System and method for using high resolution phase reconstruction in magnetic resonance images

Abstract

The present invention is entitled "System and method for using high-resolution phase reconstruction in magnetic resonance images". A method of generating an image of a subject using magnetic resonance imaging (MRI) includes acquiring a first set of partial k-space data from the subject and generating a phase-corrected image based on a phase correction factor and the first set of partial k-space data. The method also includes transforming the phase-corrected image into a second set of partial k-space data, and reconstructing the image of the subject based on the second set of partial k-space data and a weighting function.

Description

Background Art

[0001] The field of the present disclosure relates generally to systems and methods of magnetic resonance imaging (MRI), and more particularly to techniques for image reconstruction in MRI.

[0002] MRI has proven useful for the diagnosis of many diseases. MRI provides detailed images of soft tissue, abnormal tissue (such as tumors), and other structures that cannot be easily imaged by other imaging modalities such as computed tomography (CT). In addition, MRI operates without exposing the patient to ionizing radiation experienced in modalities such as CT and X-rays.

[0003] The MR signals acquired with an MRI system are signal samples of the examined subject in Fourier space, or commonly referred to in the art as "k-space." In MR imaging, portions of k-space are often sampled in order to increase acquisition efficiency and / or suppress artifacts. Reconstructing a partially sampled k-space data set results in images contaminated by truncation artifacts in the form of both blurring and characteristic oscillations that severely reduce the diagnostic value of MR images.

[0004] Therefore, there is a need for an improved magnetic resonance imaging system and method. Summary of the invention

[0005] According to an embodiment of the present technology, a method for generating an image of a subject using a magnetic resonance imaging (MRI) system is provided. The method includes acquiring a first set of partial k-space data from the subject and generating a phase-corrected image based on a phase correction factor and the first set of partial k-space data. The method also includes transforming the phase-corrected image into a second set of partial k-space data, and reconstructing the image of the subject based on the second set of partial k-space data and a weighting function.

[0006] According to another embodiment of the present technology, a magnetic resonance imaging (MRI) system is provided. The MRI system includes: a magnet configured to generate a polarizing magnetic field around at least a portion of a subject arranged in the MRI system; and a gradient coil assembly, the gradient coil assembly including a plurality of gradient coils configured to apply at least one gradient field to the polarizing magnetic field. The MRI system also includes: a radio frequency (RF) system configured to apply an RF field to the subject and receive magnetic resonance signals from the subject. The MRI system further includes: a processing system programmed to acquire a first set of partial k-space data from the subject. The processing system is also programmed to generate a phase-corrected image based on a phase correction factor and the first set of partial k-space data, and transform the phase-corrected image into a second set of partial k-space data. The processing system is further programmed to reconstruct an image of the subject based on the second set of partial k-space data and a weighting function.

[0007] According to another embodiment of the present technology, an MR imaging method is provided. The method includes acquiring a first set of k-space data that is smaller than all k-space from a subject, and reconstructing the k-space data into a coarse image. The method also includes generating a phase-corrected image based on a phase correction factor and the first set of k-space data, and transforming the phase-corrected image into a second set of k-space data. The method further includes reconstructing an image of the subject based on the second set of k-space data and a weighting function. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] These and other features, aspects and advantages of the present invention will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters refer to like parts throughout the several views, and in which:

[0009] Figure 1 is a schematic diagram of an exemplary magnetic resonance imaging (MRI) system;

[0010] Figure 2 is a schematic diagram of partial k-space sampling according to an embodiment of the present technology;

[0011] Figure 3 is a schematic diagram of a k-space sampling pattern according to an embodiment of the present technology;

[0012] Figure 4 is a schematic diagram of a conventional homodyne method for reconstructing partial k-space data;

[0013] Figure 5 is a schematic diagram of a method for reconstructing partial k-space data according to an embodiment of the present technology;

[0014] Figure 6According to an embodiment of the present invention, Figure 5 An exemplary neural network model of the method shown in .

[0015] Fig. 7A is the phantom image corresponding to the fully sampled k-space data;

[0016] Figure 7B yes Fig. 7A A phase image of a phantom image;

[0017] Figure 7C is a zero-filled phantom image corresponding to a partial k-space data set;

[0018] Fig.7D is a phantom image after phase correction corresponding to the conventional homodyne method;

[0019] Fig. 7E is a phase-corrected phantom image according to a modified homodyne technique in accordance with an embodiment of the present technology;

[0020] Fig. 8A is a simulated abdominal image after phase correction corresponding to the conventional homodyne method;

[0021] Figure 8B is a phase-corrected simulated abdominal image according to a modified homodyne technique in accordance with an embodiment of the present technology; and

[0022] Fig. 9 is a flow chart depicting a method of generating an image of a subject utilizing a magnetic resonance imaging (MRI) system in accordance with an embodiment of the present technology. DETAILED DESCRIPTION

[0023] One or more specific embodiments will be described below. In order to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be understood that in the development of any such actual implementation, as in any engineering or design project, many implementation-specific decisions must be made to achieve the developer's specific goals, such as complying with system-related and business-related constraints that may vary from implementation to implementation. In addition, it should be understood that such development efforts may be complex and time-consuming, but are still routine tasks for design, fabrication, and manufacturing for ordinary technicians who benefit from this disclosure.

[0024] When introducing the elements of the various embodiments of the present embodiment, the articles "a", "an", "the" and "said" are intended to mean that there are one or more of these elements. The terms "comprise", "comprising" and "having" are intended to be inclusive, and mean that there may be additional elements in addition to the listed elements. In addition, any numerical examples in the following discussion are intended to be non-limiting, and therefore additional numerical values, ranges and percentages are within the scope of the disclosed embodiments. In addition, the terms "circuit" and "circuitry" and "controller" may include a single component or multiple components, which are active and / or passive, and are connected or otherwise coupled together to provide the functions.

[0025] In magnetic resonance imaging (MRI), an object is placed in a magnet. When the object is in the magnetic field generated by the magnet, the magnetic moments of the nuclei (such as protons) attempt to align with the magnetic field, but precess around the magnetic field in a random order at the Larmor frequency of the nuclei. The magnetic field of the magnet is called B0 and extends in the longitudinal or z direction. During the acquisition of MR images, a magnetic field in the xy plane and close to the Larmor frequency (called the excitation field B1) is generated by a radio frequency (RF) coil and can be used to align the net magnetic moment M of the nuclei. z Rotation or "tilt" from the z direction toward the lateral or xy plane. After the excitation signal B1 is terminated, the nucleus emits a signal, which is called an MR signal. In order to use the MR signal to generate an image of the object, a magnetic field gradient pulse (G x , G y and G z ). Gradient pulses are used to scan through k-space, the inverse of spatial frequency or distance. There is a Fourier relationship between the acquired MR signals and the image of the object, so the image of the object can be derived by reconstructing the MR signals. The image of the object may include a two-dimensional (2D) or three-dimensional (3D) image.

[0026] Embodiments of the present disclosure will now be described by way of example with reference to the accompanying drawings, in which Figure 11 is a schematic diagram of a magnetic resonance imaging (MRI) system 10. The operation of the system 10 can be controlled from an operator console 12, which includes an input device 13, a control panel 14, and a display screen 16. The input device 13 can be a mouse, a joystick, a keyboard, a trackball, a touch-activated screen, a light stick, a voice controller, and / or other input devices. The input device 13 can be used for interactive geometric shape designation. The console 12 communicates with a computer system 20 via a link 18, which enables an operator to control the generation and display of images on the display screen 16. The link 18 can be a wireless or wired connection. The computer system 20 may include modules that communicate with each other via a backplane 20a. The modules of the computer system 20 may include, for example, an image processor module 22, a central processing unit (CPU) module 24, and a memory module 26, which may include, for example, a frame buffer for storing an array of image data. The computer system 20 may be linked to an archival media device, a permanent or backup memory, or a network for storing image data and programs, and communicates with an MRI system control 32 via a high-speed signal link 34. The MRI system control 32 may be separate from or integrated with the computer system 20. The computer system 20 and the MRI system control 32 together form an "MRI controller" 33 or "controller."

[0027] In an exemplary embodiment, the MRI system control 32 includes modules connected by a base plate 32a. These modules include a CPU module 36 and a pulse generator module 38. The CPU module 36 is connected to the operator console 12 via a data link 40. The MRI system control 32 receives commands from the operator via the data link 40 to indicate the scan sequence to be performed. The CPU module 36 operates the system components to perform the desired scan sequence and generates data indicating the timing, intensity and shape of the generated RF pulses and the timing and length of the data acquisition window. The CPU module 36 is connected to the components operated by the MRI controller 32, including the pulse generator module 38 that controls the gradient amplifier 42, the physiological acquisition controller (PAC) 44, and the scan room interface circuit 46.

[0028] In one example, the CPU module 36 receives patient data from a physiological acquisition controller 44, which receives signals from sensors connected to the subject, such as ECG signals received from electrodes attached to the patient. The CPU module 36 receives signals from the sensors associated with the condition of the patient and the magnet system via a scan room interface circuit 46. The scan room interface circuit 46 also enables the MRI controller 33 to command a patient positioning system 48 to move the patient to a desired position for scanning.

[0029] The whole body RF coil 56 is used to transmit the waveform toward the subject's anatomical structure. The whole body RF coil 56 can be a body coil. The RF coil can also be a local coil, which can be placed closer to the subject's anatomical structure than the body coil. The RF coil 56 can also be a surface coil. The RF coil containing the RF receiver channel can be used to receive signals from the subject's anatomical structure. A typical surface coil will have eight receiving channels; however, different numbers of channels are possible. It is known to use a combination of both the body coil 56 and the surface coil to provide better image quality.

[0030] The pulse generator module 38 can operate the gradient amplifier 42 to achieve the desired timing and shape of the gradient pulses generated during the scan. The gradient waveform generated by the pulse generator module 38 can be applied to the gradient amplifier system 42 having Gx, Gy and Gz amplifiers. Each gradient amplifier excites a corresponding physical gradient coil in the gradient coil assembly 50 to generate a magnetic field gradient for spatially encoding the acquired signal. Specifically, Gx corresponds to a flow / frequency encoding gradient, Gy corresponds to a phase encoding gradient, and Gz corresponds to a slice selection gradient. The gradient coil assembly 50 can form a part of a magnet assembly 52, which also includes a polarizing magnet 54 (in operation, the polarizing magnet provides a longitudinal magnetic field B0 throughout a target space 55 surrounded by the magnet assembly 52) and a whole-body RF coil 56 (in operation, the coil provides a transverse magnetic field B1, which is approximately perpendicular to B0 throughout the target space 55). The transceiver module 58 in the MRI system control 32 generates pulses that can be amplified by an RF amplifier 60, which is coupled to the RF coil 56 through a transmit / receive switch 62. The resulting signals emitted by the excited nuclei in the subject's anatomy may be sensed by a receive coil (not shown) and provided to a preamplifier 64 via a transmit / receive switch 62. The amplified MR signals are demodulated, filtered, and digitized in the receiver portion of the transceiver 58. The transmit / receive switch 62 is controlled by a signal from the pulse generator module 38 to electrically connect the RF amplifier 60 to the coil 56 during a transmit mode and to connect the preamplifier 64 to the receive coil during a receive mode.

[0031] The MR signals resulting from the excitation of the target are digitized by the transceiver module 58. The digitized signals are then processed by the MR system control 32 via Fourier transform to produce k-space data, which are transmitted to the memory module 66 or other computer-readable medium via the MRI system control 32. "Computer-readable medium" may include, for example, a structure configured so that an electrical, optical, or magnetic state can be fixed in a manner that is perceptible and reproducible by a conventional computer (e.g., text or images printed on paper or displayed on a screen, a compact disk or other optical storage medium, "flash" memory, EEPROM, SDRAM or other electrical storage medium; a floppy disk or other magnetic disk, a magnetic tape or other magnetic storage medium).

[0032] The scan is complete when an array of raw k-space data is acquired in the computer readable medium 66. For each image to be reconstructed, the raw k-space data is rearranged into a separate k-space data array, and each of these k-space data arrays is input to the array processor 68, which operates to reconstruct the data into an array of image data using a reconstruction algorithm such as a Fourier transform. When complete k-space data is obtained, it represents the entire volume of the subject's body, and the k-space so obtained may be referred to as reference k-space. Similarly, when only partial k-space data is obtained, the image may be referred to as partial k-space. The image data is transmitted to the computer system 20 via the data link 34 and stored in the memory. In response to commands received from the operator console 12, the image data may be archived in a long-term storage device, or may be further processed by the image processor 22 and transmitted to the operator console 12 and presented on the display 16.

[0033] The MR signal is represented by a complex number, where each position at k-space is represented by a complex number, where the I and Q quadrature MR signals are real and imaginary components. A composite MR image can be reconstructed based on the I quadrature MR signal and the Q quadrature MR signal using a process such as Fourier transform of the k-space MR data. A complex MR image is an MR image having each pixel represented by a complex number, which also has a real component and an imaginary component. The magnitude M of the received MR signal can be determined as the square root of the sum of the squares of the I quadrature component and the Q quadrature component of the received MR signal, as shown in equation (3) below:

[0034]

[0035] And the phase φ of the received MR signal can also be determined as follows:

[0036]

[0037] In MRI, asymmetric sampling in the frequency and phase encoding directions or dimensions is called fractional echo and fractional acquisition number (NEX), respectively, and is widely used in both 2D and 3D MR imaging. These undersampling techniques are typically used to shorten the echo time (e.g., to increase SNR or change tissue contrast), shorten the repetition time (e.g., to reduce scan time), and / or suppress unwanted artifacts (such as thin line artifacts in fast spin echo (FSE) imaging, or off-resonance artifacts in gradient echo sequences (GRE) and echo planar imaging (EPI)). Asymmetric sampling of k-space introduces truncation artifacts into the reconstructed image in the form of blurring and oscillation. Therefore, various image reconstruction techniques have been designed for reconstructing partial k-space data, such as conjugate synthesis, homodyne, and projection on convex sets (POCS). These known techniques rely on some inherent estimate of the underlying image phase, which can then be removed (or "corrected"), thereby allowing the synthesis of missing or unsampled data based on the Hermitian symmetry principle of real-valued signals. This phase estimate tends to be derived from a centrally symmetric sampled portion of k-space and is limited in several important ways. First, the phase estimate is contaminated by thermal noise, which is particularly problematic in low signal image regions and / or when this phase estimate is performed on a per-channel (or per-view) basis. Second, this phase estimate is inherently bandwidth limited and must be further low-pass filtered when applied to prevent the introduction of additional truncation artifacts. As a result, high spatial frequency phase information is not corrected, leaving residual blur in the final reconstructed image. The application of this low-frequency phase estimate also tends to bias the noise in the reconstructed image, which would otherwise tend to be normally distributed. The presence of this biased noise signal in the reconstructed image reduces image contrast, especially in low signal regions, and the altered distribution of this noise reduces noise averaging performance (such as in multi-NEX EPI diffusion) and / or complicates downstream denoising work, which is typically based on an assumed noise model. In addition, known partial k-space reconstruction techniques tend to exhibit various strengths and weaknesses, and the choice of method tends to result in various performance trade-offs. For example, POCS tends to localize reconstruction artifacts, while homodyne tends to result in contrast errors. Finally, in the case of homodyne and conjugate synthesis, phase information is discarded during reconstruction, making them unsuitable for phase-sensitive applications such as Dixon chemical shift imaging, phase-sensitive inversion restoration imaging, and image-based phase generation of phase-sensitive maps.

[0038] Figure 2 is a schematic diagram of a partial sampling pattern or truncated pattern 259 of the complete k-space 261. The complete k-space 261 is composed of the maximum kx or ky value k x，max and k y，maxThe maximum kx or ky value is defined by the maximum frequency encoding gradient or phase encoding gradient. In partial sampling, a portion of the high spatial frequency data 263 is not acquired. The truncation can be in the kx dimension and / or the ky dimension, and can be in the kz dimension in three-dimensional (3D) acquisition. The complete k-space 261 is truncated into partial k-space 264. Figure 2 The partial k-space 264 shown in is the complete k-space 261 truncated in the ky dimension, wherein negative high spatial frequency data are not acquired during image acquisition of the partial k-space 264. The truncation may be asymmetric, wherein the k-space is truncated asymmetrically in a certain dimension. Figure 2 The portion of k-space 264 shown in FIG. 2 is truncated asymmetrically in the ky dimension. The truncation reduces high spatial frequency data and causes truncation artifacts. Figure 2 The truncation shown along the axes of the 2D Cartesian coordinate system is shown by way of example only.

[0039] Figure 3 is a schematic diagram of a k-space sampling pattern according to an embodiment of the present technology. Figure 3 In FIG. 3 , curve 352 shows a projection reconstruction acquisition, while curve 354 shows a three-dimensional Fourier transform (3DFT) acquisition. In general, the outer circles in both curves 352 and 354 refer to a complete acquisition / sampling or complete k-space. However, curve 352 has only 2 planes kx and ky while curve 354 has 3 planes k x , k y and k z The k in curve 354 x The plane corresponds to an axis out of the center point 356. Typically, in partial sampling, only a portion of the high spatial frequency data is acquired, which is represented by the smaller circle 358 in the curve 352. Similarly, in the curve 354, the acquired data is represented by the shaded bar 360. The systems and methods described herein can also be used to remove truncation artifacts in images based on k-space data from k-space that is asymmetrically truncated along an axis of a 2D / 3D Cartesian coordinate system, a 2D / 3D non-Cartesian coordinate system (such as a polar, spherical, or cylindrical coordinate system), or a combination thereof. For example, the partial sampling pattern is k-space that is asymmetrically truncated in the radial dimension. In another example, the k-space data is acquired as a stack of radial lines along the kz direction in the kx-ky plane, and the partial sampling pattern is k-space that is asymmetrically truncated in the radial dimension in the kx-ky plane and asymmetrically truncated in the kz dimension.

[0040] Figure 4 FIG. 1 is a schematic diagram of a conventional homodyne method 100 for reconstructing partial k-space data. In the method 100 , a partial asymmetric k-space data set M is initially generated. pk (k x , ky )(102) and the centrosymmetric k-space dataset M s (k x , k y )(104). As mentioned above, a phase correction factor needs to be applied, and then the k-space symmetry can be exploited to obtain the partial k-space data M pk (k x , k y ) to synthesize the missing data. This phase estimate is derived from the centrosymmetric sampled k-space data M s (k x , k y ).

[0041] In addition, the method 100 includes defining a pre-weighting function W(k y )(106). The partial k-space data is then multiplied by a pre-weighting function via a multiplier block 108. The weighted partial k-space data from the multiplier block 108 is then inverse Fourier transformed to produce a weighted image m pk (x, y)*w(x, y) (112). Similarly, the central symmetric k-space data set M s (k x , k y ) performs an inverse Fourier transform to produce image m s (x, y)(110). Phase correction factor p * (x, y) is a unit amplitude / magnitude image with phase m s The conjugate of (x, y) and is given by:

[0042]

[0043] The phase correction factor is then used to multiply p by another multiplier block 114 * (x, y) and m pk The weighted image is corrected by multiplying (x, y)*w(x, y). The desired image 116 is then obtained by taking the real part of the result of the multiplier block 114.

[0044] like Figure 4 As shown, conventional homodyne reconstruction methods use low-resolution phases for phase correction, resulting in severe artifacts in regions with rapid phase changes due to motion, chemical shift, air-tissue interfaces, etc. Therefore, the reconstruction method in the present invention utilizes deep learning methods and / or other methods to derive high-resolution denoised phases from zero-filled complex data, and then applies high-resolution phases during the phase correction step to minimize these artifacts. It should be noted that the term high-resolution phase refers to a phase with a high resolution compared to the phase determined by the conventional homodyne reconstruction method or the phase determined by the centralized symmetric k-space data.

[0045] Figure 5 2 is a schematic diagram of a method 200 for reconstructing partial k-space data according to an embodiment of the present technology. The method 200 may also be referred to as a modified homodyne reconstruction method. In the method 200, a partial asymmetric k-space data set M(k x , k y ), i.e., the first set of partial k-space data 202. A coarse image m is then generated from the partial asymmetric k-space data set 202. z (x, y) 204. The coarse image 204 may be reconstructed by zero-filling the partial k-space data to have zeros at positions corresponding to the skipped k-space positions, thereby deriving complete k-space data, and then reconstructing the coarse image based on the zero-filled k-space data. The complete k-space data of the coarse image may be reconstructed by a method other than zero-filling, such as interpolation.

[0046] The method also includes determining a high-resolution denoised phase In one embodiment, the high-resolution denoised phase The high resolution denoised phase 206 is determined from the coarse image 204 using a deep learning (DL) technique (e.g., a neural network 220). The high resolution denoised phase 206 is determined in radians and may also be referred to as a phase correction factor. In addition to the DL method, other denoising methods 222 (such as principal component analysis (PCA), wavelet analysis, total variance, etc.) may also be used to generate the high resolution denoised phase 206. In yet another embodiment, the high resolution denoised phase 206 may be determined using an external pre-acquired high resolution image 224. The multiplier block 207 phase multiplies the coarse image with the high resolution denoised phase to generate a phase corrected image m z * (x, y)(210), which is given by:

[0047]

[0048] The phase-corrected image 210 is then Fourier transformed to produce a truncated phase-corrected k-space dataset M z * (k x , k y ), i.e., a second set of partial k-space data 212. In addition, method 200 includes defining a pre-weighting function W(ky) (214). Typically, the pre-weighting function is selected to emphasize a subspace of the acquisition space or a portion of the data for better reconstruction. In one embodiment, the pre-weighting function represents the spectral sampling density. In another embodiment, the pre-weighting function W(k y ) can be determined as

[0049]

[0050] Where H(k y ) is a Hanning window. For example, the Hanning window may have a value of k ymin 4k in the center ymin Width, such as the following Hanning window:

[0051]

[0052] The phase corrected k-space data set 212 is then multiplied by a pre-weighting function 214 by a multiplier block 216. The desired image 218 is then obtained by taking the real portion of the result of the multiplier block 216.

[0053] Figure 6 Yes, you can Figure 5 Schematic diagram of an exemplary neural network model 300 for determining high-resolution denoised phase in an embodiment of the present invention. The neural network model 300 may include a convolutional neural network 302. The neural network 302 receives an MR phase image 304 contaminated by impaired phase information (i.e., an impaired image) and returns an original phase image 306 in which the background phase 308 has been substantially denoised. The input and output of the neural network 302 may also be a composite image, a magnitude and phase image pair, or a real or virtual image pair. In one embodiment, the neural network 302 may be trained with a loss function that is a function that measures the inference error of the neural network 302. The loss function may be expressed as min(f1(input)-f2(output)), where f1 and f2 are functions of the input and output of the neural network 302, respectively. In some embodiments, the loss function includes constraints based on prior knowledge of phase information. For example, the prior knowledge is that phase aliasing or phase wrapping may be caused by the method of calculating the phase. Substituting the phase-based equation The calculated phase is restricted to the range of [-π, π]. However, in real life, the phase can be any real value. Therefore, adding multiple 2π phases appears as the same phase value, resulting in phase aliasing. In phase-sensitive imaging applications, where the phase is used to encode physical phenomena such as flow, motion or temperature, the prior knowledge includes the laws of physics. Phases that appear to violate the laws of physics are penalized by a penalty function. For example, in flow imaging or quantitative magnetic susceptibility measurement (QSM), the flow is divergence-free. In thermal imaging, it is expected that heat is dissipated relatively uniformly. For displacement imaging such as elastic imaging, it is expected that the displacement obeys the laws of physical motion.

[0054] 7A to 7E A comparison of k-space data reconstruction using a conventional homodyne method and the modified homodyne technique provided herein is provided. Fig. 7A is a phantom image 402 corresponding to a fully sampled k-space. Figure 7B is a phase image 404 of the phantom image 402 . Figure 7C is a zero-filled phantom image 406 corresponding to the partial k-space dataset. Fig.7D is corresponding to Figure 5 Phantom image 408 after phase correction of the conventional homodyne method described. In addition, Fig. 7E is a phase-corrected phantom image 410 according to the modified homodyne technique provided herein. It should be noted that Figures 7A-7E All images provided in the are simulated phantom images. Fig.7D It can be seen that the phantom image 408 generated by the conventional homodyne method includes artifacts 412, 414, 416 and 418 corresponding to the rapid phase change regions. These artifacts are not seen in the phantom image 410 ( Fig. 7E ) is minimized in the image, the artifacts appear very similar to the phantom image 402 generated with a fully sampled k-space.

[0055] FIG. 8A to FIG. 8B Another comparison of k-space data reconstruction using a conventional homodyne method and the modified homodyne technique provided herein is provided. Fig. 8A is corresponding to Figure 5 Simulated abdominal image 502 after phase correction of the conventional homodyne method described. In addition, Figure 8B is a simulated abdominal image 504 after phase correction according to the modified homodyne technique provided herein. It can be seen that the corresponding Fig. 8A The rapid phase change artifacts 506, 508, 510 in Figure 8B 504. In other words, the abdominal image generated using conventional homodyne is much worse than the abdominal image generated using the modified homodyne technique with high resolution phase according to embodiments of the present technology.

[0056] Fig. 9800 is a flowchart depicting a method for generating an image of a subject using a magnetic resonance imaging (MRI) system. The method includes acquiring a first set of partial k-space data from the subject at step 802. In one embodiment, the first set of k-space data is less than all of the k-space from the subject. In other words, the first set of partial k-space data includes k-space data from a portion of k-space that is truncated in at least one k-space dimension. At step 804, the method includes determining a phase correction factor. The phase correction factor can be determined based on the first set of partial k-space data. In order to determine the phase correction factor from the partial k-space data, a coarse image is first reconstructed based on the first set of partial k-space data. In one embodiment, the coarse image is reconstructed by zero-padding the first set of partial k-space data to have zeros at positions corresponding to skipped k-space positions, thereby deriving complete k-space data, and then reconstructing the coarse image based on the zero-padding k-space data.

[0057] Thereafter, the phase correction factor is determined using the coarse image using one of a neural network model, a wavelet transform algorithm, a principal component analysis (PCA) algorithm, or a total variance (TV) algorithm. In one embodiment, the neural network model is trained with a pair of an original image and a damaged image, wherein the damaged image includes damaged phase information and the original image is a damaged image with reduced damaged phase information. In another embodiment, the phase correction factor can be determined based on an external image that is a pre-acquired high-resolution image. In addition, at step 806, the coarse image and the phase correction factor are multiplied to generate a phase-corrected image. At step 808, the method includes transforming the phase-corrected image into a second set of partial k-space data. The transformation can be performed by applying a fast Fourier transform (FFT) on the phase-corrected image.

[0058] At step 810, the method includes reconstructing an image of the subject based on the second set of partial k-space data and a weighting function. In one embodiment, the weighting function refers to the spectral sampling density and can include a Hanning window function. First, the weighting function is multiplied with the second set of partial k-space data, and then the actual portion of the result is used as the desired image of the subject.

[0059] One advantage of the inventive technique is that it systematically minimizes common artifacts in MRI images, such as in regions with rapid phase changes, and can be widely applied to improve the image quality of phase-reversed water-fat imaging, diffusion imaging, gradient echo imaging, etc. In addition, because the technique uses partial k-space data, the efficiency of MRI acquisition is improved.

[0060] This written description uses examples to disclose the invention, including the best mode, and also to enable those skilled in the art to practice the invention, including making and using any device or system and performing any included method. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements that differ slightly from the literal language of the claims.

Claims

1. A method for generating an image of a subject using a magnetic resonance imaging (MRI) system, the method comprising: acquiring a first set of partial k-space data from the subject using the MRI system, wherein the first set of partial k-space data is asymmetric and truncated in at least one k-space dimension; zero-filling the first set of partial k-space data with zeros at locations corresponding to the skipped k-space locations to derive complete k-space data; reconstructing a coarse image based on the zero-filled k-space data; determining a phase correction factor based on the coarse image; generating a phase-corrected image based on the phase correction factor and the first set of partial k-space data; transforming the phase-corrected image into a second set of partial k-space data; and The image of the subject is reconstructed based on the second set of partial k-space data and a weighting function.

2. The method of claim 1, comprising determining the phase correction factor with the coarse image using one of a neural network model, a wavelet transform algorithm, a principal component analysis (PCA) algorithm, or a total variance (TV) algorithm.

3. The method of claim 2, wherein the neural network model is trained using a pair of an original image and a damaged image, wherein the damaged image includes damaged phase information, and the original image is a damaged image with reduced damaged phase information.

4. The method of claim 2, wherein the neural network model is trained using a loss function, the loss function including constraints based on prior knowledge of phase information. 5 . The method of claim 1 , wherein generating the phase-corrected image based on the phase correction factor and the first set of partial k-space data comprises multiplying the coarse image with the phase correction factor. 6 . The method of claim 1 , wherein the image of the subject is reconstructed by determining an actual portion of a multiplication result of the weighting function and the second set of partial k-space data. The method of claim 1 , wherein the weighting function represents spectral sampling density.

8. A magnetic resonance imaging (MRI) system, comprising: a magnet configured to generate a polarized magnetic field around at least a portion of a subject disposed in the MRI system; a gradient coil assembly, the gradient coil assembly comprising a plurality of gradient coils configured to apply at least one gradient field to the polarizing magnetic field; a radio frequency (RF) system configured to apply an RF field to the subject and receive magnetic resonance signals from the subject; A processing system, the processing system being programmed to: acquiring a first set of partial k-space data from the subject, wherein the first set of partial k-space data is asymmetric and truncated in at least one k-space dimension; zero-filling the first set of partial k-space data with zeros at locations corresponding to the skipped k-space locations to derive complete k-space data; reconstructing a coarse image based on the zero-filled k-space data; determining a phase correction factor based on the coarse image; generating a phase-corrected image based on the phase correction factor and the first set of partial k-space data; transforming the phase-corrected image into a second set of partial k-space data; and The image of the subject is reconstructed based on the second set of partial k-space data and a weighting function.

Images