READOUT-SEGMENTED ECHO PLANAR IMAGING WITH K-SPACE AVERAGING
An apparatus and method are provided to correct motion artifacts in magnetic resonance imaging (MRI) data by obtaining magnetic resonance imaging (MRI) data, the MRI data including imaging segments and corresponding navigator segments, each imaging segment sampled over a respective regions of two or more regions of a k-space grid, one of the navigator segments being selected as a reference navigator segment; generating, for each imaging segment of the imaging segments, a respective phase map based on the reference navigator segment and a corresponding navigator segment of the each imaging segment; applying the respective phase maps to the corresponding imaging segments to generate corrected imaging segments; averaging the corrected imaging segments in k-space to generate averaged imaging segments; and reconstructing an MRI image based on the averaged imaging segments.
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This disclosure relates to correcting motion artifacts in magnetic resonance imaging (MRI), and, more particularly, to using readout-segmented echo planar imaging (RSEPI) with joint navigator correction to perform averaging in the k-space domain.BACKGROUND
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent the work is described in this background section, as well as aspects of the description that cannot otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.
The image quality obtained during magnetic resonance imaging (MRI) is adversely affected by movements of a patient/subject, resulting in motion artifacts. This problem arises frequently due to the prolonged time required for common MRI sequences to collect sufficient data to form an image. This imaging time is commonly longer than the timescale of most types of physiological motion, including involuntary movements, cardiac and respiratory motion, gastrointestinal peristalsis, vessel pulsation, and blood and CSF flow. Examples of motion artifacts include blurring and ghosting in the image.
Echo-planar imaging (EPI) is a fast MR imaging method which allows the acquisition of full k-space in a single shot (SSEPI), for example in the time frame of 50-100 ms, thus minimizing the effects of patient motion. Following a spin-preparation, a strong switched frequency-encoding gradient may be applied simultaneously with an intermittently “blipped” low-magnitude phase-encoding gradient. Gradient echoes (GREs) may be collected with each oscillation of the readout (frequency) gradient. The end result may be a “zig-zag” traversal of k-space. EPI is the preferred readout method for time-consuming exams such as diffusion-weighted (DWI) and functional (fMRI) imaging. SSEPI has the advantage of speed but suffers from susceptibility artifacts/distortions, low signal-to-noise, and spatial blurring.
To improve image quality (IQ) while increasing scan duration, readout-segmented EPI (RSEPI) may be utilized. In RSEPI, a full acquisition of k-space data may be acquired in multiple adjacent readout (RO) segments. The k-space from each segment are combined to create a full set of k-space suitable for image reconstruction. RSEPI may additionally introduce motion artifacts due to inconsistencies between the RO segments, but this may be mitigated via navigator data (segments) acquired during the second and subsequent echoes. However, navigator data may only be consistent and compared within each acquisition set, thus rendering data across all acquisition sets disparate.
Accordingly, better methods are desired to reconstruct high-quality images from MRI data using RSEPI with joint navigator correction and imaging data (segments) averaging in k-space.
A more complete understanding of this disclosure is provided by reference to the following detailed description when considered in connection with the accompanying drawings, wherein:
Exemplary embodiments are illustrated in the referenced figures of the drawings. It is intended that the embodiments and figures disclosed herein are to be considered illustrative rather than restrictive. No limitation on the scope of the technology and of the claims that follow is to be imputed to the examples shown in the drawings and discussed herein.
The embodiments are mainly described in terms of particular processes and systems provided in particular implementations. However, the processes and systems will operate effectively in other implementations. Phrases such as ‘an embodiment’, ‘one embodiment’, and ‘another embodiment’ can refer to the same or different embodiments. The embodiments will be described with respect to methods and compositions having certain components. However, the methods and compositions can include more or less components than those shown, and variations in the arrangement and type of the components can be made without departing from the scope of the present disclosure.
The exemplary embodiments are described in the context of methods having certain steps. However, the methods and compositions operate effectively with additional steps and steps in different orders that are not inconsistent with the exemplary embodiments. Thus, the present disclosure is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features described herein and as limited only by the appended claims.
Furthermore, where a range of values is provided, it is to be understood that each intervening value between an upper and lower limit of the range—and any other stated or intervening value in that stated range—is encompassed within the disclosure. Where the stated range includes upper and lower limits, ranges excluding either of those limits are also included. Unless expressly stated, the terms used herein are intended to have the plain and ordinary meaning as understood by those of ordinary skill in the art. Any definitions are intended to aid the reader in understanding the present disclosure, but are not intended to vary or otherwise limit the meaning of such terms unless specifically indicated.
Motion from a patient/object during an MRI scan can introduce artifacts in reconstructed images (blurring, ghosting, signal loss, etc.), leading to misdiagnosis or requiring multiple scans to mitigate said motion errors. While some motion can be prevented, involuntary movements from the patient, such as swallowing, breathing. pulsatile flow, etc. can still occur and degrade the quality of the results. This is especially common for pediatric and geriatric patients who dislike remaining in the instrument, cannot hold their breath for long periods of time, etc.
In MRI, the data acquisition can be performed using pulse sequences that sample an object in spatial frequency space and then applying a Fourier transformation to transform the sampled echoes into the image domain, rather than sampling directly in image space. Motion artifacts can materialize in a scan due to myriad factors including the image structure, type of motion, MR pulse sequence settings, and k-space acquisition strategy. The center of k-space contains low spatial frequency information correlated to objects with large-dimension spatial features and smooth intensity variations, whereas the periphery of k-space contains high spatial frequency information correlated to edges, details, and sharp transitions. A majority of biological samples show very local spectral density in k-space centered around k=0. The kx and ky axes of k-space correspond to the horizontal (x-) and vertical (y-) axes of a two-dimensional (2D) image. The k-axes, however, represent spatial frequencies in the x- and y-directions rather than positions. Since the object in k-space is described by global planar waves, each point in k-space contains spatial frequency and phase information about every pixel in the final image. Conversely, each pixel in the image maps to every point in k-space. Simple reconstruction using an inverse FFT (iFFT) assumes the object has remained stationary during the time the k-space data were sampled. Therefore, errors from object motion have a pronounced effect on the final reconstructed image because a change in a single sample in k-space can affect the entire image. Since scan durations can take minutes in order to acquire the data necessary for image reconstruction, attempts have been made to accelerate the imaging speed as well as detect and correct for motion in images.
As previously mentioned, EPI may be utilized to fully sample k-space in a single shot in a short time frame on the scale of 50-100 ms. In EPI, phase encoding (PE) k-space velocity, also known as the k-velocity, is determined via Δky/ETS, wherein Δky is the k-space distance between two consecutive PE lines and ETS is echo train-spacing which is the time between any two successive echoes. The k-velocity can be increased by increasing Δky or reducing E. However, large increases in Δky can lead to significant residual aliasing artifacts even after a parallel imaging reconstruction. And reduction in ETS leads to increased slew rate which is limited by the gradient amplifier and patient peripheral nerve stimulation safety specifications. Thus, k-velocity cannot be increased beyond a certain practical limit. This k-velocity limit is the source of two major disadvantages of EPI: image distortion and low spatial resolution. Since the amount of distortion is inversely proportional to k-velocity, the lower k-velocity in EPI leads to significant image distortions. On the other hand, for a fixed slew rate, readout (RO) resolution may be increased by increasing the ETS but this may result in lower k-velocity and increased distortion. Conversely, for a fixed ETS, slew rate can be increased to increase resolution but only to a certain extent due to the gradient hardware and patient safety limits.
To increase RO resolution and reduce distortion, Readout-Segmented EPI (RSEPI) may be utilized at the cost of increased scan duration. K-space sampling may occur in a predetermined number of shots, for example 3-7 shots, to sample a predetermined number of k-space segments, for example 3-7 segments. Each shot may have limited transversal of k-space in the readout (kx) direction, but full resolution along the phase-encode (ky) direction. The kx coverage can be increased by acquiring more RO segments, thus increasing RO image resolution without changing ETS or slew rate. Adding more segments does not change k-velocity, so distortion remains the same, independent of the RO image resolution. By using narrow segments, for example segments with 32-64 RO points, short ETS of 300-400 μs can be achieved. This increases k-velocity, thereby resulting in lower distortion in the phase encode direction.
Referring now to the drawings, wherein like reference numerals designate identical or corresponding parts throughout the several views,
Even with single-shot EPI, DWI and fMRI exams may be several minutes long. RSEPI may further increase the scan time, wherein the scan time is a linear product of the single-shot EPI scan time and the number of segments 205. For example, if five segments 205 are acquired, the scan time may increase five-fold as compared to SSEPI. Moreover, the scan time may increase when averaging of all the shots in an acquisition set, as well as when scanning additional acquisition sets. For example, as illustrated in
In one implementation, which is illustrated in
In a scenario where the total NAQs are limited to a fixed quantity (e.g., the MRI scan is time constrained), the non-uniform pattern of sampling may provide benefit in targeting the central, low spatial-frequency content more, whereas uniformly sampling over the entire k-space grid (i.e. five segments evenly across the k-space grid in each acquisition set) would devote a greater time and resources to sampling the less information rich, peripheral portion of k-space. In a scenario where the total scan time is limited, the non-uniform pattern of sampling may provide benefit in skipping shots that may sample the segments 205(1,a), 205(1,e) on the periphery of the k-space grid in order to acquire more shots of the central segments 205(1,c), 205(2,b), 205(3,a).
While a specific example has been provided, it may be appreciated by those in the art that other non-uniform sampling patterns may be implemented. In general, the k-space grid may include N segments 205 to cover the k-space grid and the N segments 205 may be sampled with M acquisitions, wherein M≥N. The k-space grid may be divided into a plurality of portions P, wherein each portion may comprise one or more segments 205. For example, as shown in
Due to changes (e.g., motion) between the shots, each shot acquired using RSEPI may require a correction (e.g., a phase correction due to motion) prior to averaging in k-space as compared to other EPI-based methods using image-domain averaging.
In step S301, MRI data may be acquired including imaging segments with corresponding navigator segments.
In step S303, a first correction of the imaging segments may be applied, for example a ghost correction. After this step, the navigator segment acquired with each imaging segment acquisition may be used to correct a phase difference of each of the imaging segments within each acquisition set. However, correction of imaging segments across more than one acquisition set may not be possible if the navigator segments acquired in respective acquisition sets are independently corrected and not corrected with respect to a reference across all acquisition sets.
In step S305, after all acquisitions sets have been acquired, a reference navigator segment may be selected from all the acquired navigator segments. The navigator segments may then be corrected with respect to the reference navigator segment.
In step S309, respective phase maps are generated for the imaging segments based on differences between the reference navigator and the navigator corresponding to a respective imaging segment. For example, the navigator segments and the reference navigator segment may be inverse Fourier Transformed to generate navigator images and a reference navigator image. Each of the navigator segment images may be multiplied with a conjugate of the reference navigator segment image to generate a phase map, for example a 2D phase map for a sampled 2D k-space grid.
In step S311, optional post processing, such as smoothing, may be applied to the phase map.
In step S313, the imaging segments may be inverse Fourier Transformed to generate imaging segment images.
In step S315, it is determined whether or not phase correction is needed. For example, if no motion has occurred and the phase map represents a phase correction of zero, then no phase correction is needed. In this case, method 300 can skip step S317 and proceed directly to step S325. If on the other hand a phase correction is needed, then, in step S317, the imaging segments may be multiplied with the phase map generated in step S309. The determination of whether or not phase correction is needed can proceed on an imaging segment by imaging segment basis. The phase correction may be insufficient in correcting the error due to motion, for example if the user exhibits a large motion of a body part, in which case a usability threshold may be determined for each of the imaging segments after phase correction. If the imaging segment does not meet the usability threshold, the imaging segment may be rejected.
In step S325, for the imaging segments meeting the usability threshold, the imaging segments are averaged in the k-space domain. For example, the imaging segments that did not need phase correction plus the imaging segments that were phase corrected in step S317 may be Fourier Transformed to k-space to generate corrected imaging segments. Then these corrected imaging segments can be averaged in the k-space domain to generate averaged imaging segments. The averaged imaging segments may be mapped (e.g., interpolated) onto a common grid in k-space.
In step S329, the averaged imaging segments may be inverse Fourier Transformed to generate an MRI image. To generate the MRI image, the one or more MRI system 100 can perform a reconstruction process on the scan data.
Notably, the joint navigator segment correction enables the aforementioned method to perform averaging in the k-space domain, as opposed to averaging in the image domain. In turn, this averaging in the k-space domain allows for a non-uniform distribution of imaging segments within k-space to obtain more samples in those regions of k-space that are deemed to have greater impact on the image quality. In contrast, averaging in the image domain generally requires a uniform distribution of imaging segments within k-space because, typically, each unique image that is being averaged in the image domain is generated from a unique set of imaging segments spanning the k-space domain. Further, the above-described joint navigator segment correction method enables the non-uniform distribution of imaging segments within k-space because all the acquisition sets are corrected via a common reference navigator segment. Additionally, since most of the MR signal is concentrated near the center of the k-space grid, the signal-to-noise ratio (SNR) of the non-uniform sampling pattern may closely match the SNR of a uniform sampling pattern, while preserving or reducing the total scan time.
In one implementation, the non-uniform sampling pattern with joint navigator correction may allow for selective data rejection from scans while preserving scan time. In the presence of bulk or large pulsatile motion, imaging segments and corresponding navigator segments may experience significant signal loss and become unusable. In one method, the navigator segments from an acquisition set may be reconstructed and analyzed inline during a scan to determine if the error for each navigator segment in the acquisition set exceeds a predetermined quality threshold. For example, the predetermined quality threshold may be determined via the navigator segment phase or k-space entropy. Upon determining that acquired MRI data is unusable, a user may perform a repeated acquisition of an entire acquisition set again. This may require a predetermined feedback mechanism. This may also increase scan time when repeated acquisition scans must be performed. In another method without the feedback mechanism, the user may define a predetermined number of acquisitions sets to acquire with uniform k-space sampling of the k-space grid. The navigator segments may be acquired with the corresponding imaging segments and compared after the scan has terminated. In this method, upon determining that acquired MRI data is unusable, an entire acquisition set may be rejected.
For example, two acquisition sets uniformly sampling the k-space grid including five segments 205 may be acquired (as shown in
Using the non-uniform k-space sampling pattern with joint navigator segment correction of the present disclosure, the example sampling pattern in
Upon determining that the navigator segment for one of the imaging segments exceeds a predetermined error threshold, the data from that specific segment 205 may be rejected without rejecting all the data in the respective acquisition set. For example, in
Notably, as described in this particular example, the uniform k-space sampling with disjointed navigator segment correction (
As previously mentioned, in practice, the segments 205 may be overlapped by a small fraction to avoid discontinuities in k-space, especially for data points sampled on the gradient ramps. However, even with overlapping segments 205, k-space discontinuities may occur due to errors in gradient calibration, which may be mitigated via increasing the overlap factor further. Increase in overlap factor increases the scan time by increasing the number of segments 205 required to cover the k-space grid.
In one implementation, the disclosed method may reduce scan time by reducing or eliminating the partial overlap of adjacent segments 205. Instead, the segments 205 acquired in consecutive acquisition sets may be shifted in the kx direction such that the segments 205 overlap any two imaging segments previously adjacently sampled. Since all the navigator segments may be jointly corrected, the segments 205 may be directly averaged in k-space to mitigate k-space discontinuities. Note in
RS-ESPI may utilize precise and accurate control of the applied gradients. There may be potential secondary costs related to the gradient control necessary to achieve precise kx sampling. Namely, eddy currents may distort the kx trajectory and thus the kx samples may not be sampled at the target location. This may introduce ringing artifacts, for example. Therefore, advantageously, sampling the same kx point with several different overlapping segments 205 allows for averaging out the error in the kx trajectory introduced by any one segment 205.
One or more smaller array RF coils 121 can be more closely coupled to the patient's head (referred to herein, for example, as “scanned object” or “object”) in imaging volume 117. As those in the art will appreciate, compared to the WBC (whole-body coil), relatively small coils and/or arrays, such as surface coils or the like, are often customized for particular body parts (e.g., arms, shoulders, elbows, wrists, knees, legs, chest, spine, etc.). Such smaller RF coils are referred to herein as array coils (AC) or phased-array coils (PAC). These can include at least one coil configured to transmit RF signals into the imaging volume, and a plurality of receiver coils configured to receive RF signals from an object, such as the patient's head, in the imaging volume.
The MRI system 100 includes a MRI system controller 130 that has input/output ports connected to a display 124, a keyboard 126, and a printer 128. As will be appreciated, the display 124 can be of the touch-screen variety so that it provides control inputs as well. A mouse or other I/O device(s) can also be provided.
The MRI system controller 130 interfaces with a MRI sequence controller 140, which, in turn, controls the Gx, Gy, and Gz gradient coil drivers 132, as well as the RF transmitter 134, and the transmit/receive switch 136 (if the same RF coil is used for both transmission and reception). The MRI sequence controller 140 includes suitable program code structure 138 for implementing MRI imaging (also known as nuclear magnetic resonance, or NMR, imaging) techniques including parallel imaging. MRI sequence controller 140 can be configured for MR imaging with or without parallel imaging.
Moreover, the MRI sequence controller 140 can facilitate one or more preparation scan (pre-scan) sequences, and a scan sequence to obtain a main scan magnetic resonance (MR) image (referred to as a diagnostic image). MR data from pre-scans can be used, for example, to determine sensitivity maps for RF coils 115 and/or 121 (sometimes referred to as coil sensitivity maps or spatial sensitivity maps), and to determine unfolding maps for parallel imaging.
The MRI system components 103 include an RF receiver 141 providing input to data processor 142 so as to create processed image data, which is sent to display 124. The MRI data processor 142 is also configured to access previously generated MR data, images, and/or maps, such as, for example, coil sensitivity maps, parallel image unfolding maps, distortion maps and/or system configuration parameters 146, and MRI image reconstruction program code structures 144 and 150.
In one embodiment, the MRI data processor 142 includes processing circuitry. The processing circuitry can include devices such as an application-specific integrated circuit (ASIC), configurable logic devices (e.g., simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field programmable gate arrays (FPGAs), and other circuit components that are arranged to perform the functions recited in the present disclosure.
The processor 142 executes one or more sequences of one or more instructions contained in the program code structures 144 and 150. Alternatively, the instructions can be read from another computer-readable medium, such as a hard disk or a removable media drive. One or more processors in a multi-processing arrangement can also be employed to execute the sequences of instructions contained in the program code structures 144 and 150. In alternative embodiments, hard-wired circuitry can be used in place of or in combination with software instructions. Thus, the disclosed embodiments are not limited to any specific combination of hardware circuitry and software.
Additionally, the term “computer-readable medium” as used herein refers to any non-transitory medium that participates in providing instructions to the processor 142 for execution. A computer readable medium can take many forms, including, but not limited to, non-volatile media or volatile media. Non-volatile media includes, for example, optical, magnetic disks, and magneto-optical disks, or a removable media drive. Volatile media includes dynamic memory.
Also illustrated in
Additionally, the MRI system 100 as depicted in
Furthermore, not only does the physical state of the processing circuits (e.g., CPUs, registers, buffers, arithmetic units, etc.) progressively change from one clock cycle to another during the course of operation, the physical state of associated data storage media (e.g., bit storage sites in magnetic storage media) is transformed from one state to another during operation of such a system. For example, at the conclusion of an image reconstruction process and/or sometimes an image reconstruction map (e.g., coil sensitivity map, unfolding map, ghosting map, a distortion map etc.) generation process, an array of computer-readable accessible data value storage sites in physical storage media will be transformed from some prior state (e.g., all uniform “zero” values or all “one” values) to a new state wherein the physical states at the physical sites of such an array vary between minimum and maximum values to represent real world physical events and conditions (e.g., the internal physical structures of a patient over an imaging volume space). As those in the art will appreciate, such arrays of stored data values represent and also constitute a physical structure, as does a particular structure of computer control program codes that, when sequentially loaded into instruction registers and executed by one or more CPUs of the MRI system 100, causes a particular sequence of operational states to occur and be transitioned through within the MRI system 100.
MRI images are formed by acquiring NMR (nuclear magnetic resonance) RF response signals (e.g. echo data) spatially encoded for respectively corresponding points in k-space. The RF response values are typically generated by “traversing” k-space in two or three dimensions according to a configured MRI pulse sequence. The acquisition of data in the frequency-encoded direction (e.g., along the x-axis) is typically rapid and on the order of several milliseconds. However, along the phase-encoded axis (e.g., y-axis), a different value of the applied phase-encoding gradient is used to sample each point. Therefore, the acquisition time for an MRI image can be largely determined by the number of phase-encoding steps.
While certain implementations have been described, these implementations have been presented by way of example only, and are not intended to limit the teachings of this disclosure. Indeed, the novel methods, apparatuses and systems described herein can be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the methods, apparatuses and systems described herein can be made without departing from the spirit of this disclosure.
1. An imaging apparatus, comprising:
- processing circuitry configured to obtain magnetic resonance imaging (MRI) data, the MRI data including imaging segments and corresponding navigator segments, each imaging segment sampled over a respective regions of two or more regions of a k-space grid, one of the navigator segments being selected as a reference navigator segment; generate, for each imaging segment of the imaging segments, a respective phase map based on the reference navigator segment and a corresponding navigator segment of the each imaging segment; apply the respective phase maps to the corresponding imaging segments to generate corrected imaging segments; average the corrected imaging segments in k-space to generate averaged imaging segments; and reconstruct an MRI image based on the averaged imaging segments.
2. The apparatus according to claim 1, wherein the processing circuitry is further configured to obtain the MRI data, wherein a number of imaging segments is greater than a number of the two or more regions in the k-space grid.
3. The apparatus according to claim 1, wherein the processing circuitry is further configured to obtain the MRI data, wherein more of the imaging segments are sampled at a central region of the k-space grid than are sampled at a peripheral region of the k-space grid, the central region of the k-space grid including lower spatial frequencies than the peripheral region.
4. The apparatus according to claim 3, wherein the processing circuitry is further configured to obtain the MRI data, wherein a predetermined number of the imaging segments sampled over the at least one substantially central segment of the k-space grid is rejected.
5. The apparatus according to claim 1, wherein the processing circuitry is further configured to generate the respective phase map, wherein the respective phase map corresponding to one of the imaging segments is a phase of a product of a navigator segment image of the navigator segments corresponding to the one of the imaging segments and a conjugate of a reference navigator image of the reference navigator segment.
6. The apparatus according to claim 1, wherein the processing circuitry is further configured to reconstruct the MRI image, wherein the MRI image is reconstructed by an inverse Fourier Transform of the averaged imaging segments.
7. The apparatus according to claim 1, wherein the processing circuitry is further configured to apply the respective phase map to the corresponding imaging segments by convolving, in the k-space, the respective imaging segments with the corresponding phase maps.
8. The apparatus according to claim 1, wherein the processing circuitry is further configured to obtain the MRI data, wherein the imaging segments includes a third imaging segment that spans a boundary in the k-space between a first imaging segment and a second imaging segment that are sampled in adjacent regions of the k-space.
9. A method for non-uniform k-space sampling, comprising:
- obtaining magnetic resonance imaging (MRI) data, the MRI data including imaging segments and corresponding navigator segments, each imaging segment sampled over a respective regions of two or more regions of a k-space grid, one of the navigator segments being selected as a reference navigator segment;
- generating, for each imaging segment of the imaging segments, a respective phase map based on the reference navigator segment and a corresponding navigator segment of the each imaging segment;
- applying the respective phase maps to the corresponding imaging segments to generate corrected imaging segments;
- averaging the corrected imaging segments in k-space to generate averaged imaging segments; and
- reconstructing an MRI image based on the averaged imaging segments.
10. The method according to claim 9, wherein a number of imaging segments is greater than a number of the two or more regions in the k-space grid.
11. The method according to claim 9, wherein more of the imaging segments are sampled at a central region of the k-space grid than are sampled at a peripheral region of the k-space grid, the central region of the k-space grid including lower spatial frequencies than the peripheral region.
12. The method according to claim 11, wherein a predetermined number of the imaging segments sampled over the at least one substantially central segment of the k-space grid is rejected.
13. The method according to claim 9, wherein the respective phase map corresponding to one of the imaging segments is a phase of a product of a navigator segment image of the navigator segments corresponding to the one of the imaging segments and a conjugate of a reference navigator image of the reference navigator segment.
14. The method according to claim 9, wherein the MRI image is reconstructed by an inverse Fourier Transform of the averaged imaging segments.
15. The method according to claim 9, wherein the processing circuitry is further configured to apply the respective phase map to the corresponding imaging segments by convolving, in the k-space, the respective imaging segments with the corresponding phase maps.
16. The method according to claim 9, wherein the processing circuitry is further configured to obtain the MRI data, wherein the imaging segments includes a third imaging segment that spans a boundary in the k-space between a first imaging segment and a second imaging segment that are sampled in adjacent regions of the k-space.
17. A non-transitory computer readable storage medium including executable instructions, wherein the instructions, when executed by circuitry, cause the circuitry to perform the method according to claim 9.