an input MR image of a part of the subject (e.g., the brain). Image 284 is a channel combination image of the input channels (i.e., the receiving channels of one or more coils) utilized with different techniques. Principal Component Analysis is the most commonly used channel compression technique. Image 286 is an output MR image generated using Principal Component Analysis. Image 288 is residual data discarded using Principal Component Analysis. Image 290 is... Figure 7 The output MR image generated by method 250. Image 292 is generated using... Figure 7 The residual data discarded by method 250. As depicted, compared to method 250, principal component analysis failed to remove zipper artifacts and even lost more image information.

[0075] The technical effects of the disclosed subject matter include providing simultaneous channel compression and artifact / noise removal. The technical effects of the disclosed subject matter include enabling radiologists and medical professionals to obtain / utilize higher-quality images while reducing artifacts, leading to more accurate diagnoses and better patient outcomes. The technical effects of the disclosed subject matter include providing reduced hardware costs, as the need for additional sensors or expensive components is minimized / eliminated. The technical effects of the disclosed subject matter include providing a software-based solution that is easier to integrate into existing systems, allowing for seamless upgrades and enhancements without significant hardware changes.

[0076] This disclosure also provides support for a computer-implemented method for removing radio frequency interference, the computer-implemented method comprising: obtaining k-space data of a subject acquired during scanning from multiple receiving channels of a magnetic resonance imaging scanner via a processing system including one or more processors, wherein at least some of the multiple receiving channels are located at spatial locations different from other receiving channels of the multiple receiving channels; obtaining channel sensitivity data of the multiple receiving channels acquired using the magnetic resonance imaging scanner prior to the acquisition of the k-space data via the processing system; obtaining noise data of the multiple receiving channels acquired using the magnetic resonance imaging scanner prior to the acquisition of the k-space data via the processing system; and estimating a virtual channel having virtual k-space data, the virtual channel maximizing the signal-to-RF interference ratio while isolating zipper artifacts, via the processing system. In a first example of the computer-implemented method, the computer-implemented method further comprises calculating an inter-channel signal correlation matrix using the channel sensitivity data via the processing system. In a second example of the computer-implemented method, optionally including the first example, the computer-implemented method further comprises calculating an inter-channel radio frequency interference correlation matrix using the noise data via the processing system. In a third example of the computer-implemented method, optionally including one or both of the first and second examples, the computer-implemented method further includes, via a processing system, determining channel combination weights that maximize signal and minimize radio frequency interference when calculating the signal-to-interference ratio of each virtual channel based on the inter-channel signal correlation matrix and the inter-channel radio frequency interference correlation matrix. In a fourth example of the computer-implemented method, optionally including one or more or each of the first to third examples, the estimation of virtual channels is based on channel combination weights. In a fifth example of the computer-implemented method, optionally including one or more or each of the first to fourth examples, the computer-implemented method further includes, via a processing system, generating a reconstructed image free of zipper artifacts using virtual k-space data from a subset of the virtual channels. In a sixth example of the computer-implemented method, optionally including one or more or each of the first to fifth examples, the computer-implemented method further includes, via a processing system, determining a subset of virtual channels to be used to generate the reconstructed image based on the percentile of the total variance retained from the eigenvalues ​​used in estimating each corresponding virtual channel in the virtual channels. In a seventh example of the computer-implemented method, one or more or each of the first to sixth examples is optionally included, wherein the zipper artifact is removed without utilizing external sensors to determine radio frequency interference. In an eighth example of the computer-implemented method, one or more or each of the first to seventh examples is optionally included, wherein the plurality of receiving channels are part of a plurality of radio frequency coils.In the ninth example of the computer-implemented method, one or more or each of the first to eighth examples may be included, wherein the plurality of receiving channels are part of a single radio frequency coil.

[0077] This disclosure also provides support for a system for removing radio frequency interference, the system comprising: a memory encoding processor-executable routines; and a processing system including one or more processors and configured to access the memory and execute the processor-executable routines, wherein the processor-executable routines, when executed by the processing system, cause the processing system to: acquire k-space data of a subject acquired during a scan from a plurality of receiving channels of a magnetic resonance imaging scanner, wherein at least some of the plurality of receiving channels are located at spatial locations different from other of the plurality of receiving channels; acquire channel sensitivity data of the plurality of receiving channels acquired using the magnetic resonance imaging scanner prior to the acquisition of the k-space data; acquire noise data of the plurality of receiving channels acquired using the magnetic resonance imaging scanner prior to the acquisition of the k-space data; and estimate a virtual channel having virtual k-space data based on a linear combination of both the channel sensitivity data and the noise data from the plurality of receiving channels, the virtual channel maximizing the signal-to-RF interference ratio while isolating zipper artifacts. In a first example of the system, the processor-executable routines, when executed by the processing system, also cause the processing system to compute an inter-channel signal correlation matrix using the channel sensitivity data. In a second example of the system, optionally including the first example, the processor-executable routines, when executed by the processing system, also cause the processing system to compute an inter-channel radio frequency interference correlation matrix using the noise data. In a third example of the system, optionally including one or both of the first and second examples, the processor-executable routines, when executed by the processing system, also cause the processing system to determine, when computing the signal-to-radio frequency interference ratio for each virtual channel based on the inter-channel signal correlation matrix and the inter-channel radio frequency interference correlation matrix, a channel combination weight that maximizes the signal and minimizes the radio frequency interference using generalized eigenvector decomposition. In a fourth example of the system, optionally including one or more, or each, of the first to third examples, the estimation of the virtual channels is based on the channel combination weights. In a fifth example of the system, one or more or each of the first to fourth examples is optionally included, wherein these processor-executable routines, when executed by the processing system, further cause the processing system to generate a reconstructed image free of zipper artifacts using virtual k-space data from a subset of virtual channels. In a sixth example of the system, one or more or each of the first to fifth examples is optionally included, wherein these processor-executable routines, when executed by the processing system, further cause the processing system to determine a subset of virtual channels to be used for generating the reconstructed image based on the percentile of the total variance retained from the eigenvalues ​​used in estimating each corresponding virtual channel in the virtual channels. In a seventh example of the system, one or more or each of the first to sixth examples is optionally included, wherein zipper artifacts are removed without utilizing external sensors to determine radio frequency interference.

[0078] This disclosure also provides support for a non-transitory computer-readable medium including processor-executable code that, when executed by a processing system comprising one or more processors, causes the processing system to: acquire k-space data of a subject acquired during scanning from multiple receiver channels of a magnetic resonance imaging scanner, wherein at least some of the multiple receiver channels are located at spatial locations different from other receiver channels; acquire channel sensitivity data of the multiple receiver channels acquired using the magnetic resonance imaging scanner prior to the acquisition of the k-space data; acquire noise data of the multiple receiver channels acquired using the magnetic resonance imaging scanner prior to the acquisition of the k-space data; and linearly combine the k-space data from the multiple receiver channels based on both the channel sensitivity data and the noise data to estimate a virtual channel with virtual k-space data that maximizes the signal-to-radio frequency interference ratio while isolating zipper artifacts. In a first example of the non-transitory computer-readable medium, the processor-executable code, when executed by the processing system, also causes the processing system to generate a reconstructed image free of zipper artifacts using virtual k-space data from a subset of the virtual channels.

[0079] Referring to the technology presented herein and protected by the claims, and applying it to physical objects and concrete examples of practical nature, which explicitly improves the present art, and is therefore not abstract, intangible, or purely theoretical. Furthermore, if any claim appended to the end of this specification contains one or more elements designated as “component for [performing]…the function” or “step for [performing]…the function,” such elements are intended to be interpreted according to 35 USC 112(f). However, for any claim containing elements designated in any other manner, such elements are not intended to be interpreted according to 35 USC 112(f).

[0080] This written description uses examples to disclose the subject matter of the invention, including best practices, and also enables those skilled in the art to practice the subject matter, including making and using any device or system and performing any included methods. The patent scope of this subject matter is defined by the claims and may include other examples that would occur to those skilled in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that are not indistinguishable from the literal language of the claims, or if they include equivalent structural elements that have minor differences from the literal language of the claims.

Claims

1. A computer-implemented method for removing radio frequency interference, the computer-implemented method comprising: k-space data of a subject acquired during scanning from multiple receiving channels of a magnetic resonance imaging scanner (102) via a processing system (202) including one or more processors (204), wherein at least some of the multiple receiving channels are located in a different spatial location than the other multiple receiving channels; The processing system (202) obtains channel sensitivity data of the plurality of receiving channels acquired by the magnetic resonance imaging scanner (102) before acquiring the k-space data; The processing system (202) obtains noise data of the plurality of receiving channels acquired by the magnetic resonance imaging scanner (102) before acquiring the k-space data; as well as The processing system (202) linearly combines the k-space data from the plurality of receiving channels based on both the channel sensitivity data and the noise data to estimate a virtual channel with virtual k-space data, which maximizes the signal-to-RF interference ratio while isolating zipper artifacts.

2. The computer-implemented method according to claim 1, further comprising calculating an inter-channel signal correlation matrix via the channel sensitivity data using the processing system (202).

3. The computer-implemented method according to claim 2, further comprising calculating an inter-channel radio frequency interference correlation matrix using the noise data via the processing system (202).

4. The computer-implemented method according to claim 3, further comprising, via the processing system (202), determining, by generalized eigenvector decomposition, channel combination weights that maximize signal and minimize radio frequency interference when calculating the signal-to-radio frequency interference ratio of each virtual channel based on the inter-channel signal correlation matrix and the inter-channel radio frequency interference correlation matrix.

5. The computer-implemented method of claim 4, wherein the estimation of the virtual channel is based on the channel combination weights.

6. The computer-implemented method of claim 5, further comprising generating a reconstructed image free of zipper artifacts via the processing system (202) using virtual k-space data from a subset of the virtual channel.

7. The computer-implemented method of claim 6, further comprising determining, via the processing system (202), the subset of virtual channels to be used for generating the reconstructed image based on the percentile of the total variance retained from the eigenvalues ​​used in estimating each of the respective virtual channels.

8. The computer-implemented method of claim 5, wherein the zipper artifact is removed without utilizing external sensors to determine the radio frequency interference.

9. The computer-implemented method of claim 1, wherein the plurality of receiving channels are part of a plurality of radio frequency coils.

10. The computer-implemented method of claim 1, wherein the plurality of receiving channels are part of a single radio frequency coil.

11. A system for removing radio frequency interference, the system comprising: Memory (206) that encodes processor-executable routines; as well as A processing system (202) comprising one or more processors (204) and configured to access the memory (206) and execute processor-executable routines, wherein the processor-executable routines, when executed by the processing system (202), cause the processing system (202) to: Acquire k-space data of a subject acquired during scanning from multiple receiving channels of a magnetic resonance imaging scanner (102), wherein at least some of the multiple receiving channels are located in a different spatial location than the other multiple receiving channels; Obtain channel sensitivity data of the plurality of receiving channels acquired using a magnetic resonance imaging scanner (102) prior to acquiring the k-space data; Noise data of the plurality of receiving channels acquired using a magnetic resonance imaging scanner (102) prior to the acquisition of the k-space data; as well as The k-space data from the plurality of receiving channels are linearly combined based on the channel sensitivity data and the noise data to estimate a virtual channel with virtual k-space data, which maximizes the signal-to-RF interference ratio while isolating zipper artifacts.

12. The system of claim 11, wherein the processor executable routine, when executed by the processing system (202), further causes the processing system (202) to use the channel sensitivity data to calculate an inter-channel signal correlation matrix.

13. The system of claim 12, wherein the processor executable routine, when executed by the processing system (202), further causes the processing system (202) to use the noise data to calculate an inter-channel radio frequency interference correlation matrix.

14. The system of claim 13, wherein the processor executable routine, when executed by the processing system (202), further causes the processing system (202) to determine, when calculating the signal-to-RF interference ratio of each virtual channel based on the inter-channel signal correlation matrix and the inter-channel RF interference correlation matrix, a channel combination weight that maximizes the signal and minimizes the RF interference using generalized eigenvector decomposition.

15. The system of claim 14, wherein the estimation of the virtual channel is based on the channel combination weights.

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