Accelerating magnetic resonance imaging using parallel imaging and iterative image reconstruction

Abstract

The present disclosure provides various systems and methods for magnetic resonance imaging. In one aspect, a method for magnetic resonance imaging can include receiving k-space datasets acquired by radio frequency (RF) coils. Each of the k-space datasets can correspond to a different one of the RF coils. Each of the k-space datasets can be truncated and / or undersampled. The method can further include generating a partial image of a field of view based on the k-space datasets and generating an initial image based on the partial image. The initial image can be a full image of the field of view. The method can further include applying an iterative image reconstruction technique to generate an updated image based on the initial image.

Description

[Background technology]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of and priority under 35 U.S.C. § 120 of U.S. patent application Ser. No. 18 / 153,111, entitled "ACCELERATING MAGNETIC RESONANCE IMAGING USING PARALLEL IMAGING AND ITERATIVE IMAGE RECONSTRUCTION," filed Jan. 11, 2023, the entire disclosure of which is incorporated herein by reference.

[0002] The present disclosure relates to magnetic resonance imaging (MRI), medical imaging, medical intervention, and surgical intervention. MRI systems often involve large, complex machines that generate very high magnetic fields and pose significant constraints on the feasibility of certain surgical interventions. Constraints can include limited physical access to the patient by surgeons and / or surgical robots, and / or restrictions on the use of certain electrical and mechanical components in the vicinity of the MRI scanner. Such limitations are inherent in the underlying designs of many existing systems and are difficult to overcome. Summary of the Invention [Means for solving the problem]

[0003] According to one aspect, the present disclosure provides a method for magnetic resonance imaging. The method may include receiving k-space data sets acquired by RF coils of a radio frequency (RF) coil assembly. Each of the k-space data sets may correspond to a different one of the RF coils. Each of the k-space data sets may be truncated and / or undersampled. The method may further include generating partial images of a field of view based on the k-space data sets. Each of the partial images may correspond to a different one of the k-space data sets. The method may further include generating an initial image based on the partial images. The initial image is a full image of the field of view. The method may further include applying an iterative image reconstruction technique to generate an updated image based on the initial images.

[0004] In some aspects, applying an iterative image reconstruction technique to generate an update image based on an initial image can include designating the initial image as an input image. Applying the iterative image reconstruction technique can further include applying phase correction to the input image to generate a first intermediate image, applying k-space conjugate compounding to the input image to generate a second intermediate image, and calculating an output image based on the first intermediate image and the second intermediate image. The output image can be designated as an input image for a next iteration. Applying the iterative image reconstruction technique can further include repeating applying phase correction to the input image, applying k-space conjugate compounding to the input image, calculating an output image, and designating the output image as the input image for the next iteration. The update image can be generated based on a final output image of the iterative image reconstruction technique.

[0005] According to another aspect, the present disclosure provides a system. The system may include an array of magnets, a radio frequency (RF) coil assembly, and control circuitry. The array of magnets may be configured to generate a magnetic field of low or very low magnetic field strength toward a target object located within a field of view. The RF coil assembly may include an array of RF coils. The RF coils may be positionable around the target object within the field of view. The RF coils may be configured to acquire magnetic resonance signals. The control circuitry may include a processor and a memory. The memory may store instructions executable by the processor to receive k-space datasets corresponding to the magnetic resonance signals acquired by the RF coils. Each of the k-space datasets may correspond to a different one of the RF coils. Each of the k-space datasets may be truncated and / or undersampled. The memory may further store instructions executable by the processor to generate partial images of the field of view based on the k-space datasets. Each of the partial images may correspond to a different one of the k-space datasets. The memory may further store instructions executable by the processor to apply an iterative image reconstruction technique to generate an initial image, which is a full image of the field of view, based on the partial images, and to generate an updated image based on the initial image. [Brief explanation of the drawings]

[0006] The various aspects described herein, both as to organization and method of operation, together with further objects and advantages thereof, may best be understood by reference to the following description taken in conjunction with the accompanying drawings, in which:

[0007] [Figure 1] FIG. 1 illustrates components of an MRI scanning system including a dome-shaped housing for a magnetic array in accordance with at least one aspect of the present disclosure, the dome-shaped housing enclosing a region of interest therein and further illustrating the dome-shaped housing positioned to receive at least a portion of a patient's head lying on a table within the region of interest.

[0008] [Figure 1A] FIG. 1A shows a patient's head positioned in the region of interest of the MRI scanning system of FIG.

[0009] [Figure 2] FIG. 2 is a perspective view of an alternative dome-shaped housing for a magnetic array for use in the MRI scanning system of FIG. 1 in accordance with at least one aspect of the present disclosure, the dome-shaped housing having an access opening defined therein.

[0010] [Figure 3] FIG. 3 is a perspective view of an alternative dome-shaped housing for a magnetic array for use in the MRI scanning system of FIG. 1 in accordance with at least one aspect of the present disclosure, the dome-shaped housing having an access opening and an adjustable gap defined therein.

[0011] [Figure 4] FIG. 4 illustrates a dome-shaped housing for use in an MRI scanning system having an access opening in the form of a centrally defined hole in accordance with at least one aspect of the present disclosure.

[0012] [Figure 5] FIG. 5 is a cross-sectional view of the dome-shaped housing of FIG. 4 in accordance with at least one aspect of the present disclosure.

[0013] [Figure 6] FIG. 6 illustrates a control schematic of an MRI system in accordance with at least one aspect of the present disclosure.

[0014] [Figure 7] FIG. 7 is a flowchart illustrating a method for acquiring imaging data from an MRI system in accordance with at least one aspect of the present disclosure.

[0015] [Figure 8]FIG. 8 illustrates an MRI scanning system and a robotic system in accordance with at least one aspect of the present disclosure.

[0016] [Figure 9] FIG. 9 shows an exemplary diagram of a fully sampled k-space in accordance with various aspects of the present disclosure.

[0017] [Figure 10] FIG. 10 shows an exemplary diagram of undersampled k-space in accordance with various aspects of the present disclosure.

[0018] [Figure 11] FIG. 11 shows an exemplary diagram of truncated undersampled k-space in accordance with various aspects of the present disclosure.

[0019] [Figure 12] FIG. 12 is a flowchart illustrating a method for magnetic resonance imaging based on a truncated and undersampled k-space data set in accordance with at least one aspect of the present disclosure.

[0020] [Figure 13] FIG. 13 is a flowchart illustrating an iterative image reconstruction technique that may be implemented as part of a method for magnetic resonance imaging based on a truncated and undersampled k-space data set in accordance with at least one aspect of the present disclosure.

[0021] [Figure 14] FIG. 14 is a flowchart illustrating an exemplary implementation of the iterative image reconstruction technique described with respect to FIG. 13 in accordance with at least one aspect of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0022] Corresponding reference characters indicate corresponding parts throughout the several views. The exemplifications set forth herein are illustrative of various disclosed embodiments and are in one form only, and such exemplifications should not be construed as limiting the scope thereof in any way.

[0023] The applicant of the present application owns the following patent applications, each of which is incorporated herein by reference in its respective entirety: -International Patent Application No. PCT / US2022 / 72143, filed May 5, 2022, entitled "NEURAL INTERVENTIONAL MAGNETIC RESONANCE IMAGING APPARATUS." -U.S. Patent Application No. 18 / 057,207, filed November 19, 2022, entitled "SYSTEM AND METHOD FOR REMOVING ELECTROMAGNETIC INTERFERENCE FROM LOW-FIELD MAGNETIC RESONANCE IMAGES." -U.S. Patent Application No. 18 / 147,418, filed December 28, 2022, entitled "MODULARIZED MULTI-PURPOSE MAGNETIC RESONANCE PHANTOM." -U.S. Patent Application No. 18 / 147,542, filed December 28, 2022, entitled "INTRACRANIAL RADIO FREQUENCY COIL FOR INTRAOPERATIVE MAGNETIC RESONANCE IMAGING." -U.S. Patent Application No. 18 / 147,556, filed December 28, 2022, entitled "DEEP LEARNING SUPER-RESOLUTION TRAINING FOR ULTRA LOW-FIELD MAGNETIC RESONANCE IMAGING." -U.S. Patent Application No. 18 / 153,175, filed January 11, 2023, entitled "FAST T2-WEIGHTED AND DIFFUSION-WEIGHTED CHIRPED-CPMG SEQUENCES."

[0024] Before describing various aspects of the neurointerventional magnetic resonance imaging device in detail, it should be noted that the illustrative examples are not limited in application or use to the details of construction and arrangement of parts shown in the accompanying drawings and description. The illustrative examples may be implemented or incorporated with other aspects, variations, and modifications, and may be practiced or carried out in various ways. Furthermore, unless otherwise indicated, the terms and phrases used herein have been chosen to describe the illustrative examples for the convenience of the reader, and not to limit them. It will also be understood that one or more of the aspects, aspect expressions, and / or examples described below may be combined with any one or more of the other aspects, aspect expressions, and / or examples described below.

[0025] Various aspects relate to MRI devices that enable the integration of surgical intervention and guidance by neurointerventional magnetic resonance imaging (MRI). This includes allowing not only physical access to the area around the patient, but also access to the patient's head with one or more access openings. Furthermore, neurointerventional MRI devices may enable the use of robotic-guided tools and / or conventional surgical instruments. In various instances, neurointerventional MRI can be used intraoperatively to obtain scans of a patient's head and / or brain during a surgical intervention, such as a surgical procedure such as a biopsy or neurosurgery.

[0026] 1 illustrates an MRI scanning system 100 including a dome-shaped housing 102 configured to receive a patient's head. The dome-shaped housing 102 may further include at least one access opening configured to allow access to the patient's head to enable neurointervention. The space within the dome-shaped housing 102 forms a region of interest for the MRI scanning system 100. Target tissue within the region of interest is subjected to magnetization fields / pulses, as further described herein, to obtain imaging data representative of the target tissue.

[0027] 1A , a patient can be positioned such that his / her head is positioned within a region of interest within the dome-shaped housing 102. The brain can be positioned entirely within the dome-shaped housing 102. In such cases, to facilitate intracranial intervention (e.g., neurosurgery) in coordination with MR imaging, the dome-shaped housing 102 can include one or more openings that provide access to the brain. The openings can be spaced around the dome-shaped housing.

[0028] The MRI scanning system 100 may include an auxiliary cart (see, e.g., auxiliary cart 540 in FIG. 6 ) that houses certain conventional MRI electrical and electronic components, such as, for example, a computer, a programmable logic controller, a power distribution unit, and amplifiers. The MRI scanning system 100 may also include a magnet cart that holds the dome-shaped housing 102, gradient coils, and / or transmission coils, as described further herein. Additionally, the magnet cart may be attached to a receive coil in various instances. Referring primarily to FIG. 1 , the dome-shaped housing 102 may further include an RF transmission coil, a gradient coil 104 (shown external to the housing), and a shim magnet 106 (shown internal to the housing). Alternative configurations of the gradient coil 104 and / or shim magnet 106 are also contemplated. In various instances, the shim magnet 106 may be adjustably positioned on a shim tray within the dome-shaped housing 102, allowing a technician to granularly configure the magnetic flux density of the dome-shaped housing 102.

[0029] Various structural housings for receiving a patient's head and enabling neurointervention may be utilized with MRI scanning systems, such as MRI scanning system 100. In one aspect, MRI scanning system 100 may be equipped with an alternative housing, such as dome-shaped housing 202 (FIG. 2) or a two-part housing 302 (FIG. 3) configured to form a dome shape. Dome-shaped housing 202 defines multiple access openings 203; two-part housing 302 also defines multiple access openings 303 and further includes an adjustable gap 305 between the two parts of the housing.

[0030] In various instances, the housings 202 and 302 may include a bonding agent 308 (e.g., epoxy resin, etc.) that holds the plurality of magnetic elements 310 in a fixed position. The plurality of magnetic elements 310 may be bonded to a structural housing 312 (e.g., a plastic substrate, etc.). In various aspects, the bonding agent 308 and the structural housing 312 may be non-conductive or diamagnetic materials. Referring primarily to FIG. 3 , the two-part housing 302 includes two structural housings 312. In various aspects, the structural housing for receiving the patient's head may be formed from three or more sub-parts. An access opening 303 in the structural housing 312 provides a direct passageway to the patient's head and is unobstructed by the structural housing 312, the bonding agent 308, or the magnetic elements 310. The access opening 303 may be positioned in the open space of the housing 302, for example.

[0031] There are many possible configurations of neurointerventional MRI devices that can achieve improved access for surgical intervention. Many configurations are based on two main designs, commonly known as Halbach cylinders and Halbach domes, as described in the following papers by Cooley et al. (e.g., Cooley, CZ, Haskell, MW, Cauley, SF, Sappo, C., Lapierre, CD, Ha, CG, Stockmann, JP, and Wald, LL (2018) "Design of sparse Halbach magnet arrays for portable MRI using a genetic algorithm." IEEE Transactions on Magnetics, 54(1), 5100112. The paper by Cooley et al., "Design of sparse Halbach magnet arrays for portable MRI using a genetic algorithm," published in 2018 in IEEE Transactions on Magnetics, 54(1), 5100112, is incorporated herein by reference in its entirety.

[0032] In various cases, a dome-shaped housing for an MRI scanning system such as system 100 can include, for example, a Halbach dome that defines the dome shape and is configured based on several factors, including main magnetic field B strength, magnetic field size, magnetic field uniformity, device size, device weight, and patient access for neurointervention. In various aspects, the Halbach dome includes an outer radius and an inner radius at the base of the dome. The Halbach dome can include an elongated cylindrical portion extending from the base of the dome. In one aspect, the elongated cylindrical portion includes the same outer and inner radii as the base of the dome and continues from the base of the dome for a predetermined length at a constant radius. In another aspect, the elongated cylindrical portion includes different outer and inner radii than the base of the dome (see, for example, FIGS. 2 and 3). In such cases, the different outer and inner radii of the elongated cylindrical portion can merge with the base radius in a transition region.

[0033] 4 illustrates an exemplary Halbach dome 400 for an MRI scanning system, such as system 100, defining an access opening, e.g., in the form of a hole or access opening 403, configured to receive a head and brain B of a patient P within a region of interest therein, the access opening 403 configured to allow access to the patient P and neurointervention with medical instruments and / or robotically controlled surgical tools. The Halbach dome 400 can be constructed with a single access opening 403 on a top surface 418 of the dome 400, allowing access to the top of the skull while minimizing impact with the magnetic field. Additionally or alternatively, the dome 300 can be configured with multiple access openings around the structure 416 of the dome 400, as shown in FIGS. 2 and 3.

[0034] Diameter D of access opening 403 hole can be small (e.g., about 2.54 cm) or very large (substantially the outer diameter r of the dome 400). ext ) As the access opening 403 becomes larger, the dome 400 begins to resemble, for example, a Halbach cylinder. The access opening 403 is not limited to being at the apex of the dome 400. The access opening 403 may be located anywhere on the surface or structure 416 of the dome 400. In various instances, the entire dome 400 may be rotated so that the access opening 403 is co-located with a desired physical location on the patient P.

[0035] FIG. 5 shows the diameter D of the access opening 403. hole , the length L of the dome 400, and the outer radius r of the dome 400 ext and the inner radius r in 4. The Halbach dome 400 includes a plurality of magnetic elements, which are arranged in a Halbach array to form a magnet assembly. The plurality of magnetic elements are arranged in a Halbach array to form a magnet assembly. The plurality of magnetic elements are arranged in a Halbach array to form a magnet assembly. The Halbach dome 400 includes a plurality of magnetic elements, which ... ext and the inner radius rin In one aspect, exemplary dimensions can be defined as follows: r in =19.3cm;r ext =23.6cm;L=38.7cm;2.54cm≦D<19.3cm.

[0036] Based on the above exemplary dimensions, the Halbach dome 400 with access opening 403 may be configured with a magnetic flux density B of approximately 72 mT and an overall mass of approximately 35 kg. It will be appreciated that dimensions may be selected based on the particular application to achieve the desired magnetic flux density B, total weight of the Halbach dome 400 and / or magnet cart, and geometry of the neurointerventional access opening 403.

[0037] In various aspects, the Halbach dome 400 can be configured to define a plurality of access openings 403 disposed about the structure 416 of the dome 400. These plurality of access openings 403 can be configured to allow access to the patient's head and brain B using tools (e.g., surgical tools) and / or a surgical robot.

[0038] In various aspects, the access opening 403 can be adjustable. An adjustable configuration can include, for example, adjusting the diameter D of the access opening 403. hole The ability to adjust the access opening 403 using either a motor, mechanical assistance, or a manual system with a mechanical iris configuration to adjust the access opening 403 can be provided. This allows for a configuration of the dome without the access opening 403, an imaging scan to be performed, and then allows for adjustment of the dome 400 and its mechanical iris configuration to include the access opening 403, thus allowing for surgical intervention.

[0039] Halbach domes and magnetic arrays thereof for facilitating neurointervention are further described in International Patent Application No. PCT / US2022 / 72143, entitled "NEURAL INTERVENTIONAL MAGNETIC RESONANCE IMAGING APPARATUS," filed May 5, 2022, which is incorporated herein by reference in its entirety.

[0040] Referring now to FIG. 6, a schematic diagram of an MRI system 500 is shown. For example, the MRI scanning system 100 (FIG. 1) and various dome-shaped housings and their magnetic arrays described further herein may be incorporated into the MRI system 500. The MRI system 500 includes a housing 502 that may be similar in many aspects to the dome-shaped housings 102 (FIG. 1), 202 (FIG. 2), and / or 302 (FIG. 3), for example. The housing 502 is dome-shaped and configured to form a region of interest or field of view 552 therein. For example, the housing 502 may be configured to receive a patient's head in various aspects of the present disclosure.

[0041] The housing 502 includes a magnet assembly 548 having a plurality of magnets (e.g., a Halbach arrangement of magnets) disposed therein. In various aspects, the main magnetic field B generated by the magnet assembly 548 extends within a field of view 552 that includes an object (e.g., a patient's head) being imaged by the MRI system 500.

[0042] The MRI system 500 also includes an RF transmit / receive coil 550. The RF transmit / receive coil 550 is combined into an integrated transmit / receive (Tx / Rx) coil. In other cases, the RF transmit coil may be separate from the RF receive coil. For example, to acquire imaging data, the RF transmit coil may be incorporated into the housing 502 and the RF receive coil may be positioned within the housing 502.

[0043] The housing 502 also includes one or more gradient coils 504 configured to generate gradient magnetic fields to facilitate imaging of objects within a field of view 552 generated by, for example, a magnet assembly 548 surrounded by a dome-shaped housing and a dome-shaped array of magnetic elements therein. A shim tray adapted to receive shim magnets 506 may also be incorporated into the housing 502.

[0044] During the imaging process, a main magnetic field B0 extends within the field of view 552. The direction of the effective magnetic field (B1) changes in response to RF pulses and associated electromagnetic fields transmitted by the RF transmit / receive coil 550. For example, the RF transmit / receive coil 550 may be configured to selectively transmit RF signals or pulses to objects within the field of view 552, such as tissue in a patient's brain. These RF pulses may change the effective magnetic field experienced by spins within the sample tissue.

[0045] The housing 502 is in signal communication with an auxiliary cart 530 configured to provide power to the housing 502 and send / receive control signals to / from the housing 502. The auxiliary cart 530 includes a power distribution unit 532, a computer 542, a spectrometer 544, a transmit / receive switch 545, an RF amplifier 546, and a gradient amplifier 558. In various instances, the housing 502 can be in signal communication with multiple auxiliary carts, and each cart can support one or more of the power distribution unit 532, the computer 542, the spectrometer 544, the transmit / receive switch 545, the RF amplifier 546, and / or the gradient amplifier 558.

[0046] The computer 542 is in signal communication with the spectrometer 544 and is configured to transmit and receive signals between the computer 542 and the spectrometer 544. When an object within the field of view 552 is excited with RF pulses from the RF transmit / receive coil 550, the precession of the object results in an induced current, i.e., an MR current, which is detected by the RF transmit / receive coil 550 and transmitted to an RF preamplifier 556. The RF preamplifier 556 is configured to boost or amplify the excitation data signals and transmit them to the spectrometer 544. The spectrometer 544 is configured to transmit the excitation data to the computer 542 for storage, analysis, and image construction. The computer 542 is configured, for example, to combine multiple stored excitation data signals to create an image. In various instances, the computer 542 is in signal communication with at least one database 562 that stores a reconstruction algorithm 564 and / or a pulse sequence 566. The computer 542 is configured to utilize the reconstruction algorithm to generate an MR image 568.

[0047] From the spectrometer 544, the signal may also be relayed to an RF transmit / receive coil 550 within the housing 502 via an RF power amplifier 546 and a transmit / receive switch 545 positioned between the spectrometer 544 and the RF power amplifier 546. From the spectrometer 544, the signal may also be relayed to a gradient coil 560 within the housing 502 via a gradient power amplifier 558. For example, the RF power amplifier 546 is configured to amplify the signal and transmit it to the RF transmit coil 560, and the gradient power amplifier 558 is configured to amplify the gradient coil signal and transmit it to the gradient coil 560.

[0048] In various cases, the MRI system 500 can include a noise cancellation coil 554. For example, the auxiliary cart 530 and / or the computer 542 can be in signal communication with the noise cancellation coil 554. In other cases, the noise cancellation coil 554 can be optional. For example, certain MRI systems disclosed herein may not include additional / auxiliary RF coils for detecting and canceling electromagnetic interference, i.e., noise.

[0049] A flowchart illustrating a process 570 for acquiring an MRI image is shown in FIG. 7. This flowchart may be implemented, for example, by the MRI system 500. In various instances, in block 572, a target object (e.g., a portion of a patient's anatomy) is positioned in a main magnetic field B0 within a region of interest (e.g., region of interest 552), such as within a dome-shaped housing (e.g., magnet assembly 548) of various MRI scanners described further herein. The main magnetic field B0 is configured to magnetically polarize hydrogen protons (H-protons) in the target object (e.g., all organs and tissues), known as net longitudinal magnetization M0. It is proportional to the tissue's proton density (PD) and increases exponentially in time with a time constant known as the tissue's longitudinal relaxation time, T1. The T1 value of an individual tissue depends on many factors, including, for example, the individual tissue's microstructure, water and / or lipid content, and the strength of the polarizing magnetic field. For these reasons, the T1 value of a given tissue sample depends on its age and health.

[0050] In block 574, a time-varying oscillating magnetic field B1, i.e., an excitation pulse, is applied to the magnetically polarized target object using an RF coil (e.g., RF transmit / receive coil 550). The carrier frequency of the pulsed B1 field is set to the resonant frequency of the 1H-protons, which causes the longitudinal magnetization to flip and rotate from its equilibrium longitudinal direction, resulting in a magnetization vector that can generally have transverse and longitudinal magnetization components, depending on the flip angle used. Common B1 pulses include inverse pulses, or 180-degree pulses, and 90-degree pulses. The 180-degree pulse reverses the direction of the 1H-proton magnetization in the longitudinal axis. The 90-degree pulse rotates the 1H-proton magnetization by 90 degrees so that the magnetization is in the transverse plane. The MR signal is a time-varying current proportional to the transverse component of the magnetization and is detected with an appropriate RF coil. These MR signals decay exponentially in time with a time constant known as the transverse relaxation time, T2, which also depends on, for example, the fine tissue structure, water / lipid content, and the strength of the magnetic field used.

[0051] In block 576, the MR signals are spatially encoded by exposing the target object to an additional magnetic field (known as a gradient field) generated by a gradient coil (e.g., gradient coil 560). The gradient field, which varies linearly in space, is applied for a short period in pulse form and with spatial variation in each direction. The net result is the generation of multiple spatially encoded MR signals, which can be detected in block 577 and reconstructed to form an MR image depicting a slice of the object. An RF receive coil (e.g., RF transmit / receive coil 550) can be configured to detect the spatially encoded RF signals. The slice can be oriented in a transverse, sagittal, coronal, or any oblique plane.

[0052] In block 578, the spatially encoded signals for each slice of the scan region are digitized and mathematically spatially decoded by a computer reconstruction program (e.g., by computer 542) to generate an image depicting the internal anatomical structures of the subject. In various instances, the reconstruction program may utilize an (inverse) Fourier transform to back-transform the spatially encoded data (k-space data) into geometrically decoded data.

[0053] FIG. 8 shows a diagram of a robotic system 680 that can be used for neurointervention via an MRI scanning system 600. The robotic system 680 includes a computer system 696 and a surgical robot 682. The MRI scanning system 600 can be similar to the MRI system 500 and can include a dome-shaped housing and a magnetic array with access openings, as further described herein. For example, the MRI system 500 can include one or more access openings defined in the Halbach arrangement of magnets in a permanent magnet assembly to provide access to one or more anatomical portions of a patient to be imaged during a medical procedure. In various cases, the robotic arm and / or tool of the surgical robot 682 is configured to extend through the access openings in the permanent magnet assembly to reach the patient or target site. Each access opening can provide access to the patient and / or surgical site. For example, in the case of multiple access openings, the multiple access openings can allow access from different directions and / or proximal positions.

[0054] According to various embodiments, the robotic system 680 is configured to be disposed outside the MRI system 600. As shown in FIG. 8 , the robotic system 680 can include a robotic arm 684 configured to move with one or more degrees of freedom. According to various embodiments, the robotic arm 684 includes one or more mechanical arm portions including a hollow shaft 686 and an end effector 688. The hollow shaft 686 and the end effector 688 are configured to be translated, rotated, and / or pivoted through various ranges of motion via one or more motion controllers 690. The double-headed curved arrows in FIG. 8 indicate exemplary rotational movements generated by the motion controller 690 at various joints of the robotic arm 684.

[0055] According to various embodiments, the robotic arm 684 of the robotic system 682 is configured to access various subject anatomical portions through or around the MRI scanning system 600. According to various embodiments, the access opening is designed to take into account the size of the robotic arm 684. For example, the access opening defines a circumference configured to allow the robotic arm 684, hollow shaft 686, and end effector 688 to pass therethrough. In various cases, the robotic arm 684 is configured to access various anatomical portions of the patient from around the side of the magnetic imaging device 600. The hollow shaft 686 and / or the end effector 688 may be adapted to receive a robotic tool 692, such as a biopsy needle having a cutting edge 694, for example, for obtaining a biopsy sample from the patient.

[0056] The reader will understand that the robotic system 682 can be used in combination with various dome-shaped and / or cylindrical magnetic housings, as further described herein. Furthermore, the robotic system 682 and robotic tool 692 of FIG. 8 are exemplary. Alternative robotic systems can be utilized in connection with the various MRI systems disclosed herein. Furthermore, handheld surgical instruments and / or additional imaging devices (e.g., endoscopes) and / or systems can also be utilized in connection with the various MRI systems disclosed herein.

[0057] In various aspects of the present disclosure, the MRI systems described herein can include low-field MRI (LF-MRI) systems. In such cases, the main magnetic field B generated by the permanent magnet assembly can be, for example, 0.1 T to 1.0 T. In other cases, the MRI systems described herein can include ultra-low-field MRI (ULF-MRI) systems. In such cases, the main magnetic field B generated by the permanent magnet assembly can be, for example, 0.03 T to 0.1 T.

[0058] Higher magnetic fields, such as those exceeding 1.0 T, can prevent the use of certain electrical and mechanical components near MRI scanners. For example, the presence of surgical instruments and / or surgical robot components containing metal, especially ferrous metals, can be dangerous near higher magnetic fields because such tools may be attracted toward the magnetized source. Furthermore, higher magnetic fields often require specially designed rooms with additional precautions and shielding to limit magnetic interference. Despite the limitations of high-field MRI systems, low-field and ultra-low-field MRI systems present various challenges with regard to acquiring high-quality images with sufficient resolution to achieve the desired imaging objectives.

[0059] The LF-MRI and / or ULF-MRI systems described herein may be suitable for use in environments not typically considered suitable for high-field MRI systems, such as intensive care units, emergency rooms, and / or rural medical settings. For example, LF-MRI and ULF-MRI systems may be more portable, lighter, smaller, and / or less expensive than high-field MRI systems. However, the low magnetic field B0 strength of LF-MRI and ULF-MRI systems may pose challenges related to acquisition time and image processing. For example, LF-MRI and ULF-MRI systems generally define a relatively low overall magnetic field B0 homogeneity (e.g., 1,000 ppm and 10,000 ppm within the region of interest) compared to high-field MRI systems, which may result in a reduced signal-to-noise ratio (SNR). Furthermore, LF-MRI and ULF-MRI systems may lack the shielding included in high-field MRI systems, which may lead to increased radio frequency and magnetic field background noise. These field homogeneity and noise-related challenges can make it difficult to acquire magnetic resonance (MR) signals using pulse sequences often implemented by high-field MRI systems to reduce acquisition times, such as fast spin-echo and / or fast gradient-echo sequences. Therefore, there is a need for LF-MRI and ULF-MRI systems and methods for reducing MR signal acquisition times while also addressing the above-mentioned field homogeneity and noise-related challenges.

[0060] The present disclosure provides systems and methods for reducing MR signal acquisition time using parallel imaging and iterative image reconstruction. The systems provided herein can include LF-MRI systems and / or ULF-MRI systems, and the methods provided herein can be implemented using LF-MRI systems and / or ULF-MRI systems. In at least one aspect of the present disclosure, a method for reducing MR signal acquisition time using parallel imaging and iterative image reconstruction can include receiving truncated and undersampled k-space data sets acquired in parallel using multiple radio frequency (RF) coils positioned around a target object in a field of view. Each of the k-space data sets can correspond to a different one of the coils. Inverse Fourier transforming each of the k-space data sets can generate partial images of the field of view. Furthermore, a parallel imaging reconstruction technique (e.g., sensitivity encoding (SENSE)) can be applied to generate an initial image of the full field of view based on the partial images. Furthermore, an iterative image reconstruction technique can be applied to generate an updated image based on the initial image.

[0061] In various aspects, the iterative image reconstruction technique may include designating an initial image generated via parallel imaging reconstruction as an input image. Phase correction may be applied to the input image to generate a first intermediate image. Furthermore, k-space conjugate compounding may be applied to the input image to generate a second intermediate image. An output image may be calculated based on the first intermediate image and the second intermediate image (e.g., by weighting and combining the first intermediate image and the second intermediate image). Furthermore, the output image may be designated as an input image for a next iteration. The application of phase correction to the input image, the application of k-space conjugate compounding to the input image, the calculation of the output image, and the designation of the output image as an input image for the next iteration may be repeated, for example, until a difference between the output image and the corresponding input image satisfies a predetermined threshold. The updated image may be based on a final output image of the iterative image reconstruction technique.

[0062] As described in detail below, acquiring truncated and undersampled k-space data sets in parallel using multiple RF coils can significantly reduce acquisition time, thereby improving efficiency and patient comfort. Furthermore, iterative image reconstruction techniques can improve the quality of initial images generated through parallel image reconstruction by estimating unacquired k-space from the truncated and undersampled k-space data sets. For example, applying phase correction to the input image to generate a first intermediate image can include forcing the phase of the input image to match the phase of a central zone of k-space acquired via a calibration scan. Applying k-space conjugate compounding to the input image to generate a second intermediate image can include replacing reconstructed k-space data associated with the input image with k-space data acquired from the k-space data set. Thus, by combining the first and second intermediate images, using the output image as the input image for the next iteration, and continuing to iterate until a predetermined threshold is met, the iterative image reconstruction technique can accurately estimate unacquired k-space. Therefore, the systems and methods provided herein can generate accurate images with reduced MR signal acquisition times compared to conventional signal acquisition techniques.

[0063] Figure 9 shows an exemplary diagram of fully sampled k-space 900, Figure 10 shows an exemplary diagram of undersampled k-space 1000, and Figure 11 shows an exemplary diagram of truncated undersampled k-space 1100. In some aspects, Figures 9-11 illustrate how acquiring a truncated undersampled k-space data set can significantly reduce acquisition time compared to conventional MR signal acquisition techniques.

[0064] Referring to FIG. 9, the fully sampled k-space 900 is represented by k y and k z The fully sampled k-space 900 represents the arrangement of data corresponding to spatial frequencies arranged in the k direction. zNumber of phase encoding directions N y Nik z Number of phase encoding directions N z The total number of phase encodings, N, is equal to the sum of phase For example, the phase encoding number N y and the number of phase encodings N z If each of these is equal to 256, then the phase encoding number N phase The total number of k-spaces is equal to 256x256. Figure 9 also shows a central zone 902 of the fully sampled k-space 900. Generally, the central zone 902 may correspond to a lower order phase encoding step where the echo amplitude is larger. In accordance with various aspects of the present disclosure, as used herein, the "central zone" of k-space refers to the k y and k z The central zone can refer to a zone of k-space extending from the center of k-space at each of the k y 25% to 50% of k-space in the direction, and k z It includes 25% to 50% of k-space in the direction. For example, the central zone of k-space is y In the direction, the central 50%, central 45%, central 40%, central 35%, central 30%, or central 25% of k-space, and z In a direction, the central 50%, central 45%, central 40%, central 35%, central 30%, or central 25% of k-space can be included.

[0065] 9 and 10, the undersampled k-space 1000 is obtained by dividing each phase encoding N of the fully sampled k-space 900. y and each phase encoding N z Instead, acquisition of some of the spatial frequency data of k-space 1000 is intentionally omitted or "undersampled." Thus, less time is required to acquire the spatial frequency data represented by the undersampled k-space 1000. y The degree of undersampling in the direction is determined by the undersampling ratio R y can be described by R yis the number of phase encodings in the full sampled data set, N y The number of actual phase encodings obtained is N ys It is equal to the division of k space 1000. z The degree of undersampling in the direction is determined by the undersampling ratio R z can be described by R z is the number of encodings in the full sampled data set, N z The number of actual phase encodings obtained is N zs For example, the undersampling ratio R y and the undersampling ratio R z are each equal to 2, and the number of phase encodings for the fully sampled k-space, N y and N z are each equal to 256, the number of actually sampled phase encodings in the undersampled k-space 1000, N ys and N zs is (256 / 2) x (256 / 2), or the number of sampled phase encodings N for fully sampled k-space. phase 10 also shows a central zone 1002 of undersampled k-space 1000.

[0066] 10 and 11, the truncated and undersampled k-space 1100 is shown as k y The truncated range of directions δ y is not sampled according to k z The truncated range of directions δ z δ is similar to the undersampled k-space 1000, except that it is not sampled according to the truncation range δ y and δ z represents the number of phase encodings omitted from the corresponding edge of the fully sampled k-space. For example, N y and N z The number of is equal to 256, and the undersampling rate Ry and R z are each equal to 2, and the truncation range δ y and δ z , and δ are each equal to 96, the number of actually sampled phase encodings in the truncated undersampled k-space 1100 can be represented by ((256−96) / 2)×((256−96) / 2). y and δ z is selected to be slightly oversampled (undersampled) the lower right quadrant of k-space 1100, thereby including the central zone 1102 of k-space 1100. However, in other aspects, the truncation range δ y and δ z may be configured to sample a different quadrant (e.g., upper right, upper left, lower left) or a different sub-region of k-space 1100. According to some aspects of the present disclosure, if one quadrant of k-space corresponds to 25% of the k-space phase encoding for a fully sampled k-space, a truncated k-space that is sampled "slightly more" than one quadrant of k-space can mean that 25% to 50% of the phase encoding of the fully sampled k-space is sampled, such as, for example, 30%, 35%, 40%, or 45% of the phase encoding of the fully sampled k-space is sampled.

[0067] As illustrated by FIGS. 9-11 and the accompanying description above, acquiring a truncated, undersampled k-space 1100 data set can significantly reduce acquisition time compared to acquiring a fully sampled k-space 900 data set. For example, the total acquisition time T required to sample a fully sampled k-space 900 data set is acu-f The total acquisition time T required to sample the truncated and undersampled k-space 1100 data as a function of acq-tu can be expressed as follows:

number

[0068] Although the truncated undersampled k-space 1100 may be acquired more quickly than the fully sampled k-space 900, various challenges exist with reconstructing an accurate image based on the truncated undersampled k-space 1100. For example, reconstructing an image from an undersampled k-space, such as the undersampled k-space 1000, may present challenges because the number of spatial frequencies represented in the undersampled k-space may be insufficient to generate an image that adequately represents the target object in the field of view. Thus, images generated based on an undersampled k-space data set may have aliasing and result in an image that only partially represents the field of view. For example, challenges associated with reconstructing an image from undersampled k-space may arise because at least some missing portions of k-space cannot be estimated based on conjugate symmetry, as illustrated by the truncated undersampled k-space 1100 of FIG. 11 . y Direction and k z Further complications can be introduced by truncating both in the direction and in the direction.

[0069] Various parallel imaging techniques can be applied to undersampled k-space data to address the above-mentioned problems associated with aliasing and reduced field of view. For example, according to the sensitivity encoding (SENSE) technique, multiple RF coils positioned around the target object within the field of view are used to simultaneously acquire separate undersampled k-space data sets. Each k-space data set is inverse Fourier transformed to generate a partial image of the field of view. Furthermore, based on an RF coil sensitivity map (containing information about the position of each RF coil relative to the target object) calculated from a calibration scan of each coil, the partial images are combined to generate a full-field-of-view image. Various details regarding exemplary implementation aspects of the SENSE technique are described in an article entitled "SENSE: Sensitivity Encoding for Fast MRI" by Pruessmann et al., published in Magnetic Resonance in Medicine, 42(5), 952-962 (1999), which is incorporated herein by reference in its entirety. Although SENSE and other parallel imaging techniques can address various challenges associated with reconstructing images from undersampled k-space data, there remains a need for image reconstruction techniques for reconstructing images from truncated and undersampled k-space data.

[0070] FIG. 12 is a flowchart illustrating a method 1200 for magnetic resonance imaging based on a truncated and undersampled k-space dataset in accordance with at least one aspect of the present disclosure. Method 1200 may be performed by an MRI system including an array of magnets, a radio frequency (RF) coil assembly 1204 including an array of RF coils 1204a-d, and control circuitry. The array of magnets may be configured to generate a magnetic field toward a subject object 1202 within a field of view. The RF coils 1204a-d are positionable around the subject object 1202 and configured to acquire magnetic resonance signals. The control circuitry may include a processor and memory. The memory may store instructions executable by the processor to perform method 1200. In some aspects, method 1200 may be performed by various MRI systems described herein, such as MRI system 500 of FIG. 6. For example, the RF coil assembly 1204 of FIG. 12 may be similar to the RF transmit / receive coil 550, and executable instructions for performing the method 1200 may be stored as a reconstruction algorithm 564 in at least one database 562 and executed by the computer 542.

[0071] 12, according to method 1200, k-space data sets 1206a-d acquired by RF coils 1204a-d are received 1201 (e.g., by computer 542 and / or transmit / receive switch 545 of FIG. 6). The k-space data sets 1206a-d may be acquired in parallel (e.g., simultaneously) and may be truncated and undersampled. Each of the k-space data sets 1206a-d corresponds to a different one or more of the RF coils 1204a-d. For example, the first k-space data set 1206a may correspond to the first RF coil 1204a, the second k-space data set 1206b may correspond to the second RF coil 1204b, the third k-space data set 1206c may correspond to the third RF coil 1204c, and the fourth k-space data set 1206d may correspond to the fourth RF coil 1204d.

[0072] The k-space data sets 1206a-d are sampled with an undersampling ratio R equal to 2, 2.5, or 3. y For example, undersampling ratio R is 2 or more. y So, k y The k-space data sets 1206a-d may be undersampled in the x-direction. The k-space data sets 1206a-d may be undersampled in the x-direction by an undersampling ratio R equal to 2, 2.5, or 3. y For example, undersampling ratio R is 2 or more. z So, k z The k-space data sets 1206a-d may be undersampled in the direction of the truncation range δ equal to 10%, 15%, 20%, 25%, 30%, 37.5%, 40%, 45%, or 50% of the number of phase encodings in the full sample k-space. y k within the range of 10% to 50% of the number of phase encodings in the complete sample k-space. y Cutoff range in the direction δ y The k-space data sets 1206a-d can have a truncation range δ equal to 10%, 15%, 20%, 25%, 30%, 37.5%, 40%, 45% or 50% of the number of phase encodings in the complete sample k-space. z k within the range of 10% to 50% of the number of phase encodings in the complete sample k-space. z Cutoff range δ in the direction z As explained above, acquiring the truncated and undersampled k-space data sets 1206a-d in parallel can significantly reduce acquisition time compared to the acquisition time required for a fully sampled k-space data set. For example, R equal to 2 y and R z is undersampled by an undersampling factor of δ y =δ z =96, N y =N z = 256) y and δ zBy configuring the k-space data sets 1206a-d to have , the time required to acquire the k-space data sets 1206a-d can be reduced by approximately 10% compared to fully sampled k-space.

[0073] 12 , according to method 1200, partial images 1208a-d are generated 1203 based on k-space data sets 1206a-d. The partial images 1208a-d can be generated 1203 based on the k-space data sets 1206a-d by inverse Fourier transforming each of the k-space data sets 1206a-d. The partial images 1208a-d are respective subimages of the field of view and may have aliasing because the k-space data sets 1206a-d are undersampled. Each of the partial images 1208a-d corresponds to a different one or more of the k-space data sets 1206a-d. For example, the first partial image 1208a can correspond to the first k-space data set 1206a, the second partial image 1208b can correspond to the second k-space data set 1206b, the third partial image 1208c can correspond to the third k-space data set 1206c, and the fourth partial image 1208d can correspond to the fourth k-space data set 1206d.

[0074] 12 , according to method 1200, an initial image 1210 is generated 1205 based on the partial images 1208a-1208d. The initial image may be a full image of the field of view. The initial image 1210 may be generated 1205 according to various parallel imaging techniques, such as SENSE. For example, according to some aspects of method 1200, calibration k-space data sets acquired by RF coils 1204a-1204d may be received, and a coil sensitivity map may be generated based on the calibration k-space data sets. The coil sensitivity map may include information regarding the position of each of the RF coils 1204a-1204d relative to the object 1202 in the field of view. The initial image 1210 may be generated 1205 based on the partial images 1208a-1208d and the coil sensitivity map. In some aspects, the initial image 1210 can be generated 1205 based on the partial images 1208a-1208d according to the techniques described in the article "SENSE: Sensitivity Encoding for Fast MRI" by Pruessmann et al., published in Magnetic Resonance in Medicine, 42(5), 952-962 (1999). In other aspects, the initial image 1210 can be generated 1205 based on the partial images 1208a-1208d according to a parallel imaging technique such as Array Coil Spatial Sensitivity Encoding Technique (ASSET). In yet other aspects, the initial image 1210 can be generated 1205 based on the k-space datasets 1206a-d according to a parallel imaging technique such as GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) or Autocalibrating Reconstruction for Cartesian imaging (ARC).Various details related to GRAPPA are described in an article by Griswold et al. entitled "Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA)," published in Magnetic Resonance in Medicine 47, 1202-1210, 2002, which is incorporated herein by reference in its entirety.

[0075] In some aspects, because the initial image 1210 is derived from truncated k-space data sets 1206a-d, the initial image 1210 may require further reconstruction to adequately represent the object 1202. Therefore, in accordance with the method 1200, an iterative image reconstruction technique may be applied 1207 to generate an updated image 1212 based on the initial image 1210. The iterative image reconstruction technique may estimate the unacquired k-space, thereby generating an updated image 1212 that accurately represents the object 1202. In some aspects, the iterative image reconstruction technique may be applied 1207 in accordance with an iterative image reconstruction technique 1300, as described below with respect to FIG.

[0076] 12 shows an RF coil assembly 1204 having a first RF coil 1204a, a second RF coil 1204b, a third RF coil 1204c, and a fourth RF coil 1204d, those skilled in the art will appreciate that the RF coil assembly 1204 may be configured to include any number of RF coils 1204 suitable for parallel signal acquisition. For example, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 coils may be included in the RF coil assembly. Similarly, although method 1200 is described above as generating a first k-space data set 1206a, a second k-space data set 1206b, a third k-space data set 1206c, and a fourth k-space data set 1206d, and a first partial image 1208a, a second partial image 1208b, a third partial image 1208c, and a fourth partial image 1208d, those skilled in the art will understand that the number of k-space data sets and the number of partial images generated according to method 1200 can correspond to the configuration of RF coils in RF coil assembly 1204 (e.g., the number of RF coils).

[0077] 13 is a flowchart illustrating an iterative image reconstruction technique 1300 (sometimes referred to as iterative technique 1300) in accordance with at least one aspect of the present disclosure. In some aspects, the iterative technique 1300 may be applied 1207 in accordance with method 1200 ( FIG. 12 ) to generate an updated image 1312 based on an initial image 1310. For example, the initial image 1310 ( FIG. 13 ) may correspond to the initial image 1210 ( FIG. 12 ), and the updated image 1312 ( FIG. 13 ) may correspond to the updated image 1212 ( FIG. 12 ).

[0078] Referring to FIG. 13, an initial image 1310 is designated 1301 as an input image 1314 for an iterative technique 1300. Thus, the initial image 1310 is designated 1301 as the input image 1314 (I n ) can function as (e.g., I initial =I n=1 Following the iterative technique 1300, a first intermediate image 1316 (I n+1(1) ) to generate the input image 1314 (I n ) 1303, and a second intermediate image 1318 (I n+1 (2) ) is applied 1305. A first intermediate image 1316 (I n+1 (1) ) to generate the input image 1314 (I n ) is applied 1303 to the input image 1314 (I n ) to match the phase of the central zone of k-space acquired via a calibration scan. In some aspects, the calibration scan used to determine the phase of the central zone of k-space may be the same calibration scan used to generate coil sensitivity maps for parallel image reconstruction (e.g., generating 1205 initial image 1210 as described above with respect to FIG. 12). n+1 (2) ) to generate the input image 1314 (I n ) is applied 1305 to the input image 1314 (I n ) with k-space data acquired from the k-space data set (e.g., k-space data sets 1206a-d of FIG. 12). In some aspects, the first intermediate image 1316 (I n+1 (1) ) to generate the input image 1314 (I n ) and / or applying a phase correction 1303 to the second intermediate image 1318 (I n+1 (2) ) to generate the input image 1314 (I n ) may be performed according to an exemplary implementation aspect of an iterative image reconstruction technique 1400 described below with respect to FIG.

[0079] Referring again to FIG. 13, following an iterative technique 1300, a first intermediate image 1316 (I n+1 (1) ) and the second intermediate image 1318 (In+1 (2) ) and the output image 1320 (I n+1 ) is calculated 1307. In some aspects, the output image 1320 (I n+1 ) is the first weighting factor (w1) and the first intermediate image 1316 (I n+1 (1) ) is multiplied by the second weighting factor (w2) and the second intermediate image 1318 (I n+1 (2) ) 1307, where the sum of the first weighting factor (w1) and the second weighting factor (w2) is equal to 1. In some aspects, the output image 1320 (I n+1 ) 1307 may be performed according to an exemplary implementation aspect of the iterative image reconstruction technique 1400 described below with respect to FIG.

[0080] Referring again to FIG. 13, the output image 1320 (I n+1 ) is the input image 1314 (I n ) 1309. Furthermore, for the next iteration, the phase correction may be applied to the input image 1314 (I n ) 1303, and k-space conjugate compounding can be applied to the input image 1314 (I n ) can be applied to the output image 1320 (I n+1 ) can be calculated 1307 and the output image 1320 (I n+1 ) is the input image 1314 (I n ) can be redesignated as 1309.

[0081] Updated image 1312(I updated ) is the output image 1320 (I n+1 ) for example, the iterations may be repeated until a predetermined threshold is met. Once the predetermined threshold is met, an updated image 1312 (I updated ) is the final output image 1320 (I n+1) according to some aspects of the iterative technique 1300, the predetermined threshold may be specified based on the output image 1320 (I n+1 ) and the corresponding input image 1314 (I n ) can be filled based on the difference between the output image 1320 (I n+1 ) is the corresponding input image 1314 (I n ) is less than 5%, less than 1%, less than 0.1%, less than 0.01%, less than 0.001%, etc., from the corresponding input image 1314 (I n ) is less than 10% different from the first intermediate image 1316 (I ). According to some aspects of the iterative technique 1300, the predetermined threshold may be met based on the number of iterations performed. For example, the predetermined threshold may be met when the iterative technique 1300 achieves 10 iterations or more than 10 iterations, such as 100 iterations, 1,000 iterations, or 10,000 iterations. The iterative image reconstruction technique 1300 generates a first intermediate image 1316 (I n+1 (1) ) and a second intermediate image 1318 (I n+1 (2) ) based on the output image 1320 (I n+1 ) is calculated 1307, and the output image 1320 (I n+1 ) as the input image 1314 (I n ) 1309 and continue iterating until a predetermined threshold is met, the unsampled k-space due to undersampling and truncation can be accurately estimated.

[0082] FIG. 14 is a flowchart illustrating an iterative image reconstruction technique 1400 (sometimes referred to as iterative technique 1400) in accordance with at least one aspect of the present disclosure. In some aspects, the iterative technique 1400 may represent an example implementation of the iterative image reconstruction technique 1300 described with respect to FIG. 13. For example, an input image 1414 (I n ) (FIG. 14) is an input image 1314 (I n ) (FIG. 14), and the first intermediate image 1416 (I n+1 (1)) (FIG. 14) is a first intermediate image 1316 (I n+1 (1) ) (FIG. 13), and a second intermediate image 1418 (I n+1 (2) ) (FIG. 14) is a second intermediate image 1318 (I n+1 (2) ) (FIG. 13), and the output image 1420 (I n+1 ) (FIG. 14) is the output image 1320 (I n+1 ) (Figure 13).

[0083] Referring to FIG. 14, according to an iterative technique 1400, phase correction is performed to obtain the phase and magnitude 1422 of the input image (I n =|I n |e iφn ) is determined 1401, and the size of the input image 1422 (|I n |) and the phase map of the central zone of k-space (Φ o ) 1424 and a first intermediate image 1416 (I n+1 (1) ) to obtain a first intermediate image 1416 (I n+1 (1) ) to generate the input image 1414 (I n ) can be applied to the input image size 1422(|I n |) and k-space (Φ o ) and a phase map 1424 of the central zone of the first intermediate image 1416 (I n+1 (1) ) 1403 is calculated based on the input image 1414 (I n ) is the phase of the central zone of k-space (Φ o ) to match (I n+1 (1) =|I n |e iΦo According to some aspects of the iterative technique 1400, a phase map (Φ o) 1424 is generated based on a calibration k-space data set acquired by an RF coil of an RF coil assembly (e.g., RF coils 1204a-d of RF coil assembly 1204 of FIG. 12). In one aspect, the calibration k-space data set acquired by the RF coil is the same as the calibration k-space data set acquired to generate the coil sensitivity map used to generate 1205 the initial image 1210 according to the parallel imaging technique, as described above with respect to FIG.

[0084] Referring again to FIG. 14, according to an iterative technique 1400, k-space conjugate synthesis is performed to generate a first intermediate k-space 1426 (K n ) is generated 1405 and a first intermediate k-space 1426 (K n ) based on the second intermediate k-space 1428 (K n ') and generate 1407 a second intermediate k-space 1428 (K n ') based on the second intermediate image 1418 (I n+1 (2) ) to generate a second intermediate image 1418 (I n+1 (2) ) to generate the input image 1414 (I n ) can be applied to the first intermediate k-space 1426 (K n ) is the input image 1414 (I n ) can be generated 1405. A second intermediate k-space 1428 (K n ') are the actual acquired k-space values ​​from one or more of the k-space data sets 1206a-d received 1201 according to the method 1200 of FIG. 12, and are included in the first intermediate k-space 1426 (K n ) may be generated by replacing 1407 at least some of the k-space values ​​from the k-space data sets 1206a-d. For example, all of the actually acquired k-space values ​​from the k-space data sets 1206a-d may be replaced 1407 with the second intermediate k-space 1428 (K n To generate 1407 the first intermediate k-space 1426 (K n ) can be substituted for the corresponding k-space values ​​in the second intermediate k-space 1428 (K nGenerating 1407 an output image 1420 (I ′) that accurately represents the target object 1202 n+1 ) can be used to guide the iterative technique 1400 to ultimately generate a second intermediate k-space 1428 (K n Following the generation 1407 of the second intermediate k-space 1428 (K n ') is the second intermediate image 1418 (I n '=I n+1 (2) ) can be inverse Fourier transformed to generate 1409

[0085] Still referring to FIG. 14, following an iterative technique 1400, an output image 1420 (I n+1 ) is the first weighting factor (w1) and the first intermediate image 1416 (I n+1 (1) ) is multiplied by a second weighting factor (w2) and a second intermediate image 1418 (I n+1 (2) ) and the product of 1411 (e.g., I n+1 =w1I n+1 (1) +w2I n+1 (2) ). The sum of the first weighting factor (w1) and the second weighting factor (w2) is equal to 1 (e.g., 1=w1+w2). The individual values ​​of the first weighting factor (w1) and the second weighting factor (w2) are used to calculate the output image 1420 (I n+1 ) may be selected to optimize the generation of output image 1420 (I n+1 ) is the first intermediate image 1416 (I n+1 (1) ) and the second intermediate image 1418 (I n+1 (2) ) (e.g., 0.5 = w1 = w2). In another aspect, the first weighting factor (w1) and the second weighting factor (w2) may be selected to be the average of the output image 1420 (I n+1 ) is the first intermediate image 1416 (I n+1 (1)), or the first weighting factor (w1) and the second weighting factor (w2) may be selected to be weighted towards the output image 1420 (I n+1 ) is the second intermediate image 1418 (I n+1 (2) ), (e.g., w2>w1).

[0086] Thus, iterative technique 1400 can be implemented as part of iterative technique 1300 (FIG. 13) and method 1200 (FIG. 12) to generate an image that accurately represents an object of interest within a field of view based on a truncated and undersampled k-space data set, thereby enabling LF and / or ULF MRI systems to achieve reduced acquisition times (e.g., compared to the acquisition times required for fully sampled k-space data). (Example)

[0087] Various additional aspects of the subject matter described herein are set forth in the following numbered examples.

[0088] Clause 1: A method for magnetic resonance imaging, the method comprising: receiving k-space data sets acquired by RF coils of a radio frequency (RF) coil assembly, each of the k-space data sets corresponding to a different one of the RF coils, each of the k-space data sets being truncated and undersampled; generating partial images of a field of view based on the k-space data sets, each of the partial images corresponding to a different one of the k-space data sets; generating an initial image based on the partial images, the initial image being a full image of the field of view; and applying an iterative image reconstruction technique to generate an updated image based on the initial images. and applying a phase correction to the input image to generate a first intermediate image, applying k-space conjugate compounding to the input image to generate a second intermediate image, calculating an output image based on the first intermediate image and the second intermediate image, designating the output image as an input image for a next iteration, and repeating the steps of applying the phase correction to the input image, applying k-space conjugate compounding to the input image, calculating an output image, and designating the output image as the input image for the next iteration, wherein the updated image is based on a final output image of the iterative image reconstruction technique.

[0089] Clause 2: The method described in Clause 1, wherein applying phase correction to an input image, applying k-space conjugate synthesis to the input image, calculating an output image, and designating the output image as an input image for the next iteration are repeated until the difference between the output image and the corresponding input image satisfies a predetermined threshold.

[0090] Clause 3: A method described in any of clauses 1 to 2, further comprising receiving calibration k-space data sets acquired by RF coils of the RF coil assembly, each of the calibration k-space data sets corresponding to a different one of the RF coils, and each of the calibration k-space data sets comprising a central zone, and generating a phase map of the central zone based on the calibration k-space data sets.

[0091] Clause 4: A method as described in any of clauses 3, wherein applying phase correction to the input image and generating a first intermediate image includes determining a size of the input image and calculating the first intermediate image based on the size of the input image and the phase map of the central zone.

[0092] Clause 5: A method according to any one of clauses 1 to 4, wherein the k-space data set comprises acquired k-space values, and applying k-space conjugate synthesis to the input image and generating a second intermediate image comprises generating a first intermediate k-space by Fourier transforming the input image, the first intermediate k-space comprising intermediate k-space values; generating a second intermediate k-space from the first intermediate k-space by replacing at least some of the intermediate k-space values ​​with at least some of the acquired k-space values; and generating the second intermediate image by inverse Fourier transforming the second intermediate k-space.

[0093] Clause 6: A method according to any one of clauses 1 to 5, wherein calculating the output image based on the first intermediate image and the second intermediate image includes adding the product of the first intermediate image and the first weighting factor to the product of the second intermediate image and the second weighting factor, wherein the sum of the first weighting factor and the second weighting factor is equal to 1.

[0094] Clause 7: The method of any one of clauses 3 to 6, further comprising generating a coil sensitivity map based on the calibration k-space data set.

[0095] Clause 8: The method described in Clause 7, wherein the k-space data sets are acquired in parallel and generating an initial image based on the partial images includes generating the initial image based on the partial images and a coil sensitivity map.

[0096] Clause 9: The method of clause 8, wherein generating an initial image based on the partial images and the coil sensitivity map includes generating the initial image according to a sensitivity encoding (SENSE) technique.

[0097] Clause 10: A method according to any one of clauses 1 to 9, wherein each of the k-space data sets is undersampled in the first and second lateral directions based on an undersampling ratio of at least 2, and each of the k-space data sets is truncated by at least 37.5% in the first and second lateral directions.

[0098] Clause 11: A method according to any one of clauses 1 to 10, wherein each of the k-space data sets is undersampled, truncated and acquired in parallel such that the scan time required to acquire the k-space data set is less than 10% of the scan time required to acquire a fully sampled, untruncated k-space data set with the corresponding number of phase encodings.

[0099] Clause 12. A system comprising: an array of magnets configured to generate a magnetic field of low or very low magnetic field strength toward a target object located within a field of view; an RF coil assembly having an array of radio frequency (RF) coils, the RF coils positionable around the target object within the field of view, the RF coils configured to acquire magnetic resonance signals; and control circuitry comprising a processor and a memory, the memory storing instructions executable by the processor to: receive k-space data sets corresponding to the magnetic resonance signals acquired by the RF coils, each of the k-space data sets corresponding to a different one of the RF coils, and each of the k-space data sets being truncated and undersampled; generate partial images of the field of view based on the k-space data sets, each of the partial images corresponding to a different one of the k-space data sets; generate an initial image based on the partial images, the initial image being a full image of the field of view; and apply an iterative image reconstruction technique to generate an updated image based on the initial images.

[0100] Clause 13: The system described in Clause 12, wherein the instructions executable by the processor to apply an iterative image reconstruction technique to generate an updated image include instructions to: designate an initial image as an input image for the iterative image reconstruction technique; apply phase correction to the input image to generate a first intermediate image; apply k-space conjugate compounding to the input image to generate a second intermediate image; calculate an output image based on the first intermediate image and the second intermediate image; designate the output image as an input image for the next iteration; and repeat applying phase correction to the input image, applying k-space conjugate compounding to the input image, calculating an output image, and designating the output image as the input image for the next iteration until a difference between the output image and the corresponding input image satisfies a predetermined threshold, wherein the updated image is based on the final output image of the iterative image reconstruction technique.

[0101] Clause 14: A system described in any one of clauses 12 to 13, wherein the memory further stores instructions executable by the processor to receive calibration k-space data sets corresponding to magnetic resonance signals acquired by the RF coils, each of the calibration k-space data sets corresponding to a different one of the RF coils and each of the calibration k-space data sets having a central zone, and to generate a phase map of the central zone based on the calibration k-space data sets.

[0102] Clause 15: The system described in Clause 14, wherein the instructions executable by the processor to apply phase correction to the input image to generate a first intermediate image include instructions for determining a size of the input image and calculating the first intermediate image based on the size of the input image and the phase map of the central zone.

[0103] Clause 16: The system of clause 15, wherein the memory further stores instructions executable by the processor to generate a coil sensitivity map based on the calibration k-space data set.

[0104] Clause 17: The system of clause 16, wherein the instructions executable by the processor to generate an initial image based on the partial image comprise instructions for generating the initial image based on the partial image and a coil sensitivity map.

[0105] Clause 18: The system described in Clause 17, wherein the instructions executable by the processor to generate an initial image based on the partial image and the coil sensitivity map comprise instructions for generating the initial image according to a sensitivity encoding (SENSE) technique.

[0106] Clause 19: A system described in any one of clauses 13 to 18, wherein the k-space dataset comprises acquired k-space values, and the instructions executable by the processor to apply k-space conjugate synthesis to the input image and generate a second intermediate image comprise instructions for: generating a first intermediate k-space by inverse Fourier transforming the input image, the first intermediate k-space comprising intermediate k-space values; generating a second intermediate k-space from the first intermediate k-space by replacing at least some of the intermediate k-space values ​​with at least some of the acquired k-space values; and generating the second intermediate image by Fourier transforming the second intermediate k-space.

[0107] Clause 20: A system described in any one of clauses 13 to 19, wherein the instructions executable by the processor to calculate an output image based on the first intermediate image and the second intermediate image comprise instructions for adding the product of the first intermediate image and the first weighting factor to the product of the second intermediate image and the second weighting factor, wherein the sum of the first weighting factor and the second weighting factor is equal to 1.

[0108] Clause 21: A system described in any one of clauses 12 to 20, wherein each of the k-space data sets is undersampled based on an undersampling ratio of at least 2 in the first lateral direction and the second lateral direction, and each of the k-space data sets is truncated by at least 37.5% in the first lateral direction and the second lateral direction.

[0109] While several embodiments have been illustrated and described, it is not the intention of the applicant to restrict or limit the scope of the appended claims to such details. Numerous modifications, variations, changes, substitutions, combinations, and equivalents to these embodiments may be made without departing from the scope of the present disclosure, and will occur to those skilled in the art. Moreover, the structure of each element associated with the described embodiments may alternatively be described as a means for providing the function performed by the element. Furthermore, where materials are disclosed for particular components, other materials may be used. Therefore, it should be understood that the foregoing description and the appended claims are intended to cover all such modifications, combinations, and variations that fall within the scope of the disclosed embodiments. The appended claims are intended to cover all such modifications, variations, changes, substitutions, modifications, and equivalents.

[0110] The foregoing detailed description describes various aspects of devices and / or processes through the use of block diagrams, flowcharts, and / or examples. To the extent that such block diagrams, flowcharts, and / or examples include one or more functions and / or operations, those skilled in the art will appreciate that each function and / or operation within such block diagrams, flowcharts, and / or examples can be individually and / or collectively implemented by a wide range of hardware, software, firmware, or substantially any combination thereof. Those skilled in the art will recognize that some aspects of the embodiments disclosed herein can be equivalently implemented, in whole or in part, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or substantially any combination thereof, in integrated circuits, and that designing circuitry and / or writing code for the software and / or firmware is within the skill of those skilled in the art in light of this disclosure. Furthermore, those skilled in the art will understand that the subject matter mechanisms described herein may be distributed as one or more program products in a variety of forms, and that the illustrative forms of the subject matter described herein apply regardless of the particular type of signal-bearing medium used to actually effect the distribution.

[0111] The instructions used to program the logic to execute the various disclosed aspects may be stored in memory within the system, such as dynamic random access memory (DRAM), cache, flash memory, or other storage device. Additionally, the instructions may be distributed over a network or via other computer-readable media. Thus, a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including, but not limited to, a floppy diskette, an optical disk, a compact disk, a read-only memory (CD-ROM), and a magneto-optical disk, a read-only memory (ROM), a random access memory (RAM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic or optical card, a flash memory, or any tangible machine-readable storage device used to transmit information over the Internet via an electrical, optical, acoustical, or other form of propagated signal (e.g., carrier wave, infrared signal, digital signal, etc.). Accordingly, non-transitory computer-readable media includes any type of tangible machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

[0112] As used in any aspect of this specification, the term "control circuitry" can refer to, for example, hardwired circuitry, programmable circuitry (e.g., a computer processor including one or more individual instruction processing cores, a processing unit, a processor, a microcontroller, a microcontroller unit, a controller, a digital signal processor (DSP), a programmable logic device (PLD), a programmable logic array (PLA), or a field programmable gate array (FPGA)), a state machine circuit, firmware that stores instructions executed by the programmable circuitry, and any combination thereof. Control circuitry may be embodied, collectively or individually, as circuitry that forms part of a larger system, e.g., an integrated circuit (IC), an application specific integrated circuit (ASIC), a system on a chip (SoC), a desktop computer, a laptop computer, a tablet computer, a server, a smartphone, etc. Thus, as used herein, "control circuitry" includes, but is not limited to, an electrical circuit having at least one discrete electrical circuit, an electrical circuit having at least one integrated circuit, an electrical circuit having at least one application-specific integrated circuit, an electrical circuit forming a general-purpose computing device configured by a computer program (e.g., a general-purpose computer and / or device described herein configured by a computer program that at least partially executes a process, or a microprocessor and / or device described herein configured by a computer program that at least partially executes a process), an electrical circuit forming a memory device (e.g., a form of random access memory), and / or an electrical circuit forming a communications device (e.g., a modem, a communications switch, or an optoelectronic appliance). Those skilled in the art will recognize that the subject matter described herein can be implemented in analog or digital form or some combination thereof.

[0113] As used in any aspect of this specification, the term "logic" may refer to an application, software, firmware, and / or circuitry configured to perform any of the aforementioned operations. Software may be embodied as a software package, code, instructions, instruction sets, and / or data recorded on a non-transitory computer-readable storage medium. Firmware may be embodied as code, instructions, instruction sets, and / or data hard-coded (e.g., non-volatile) within a memory device.

[0114] When used in any aspect of this specification, the terms "component," "system," "module," and the like may refer to a controlled circuit computer-related entity that is either hardware, a combination of hardware and software, software, or software in execution.

[0115] As used in any aspect of this specification, an "algorithm" refers to a self-consistent sequence of steps leading to a desired result, and the "steps" refer to manipulations of physical quantities and / or logical states, which may, but need not, take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It is common usage to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. These and similar terms may be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities and / or states.

[0116] The network may include a packet-switched network. The communication devices may be capable of communicating with each other using a selected packet-switched network communication protocol. One exemplary communication protocol may include an Ethernet communication protocol, which may enable communication using Transmission Control Protocol / Internet Protocol (TCP / IP). The Ethernet protocol may conform to or be compatible with the Ethernet standard published by the Institute of Electrical and Electronics Engineers (IEEE) entitled "IEEE 802.3 Standard," published in December 2008, and / or later versions of this standard. Alternatively or additionally, the communication devices may be capable of communicating with each other using an X.25 communication protocol. The X.25 communication protocol may conform to or be compatible with standards published by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T). Alternatively or additionally, the communication devices may be capable of communicating with each other using a frame relay communication protocol. The frame relay communication protocol may conform to or be compatible with standards published by the Consultative Committee for International Telegraph and Telephone (CCITT) and / or the American National Standards Institute (ANSI). Alternatively or additionally, the transceivers may be capable of communicating with each other using an Asynchronous Transfer Mode (ATM) communications protocol. The ATM communications protocol may conform to or be compatible with the ATM standard published by the ATM Forum entitled "ATM-MPLS Network Interworking 2.0," published in August 2001, and / or later versions of this standard. Of course, different and / or later-developed connection-oriented network communications protocols are also contemplated herein.

[0117] Unless otherwise indicated as is apparent from the foregoing disclosure, throughout the foregoing disclosure, discussions using terms such as "processing," "calculating," "computing," "determining," "displaying," and the like will be understood to refer to the operations and processes of a computer system or similar electronic computing device that manipulates and converts data represented as physical (electronic) quantities in the computer system's registers and memory into other data similarly represented as physical quantities in the computer system's memory or registers or other such information storage, transmission, or display devices.

[0118] One or more components may be referred to herein as being "configured," "configurable," "operable," "adapted," "capable," "adaptable," etc. Those skilled in the art will recognize that, unless the context requires otherwise, "configured to" may generally encompass active and / or inactive and / or standby components.

[0119] The terms "proximal" and "distal" are used herein with respect to a clinician manipulating the handle portion of a surgical instrument. The term "proximal" refers to the portion closest to the clinician, and the term "distal" refers to the portion located away from the clinician. It will be further understood that for convenience and clarity, spatial terms such as "vertical," "horizontal," "upper," and "lower" may be used herein with respect to the drawings. However, surgical instruments are used in many orientations and positions, and these terms are not intended to be limiting and / or absolute.

[0120] Those skilled in the art will recognize that, generally, the terms used in this specification, and particularly in the appended claims (e.g., the body of the appended claims), are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but not limited to," etc.). Where recitation of a specific number of introduced claim scopes is intended, such intention will be explicitly set forth in the claim, and it will be further understood by those skilled in the art that, in the absence of such recitation, no such intention exists. For example, to aid in understanding, the following appended claims include the use of the introductory phrases "at least one" and "one or more" to introduce the recitation of claim scopes. However, the use of such phrases should not be construed to mean that introducing a claim recitation with the indefinite article "a" or "an" limits any particular claim that includes such an introduced claim recitation to claims that include only one of such recitations, even when the same claim includes the introductory phrase "one or more" or "at least one" and an indefinite article such as "a" or "an" (e.g., "a" and / or "an" should typically be construed to mean "at least one" or "one or more"), and the same is true for the use of definite articles used to introduce claim recitations.

[0121] Furthermore, even if a specific number of introduced claim recitations is explicitly recited, those skilled in the art will recognize that such recitations should typically be interpreted to mean at least the recited number (e.g., a bare recitation of "two enumerations" without other modifiers typically means at least two enumerations, or more than two enumerations). Furthermore, in instances where a convention similar to "at least one of A, B, and C, etc." is used, such configuration is generally intended in the sense that one skilled in the art would understand the convention (e.g., "a system having at least one of A, B, and C" includes, but is not limited to, systems having A only, B only, C only, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). In instances where a convention similar to "at least one of A, B, or C, etc." is used, such configuration is generally intended in the sense that one of ordinary skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" includes, but is not limited to, systems having A only, B only, C only, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). It will be further understood by those of ordinary skill in the art that typically disjunctive words and / or phrases presenting two or more alternative terms in either this specification, claims, or drawings should be understood as considering the possibility of including one of the terms, either of the terms, or both terms, unless the context dictates otherwise. For example, the phrase "A or B" is typically understood to include the possibilities of "A" or "B" or "A and B."

[0122] With respect to the appended claims, those skilled in the art will understand that the operations described therein may generally be performed in any order. While various operational flow diagrams are shown sequentially, it should be understood that various operations may be performed in orders other than those shown, or may even be performed simultaneously. Examples of such alternative orderings may include overlapping, interleaved, interrupted, reordered, incremental, preliminary, supplemental, simultaneous, reverse, or other variant orderings, unless the context dictates otherwise. Furthermore, terms such as "responsive to," "related to," or other past-tense adjectives generally are not intended to exclude such variants, unless the context dictates otherwise.

[0123] It is worth noting that references to "one aspect," "aspect," "example," "one example," etc. mean that the particular features, structures, or characteristics described in connection with that aspect are included in at least one aspect. Thus, appearances of the phrases "in one aspect," "in one aspect," "in an example," and "in one example" in various places throughout this specification do not necessarily all refer to the same aspect. Furthermore, particular features, structures, or characteristics may be combined in any suitable manner in one or more aspects.

[0124] Any patent application, patent, non-patent publication, or other disclosure material referred to herein and / or listed in any Application Data Sheet is incorporated herein by reference to the extent that the incorporated material does not contradict this specification. Accordingly, to the extent necessary, the disclosure explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated herein by reference but that contradicts existing definitions, descriptions, or other disclosure material set forth herein is incorporated only to the extent that no contradiction arises between the incorporated material and the existing disclosure material.

[0125] In summary, many advantages have been described that arise from the use of the concepts described herein. The foregoing description of one or more embodiments has been presented for purposes of illustration and description. It is not intended to be exhaustive or to be limited to the precise embodiments disclosed. Modifications or variations are possible in light of the above teachings. One or more embodiments have been selected and described in order to illustrate the principles and practical applications, thereby enabling those skilled in the art to utilize various embodiments and various modifications suited to the particular use contemplated. The claims submitted herewith are intended to define the overall scope.

Claims

1. 1. A method for magnetic resonance imaging, said method comprising: receiving k-space data sets acquired by RF coils of a radio frequency (RF) coil assembly, each of the k-space data sets corresponding to a different one of the RF coils, each of the k-space data sets being truncated and undersampled; generating sub-images of a field of view based on the k-space data sets, each of the sub-images corresponding to a different one of the k-space data sets; generating an initial image based on the partial image, the initial image being a full image of the field of view; applying an iterative image reconstruction technique to generate an updated image based on the initial image; Including, The iterative image reconstruction technique comprises: designating the initial image as an input image for the iterative image reconstruction technique; applying a phase correction to the input image to generate a first intermediate image; applying k-space conjugate compounding to the input image to generate a second intermediate image; calculating an output image based on the first intermediate image and the second intermediate image; designating the output image as the input image for a next iteration; repeating the applying the phase correction to the input image, the applying the k-space conjugate compound to the input image, the calculating the output image, and the designating the output image as the input image for the next iteration; Including, The method, wherein the updated image is based on a final output image of the iterative image reconstruction technique.

2. 2. The method of claim 1, wherein the applying the phase correction to the input image, the applying the k-space conjugate compound to the input image, the calculating the output image, and the designating the output image as the input image for a next iteration are repeated until a difference between the output image and the corresponding input image meets a predetermined threshold.

3. receiving calibration k-space data sets acquired by the RF coils of the RF coil assembly, each of the calibration k-space data sets corresponding to a different one of the RF coils, each of the calibration k-space data sets comprising a central zone; generating a phase map of the central zone based on the calibration k-space data set; The method of claim 1 further comprising:

4. applying the phase correction to the input image to generate the first intermediate image, determining the size of the input image; calculating the first intermediate image based on the magnitude of the input image and the phase map of the central zone; The method of claim 3, comprising:

5. The k-space data set comprises acquired k-space values, and applying the k-space conjugate compounding to the input image to generate the second intermediate image includes: generating a first intermediate k-space by Fourier transforming the input image, the first intermediate k-space comprising intermediate k-space values; generating a second intermediate k-space from the first intermediate k-space by replacing at least some of the intermediate k-space values ​​with at least some of the acquired k-space values; generating the second intermediate image by inverse Fourier transforming the second intermediate k-space; The method of claim 4, comprising:

6. calculating the output image based on the first intermediate image and the second intermediate image includes adding a product of the first intermediate image and a first weighting factor to a product of the second intermediate image and a second weighting factor; The method of claim 5 , wherein the sum of the first weighting factor and the second weighting factor is equal to one.

7. The method of claim 6 , further comprising generating a coil sensitivity map based on the calibration k-space data set.

8. 8. The method of claim 7, wherein the k-space data sets are acquired in parallel, and generating the initial image based on the partial images comprises generating the initial image based on the partial images and the coil sensitivity maps.

9. The method of claim 8 , wherein generating the initial image based on the partial images and the coil sensitivity map comprises generating the initial image according to a sensitivity encoding (SENSE) technique.

10. 2. The method of claim 1, wherein each of the k-space data sets is undersampled based on an undersampling factor of at least 2 in a first transverse direction and a second transverse direction, and each of the k-space data sets is truncated by at least 37.5% in the first transverse direction and the second transverse direction.

11. 2. The method of claim 1, wherein each of the k-space data sets is undersampled, truncated, and acquired in parallel, whereby the scan time required to acquire the k-space data set is less than 10% of the scan time required to acquire a fully sampled, untruncate k-space data set with a corresponding number of phase encodings.

12. 1. A system comprising: an array of magnets configured to generate a low or very low magnetic field strength toward a target object located within a field of view; an RF coil assembly including an array of radio frequency (RF) coils, the RF coils positionable about a subject object within the field of view, the RF coils configured to acquire magnetic resonance signals; a control circuit having a processor and a memory; Equipped with The memory includes: receiving k-space data sets corresponding to magnetic resonance signals acquired by the RF coils, each of the k-space data sets corresponding to a different one of the RF coils, each of the k-space data sets being truncated and undersampled; generating sub-images of the field of view based on the k-space data sets, each of the sub-images corresponding to a different one of the k-space data sets; generating an initial image based on the partial image, the initial image being a full image of the field of view; applying an iterative image reconstruction technique to generate an updated image based on the initial image; storing instructions executable by the processor to perform the steps of:

13. The instructions executable by the processor to apply the iterative image reconstruction technique to generate the updated image include: designating the initial image as an input image for the iterative image reconstruction technique; applying a phase correction to the input image to generate a first intermediate image; applying k-space conjugate compounding to the input image to generate a second intermediate image; calculating an output image based on the first intermediate image and the second intermediate image; designating the output image as the input image for a next iteration; repeating the application of the phase correction to the input image, the application of the k-space conjugate compound to the input image, the calculation of the output image, and the designation of the output image as the input image for the next iteration until a difference between the output image and the corresponding input image meets a predetermined threshold, wherein the updated image is based on a final output image of the iterative image reconstruction technique; The system of claim 12 , comprising instructions for:

14. The memory includes: receiving calibration k-space data sets corresponding to magnetic resonance signals acquired by the RF coils, each of the calibration k-space data sets corresponding to a different one of the RF coils, each of the calibration k-space data sets comprising a central zone; generating a phase map of the central zone based on the calibration k-space data set; 14. The system of claim 13, further storing instructions executable by the processor for:

15. The instructions executable by the processor to apply the phase correction to the input image to generate the first intermediate image include: determining the size of the input image; calculating the first intermediate image based on the magnitude of the input image and the phase map of the central zone; 15. The system of claim 14, comprising instructions for:

16. 16. The system of claim 15, wherein the memory further stores instructions executable by the processor to generate a coil sensitivity map based on the calibration k-space data set.

17. 17. The system of claim 16, wherein the instructions executable by the processor to generate the initial image based on the partial image comprise instructions for generating the initial image based on the partial image and the coil sensitivity map.

18. 20. The system of claim 17, wherein the instructions executable by the processor to generate the initial image based on the partial images and the coil sensitivity map comprise instructions for generating the initial image according to a sensitivity encoding (SENSE) technique.

19. The k-space data set comprises acquired k-space values, and the instructions executable by the processor to apply the k-space conjugate compounding to the input image to generate the second intermediate image include: generating a first intermediate k-space by inverse Fourier transforming the input image, the first intermediate k-space comprising intermediate k-space values; generating a second intermediate k-space from the first intermediate k-space by replacing at least some of the intermediate k-space values ​​with at least some of the acquired k-space values; generating the second intermediate image by Fourier transforming the second intermediate k-space; 14. The system of claim 13, comprising instructions for:

20. The instructions executable by the processor to calculate the output image based on the first intermediate image and the second intermediate image include: instructions for adding a product of the first intermediate image and a first weighting factor to a product of the second intermediate image and a second weighting factor; The system of claim 13 , wherein the sum of the first weighting factor and the second weighting factor is equal to one.

21. 13. The system of claim 12, wherein each of the k-space data sets is undersampled based on an undersampling ratio of at least 2 in a first transverse direction and a second transverse direction, and each of the k-space data sets is truncated by at least 37.5% in the first transverse direction and the second transverse direction.

Images