Body motion correction device, method, and program

Abstract

To perform motion correction at high speed and accurately.SOLUTION: An apparatus for motion correction according to the embodiment is applied for magnetic resonance imaging and includes processing circuitry. The processing circuitry estimates an intermediate image from a first section of k-space associated with shots of the k-space determined to have minimal motion, estimates motion parameters of a second section of the k-space by using the estimated intermediate image, combines data from the first section with data from the second section on the basis of the estimated motion parameters, and reconstructs the combined data to generate a final image.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to a body motion correction device, method, and program. [Background technology]

[0002] Medical imaging produces images of organs and tissues in a patient's body. For example, magnetic resonance imaging (MRI) uses radio waves, magnetic fields, and magnetic field gradients to produce images of organs and tissues. These images are used by physicians to diagnose injuries and illnesses in patients.

[0003] However, the diagnostic quality of images is significantly degraded by motion. Motion, which occurs because patients cannot remain still, is particularly problematic in MRI. In some cases, motion can prevent diagnostic imaging. In one analysis, as many as 15.4% of head and neck scans required rescanning due to motion. In another analysis, 55 of 175 imaging studies showed some signs of motion, and 29 of those studies underwent at least one resequencing. Resequencing can be costly both financially and time-wise for patients, technicians, and hospitals.

[0004] Furthermore, attempts to reduce the motion described above are generally inadequate. For example, such attempts often reduce the diagnostic quality of images or require time-consuming processes that make them difficult to implement in clinical settings. Furthermore, motion can have a significant impact on patients with movement disorders, which can prevent them from receiving effective medical care. Therefore, new motion correction methods for medical imaging are needed.

[0005] The above description of the "Background Art" is intended to provide an overview of the background of the present disclosure. The research work of the inventors described in the Background Art section above, as well as aspects of the specification that are not otherwise prior art at the time of filing, are not explicitly or implicitly considered to be prior art to the present invention. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] US Patent Application Publication No. 2008 / 0054899 [Patent Document 2] US Patent Application Publication No. 2017 / 0285125 [Patent Document 3] International Publication No. 2012 / 011069 [Non-patent literature]

[0007] [Non-Patent Document 1] Smith et al., “MRI Artifacts and Correction Strategies”, Imaging Med., 2010, 2(4), p. 445-457 [Non-patent document 2] Melissa w. Haskell et al., “Network Accelerated Motion Estimation and Reduction(NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model”, Magnetic Resonance in Medicine, 2019, p.1-10 [Non-patent document 3] Li Feng et al.,”XD-GRASP:Golden-Angle Radial MRI with Reconstruction of Extra Motion-State Dimensions Using Compressed Sensing,Magna Reson Med.,2016,February,75(2),p.775-788 [Non-Patent Document 4] Jada B. Andre, MD et al.,”Toward Quantifying the Prevalence,Severity,and Cost Associated With Patient Motion During Clinical MR Examinations”,American College of Radiology [Non-Patent Document 5] Kiaran P. McGee, PhD.et al.,”Image Metric-Based Correction(Autocorrection) of Motion Effects:Analysis of Image Metrics”,JOURNAL OF MAGNETIC RESONANCE IMAGING,2000,11,p.174-181 [Non-Patent Document 6] Nathan White et al.,”PROMO:Real-Time Prospective Motion Correction in MRI Using Image-Based Tracking”,Magnetic Resonance in Medicine,2010,63,p.91-105 [Non-Patent Document 7] Julian Maclaren et al.,”Prospective Motion Correction in Brain Imaging:A Review”,Magnetic Resonance in Medicine,2013,69,p.621-636 [Non-Patent Document 8] Feng Huang et al., “Data Convolution and Combination Operation (COCOA) for Motion Ghost Artifacts Reduction”, Magnetic Resonance in Medicine, 2010, 64, p. 157-166 [Non-Patent Document 9] Melissa W. Haskell et al.,”Targeted Motion Estimation and Reduction(TAMER):Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization”,IEEE TRANSACTIONS ON MEDICAL IMAGING,MAY 2018,VOL.37,NO.5 Summary of the Invention [Problem to be solved by the invention]

[0008] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to perform body motion correction more quickly and accurately. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0009] An embodiment of a motion correction apparatus for magnetic resonance imaging includes a processing circuit for estimating an intermediate image from a first section of k-space associated with a shot of the k-space determined to include minimal motion, estimating motion parameters for a second section of k-space using the intermediate image, combining data from the first section and the second section based on the motion parameters, and reconstructing the combined data to generate a final image. [Brief explanation of the drawings]

[0010] The present disclosure and various attendant advantages thereof will be better understood by considering the following detailed description in conjunction with the accompanying drawings. [Figure 1] FIG. 1 is a flow diagram of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 2] FIG. 2 is a diagram illustrating an implementation of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 3A] FIG. 3A is a flow diagram of a sub-process of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 3B] FIG. 3B is a flow diagram of a sub-process of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 3C] FIG. 3C is a flow diagram of a sub-process of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 3D] FIG. 3D is a flow diagram of a sub-process of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 4A] FIG. 4A is a schematic block diagram of the sub-processes of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 4B] FIG. 4B is a schematic block diagram of a sub-process of a progressive motion correction method according to an exemplary embodiment of the present disclosure. [Figure 5] FIG. 5 is a flow diagram of a sub-process of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 6] FIG. 6 is a flow diagram of a sub-process of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 7] FIG. 7 is a diagram illustrating an implementation of a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 8] FIG. 8 illustrates a medical imaging system configured to implement a progressive motion correction method according to an example embodiment of the present disclosure. [Figure 9]FIG. 9 is a schematic block diagram of a magnetic resonance imaging system according to an example embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] The present disclosure relates to motion correction in medical imaging.

[0012] An embodiment of a motion correction device is a motion correction device applied to magnetic resonance imaging, and includes a processing circuit that estimates an intermediate image from a first section of k-space associated with a shot of the k-space determined to include minimal motion, uses the intermediate image to estimate motion parameters for a second section of k-space, combines data from the first section and the second section based on the motion parameters, and reconstructs the combined data to generate a final image.

[0013] A motion correction method according to an embodiment is a motion correction method applied to magnetic resonance imaging, and includes the steps of: estimating an intermediate image from M shots in k-space determined to include minimal motion; estimating motion parameters of other shots in the k-space that are different from the M shots using the intermediate image; estimating a new intermediate image based on M+1 shots including the M shots and the other shots and the motion parameters; and estimating motion parameters of other shots in the k-space that are different from the M+1 shots using the new intermediate image.

[0014] A body motion correction program according to an embodiment is a body motion correction program applied to magnetic resonance imaging, and causes a computer to execute the following steps: estimating an intermediate image from M shots in k-space determined to contain minimal body motion; using the intermediate image to estimate body motion parameters of other shots in the k-space that are different from the M shots; estimating a new intermediate image based on M+1 shots including the M shots and the other shots and the body motion parameters; and using the new intermediate image to estimate body motion parameters of other shots in the k-space that are different from the M+1 shots.

[0015] The foregoing paragraphs are intended to be general and are not intended to limit the scope of the claims that follow. The described embodiments, together with their advantages, will be more clearly understood by reference to the following detailed description taken in conjunction with the accompanying drawings, in which:

[0016] As used herein, the term "a" or "an" shall mean one or more. As used herein, the term "plurality" shall mean two or more. As used herein, the term "another" shall mean second or more. As used herein, the terms "including" and / or "having" are synonymous with "comprises" (i.e., open language). Throughout this specification, terms referring to embodiments and similar terms mean that a particular feature, structure, or feature described in connection with that embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of these phrases in various places throughout this specification do not necessarily all refer to the same embodiment. Furthermore, the particular feature, structure, or feature may be combined in any suitable manner with one or more embodiments without limitation.

[0017] The example embodiments are described in the context of methods having certain steps, although the methods and configurations will function effectively with additional steps and steps in a different order than the example embodiments. Thus, the present disclosure is not limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and features described herein and is limited only by the appended claims.

[0018] Furthermore, when a range of values ​​is given, it is understood that each value between the upper and lower limits of that range, as well as any stated or intervening value in any other stated range, is included within the scope of the disclosure. When a stated range includes both an upper and lower limit, ranges that do not include either of those limits are also included within the scope of the disclosure. Unless expressly defined, terms used herein shall have their plain and ordinary meanings that are apparent to those of ordinary skill in the art. All definitions are intended to aid the reader in understanding this disclosure and are not intended to alter or limit the meaning of such terms unless specifically stated.

[0019] Medical imaging can be challenging when patients have movement disorders or neurological conditions that affect movement. For such patients, i.e., those with ataxia, dystonia, Huntington's disease, Parkinson's disease, Tourette's syndrome, and tremors, or those who are generally restless, the immobility required for many imaging modalities is nearly impossible. As a result, the diagnostic quality of the resulting images is significantly reduced because the ability to restrain the patient is limited or impractically time-consuming.

[0020] Therefore, during a magnetic resonance (MR) scan, movement of the patient or other object can cause artifacts in the reconstructed image (e.g., blurring, ghosting, signal loss, etc.), which can lead to misdiagnosis or the need for reimaging to mitigate motion errors. While motion can be suppressed to some extent, patients with neurological disorders such as those mentioned above often contribute to the task of MR scanning because they cannot suppress limb movement to the extent that motion-free imaging can be performed.

[0021] As is known in the background art, MRI systems do not acquire data directly in image space, but in frequency space (i.e., Fourier space). Motion artifacts can occur during a scan due to many factors, including patient motion, image configuration, type of motion, MR pulse sequence settings, and k-space acquisition strategy. The center of k-space contains low spatial frequency information that correlates with large, low-contrast, and smoothly varying objects, while the periphery of k-space contains high spatial frequency information that correlates with edges, details, and objects with sharp changes. Most biological samples exhibit highly localized spectral density in k-space centered at k=0. The k in k-space x axis and k y The k axes correspond to the horizontal (x) and vertical (y) axes of a two-dimensional (2D) image. However, the k axis represents spatial frequency in the x and y directions, not position. In the case of a three-dimensional (3D) image volume, the k axis corresponds to the slice dimension of the image volume. zThe k-space axis is also sampled. Because the object in k-space is described by a global plane wave, each point in k-space contains spatial frequency and phase information for every pixel in the final image. Conversely, each pixel in the final image is located at a point in k-space. Simple reconstruction using an inverse FFT (iFFT) assumes that the object remains stationary while the k-space data is sampled. Therefore, a change in one sample in k-space affects the entire image. Therefore, errors caused by object motion have a noticeable effect on the final image. Because scans must last several minutes to collect the data required for image reconstruction, efforts have been made to increase imaging speed and to detect and correct for motion in the image, as described herein.

[0022] Several previous efforts have been made to avoid or correct motion artifacts in MRI. These approaches are generally defined as prospective and retrospective motion correction methods. Prospective motion correction methods use continuous or semi-continuous measurements of patient motion to track the patient's position over time and predict patient motion to update acquisition parameters. Optical cameras deploying structured light and fiducial markers are used. Prospective motion correction offers superior accuracy and temporal resolution, but often requires specialized hardware and calibration, making it expensive and difficult to consistently perform accurately. Retrospective motion correction methods include both machine learning-based and non-machine learning-based methods. These methods typically rely on radial acquisition, resulting in slow acquisition times and limited contrast. Machine learning-based methods combine physics-based models with machine learning networks to resolve motion using data consistency metrics. This method allows the machine learning network to quickly derive a solution for a set of motion parameters. However, a fundamental drawback of machine learning-based methods is the large parameter space to be solved. In most cases, these methods require simultaneously solving a set of motion parameters for each field of view in the imaging space, i.e., for each shot. For an in-plane affine transformation involving two translations and one rotation, the number of independent parameters to be solved can easily approach 100, taking into account the motion parameters and the number of shots. Therefore, the size and complexity of such a non-convex problem can make the solution slow and unstable.

[0023] As such, one embodiment of the present disclosure describes a method for reducing the parameter space to be solved. The size of the problem to be solved can be reduced by approximately a factor of 300 by solving the set of motion parameters one shot at a time. In one embodiment, this enables machine learning-based methods.

[0024] In one embodiment of the present disclosure, the method includes first generating an initial "clear image" consisting of a subset of all shots in the MR scan. The initial "clear image" reflects minimal patient motion and is used to early terminate subsequent convergence calculations when considering a new data set. The new data set is data from a single shot of k-space that has comparable differences from the "clear image" so that it can be compared to the clear image. In one embodiment, image quality (IQ) changes gradually with small changes in the acceleration factor (e.g., acceleration factors R of 2.6 and 2.3 produce very similar images). As new data is added to the intermediate images, the acceleration factor is slightly decreased.

[0025] In other words, for example, the first K seconds of an MRI acquisition are assumed to be motionless. For example, assume that the patient remains motionless for 10-30 seconds of the MRI acquisition. In this case, data from the first M shots (hereinafter, M shots) corresponding to the first K seconds (e.g., 10-30 seconds) are used to generate an initial "clear image." Here, M is an operator parameter that can be easily adjusted based on an assessment of the patient's "motion risk" over time. As previously described, one image is reconstructed using M shots of k-space data. Although the image reconstructed from undersampled k-space data is an intermediate image and not of high quality, it can be used to define a baseline for estimating motion parameters for subsequent intermediate and final images. Using the intermediate image reconstructed from the M shots, a subsequent shot of data, i.e., the M+1th shot, can be considered, and motion parameters for the M+1th shot can be calculated using a motion estimation method. If the calculated motion parameter is particularly large compared to other comparisons, i.e., if the data between the Mth and M+1th shots is deemed inconsistent beyond the data consistency threshold, the data from the M+1th shot is discarded. Otherwise, the data from the M+1th shot is added to the data from the Mth shot, and the motion parameter estimated for the M+1th shot is added to a motion parameter vector representing the final image. After the data from the M+1th shot has been considered and incorporated, the M+2th shot is considered in the same way as the M+1st shot, and the above process is repeated. Here, the motion parameter vector is used to generate subsequent intermediate images, or, if no further shots are considered, to generate the final image.

[0026] In light of the above, it can be seen that if the first image is generated using all the shots in k-space, it will include shots with significant body motion (as shown in FIG. 7), resulting in significant image artifacts. On the other hand, if an image is generated using one or a few shots without body motion, the image reconstructed from such little data is likely to be of low quality, and therefore will be an insufficient image. Therefore, in this specification, the intermediate image reconstructed from M shots is an image with very little body motion compared to an image generated from all the shots in k-space, and is an image derived from sufficient data to be used as a sufficiently "clean" image for estimating body motion for subsequent shots.

[0027] In one embodiment, a minimal motion k-space shot may have zero motion. In another embodiment, a minimal motion k-space shot may include a small amount of motion compared to other shots of k-space, in which case the minimal motion k-space shot may be considered relatively motionless.

[0028] Thus, "minimal motion" is defined as a k-space shot in a k-space data set that contains the least amount of motion. For example, a shot that has a high level of motion in isolation, but has "minimal motion" when compared to other shots in the k-space data set, may be identified as containing "minimal motion."

[0029] Here, "minimal motion" depends on the scan conditions and the behavior of the individual patient. As will be described below with reference to Figures 3A-3D, "minimal motion" may be determined by comparison with a threshold or by evaluation of a time series of MR scans.

[0030] In any case, the number of k-space shots selected to contain "minimal motion" is large enough to provide sufficient data so that the first intermediate image constructed from those shots has image quality that allows other methods to be performed.

[0031] In one embodiment, a first "clear image," i.e., an intermediate image, is reconstructed from data obtained from the identified minimally motioned k-space shots and motion parameter vectors. In one example, the reconstruction is performed using a fast image reconstruction method such as compressed sensing or parallel imaging. In another example, to expedite this method, reconstruction is performed at one or more resolutions. The one or more resolutions are achieved by appropriately erasing data contained in the k-space shots. Here, machine learning-based reconstruction methods may be used to increase speed.

[0032] In one embodiment, the final image, i.e., the image generated after all the shots in k-space have been considered and either accepted or discarded, is reconstructed by any type of reconstruction method based on the vector of motion parameters. In one example, this reconstruction method may be the same as the method used to reconstruct the intermediate images, or another method that results in higher quality images. In another example, the reconstruction is performed at multiple resolutions. For example, the reconstruction of the final image is performed at a higher resolution than the reconstruction of the intermediate images. Reducing the resolution in the reconstruction of the intermediate images allows for faster shot-by-shot motion correction.

[0033] In one embodiment, the method of the present disclosure is not limited to estimating the first image using the first M shots acquired in chronological order. Instead, the first image may be estimated based on a motion metric minimization calculation for each shot in k-space. As a result, the first image may be estimated based on M shots out of the total N shots in k-space that contain the least motion. The M shots may be acquired at any time during the MR scan.

[0034] In one embodiment, the method of the present disclosure is implemented as follows: First, the number of M shots required to generate the first intermediate image is calculated. This number of M shots is based on the number of shots and an acceleration factor. In one example, the number of M shots is selected to obtain an acceleration factor R between 3 and 4. Next, all N shots in k-space are considered in chronological order, and M shots are selected from among them. Alternatively, all N shots in k-space are sorted in order of motion (or the motion score calculated for each shot). Then, M shots with minimal motion are selected to generate the first intermediate image. Here, the M shots with minimal motion can be selected using a quantitative metric in k-space (image space) that is used as a surrogate measure of motion. Based on this minimum motion frame of reference, a vector of motion parameters representing the final image is updated.

[0035] In one embodiment, for all shots from the (M+1)th to the (N)th, the motion contained in each shot is estimated using the immediately preceding intermediate image. For example, based on the intermediate images generated by the first eight evaluated shots of k-space, the motion parameters of the ninth shot of k-space are estimated. The motion parameters of the ninth shot of k-space are included in a motion parameter vector and used in the reconstruction of the intermediate images and the final image to remove motion artifacts.

[0036] In one embodiment, the final image is estimated using the desired reconstruction method using the data from the first section and any subsequent sections deemed appropriate, and a vector of motion parameters.

[0037] Referring now to the drawings, the above-mentioned method will be outlined with reference to the flow chart of Figure 1. Figure 1 illustrates a method 100 for progressive motion correction in large medical imaging modalities, specifically MRI.

[0038] As shown, progressive motion correction requires an initial "clear image" to be estimated based on one or more shots of k-space data determined to contain minimal motion. Therefore, in subprocess 110 of method 100, an intermediate image is estimated from a first section of k-space. The first section of k-space is one or more shots (M shots) of the total N shots of k-space data acquired in the MR scan. As described below, the first section of k-space is selected based on the acquisition time and / or the quality of the data from the underlying shots. The intermediate image of the first section of k-space is reconstructed using a reconstruction method. In one non-limiting example, the reconstruction method is a fast image reconstruction method such as compressed sensing (CS) and parallel imaging (PI). In another example, the reconstruction method is a machine learning-based reconstruction method that speeds up the reconstruction of the intermediate image.

[0039] In one exemplary embodiment, the intermediate image is estimated by the following equation (1):

number

[0040] Based on the intermediate images from the first M shots, a vector consisting of the body motion parameters of all possible N shots in k-space included in the final image is expressed by the following equation (2).

number

[0041] In one embodiment, the motion parameters for all N shots of the final image include two translational components and one rotational component in two-dimensional image space. In another embodiment, additional dimensions of data (i.e., three-dimensional space) are used, and the motion parameters are defined to have additional translational and / or rotational components. Also, as used herein, the motion parameters are not considered limiting and may be defined in any way that can correct for motion in subsequent sections of k-space data.

[0042] Once the intermediate image has been estimated (reconstructed) in sub-process 110, method 100 proceeds to sub-process 120, where motion is assessed for data from a second section of k-space using the estimated intermediate image as a minimum motion criterion. The second section of k-space consists of one or more shots of k-space. In one example, the second section of k-space consists of the next shot i of k-space data. Thus, the motion parameters for the second section of k-space are estimated according to equation (3) below:

number

[0043] Subsequently, in subprocess 130 of method 100, the estimated motion parameters for the second section of k-space are evaluated to determine whether to add the data from the second section to the data from the first section of k-space that defines the final image. This evaluation further includes combining the estimated motion parameters into a vector of motion parameters for all N shots of k-space (i.e., T(^) all ) is included in the T(^). all is stored in data buffer 145 and is accessible in sub-process 110 and step 150 of method 100.

[0044] In determining the value for the second section of k-space, the data consistency metric may be a motion score calculated based on estimates of motion parameters for the second section of k-space, as described below with reference to Figure 6. The value of the data consistency metric is compared to a tolerance threshold to combine or discard data from the second section of k-space as appropriate.

[0045] In one embodiment, once the motion parameters are estimated for shot i, T(^) i is the vector of body motion parameters (T(with ^) all ) so that T(^)i is used when the next intermediate image is reconstructed in subprocess 110 of method 100 and when the final image is reconstructed in step 150 of method 100.

[0046] If subprocess 130 of method 100 determines that data from the second section of k-space should be added, the combined k-space data set includes "motion-corrected" data from the second section of k-space and data from the first section of k-space. Then, in step 140 of method 100, an evaluation is performed to determine whether additional k-space shots need to be considered. This evaluation is performed by either (1) determining whether the number of evaluated shots equals the total number of k-space shots, or (2) determining that none of the remaining shots out of the total N shots ranked based on motion score would improve the quality of the final image. If step 140 of method 100 determines that additional k-space shots need to be considered, method 100 returns to subprocess 110 to reconstruct an intermediate image including the first section of k-space and the second section of k-space. Then, the third section of k-space is considered. Alternatively, if step 140 of method 100 determines that none of the ranked N shots improves the quality of the final image, method 100 proceeds to step 150, where a final image of the combined k-space data is generated using a final reconstruction technique using the vector of motion parameters stored in data buffer 145. For example, this corresponds to the case where it is determined that the number of evaluated shots is equal to the total N k-space shots, as described below with reference to FIG. 3B. In another example, this corresponds to the case where it is determined that the next ranked shot of the total N shots cannot improve the quality of the final image, as described below with reference to FIG. 3C.

[0047] In one embodiment, the final reconstruction method may be the same as the iterative reconstruction method selected above, or may be any other high-quality image reconstruction method that produces images of sufficient diagnostic quality. Thus, the iterative and final reconstruction methods may be performed at different resolutions and by different techniques based on the constraints and goals at each step.

[0048] The method described with reference to FIG. 1 will now be described with reference to the images in FIG. 2. FIG. 2 shows a motion-corrupted image 201, an intermediate image 202 including the first M of the N total shots, an intermediate image 203 including the M+1 of the N total shots, an intermediate image 204 including the M+2 of the N total shots, and an intermediate image 205 including the M+3 of the N total shots. As more shots are added, the accuracy of the image estimation by CG-SENSE improves and the acceleration factor decreases. For example, intermediate image 202 including the first M of the N total shots has an acceleration factor of 4, intermediate image 203 including the M+1 of the N total shots has an acceleration factor of 3, intermediate image 204 including the M+2 of the N total shots has an acceleration factor of 2.4, and intermediate image 205 including the M+3 of the N total shots has an acceleration factor of 2. These intermediate images are further improved by adding more “motion-corrected” data from subsequent shots of the N total shots. For example, the motion parameters estimated for the intermediate image 205 including M+3 shots out of the total N shots are expressed as follows: the PE translation vector is T(^) all,PE = [0, 0, 0, -1.2960, -2, 7992, -3.2990] mm. This estimated motion parameter value is compared with the true motion parameter value for the intermediate image 205 including M+3 shots out of the total N shots. This true motion parameter value, when expressed in the form of a PE translation vector, is expressed as T(^) all,PE = [0, 0, 0, -1.2960, -2, 7992, -3.2990] mm. As previously mentioned, similar vectors are determined for the remaining motion parameters of this disclosure: rotation and readout (RO) translation.

[0049] Next, subprocess 110 of method 100 will be described with reference to FIG. 3A. To estimate an intermediate image from the first section of k-space, a subset of k-space must first be selected as the first section of k-space. Therefore, in subprocess 311 of subprocess 110, a subset of k-space is selected as the first section of k-space. This selection is based on a calculated data consistency metric (e.g., a motion score) or a time-series evaluation of the MR scan. In particular, the first section of k-space is selected to minimize motion and to develop a minimum motion criterion as the intermediate image. Once the first section of k-space is selected in subprocess 311 of subprocess 110, an intermediate image is reconstructed based on the first section of k-space in step 319 of subprocess 110. Thereafter, in subprocess 120 of method 100, motion parameters for the second section of k-space are estimated using this reconstructed intermediate image.

[0050] Next, with reference to Figures 3B-3D, different implementations of sub-process 311 of sub-process 110 will be described.

[0051] First, as shown in FIG. 3B, a first section of k-space is selected according to the chronological ordering of the data. That is, subprocess 311 of subprocess 110 is performed under the assumption that the patient is stationary or experiencing minimal motion during at least the initial portion of the MR scan length. Therefore, in step 312 of subprocess 311, all N shots acquired during the MR scan are ordered according to the time of acquisition. As shown in FIG. 4A, the first acquired shot is assumed to involve minimal motion because the patient is expected to remain stationary during the initial portion of the examination. Here, the first acquired shot corresponds to the first M shots of the total N shots. Therefore, in step 313 of subprocess 311, the first M shots are selected as the first section of k-space. These first M shots correspond to a subset of the total N ordered shots of the MR scan. The first section is one or more shots of k-space acquired during the MR scan, for example, the first 20 seconds of acquisition during the MR scan.

[0052] In one embodiment, the first section of k-space includes one or more shots of k-space determined to minimize a metric used as a direct or surrogate metric of motion. To this end, a motion score is calculated for each of the N total k-space shots acquired during the MR scan. The N total k-space shots are then evaluated chronologically based on their motion scores. In this method, the first M of the N total shots are the first L seconds of the MR scan, and each motion score of those shots is within a predetermined percentage (e.g., 1%, 2%, 3%, 5%, 7.5%, 10%, etc.) of the minimum motion score of all N shots in the MR scan. In another example, the first M of the N total shots are J seconds of the MR scan, and each motion score of those shots has an average motion score within a predetermined percentage (e.g., 1%, 2%, 3%, 5%, 7.5%, 10%, etc.) of the minimum motion score. Note that in both of the above examples, the amount of motion in the first M of the N total shots varies depending on patient motion during the MR scan.

[0053] In one embodiment, additional navigator data generated by additional RF pulses (e.g., spin echoes or gradient echoes) are acquired with each shot, and this navigator data is used to determine a motion score for each of the N total shots in k-space.

[0054] In one embodiment, a motion score is determined for each of the N k-space shots by determining the image gradient entropy of the image-to-space transform. In one example, the image-to-space transform is a low-resolution transform, which provides a quick, rough motion assessment. In another embodiment, a motion score is determined for each of the N k-space shots using the entropy of the k-space.

[0055] Here, as shown in Figure 3C, the first section of k-space may be selected in another manner. For example, as shown in Figure 4B, subprocess 311 of subprocess 110 may select, as the first section of k-space, M shots determined to minimize a motion metric, which is used as a direct or alternative metric of motion, among all N shots of k-space.

[0056] For this reason, in step 314 of sub-process 311, a motion score similar to that described above is calculated for each of all N k-space shots acquired during the MR scan. Then, in step 315 of sub-process 311, all N k-space shots are ranked based on the motion scores calculated in step 314 of sub-process 311. Various methods can be used to derive motion scores for all N k-space shots.

[0057] In one embodiment, additional navigator data generated by additional RF pulses (e.g., spin echoes or gradient echoes) are acquired with each shot, and this navigator data is used to determine a motion score for each of the N total shots in k-space.

[0058] In one embodiment, a motion score is determined for each of the N k-space shots by determining the image gradient entropy of the image-to-space transform. In one example, the image-to-space transform is a low-resolution transform, which provides a quick, rough motion assessment. In another embodiment, a motion score is determined for each of the N k-space shots using the entropy of the k-space.

[0059] Then, in step 316 of sub-process 311, all N ranked k-space shots are evaluated, and the M shots with the smallest motion scores are selected as the first section of k-space. In one example, the M shots are one or more shots of k-space. In one embodiment, the M shots for the first section of k-space are the k-space shots with motion scores within a predetermined deviation from the smallest motion score of all N shots. Note that other metrics and constraints may be used to define the shots of k-space without departing from the spirit of this disclosure.

[0060] In one embodiment, the M shots of the total N shots are P-ranked shots that have a motion score within a predetermined percentage (e.g., 1%, 2%, 3%, 5%, 7.5%, 10%, etc.) of the highest-ranked shot (i.e., the shot with the lowest motion score) of the total N shots of the MR scan. In another example, the M shots of the total N shots are shots that have a motion score within a predetermined percentage (e.g., 1%, 2%, 3%, 5%, 7.5%, 10%, etc.) of the highest-ranked shot (i.e., the shot with the lowest motion score) of the total N shots of the MR scan. Note that in both of the above examples, the amount of M shots out of the total N shots varies depending on patient motion during the MR scan.

[0061] Alternatively, as shown in Figure 3D, the first section of k-space may be selected based on a data consistency error value as the data consistency metric. To this end, in step 317 of sub-process 311, a data consistency error value is calculated for each of the N shots based on the estimated motion parameters. Then, in step 318 of sub-process 311, each data consistency error value is evaluated, and the shot having the smallest data consistency error value or a shot having a data consistency error value within a predetermined range of the smallest data consistency error value (e.g., less than 1%, less than 2%, less than 3%, less than 5%, less than 7.5%, less than 10%) is selected as M of the N k-space shots to be used as the first section of k-space.

[0062] In one embodiment, the data consistency error value reflects, for each of the N shots, the difference between the data of the acquired shot and the data predicted by a forward model derived from the estimated intermediate images and motion. That is, the data consistency error value may be equal to the error value calculated from equation (3) used to estimate motion for each of the N shots.

[0063] In one embodiment, the data consistency error value may be calculated using a multi-resolution reconstruction method. For example, at a first resolution level, all N shots are "motion corrected" and a data consistency error value is recorded. Then, at the next resolution level, M shots with the smallest calculated data consistency error value at the first resolution level are used to generate intermediate images and incrementally correct as described herein. In another embodiment, after "motion correction" is performed at the first resolution level, the estimation at the first resolution level is repeated using intermediate images reconstructed from the M shots with the smallest data consistency error value.

[0064] Next, FIGS. 3B and 3C will be further described with reference to FIGS. 4A and 4B.

[0065] First, sub-process 311 of Fig. 3B will be described. Fig. 4A is a schematic diagram illustrating a method for selecting M shots to be used as the first section of k-space, according to an example embodiment of the present disclosure.

[0066] Here, an MR scan, i.e., an MR image data set, comprises k-space 406, which consists of N shots of k-space data. The k-space data comprising each of the N shots consists of time-dependent signals acquired at different spatial frequencies in k-space, as shown at 406a-406g in FIG. 4A. Fourier transforms (e.g., two-dimensional Fourier transforms) of all N shots are calculated to generate corresponding grayscale images 408a-408g.

[0067] As shown in FIG. 4A, the dashed block indicates that the first three shots of a total N chronologically ordered shots (here, N is 7) are selected. When performing steps 312 and 313 of sub-process 311, it is assumed that the patient may be stationary for the first three shots of k-space data, but that the remaining portion of the k-space data, i.e., four shots, may be at least somewhat corrupted by patient motion. In one example, the first three shots of k-space data may have an average motion score within 1% of the shot of k-space data with the lowest motion score. Therefore, as described with reference to FIG. 3B, the total N shots of k-space data are chronologically ordered, and M shots therefrom are selected as the first section of k-space.

[0068] Next, sub-process 311 of Figure 3C will be described. Figure 4B is a schematic diagram illustrating the selection of M shots to be used as the first section of k-space, according to an example embodiment of the present disclosure.

[0069] Here, an MR scan, i.e., an MR image data set, comprises k-space 406, which consists of N shots of k-space data. The k-space data comprising each of the N shots consists of time-dependent signals acquired at different spatial frequencies in k-space, as shown at 406a-406g in FIG. 4B. Fourier transforms (e.g., two-dimensional Fourier transforms) of all N shots are calculated to generate corresponding grayscale images 408a-408g.

[0070] As can be seen from FIG. 4B , assuming that grayscale images 408a-408g are in chronological order from left to right, the first M shots of the total N k-space shots may not contain the minimum motion required for the first section of k-space. Therefore, as shown in subprocess 311 of FIG. 3C , a motion score is calculated for each of the total N k-space shots, and the N shots are ranked accordingly (not shown). M shots are selected as the first section of k-space. In FIG. 4B , dashed blocks 406c, 408c, 406d, 408d, 406f, and 408f have the minimum motion scores, and are selected as the M shots. In one example, the minimum motion score is defined as D shots with motion scores within 1.5% of the k-space shots determined to have the minimum motion scores. As can be seen, the M shots selected for the first section of k-space do not have to be shots acquired within a particular time window in the MR scan, but can be any shots among the total N shots that meet the motion score requirements.

[0071] Sub-process 120 of method 100 will now be described with reference to FIG. 5. First, in step 521 of sub-process 120, an estimated intermediate image is acquired based on the first section of k-space selected in sub-process 110 of method 100. Then, in step 522 of sub-process 120, motion parameters are estimated for a second section of k-space. In one embodiment, the second section of k-space is one or more shots out of the total N k-space shots of the acquired MR scan. In one example, the second section of k-space is the next shot out of the total N k-space shots.

[0072] As previously mentioned, in step 522 of sub-process 120, the motion parameters of the second section of k-space data are estimated according to equation (3) below:

number

[0073] Sub-process 130 of method 100 will now be described with reference to Figure 6. Once motion parameters have been estimated for the data from the second section of k-space in sub-process 120 of method 100, sub-process 130 of method 100 combines the data from the first section of k-space and the data from the second section of k-space.

[0074] At a high level, subprocess 130 of method 100 evaluates whether the motion present in the second section of k-space exceeds a level that is considered significant for the final image containing the data.

[0075] Thus, in step 631 of sub-process 130, a data consistency metric value is calculated for the second section of k-space. In one embodiment, as shown in Figure 3D, the data consistency metric may be a data consistency error value, such as the l2 norm of the difference between the collected data and the data estimated by a forward model (i.e., equation (3)) that includes the estimated motion value for the second section of k-space.

[0076] Then, in step 632 of sub-process 130, the calculated data consistency metric value is compared to a tolerance threshold. In one embodiment, the tolerance threshold is a predetermined percentage (e.g., less than 1%, 2%, 3%, 4%, 5%, 7.5%, 10%, etc.) of deviation from the shot or section of k-space having the smallest data consistency metric value. In the example shown in FIG. 3D , the tolerance threshold is a predetermined percentage (e.g., less than 1%, 2%, 3%, 4%, 5%, 7.5%, 10%, etc.) or other statistical value that defines the level of error in the forward model (i.e., Equation (3)).

[0077] That is, if it is determined that the motion in the second section of k-space is at a level that would degrade the quality of the final image, the data from the second section of k-space is discarded. Thus, if it is determined in step 632 of sub-process 130 that the data from the second section of k-space does not meet the tolerance threshold, then in step 633 of sub-process 130, the second section of k-space is discarded, and the next section of k-space (if any) is reconsidered in sub-process 110 of method 100. On the other hand, if it is determined in step 632 of sub-process 130 that the data in the second section of k-space meets the tolerance threshold, then in step 634 of sub-process 130, the data in the second section of k-space is included in the first section of k-space, and the motion parameter vector is updated. That is, if the second section of k-space is accepted, then T(^) all =T(with ^) i Here, T(^) i is the motion parameter estimated for the second section of k-space.

[0078] In either case, the results of subprocess 130 of method 100 are then passed to step 140 of method 100 to determine whether additional sections of k-space need to be considered. If the k-space data is ordered chronologically, additional shots of k-space may need to be evaluated in subprocess 110 of method 100. Similarly, if the k-space data is ranked based on motion, additional shots of k-space may need to be evaluated in subprocess 110 of method 100, as these may improve the quality of the final image.

[0079] Finally, if step 140 of method 100 determines that there are no sections of k-space data that will improve the quality of the final image, method 100 proceeds to step 150, where the combined data is reconstructed to produce the final image.

[0080] 7 illustrates the performance of method 100 when applied to an example that simulates large motion. For example, using true image 755 as a reference, it can be seen that estimated image 756 without motion correction, estimated image 757 with correction applied to all shots simultaneously, and estimated image 758 with progressive correction applied to all N shots as described in this disclosure show distinctly different results. Furthermore, it can be seen that the method of this disclosure produces a final image 758 that is most similar to true image 755.

[0081] 8 illustrates an example embodiment of a medical imaging system 860 in which the method 100 of the present disclosure may be implemented. The medical imaging system 860 includes at least one scanning device 862, one or more image generating devices 864, each of which may be a dedicated computing device (e.g., a dedicated desktop computer, a dedicated laptop computer, a dedicated server, etc.), and a display device 866.

[0082] The scanning device 862 collects scan data by scanning a portion (e.g., a region, volume, slice, etc.) of an object (e.g., a patient). The scanning device 862 may be, for example, a magnetic resonance imaging (MRI) device, a computed tomography (CT) device, a positron emission tomography (PET) device, an x-ray device, an ultrasound device, etc.

[0083] One or more image generation devices 864 acquire scan data from scanning device 862 and generate images of the object region based on the scan data. To generate the images, one or more image generation devices 864 may apply a reconstruction process to the scan data, for example, during generation of intermediate images or during reconstruction of a final image. Reconstruction processes include, for example, Generalized Auto-Calibrating Partially Parallel Acquisition (GRAPPA), Conjugate Gradient Sensitivity Encoding (CG-SENSE), Sensitivity Encoding (SENSE), Autocalibrating Reconstruction for Cartesian Imaging (ARC), Iterative Self-Consistent Parallel Imaging Reconstruction (SPIRiT), Low-Rank Modeling of Local K-Space Neighborhoods (LORAKS), etc.

[0084] In one embodiment, one or more image generating devices 864 generate images and send them to a display device 866, which displays the images.

[0085] In another embodiment, further to the above, one or more image generators 864 may generate two images from the same scan data. One or more image generators 864 may generate two images with different resolutions from the same scan data using different reconstruction processes, where one image may have a lower resolution than the other image. Alternatively, one or more image generators 864 may generate a single image.

[0086] FIG. 9 illustrates a non-limiting example of a magnetic resonance imaging (MRI) system 970. The MRI system 970 shown in FIG. 9 includes a gantry 971 (shown in schematic cross section) and various associated MRI system components 972 connected to the gantry 971. Typically, at least the gantry 971 is located within a shielded room. The MRI system architecture shown in FIG. 9 includes a substantially coaxial, cylindrically arranged static B magnet 973, a Gx, Gy, Gz gradient coil set 974, and a large whole-body RF coil (WBC) assembly 975. An imaging volume 976 is illustrated along the horizontal axis of this cylindrical array of elements, substantially enclosing the head of a patient 977 supported on a patient table 978.

[0087] Within the imaging volume 976, one or more small array RF coils 979 are connected near the patient's head (e.g., referred to herein as the "scan target" or "object"). As will be appreciated by those skilled in the art, in many cases, relatively small coils and / or arrays, such as surface coils, compared to whole-body coils (WBC) are customized for specific body parts (e.g., arms, shoulders, elbows, wrists, knees, legs, chest, spine, etc.). These small RF coils are referred to herein as array coils (AC) or phased-array coils (PAC). These coils include at least one coil that transmits RF signals into the imaging volume 976 and multiple receive coils that receive RF signals from the object (e.g., the patient's head) within the imaging volume 976.

[0088] The MRI system 970 also includes an MRI system controller 983 having a plurality of input / output ports connected to a display 980, a keyboard 981, and a printer 982. The display 980 may be a touch screen type that also allows input control. A mouse or other input / output (I / O) device may also be provided.

[0089] The MRI system controller 983, in conjunction with an MRI sequence controller 984, controls a Gx, Gy, and Gz gradient coil driver 985, an RF transmitter 986, and a transmit / receive switch 987 (if the same RF coil is used for transmit and receive). The MRI sequence controller 984 includes appropriate program code structure 988 for implementing MRI imaging (also called nuclear magnetic resonance (NMR) imaging) techniques, including parallel imaging. The MRI sequence controller 984 may perform MR imaging with or without parallel imaging. Furthermore, the MRI sequence controller 984 can facilitate the execution of one or more pre-scan sequences and a scan sequence for acquiring main-scan magnetic resonance (MR) images (referred to as diagnostic images). The MR data acquired by the pre-scans can be used, for example, to measure sensitivity maps (also called coil sensitivity maps or spatial sensitivity maps) of the RF coils 975 and / or 979 to measure unfolded maps for parallel imaging.

[0090] MRI system components 972 include an RF receiver 989, which provides input to an MRI data processor 990 to generate processed image data that is sent to a display 980. The MRI data processor 990 also accesses previously created MR data, images and / or maps (e.g., coil sensitivity maps, parallel image unfolding maps, distortion maps, etc.), and / or system configuration parameters 991, an MRI image reconstruction program code structure 992, and an MRI system program storage 993.

[0091] In one embodiment, the MRI data processor 990 includes processing circuitry, such as application-specific integrated circuits (ASICs), configurable logic devices (e.g., simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field programmable gate arrays (FPGAs)), and other circuitry configured to perform the functions described in this disclosure.

[0092] The MRI data processor 990 executes one or more sequences of one or more instructions (such as the method 100 described herein) contained in the MRI image reconstruction program code structure 992 and the MRI system program storage 993. Alternatively, the instructions may be read from other computer-readable media, such as a hard disk or a removable media drive. Alternatively, one or more processors in a multi-processing arrangement may execute the sequences of instructions contained in the MRI image reconstruction program code structure 992 and the MRI system program storage 993. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments of the present disclosure are not limited to any particular combination of hardware circuitry and software.

[0093] Additionally, the term "computer-readable medium" as used herein refers to any non-transitory medium that participates in transmitting instructions to the MRI data processor 990 for execution. Computer-readable media may take various forms, including but not limited to, non-volatile media and volatile media. Non-volatile media include, for example, optical disks, magnetic disks, magneto-optical disks, and removable media drives. Volatile media include, for example, dynamic memory.

[0094] 9 also illustrates, as referenced above, a schematic diagram of the MRI system program storage (memory) 993, in which stored program code structures are stored on a computer-readable, non-transitory storage medium accessible to the various data processing components of the MRI system 970. Those skilled in the art will appreciate that the MRI system program storage 993 may be partitioned, with at least a portion directly connected to another processing computer of the MRI system 972 that most immediately needs the stored program code structures during normal operation (i.e., the program code structures are not permanently stored and directly connected to the MRI system controller 983).

[0095] Additionally, the MRI system 970 shown in Figure 9 can be utilized to implement the example embodiments described below. The MRI system components 972 can be divided into different logical collections of "boxes" and typically include multiple digital signal processors (DSPs), microprocessors, and specialized processing circuits (e.g., for high-speed A / D conversion, fast Fourier transforms, array processing, etc.). Typically, these processors are timed "state machines" where each clock period (or a predetermined number of clock periods) causes the physical data processing circuitry to advance from one physical state to another.

[0096] Furthermore, during this operation, not only do the physical states of processing circuitry (e.g., CPUs, registers, buffers, arithmetic units, etc.) continuously change from one clock cycle to another, but the physical states of associated data storage media (e.g., bit storage sites of a magnetic storage medium, etc.) also change from one state to another. For example, at the end of the image reconstruction process and / or sometimes the process of generating an image reconstruction map (e.g., coil sensitivity map, unfolding map, ghost map, distortion map, etc.), an array of computer-readable and accessible data value storage sites in the physical storage medium changes from a previous state (e.g., all uniformly "0" values ​​or all uniformly "1" values) to a new state in which the physical states at the physical sites of such array change between minimum and maximum values ​​to represent actual physical events and physical conditions (e.g., a patient's internal anatomy across the imaging volume space, etc.). As will be apparent to those skilled in the art, this array of stored data values ​​represents and is a component of a body structure, as well as a particular structure of computer control program code that, when sequentially loaded into instruction registers and executed by one or more CPUs of the MRI system 970, causes a particular sequence of operating states to occur and progress within the MRI system 970.

[0097] Obviously, many modifications and variations are possible in light of the above teachings and, therefore, it is evident that, within the scope of the appended claims, the invention may be practiced other than as specifically described herein.

[0098] According to at least one of the embodiments described above, body movement correction can be performed more quickly and with higher accuracy.

[0099] Although several embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims.

[0100] The embodiments of the present disclosure are further described below in parentheses.

[0101] (1) An apparatus for progressive motion correction in magnetic resonance imaging, the apparatus comprising: a processing circuit configured to estimate an intermediate image from a first section of k-space, the first section of k-space corresponding to an acquisition time point during a magnetic resonance scan of a subject, the corresponding acquisition time point during the magnetic resonance scan being associated with a k-space shot determined to include minimal motion; estimate motion parameters for a second section of k-space using the estimated intermediate image; combine data from the first section of k-space and the second section of k-space based on the estimated motion parameters; and reconstruct the combined k-space data to generate a final image.

[0102] (2) The apparatus described in (1), wherein the processing circuitry is configured to time-sequence N shots of k-space in the magnetic resonance scan and select the first M shots of the time-sequenced N shots of k-space in the magnetic resonance scan as the first section of k-space.

[0103] (3) The apparatus described in (1) or (2), wherein the processing circuitry is configured to calculate a motion score for each of the N k-space shots in the magnetic resonance scan, rank the N k-space shots in the magnetic resonance scan based on the motion score calculated for each of the N k-space shots, the ranked N shots being acquired over an entire period of the magnetic resonance scan, and select a section of k-space that includes the highest ranked shot of the N k-space shots and at least one other shot of the ranked N shots as the first section of k-space.

[0104] (4) The device described in any one of (1)-(3), wherein the processing circuitry is configured to update a vector of motion parameters to include estimated motion parameters of the second section of k-space, and the vector of motion parameters is updated to include motion parameters corresponding to the first section of k-space.

[0105] (5) The device described in any one of (1) to (4), wherein the processing circuitry is configured to reconstruct the k-space combined data to generate a final image based on the updated vector of body motion parameters.

[0106] (6) An apparatus described in any one of (1)-(5), wherein the vector of body motion parameters includes two translation values ​​and one rotation value for each of the combined sections of the k-space.

[0107] (7) The device described in any one of (1) to (6), wherein the processing circuitry is configured to calculate a value of a data consistency metric for the second section of k-space, and if the calculated value of the data consistency metric falls below a tolerance threshold as a result of the comparison, discard the data from the second section of k-space, thereby combining the data from the first section of k-space and the data from the second section of k-space based on the estimated body motion parameter.

[0108] (8) The device described in any one of (1) to (7), wherein the processing circuitry is configured to calculate a data consistency error value for each of the N shots of the k-space in the magnetic resonance scan, and to select the first section of the k-space based on the data consistency error value calculated for each of the N shots of the k-space in the magnetic resonance scan.

[0109] (9) A method for progressive motion correction in magnetic resonance imaging, the method including: estimating, by a processing circuit, an intermediate image from a first section of k-space, the first section of k-space corresponding to an acquisition time point during a magnetic resonance scan of a subject, the corresponding acquisition time point during the magnetic resonance scan being associated with a k-space shot determined to include minimal motion; estimating, by the processing circuit, motion parameters for a second section of k-space using the estimated intermediate image; combining, by the processing circuit, data from the first section of k-space and data from the second section of k-space based on the estimated motion parameters; and reconstructing, by the processing circuit, the combined k-space data to generate a final image.

[0110] (10) The method of (9), comprising: ordering N shots in the magnetic resonance scan in chronological order by the processing circuitry; and selecting, by the processing circuitry, the first M shots of the ordered N shots in the magnetic resonance scan as the first section of k-space.

[0111] (11) The method of (9) or (10), further comprising: calculating, by the processing circuitry, a motion score for each of N shots in the magnetic resonance scan; ranking, by the processing circuitry, the N shots in the magnetic resonance scan based on the motion score calculated for each of the N shots, wherein the ranked N shots are acquired across the entire duration of the magnetic resonance scan; and selecting, by the processing circuitry, a section of k-space that includes the highest ranked shot of the N shots of k-space and at least one other shot of the ranked N shots as the first section of k-space.

[0112] (12) A method according to any one of (9)-(11), including updating, by the processing circuit, a vector of motion parameters to include estimated motion parameters of the second section of k-space, wherein the vector of motion parameters is updated to include motion parameters corresponding to the first section of k-space.

[0113] (13) A method according to any one of (9) to (12), wherein the reconstruction of the k-space combined data to generate the final image is performed based on the updated vector of body motion parameters.

[0114] (14) A method according to any one of (9)-(13), wherein the updated vector of body motion parameters includes two translation values ​​and one rotation value for each of the combined sections of the k-space.

[0115] (15) A method according to any one of (9)-(14), wherein the combining of the data from the first section of k-space and the data from the second section of k-space based on the estimated body motion parameters includes calculating a value of a data consistency metric for the second section of k-space by the processing circuitry, and discarding the data from the second section of k-space by the processing circuitry if the calculated value of the data consistency metric falls below a tolerance threshold as a result of the comparison.

[0116] (16) A computer-readable non-transitory storage medium storing computer-readable instructions that, when executed by a computer, cause the computer to perform a method for progressive motion correction in magnetic resonance imaging, the method including: estimating an intermediate image from a first section of k-space, the first section of k-space corresponding to an acquisition time point during a magnetic resonance scan of a subject, the corresponding acquisition time point during the magnetic resonance scan being associated with a k-space shot determined to include minimal motion; estimating motion parameters for a second section of k-space using the estimated intermediate image; combining data from the first section of k-space and data from the second section of k-space based on the estimated motion parameters; and reconstructing the combined k-space data to generate a final image.

[0117] (17) The computer-readable non-transitory storage medium of (16), further comprising: chronologically ordering N shots in the magnetic resonance scan; and selecting the first M shots of the chronologically ordered N shots in the magnetic resonance scan as the first section of k-space.

[0118] (18) A computer-readable non-transitory storage medium according to either (16) or (17), further comprising: calculating a motion score for each of N shots in the magnetic resonance scan; ranking the N shots in the magnetic resonance scan based on the motion score calculated for each of the N shots, wherein the ranked N shots are acquired across the entire duration of the magnetic resonance scan; and selecting a section of the k-space that includes the highest ranked shot of the N shots of the k-space and at least one other shot of the ranked N shots as the first section of the k-space.

[0119] (19) A computer-readable non-transitory storage medium according to any one of (16) to (18), further comprising updating a vector of motion parameters to include the estimated motion parameters of the second section of k-space, wherein the vector of motion parameters includes motion parameters corresponding to the first section of k-space.

[0120] (20) The computer-readable non-transitory storage medium of any one of (16)-(19), wherein the combining of the data from the first section of k-space and the data from the second section of k-space based on the estimated body motion parameters includes calculating a value of a data consistency metric for the second section of k-space, and discarding the data from the second section of k-space if the calculated value of the data consistency metric falls below a tolerance threshold as a result of the comparison.

[0121] The foregoing description discloses and describes merely exemplary embodiments of the present invention. As will be apparent to those skilled in the art, the present invention may be embodied in other forms without departing from the spirit or essential characteristics thereof. Accordingly, the disclosure of the present invention is intended to be illustrative and not limiting of the scope of the present invention and the appended claims. It is to be understood that the present disclosure (and all readily discernible variations thereof) partially defines the scope of the following claim terms so as not to render the subject matter of the invention publicly known. [Explanation of symbols]

[0122] 970 Magnetic Resonance Imaging (MRI) System 990 MRI Data Processor

Claims

1. A body motion correction device applied to magnetic resonance imaging, estimating a first intermediate image from data of a first shot determined to include minimal body motion among multiple shots in k-space in the magnetic resonance imaging; using the first intermediate image, estimating a body movement parameter representing a body movement in a second shot of the plurality of shots, the second shot being different from the first shot; calculating a value of a data consistency metric representing data consistency between the first shot and the second shot based on a body motion parameter representing body motion in the second shot; discarding the second shot data if the value of the data consistency metric is below a threshold; If the value of the data consistency metric is greater than the threshold, include the data of the second shot in a data set including the data of the first shot, and add a body motion parameter representing the body motion in the second shot to a body motion parameter vector that lists values ​​of a plurality of body motion parameters including a value of a body motion parameter representing the body motion in the first shot; determining whether other shots of the plurality of shots require further consideration; If it is determined that the other shot needs to be further considered, a second intermediate image is estimated based on the data included in the data set and the vector of the body motion parameter to estimate a body motion parameter representing a body motion in the other shot; If it is determined that the other shots do not need to be further considered, a final image is generated based on the data contained in the data set and the vector of the body motion parameters. A motion correction device comprising a processing circuit.

2. the processing circuitry estimates the first intermediate image from data of the first shot determined to include minimal body motion among the N shots of the k-space in the magnetic resonance imaging; The body motion correction device according to claim 1 .

3. The processing circuitry ordering the N shots in chronological order; selecting the first M shots from the N shots ordered in chronological order as the first shots to estimate the first intermediate image; The body motion correction device according to claim 2 .

4. The processing circuitry calculating a motion score that is a direct or surrogate measure of motion in each of the N shots; selecting M shots from the N shots as the first shots based on the body motion score to estimate the first intermediate image; The body motion correction device according to claim 2 .

5. the processing circuitry selects a shot having a minimum motion score from the N shots as the first shot to estimate the first intermediate image; The body motion correction device according to claim 4 .

6. The body motion parameters include two translational values ​​and one rotational value. The body motion correction device according to any one of claims 1 to 5.

7. The processing circuitry For each of the N shots, calculate a data consistency error value that reflects the difference between the data of the acquired shot and the data predicted by a forward model obtained from the estimated intermediate images and body motion parameters; selecting M shots out of the N shots as the first shots based on the data consistency error value to estimate the first intermediate image; The body motion correction device according to claim 2 .

8. 1. A motion correction method applied to magnetic resonance imaging, comprising: estimating a first intermediate image from data of a first shot determined to include minimal body motion among multiple shots in k-space in the magnetic resonance imaging; estimating a body movement parameter representing a body movement in a second shot of the plurality of shots, the second shot being different from the first shot, using the first intermediate image; calculating a value of a data consistency metric representing data consistency between the first shot and the second shot based on a motion parameter representing motion in the second shot; discarding the second shot data if the value of the data consistency metric is below a threshold; If the value of the data consistency metric is greater than the threshold, including the data of the second shot in a data set including the data of the first shot, and adding a motion parameter representing the motion of the second shot to a motion parameter vector that lists values ​​of a plurality of motion parameters including a value of a motion parameter representing the motion of the first shot; determining whether other shots of the plurality of shots require further consideration; When it is determined that the other shot needs to be further considered, estimating a second intermediate image for estimating a motion parameter representing a motion in the other shot based on the data included in the data set and the motion parameter vector; generating a final image based on the data included in the data set and the vector of the body motion parameters if it is determined that the other shots do not need to be further considered; A body motion correction method comprising:

9. A motion correction program applied to magnetic resonance imaging, comprising: a step of estimating a first intermediate image from data of a first shot determined to include minimal body motion among multiple shots in k-space in the magnetic resonance imaging; a step of estimating a body movement parameter representing a body movement in a second shot different from the first shot among the plurality of shots using the first intermediate image; calculating a value of a data consistency metric representing data consistency between the first shot and the second shot based on a body motion parameter representing body motion in the second shot; discarding the data of the second shot if the value of the data consistency metric is below a threshold; a step of including the data of the second shot in a data set including the data of the first shot when the value of the data consistency metric is greater than the threshold, and adding a body motion parameter representing the body motion in the second shot to a body motion parameter vector that lists values ​​of a plurality of body motion parameters including a value of a body motion parameter representing the body motion in the first shot; determining whether other shots of the plurality of shots require further consideration; a step of estimating a second intermediate image for estimating a body motion parameter representing a body motion in the other shot based on the data included in the data set and the vector of the body motion parameter when it is determined that the other shot needs to be further considered; generating a final image based on the data included in the data set and the vector of the body motion parameters if it is determined that the other shots do not need to be further considered; A body movement correction program that causes a computer to execute the above.

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