Patent Description:
MRI has proven useful in diagnosis of many diseases. MRI provides detailed images of soft tissues, abnormal tissues such as tumors, and other structures, which cannot be readily imaged by other imaging modalities, such as computed tomography (CT). Further, MRI operates without exposing patients to ionizing radiation experienced in modalities such as CT and x-rays.

The MR signals acquired with an MRI system are signal samples of the subject of the examination in Fourier space, or what is often referred to in the art as "k-space". In MR imaging, a partial k-space is often sampled in order to increase the efficiency of the acquisition and/or to suppress artifacts. Reconstructing a partially-sampled k-space dataset results in an image that is contaminated by truncation artifacts in the form of both blurring and a characteristic ringing that severely degrades the diagnostic value of the MR image.

Therefore, there is a need for an improved magnetic resonance imaging system and method.

<NPL>et al. , describes a convolutional neural network model for partial Fourier MRI reconstruction.

<NPL>et al. , describes procedural steps for extension of a 1D homodyne phase correction for k-space truncation in all gradient encoding directions.

<CIT> describes a magnetic resonance imaging apparatus including an imaging unit and data processing condition setting unit. The imaging unit is configured to acquire magnetic resonance data corresponding to a sampling region asymmetric in a wave number direction in k-space from an object to generate image data based on the magnetic resonance data by data processing including phase correction and filter processing for obtaining a complex conjugate. The data processing condition setting unit is configured to set a condition for the data processing according to an imaging condition influencing a phase distribution used for the phase correction or the phase distribution.

In accordance with an embodiment of the present technique, a method for producing an image of a subject with a magnetic resonance imaging (MRI) system is provided in accordance with claim <NUM>.

Disclosed herein is a method for producing an image of a subject with an MRI system that 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 the partial k-space data. The method further includes transforming the phase corrected image into a second set of partial k-space data and reconstructing the image of the subject from the second set of the partial k-space data and a weighting function.

In accordance with another embodiment of the present technique, a magnetic resonance imaging (MRI) system is provided in accordance with claim <NUM>.

Disclosed herein is an MRI system that includes a magnet configured to generate a polarizing magnetic field about at least a portion of a subject arranged in the MRI system and a 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 to 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 the 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 the image of the subject from the second set of the partial k-space data and a weighting function.

Disclosed herein is a method of MR imaging that includes acquiring a first set of k-space data for less than all of k-space from a subject and reconstructing the k-space data into a crude 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, from the second set of the k-space data and a weighting function, the image of the subject.

When introducing elements of various embodiments of the present embodiments, the articles "a," "an," "the," and "said" are intended to mean that there are one or more of the elements. Furthermore, any numerical examples in the following discussion are intended to be non-limiting, and thus additional numerical values, ranges, and percentages are within the scope of the disclosed embodiments. Furthermore, the terms "circuit" and "circuitry" and "controller" may include either a single component or a plurality of components, which are either active and/or passive and are connected or otherwise coupled together to provide the described function.

In magnetic resonance imaging (MRI), an obj ect is placed in a magnet. When the object is in the magnetic field generated by the magnet, magnetic moments of nuclei, such as protons, attempt to align with the magnetic field but process about the magnetic field in a random order at the nuclei's Larmor frequency. The magnetic field of the magnet is referred to as B0 and extends in the longitudinal or z direction. In acquiring a MR image, a magnetic field (referred to as an excitation field B <NUM>), which is in the x-y plane and near the Larmor frequency, is generated by a radio-frequency (RF) coil and may be used to rotate, or "tip," the net magnetic moment Mz of the nuclei from the z direction to the transverse or x-y plane. A signal, which is referred to as a MR signal, is emitted by the nuclei, after the excitation signal B <NUM> is terminated. To use the MR signals to generate an image of an object, magnetic field gradient pulses (Gx, Gy, and Gz) are used. The gradient pulses are used to scan through the k space, the space of spatial frequencies or inverse of distances. A Fourier relationship exists between the acquired MR signals and an image of the object, and therefore the image of the object can be derived by reconstructing the MR signals. The images of the object may include two dimensional (2D) or three-dimensional (3D) images.

Embodiments of the present disclosure will now be described, by way of an example, with reference to the figures, in which <FIG> is a schematic diagram of a magnetic resonance imaging (MRI) system <NUM>. Operation of the system <NUM> may be controlled from an operator console <NUM>, which includes an input device <NUM>, a control panel <NUM>, and a display screen <NUM>. The input device <NUM> may be a mouse, joystick, keyboard, track ball, touch activated screen, light wand, voice control, and/or other input device. The input device <NUM> may be used for interactive geometry prescription. The console <NUM> communicates through a link <NUM> with a computer system <NUM> that enables an operator to control the production and display of images on the display screen <NUM>. The link <NUM> may be a wireless or wired connection. The computer system <NUM> may include modules that communicate with each other through a backplane 20a. The modules of the computer system <NUM> may include an image processor module <NUM>, a central processing unit (CPU) module <NUM>, and a memory module <NUM> that may include a frame buffer for storing image data arrays, for example. The computer system <NUM> may be linked to archival media devices, permanent or back-up memory storage or a network for storage of image data and programs and communicates with MRI system control <NUM> through a high-speed signal link <NUM>. The MRI system control <NUM> may be separate from or integral with the computer system <NUM>. The computer system <NUM> and the MRI system control <NUM> collectively form an "MRI controller" <NUM> or "controller".

In the exemplary embodiment, the MRI system control <NUM> includes modules connected by a backplane 32a. These modules include a CPU module <NUM> as well as a pulse generator module <NUM>. The CPU module <NUM> connects to the operator console <NUM> through a data link <NUM>. The MRI system control <NUM> receives commands from the operator through the data link <NUM> to indicate the scan sequence that is to be performed. The CPU module <NUM> operates the system components to carry out the desired scan sequence and produces data which indicates the timing, strength and shape of the RF pulses produced, and the timing and length of the data acquisition window. The CPU module <NUM> connects to components that are operated by the MRI controller <NUM>, including the pulse generator module <NUM> which controls a gradient amplifier <NUM>, a physiological acquisition controller (PAC) <NUM>, and a scan room interface circuit <NUM>.

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

A whole-body RF coil <NUM> is used for transmitting the waveform towards subject anatomy. The whole body-RF coil <NUM> may be a body coil. An RF coil may also be a local coil that may be placed in more proximity to the subject anatomy than a body coil. The RF coil <NUM> may also be a surface coil. RF coils containing RF receiver channels may be used for receiving the signals from the subject anatomy. Typical surface coil would have eight receiving channels; however, different number of channels are possible. Using the combination of both a body coil <NUM> and a surface coil is known to provide better image quality.

The pulse generator module <NUM> may operate the gradient amplifiers <NUM> to achieve desired timing and shape of the gradient pulses that are produced during the scan. The gradient waveforms produced by the pulse generator module <NUM> may be applied to the gradient amplifier system <NUM> having Gx, Gy, and Gz amplifiers. Each gradient amplifier excites a corresponding physical gradient coil in a gradient coil assembly <NUM>, to produce the magnetic field gradients used for spatially encoding acquired signals. Specifically, Gx corresponds to a flow/frequency encoding gradient, Gy corresponds to a phase encoding gradient and Gz corresponds to a slice select gradient. The gradient coil assembly <NUM> may form part of a magnet assembly <NUM>, which also includes a polarizing magnet <NUM> (which, in operation, provides a longitudinal magnetic field B<NUM> throughout a target volume <NUM> that is enclosed by the magnet assembly <NUM> and a whole-body RF coil <NUM> (which, in operation, provides a transverse magnetic field B1 that is generally perpendicular to B0 throughout the target volume <NUM>. A transceiver module <NUM> in the MRI system control <NUM> produces pulses that may be amplified by an RF amplifier <NUM> and coupled to the RF coil <NUM> by a transmit/receive switch <NUM>. The resulting signals emitted by the excited nuclei in the subject anatomy may be sensed by receiving coils (not shown) and provided to a preamplifier <NUM> through the transmit/receive switch <NUM>. The amplified MR signals are demodulated, filtered, and digitized in the receiver section of the transceiver <NUM>. The transmit/receive switch <NUM> is controlled by a signal from the pulse generator module <NUM> to electrically connect the RF amplifier <NUM> to the coil <NUM> during the transmit mode and to connect the preamplifier <NUM> to the receiving coil during the receive mode.

The MR signals produced from excitation of the target are digitized by the transceiver module <NUM>. The MR system control <NUM> then processes the digitized signals by Fourier transform to produce k-space data, which is transferred to a memory module <NUM>, or other computer readable media, via the MRI system control <NUM>. "Computer readable media" may include, for example, structures configured so that electrical, optical, or magnetic states may be fixed in a manner perceptible and reproducible by a conventional computer (e.g., text or images printed to paper or displayed on a screen, optical discs, or other optical storage media, "flash" memory, EEPROM, SDRAM, or other electrical storage media; floppy or other magnetic discs, magnetic tape, or other magnetic storage media).

A scan is complete when an array of raw k-space data has been acquired in the computer readable media <NUM>. This raw k-space data is rearranged into separate k-space data arrays for each image to be reconstructed, and each of these k-space data arrays is input to an array processor <NUM>, which operates to reconstruct the data into an array of image data, using a reconstruction algorithm such as a Fourier transform. When the full k-space data is obtained, it represents entire volume of the subject body and the k-space so obtained may be referred as the reference k-space. Similarly, when only the partial k-space data is obtained, the image may be referred as the partial k-space. This image data is conveyed through the data link <NUM> to the computer system <NUM> and stored in memory. In response to the commands received from the operator console <NUM>, this image data may be archived in a long-term storage or may be further processed by the image processor <NUM> and conveyed to the operator console <NUM> and presented on the display <NUM>.

MR signals are represented by complex numbers, where each location at the k-space is represented by a complex number, with I and Q quadrature MR signals being the real and imaginary components. Complex MR images may be reconstructed based on I and Q quadrature MR signals, using processes such as Fourier transform of the k-space MR data. Complex MR images are MR images with 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 may be determined as the square root of the sum of the squares of the I and Q quadrature components of the received MR signal as in Eq. (<NUM>) below: <MAT> and the phase φ of the received MR signal may also be determined as in eq. (<NUM>) below: <MAT>.

In MRI, asymmetric sampling in the frequency and phase encoding directions or dimensions is referred to as fractional echo and partial number of acquisition (NEX), respectively, and is widely used in both 2D and 3D MR imaging. These undersampling techniques are typically used to shorten echo times (e.g. to increase SNR or alter tissue contrast), to shorten repetition times (e.g. to reduce scan time), and/or to suppress unwanted artifacts (such as fineline artifact in fast-spin echo (FSE) imaging or off-resonance artifacts in gradient recalled echo (GRE) and echo planar imaging (EPI)). Asymmetric sampling of k-space introduces truncation artifacts into the reconstructed images, both in the form of blurring and ringing. Various image reconstruction techniques have been devised, therefore, for reconstructing partial k-space data, such as conjugate synthesis, homodyne, and projection onto convex sets (POCS). These known techniques rely on some intrinsic estimate of the underlying image phase, which can be subsequently removed (or "corrected"), allowing the synthesis of the missing or unsampled data based on the principle of Hermitian symmetry of real-valued signals. This phase estimate is often derived from the central, symmetrically-sampled portion of k-space, and is limited in several important ways. First, the phase estimate is contaminated by thermal noise, which is especially problematic in low-signal image regions and/or when this phase estimate performed on a per-channel (or per-view) basis. Secondly, this phase estimate is inherently bandlimited, and must be further low-pass filtered upon application to prevent the introduction of additional truncation artifacts. Therefore, high spatial frequency phase information is not corrected, leaving residual blurring 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 appearance of this biased noise signal in the reconstructed image degrades the image contrast, especially in low-signal regions, and the altered distribution of this noise degrades noise-averaging performance (as in multi-NEX EPI-diffusion) and/or complicate downstream denoising efforts, which are generally based on an assumed noise model. Further, the known partial k-space reconstruction techniques tend to exhibit various strengths and weaknesses, and the choice of method tends to result in various performance tradeoffs. POCS, for example, tends to localize reconstruction artifacts, whereas homodyne tends to result in contrast errors. Finally, in the case of homodyne and conjugate synthesis, the phase information is discarded during reconstruction, making them unsuitable for phase-sensitive applications, such as Dixon chemical shift imaging, phase-sensitive inversion recovery imaging, and generation of phase-sensitive maps based on the phases of the images.

<FIG> is a schematic diagram of a partial sampling pattern or truncation pattern <NUM> of a full k-space <NUM>. A full k-space <NUM> is defined by the maximum kx or ky values kx,max and ky,max, which is defined by maximum frequency- or phase-encoding gradients. In partial sampling, part of the high spatial frequency data <NUM> is not acquired. Truncation may be in the kx dimension and/or the ky dimension, and may be in the kz dimension in a three-dimension (3D) acquisition. The full k-space <NUM> is truncated into a partial k-space <NUM>. The partial k-space <NUM> shown in <FIG> is the full k-space <NUM> truncated in the ky dimension, where negative high spatial frequency data are not acquired during the image acquisition of the partial k-space <NUM>. Truncation may be asymmetrical, where the k-space is truncated asymmetrically in a dimension. The partial k-space <NUM> shown in <FIG> is asymmetrical truncated in the ky dimension. Truncation reduces high-spatial frequency data and causes truncation artifacts. Truncation along the axes of a 2D Cartesian coordinate system as shown in <FIG> is illustrated as an example only.

<FIG> is a schematic diagram of k-space sampling patterns in accordance with an embodiment of the present technique. In <FIG>, plot <NUM> shows a projection reconstruction acquisition whereas plot <NUM> shows a three-dimensional Fourier transformation (3DFT) acquisition. In general, the outer circles in both plots <NUM> and <NUM> relates to full acquisition/sampling or full k-space. However, plot <NUM> has only <NUM> planes kx and ky whereas plot <NUM> has <NUM> planes kx, ky and kz. The kx plane in plot <NUM> corresponds to an axis that will come out of the central dot <NUM>. In general, in partial sampling, only a part of the high spatial frequency data is acquired which is represented by a smaller circle <NUM> in plot <NUM>. Similarly, in plot <NUM>, the data that is acquired is represented by the shaded bars <NUM>. The systems and methods described herein may also be used for removal of truncation artifacts in images based on k-space data from a k-space that is asymmetrically truncated along the axes 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 the k-space being asymmetrically truncated in a radial dimension. In another example, the k-space data are acquired as a stack of radial lines in the kx-ky planes along the kz direction and a partial sampling pattern is the k-space being asymmetrically truncated in a radial dimension in the kx-ky plane and asymmetrically truncated in the kz dimension.

<FIG> is a schematic diagram of a conventional homodyne method <NUM> for reconstructing partial k-space data. In method <NUM>, initially a partial asymmetric k-space data set Mpk(kx, ky) (<NUM>) and a central symmetric k-space data set Ms(kx, ky) (<NUM>) is generated. As discussed earlier, a phase correction factor needs to be applied before the k-space symmetry can be exploited to synthesize the missing data from the partial k-space data Mpk(kx, ky). This phase estimate is derived from the central, symmetrically-sampled k-space data Ms(kx, ky).

Further, the method <NUM> includes defining a pre-weighting function W(ky) (<NUM>). The partial k-space data is then multiplied by the pre-weighting function by a multiplier block <NUM>. The weighted partial k-space data from the multiplier block <NUM> is then inverse Fourier transformed to produce an weighted image mpk(x, y)*w(x, y) (<NUM>). Similarly, central symmetric k-space data set Ms(kx, ky) is inverse Fourier transformed to produce the image ms(x, y) (<NUM>). The phase correction factor p*(x,y) is a unit amplitude/magnitude image with a phase that is the conjugate of ms(x, y) and is given as: <MAT> The phase correction factor is then used to correct the weighted image by multiplying p*(x,y) with mpk(x, y)*w(x, y) by another multiplier block <NUM>. A desired image <NUM> is then obtained by taking the real part of the result of the multiplier block <NUM>.

As shown in <FIG>, the conventional Homodyne reconstruction method uses low-resolution phase for phase correction, resulting in severe artifacts in regions with rapid phase change due to motion, chemical shift, air-tissue interfaces, etc. Therefore, the reconstruction method in the present invention derives a high-resolution denoised phase from zero-filled complex data with deep learning and/or other methods, then applies the high-resolution phase during the phase correction step to minimize these artifacts. It should be noted that the term high-resolution phase refers to a phase having high resolution compared to the phase determined with conventional Homodyne reconstruction method or the phase determined with centralized symmetric k-space data.

<FIG> is a schematic diagram of a method <NUM> for reconstructing partial k-space data, in accordance with an embodiment of the present technique. The method <NUM> may also be referred to as a modified homodyne reconstruction method. In method <NUM>, initially a partial asymmetric k-space data set M(kx, ky) i.e., a first set of partial k-space data <NUM> is generated. A crude image mz(x, y) <NUM> is then generated from the partial asymmetric k-space data set <NUM>. The crude image <NUM> may be reconstructed by zero-filling the partial k-space data with zeros at locations corresponding to the skipped k-space locations to derive full k-space data, and then reconstructing the crude image based on the zero-filled k-space data. The full k-space data for the crude image may be reconstructed by methods other than zero-filling, such as interpolation.

The method further includes determining a high-resolution denoised phase ϕ(x,y) <NUM>. In one embodiment, the high-resolution denoised phase ϕ(x,y) <NUM> is determined from the crude image <NUM> using deep learning (DL) techniques (e.g., a neural network <NUM>). The high-resolution denoised phase <NUM> is determined in radians and may also be referred to as a phase correction factor. Besides DL methods, other denoising method <NUM> such as principal component analysis (PCA), Wavelet Analysis, Total Variance, etc.) may also be used to generate the high-resolution denoised phase <NUM>. In an example useful for understanding the present invention, the high-resolution denoised phase <NUM> may be determined using an external pre-acquired high resolution image <NUM>. A multiplier block <NUM> multiplies the crude image with the high-resolution denoised phase to generate a phase corrected image mz*(x, y) (<NUM>) given by: <MAT>.

The phase corrected image <NUM> is then Fourier transformed to produce a truncated phase corrected k-space data set Mz*(kx, ky), i.e., a second set of partial k-space data <NUM>. Further, the method <NUM> includes defining a pre-weighting function W(ky) (<NUM>). In general, the pre-weighting function is chosen to emphasize a subspace of the acquisition space or for a better reconstruction of a portion of the data. In one embodiment, the pre-weighting function represents spectral sampling density. In another embodiment, the pre-weighting function W(ky) may be determined as <MAT> where H(ky) is a Hanning window. By way of example, the Hanning window can have a width of <NUM>ymin, that is centered around kymin, such as the following Hanning window: <MAT>.

The phase corrected k-space data set <NUM> is then multiplied with the pre-weighting function <NUM> by a multiplier block <NUM>. A desired image <NUM> is then obtained by taking the real part of the result of the multiplier block <NUM>.

<FIG> is a schematic diagram of an exemplary neural network model <NUM> that may be used in the embodiment of <FIG> for determining high-resolution denoised phase. The neural network model <NUM> may include a convolutional neural network <NUM>. The neural network <NUM> accepts a MR phase image <NUM> contaminated with corrupted phase information (i.e., a corrupted image) and returns a pristine phase image <NUM>, where the background phase <NUM> has been substantially denoised. Inputs and outputs to the neural network <NUM> may also be complex images, magnitude and phase image pairs, or real or imaginary image pairs. In one embodiment, the neural network <NUM> may trained with a loss function, which is a function measuring the inference error by the neural network <NUM>. The loss function may be expressed as min(f1(input) - f2(output)), where f1 and f2 are functions of the input and the output to the neural network <NUM>, respectively. In some embodiment, the loss function includes constraints based on prior knowledge of the phase information. For example, prior knowledge is that phase aliasing or phase wrapping may be caused by the computation method of phases. The phase calculated based on the equation of phase Φ(x,y) = <MAT> is limited to the range of [-π, π]. However, in real life, phases may be any real values. As a result, phases with addition of multiple 2π's appear as same phase values, causing phase aliasing. In phase sensitive imaging applications, where phase is used to encode a physical phenomenon, such as flow, motion, or temperature, prior knowledge includes physical law. Phase that behaves against physical law is penalized by the loss function. For example, in flow imaging or quantitative susceptibility measurements (QSM), flow is divergence free. In thermal imaging, heat is expected to dissipate relatively uniformly. For displacement imaging such as elastography, the displacement is expected to obey physical law of motion.

<FIG> provide a comparison of k-space data reconstruction using conventional homodyne method with the modified homodyne technique presented herein. <FIG> is a phantom image <NUM> corresponding to a fully sampled k-space. <FIG> is a phase image <NUM> of the phantom image <NUM>. <FIG> is a zero filled phantom image <NUM> corresponding to a partial k-space data set. <FIG> is a phantom image <NUM> after phase correction corresponding to a conventional homodyne method as described in <FIG>. Further, <FIG> is a phase corrected phantom image <NUM> according to the modified homodyne technique presented herein. It should be noted that all the images presented in <FIG> are simulated phantom images. As can be seen from <FIG>, the phantom image <NUM> generated with the conventional homodyne method includes artifacts <NUM>, <NUM>, <NUM> and <NUM> corresponding to the regions of rapid phase change. These artifacts are minimized in the phantom image <NUM> (<FIG>) generated with the proposed modified Homodyne reconstruction method which looks very similar to the phantom image <NUM> generated with fully sampled k-space.

<FIG> provide another comparison of k-space data reconstruction using conventional homodyne method with the modified homodyne technique presented herein. <FIG> is a simulated abdominal image <NUM> after phase correction corresponding to a conventional homodyne method as described in <FIG>. Further, <FIG> is a phase corrected simulated abdominal image <NUM> according to the modified homodyne technique presented herein. As can be seen from, the artifacts <NUM>, <NUM>, <NUM> corresponding to rapid phase change in <FIG> have been minimized to a great extent in image <NUM> of <FIG>. In other words, the abdominal image generated using the conventional Homodyne is quite inferior to the abdominal image of the modified Homodyne technique with high resolution phase according to an embodiment of the present technique.

<FIG> is a flow chart <NUM> depicting a method for producing an image of a subject with a magnetic resonance imaging (MRI) system. The method includes acquiring a first set of partial k-space data from the subject at step <NUM>. In one embodiment, the first set of k-space data is for less than all of k-space from the subject. In other words, the first set of partial k-space data include k-space data from a partial k-space that is truncated in at least one k-space dimension. At step <NUM>, the method includes determining a phase correction factor. The phase correction factor may be determined from the first set of the partial k-space data. To determine the phase correction factor from the partial k-space data, first a crude image is reconstructed from the first set of the partial k-space data. In one embodiment, the crude image is reconstructed by zero-filling the first set of partial k-space data with zeros at locations corresponding to the skipped k-space locations to derive full k-space data, and then reconstructing the crude image based on the zero-filled k-space data.

Thereafter, one of a neural network model, a wavelet transform algorithm, a principal component analysis (PCA) algorithm or a total variance (TV) algorithm is used with the crude image to determine the phase correction factor. In one embodiment, the neural network model is trained with a pair of pristine images and corrupted images, wherein the corrupted images include corrupted phase information, the pristine images are the corrupted images with the corrupted phase information reduced. In another embodiment, the phase correction factor may be determined from an external image which is a pre-acquired high resolution image. Further, the crude image and the phase correction factor is multiplied to generate a phase corrected image at step <NUM>. At step <NUM>, the method includes transforming the phase corrected image into a second set of partial k-space data. The transformation may be performed by applying a Fast Fourier Transform (FFT) on the phase corrected image.

At step <NUM>, the method includes reconstructing the image of the subject, from the second set of the partial k-space data and a weighting function. In one embodiment, the weighting function refers to spectral sampling density and may include a Hanning window function. First, the weighting function is multiplied with the second set of the partial k-space data to and then the real portion of the result is used as the desired image of the subject.

One advantage of the present technique is that it systematically minimizes common artifacts in MRI images such as in regions with rapid phase variations and can be broadly applied to improve the image quality of out-of-phase water-fat imaging, diffusion imaging, gradient echo imaging, etc. Further, since the technique uses a partial k-space data, efficiency of the MRI acquisition is improved.

Claim 1:
A method for producing an image of a subj ect with a magnetic resonance imaging (MRI) system, the method comprising:
acquiring (<NUM>), using the MRI system, a first set of partial k-space data (<NUM>) from the subject;
reconstructing a crude image (<NUM>) from the first set of partial k-space data;
using one of a neural network model, a wavelet transform algorithm, a principal component analysis (PCA) algorithm or a total variance (TV) algorithm with the crude image to determine (<NUM>) a phase correction factor, wherein the phase correction factor is a high-resolution denoised phase (<NUM>);
generating (<NUM>) a phase corrected image (<NUM>) by multiplying the phase correction factor and the crude image;
Fourier transforming (<NUM>) the phase corrected image into a second set of partial k-space data (<NUM>) ; and
reconstructing (<NUM>), by multiplying the second set of the partial k-space data and a weighting function, the image (<NUM>) of the subject.