System and method for motion correction of medical images for validation of quantized parameter maps

Abstract

The present invention is entitled "System and method for verifying motion correction of medical images in a quantized parameter map". The present invention provides a computer-implemented method for verifying motion correction of magnetic resonance (MR) images in a quantized parameter map of parameters. The method includes receiving a series of motion-corrected images in a series of MR images, wherein the series of MR images are MR images of anatomical regions at different time points. For each pixel of a plurality of pixels in a series of motion-corrected images in the series of motion-corrected images, the method also includes: generating a time series at the pixel based on the series of motion-corrected images; fitting the time series to a model of the parameters; and estimating an indication of the goodness of fit of the time series to the model. The method also includes generating a metric indicating the effectiveness of the motion correction based on the indication of the estimated goodness of fit of the plurality of pixels, and verifying the motion correction based on the metric.

Description

Background Art

[0001] The field of the present disclosure relates generally to systems and methods for validating motion correction, and more particularly to systems and methods for validating motion correction of medical images for quantization parameter maps.

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

[0003] Quantitative parametric maps are used to measure biological and pathological properties of tissue. Parametric maps are derived based on a series of medical images of the anatomical structure at different time points. The patient and / or the anatomical structure moves between different time points, and thus the motion generally deteriorates the quality of the estimated parametric map. Motion correction is often used to mitigate the problem, but does not always improve the quality of the parametric map. Summary of the invention

[0004] In one aspect, a computer-implemented method for validating motion correction of magnetic resonance (MR) images for a quantized parameter map of a parameter is provided. The method includes receiving a series of motion-corrected images in a series of MR images, wherein the series of MR images are MR images of an anatomical region at different time points. For each of a plurality of pixels in a series of motion-corrected images in the series of motion-corrected images, the method further includes: generating a time series at the pixel based on the series of motion-corrected images; fitting the time series to a model of the parameter; and estimating an indication of the goodness of fit of the time series to the model. The method also includes generating a metric indicating the effectiveness of the motion correction based on the indication of the estimated goodness of fit of the plurality of pixels, and validating the motion correction based on the metric.

[0005] In another aspect, a motion correction verification system for verifying motion correction of MR images of quantized parameter maps of parameters is provided. The system includes a motion correction verification computing device. The motion correction verification computing device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to receive a series of motion correction images of a series of MR images, wherein the series of MR images are MR images of an anatomical region at different time points. For each pixel of a plurality of pixels in a series of motion correction images in the series of motion correction images, the at least one processor is further programmed to generate a time series at the pixel based on the series of motion correction images; fit the time series to a model of the parameters; and estimate an indication of the goodness of fit of the time series to the model. The at least one processor is also programmed to generate a metric indicating the effectiveness of the motion correction based on the indication of the estimated goodness of fit of the plurality of pixels, and to verify the motion correction based on the metric.

[0006] In one or more aspects, a computer-implemented method for validating motion correction of a medical image using a quantized parameter map of a parameter is provided. The method includes receiving a series of motion-corrected images in a series of medical images, wherein the series of medical images are medical images of an anatomical region at different points in time. For each of a plurality of pixels in a series of motion-corrected images in the series of motion-corrected images, the method further includes: generating a time series at the pixel based on the series of motion-corrected images; fitting the time series to a model of the parameter; and estimating an indication of the goodness of fit of the time series to the model. The method also includes generating a metric indicating the effectiveness of the motion correction based on the indication of the estimated goodness of fit of the plurality of pixels, and validating the motion correction based on the metric. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The patent or application file contains at least one drawing executed in color. Copies of the patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0008] Figure 1 is a schematic diagram of an exemplary magnetic resonance imaging (MRI) system.

[0009] Figure 2A is an exemplary motion correction verification system.

[0010] Figure 2B is a flow chart of an exemplary method of verifying motion correction.

[0011] Figure 3 is a series of cardiac images used for T1 mapping.

[0012] Figure 4 R shows the T1 map without motion correction2 A map having a first motion correction algorithm and having a second motion correction algorithm.

[0013] Figure 5 is a block diagram of an exemplary computing device. DETAILED DESCRIPTION

[0014] The present disclosure includes systems and methods for motion correction of magnetic resonance (MR) images of a validated individual for quantifying parameter maps. As used herein, a subject is a human, an animal, or a sham animal. Method aspects will be apparent in part and explicitly discussed in part in the following description.

[0015] In magnetic resonance imaging (MRI), a subject is placed in a magnet. When the subject is in a magnetic field generated by a magnet, the magnetic moments of nuclei such as protons attempt to align with the magnetic field, but precess around the magnetic field in a random order at the Larmor frequency of the nuclei. The magnetic field of the magnet is called B0 and extends in the longitudinal or z direction. During the acquisition of MRI images, a magnetic field in the xy plane and close to the Larmor frequency (called the excitation field B1) is generated by a radio frequency (RF) coil and can be used to rotate or "tilt" the net magnetic moment or magnetization Mz of the nucleus from the z direction toward the transverse or xy plane. After the excitation signal B1 terminates, the nucleus emits a signal, which is called an MR signal. In order to use the MR signals to generate an image of the subject, magnetic field gradient pulses (Gx, Gy, and Gz) are used. Gradient pulses are used to scan through k-space, the space or distance of spatial frequency in reverse. There is a Fourier relationship between the acquired MR signals and the image of the subject, so the image of the subject can be derived by reconstructing the MR signals.

[0016] Figure 1 A schematic diagram of an exemplary MRI system 10 is shown. In an exemplary embodiment, the MRI system 10 includes a workstation 12 having a display 14 and a keyboard 16. The workstation 12 includes a processor 18, such as a commercially available programmable machine running a commercially available operating system. The workstation 12 provides an operator interface that allows a scan plan to be entered into the MRI system 10. The workstation 12 is coupled to a pulse sequence server 20, a data acquisition server 22, a data processing server 24, and a data storage server 26. The workstation 12 and each of the servers 20, 22, 24, and 26 communicate with each other.

[0017] In an exemplary embodiment, the pulse sequence server 20 responds to instructions downloaded from the workstation 12 to operate the gradient system 28 and the radio frequency ("RF") system 30. The instructions are used to generate gradient waveforms and RF waveforms in the MR pulse sequence. The RF coil 38 and the gradient coil assembly 32 are used to perform the prescribed MR pulse sequence. The RF coil 38 is shown as a whole body RF coil. The RF coil 38 can also be a local coil that can be placed near the anatomical structure to be imaged, or a coil array including multiple coils.

[0018] In the exemplary embodiment, gradient waveforms for performing a prescribed scan are generated and applied to the gradient system 28, which excites the gradient coils in the gradient coil assembly 32 to generate magnetic field gradients G for position encoding the MR signals. x , G y and G z The gradient coil assembly 32 forms part of a magnet assembly 34 , which also includes a polarizing magnet 36 and an RF coil 38 .

[0019] In an exemplary embodiment, the RF system 30 includes an RF transmitter for generating RF pulses used in an MR pulse sequence. The RF transmitter responds to the scanning scheme and direction from the pulse sequence server 20 to generate RF pulses with a desired frequency, phase and pulse amplitude waveform. The generated RF pulses can be applied to the RF coil 38 by the RF system 30. The response MR signal detected by the RF coil 38 is received by the RF system 30 and amplified, demodulated, filtered and digitized under the instruction of the command generated by the pulse sequence server 20. The RF coil 38 is described as a transmitter and receiver coil, so that the RF coil 38 transmits RF pulses and detects MR signals. In one embodiment, the MRI system 10 may include a transmitter RF coil that transmits RF pulses and a separate receiver coil that detects MR signals. The transmission channel of the RF system 30 can be connected to the RF transmission coil, and the receiver channel can be connected to a separate RF receiver coil. Typically, the transmission channel is connected to the whole body RF coil 38, and each receiver section is connected to a separate local RF coil.

[0020] In an exemplary embodiment, the RF system 30 also includes one or more RF receiver channels. Each RF receiver channel includes an RF amplifier that amplifies the MR signals received by the RF coil 38 to which the channel is connected; and a detector that detects and digitizes the I quadrature component and the Q quadrature component of the received MR signal. The magnitude of the received MR signal can then be determined as the square root of the sum of the squares of the I component and the Q component, as shown in the following equation (1):

[0021]

[0022] And the phase of the received MR signal can also be determined as shown in the following equation (2):

[0023]

[0024] In an exemplary embodiment, the digitized MR signal samples generated by the RF system 30 are received by the data acquisition server 22. The data acquisition server 22 can operate in response to instructions downloaded from the workstation 12 to receive real-time MR data and provide a buffer memory so that no data is lost due to data overflow. In some scans, the data acquisition server 22 only passes the acquired MR data to the data processing server 24. However, in scans where information derived from the acquired MR data is needed to control further execution of the scan, the data acquisition server 22 is programmed to generate the required information and transmit it to the pulse sequence server 20. For example, during a pre-scan, MR data is acquired and used to calibrate the pulse sequence executed by the pulse sequence server 20. In addition, navigator signals can be acquired during the scan and used to adjust the operating parameters of the RF system 30 or the gradient system 28, or to control the view order in which k-space is sampled.

[0025] In an exemplary embodiment, the data processing server 24 receives MR data from the data acquisition server 22 and processes the MR data according to instructions downloaded from the workstation 12. Such processing may include, for example, Fourier transforming the raw k-space MR data to produce a two-dimensional or three-dimensional image, applying filters to the reconstructed image, performing back-projection image reconstruction on the acquired MR data, generating functional MR images, and calculating motion or flow images.

[0026] In an exemplary embodiment, the image reconstructed by the data processing server 24 is transmitted back to the workstation 12 and stored there. In some embodiments, the real-time image is stored in a database memory cache ( Figure 1 In the example of FIG. 1 , the real-time images can be output from the database memory cache to the operator display 14 or a display 46 located near the magnet assembly 34 for use by the attending physician. Batch mode images or selected real-time images can be stored in a disk storage device 48 or a host database on the cloud. When such images have been reconstructed and transferred to the storage device, the data processing server 24 notifies the data storage server 26. The operator can use the workstation 12 to archive images, generate films, or send images to other facilities via the network.

[0027] In some applications, in order to study and examine the biological and pathological characteristics of an individual, a series of MR images are used to estimate parameters such as T1 and T2 and derive a map of the parameters or a parameter map. The parameter map indicates the value of the parameter at each position. For example, a T1 map of an anatomical region is an image of the anatomical region, wherein each pixel of the image indicates the T1 value at the position in the anatomical region corresponding to the pixel. The T1 value in the T1 map can be used to examine the biological and pathological characteristics of an individual, especially the myocardium. Myocardial T1 is prolonged in most forms of pathology, such as edema and chronic cardiac and systemic disorders. Myocardial T1 shortening can be seen in disorders such as Fabry disease. Maps of other parameters can be generated, such as proton density or diffusion coefficient.

[0028] As mentioned above, in MRI, when an excitation RF pulse is applied, the magnetization M rotates from the z-axis to the xy plane. The magnetization can be represented by a vector M with three components Mx, My and Mz along the three axes of the x, y and z axes. When the RF pulse is turned off, the magnetization M is placed back to the z-axis and the transverse components Mx and My decay because of the rotation of the nuclear dephasing. The simplified equation for the magnetization M after a 90° excitation pulse is:

[0029]

[0030]

[0031] Among them, M ⊥ is the vector of the magnetization M in the transverse plane and includes transverse components Mx and My.

[0032] The time for the z component Mz to grow from 0 to (1-l / e) or about 63% of the original magnetization before excitation is called the longitudinal relaxation time T1. The time for the transverse component Mx or My to decay from 1 to 1 / e, about 37% of its initial maximum value immediately after excitation is called the transverse relaxation time T2. In other words, the components of the magnetization M can be approximated by an exponential model with a time constant of T1 or T2, as expressed in equations (3) and (4).

[0033] In the derived parameter map, a series of MR images across time is used, and the time series of multiple pixels are each fitted to the signal model of the parameter to derive the parameters at multiple pixels. The series of MR images are MR images of the same anatomical structure at different time points. For the T1 map, an inversion recovery pulse sequence can be used, in which the magnetization is inverted to the -z axis by an inversion RF pulse, and then flipped to the transverse plane by an excitation RF pulse to acquire the MR signal after the inversion time between the inversion RF pulse and the excitation RF pulse. A series of MR images with different inversion times are collected and used for T1 mapping. The series of MR images can be represented as a three-dimensional (3D) data set, in which the third dimension is the time dimension. The series of images can be represented by a four-dimensional (4D) data set, in which the series of images are a series of 3D images of anatomical volumes at different time points and the fourth dimension is time. For each pixel, a time series along the time dimension is used to fit to a model of the parameter to be estimated. For the T1 map, a pixel-by-pixel fit of a T1 exponential decay model such as formula (3) is used.

[0034] Because the series of images are images of the same anatomical region at different time points, if the individual moves, the pixels at different time points do not correspond to the same position in the anatomical region, and the parameter map will be inaccurate. In fact, individuals are always moving, as physiological movements such as heartbeats or breathing or involved movements. Movement also causes motion artifacts in the image, which further deteriorates the quality of the parameter map. Motion correction is used to align the series of images, which relies on the similarity between the images. Motion correction generally includes three calculation parts of a similarity metric, deriving motion parameters by optimizing the similarity metric, and registering the images using the derived motion parameters. The similarity metric between two images indicates how similar the two images are. An exemplary similarity metric is the root mean square error or mutual information between the two images. Motion parameters, such as translation or rotation parameters, are derived by optimizing their values ​​relative to the resulting similarity metric. Once the motion parameters are derived, one of the two images is transformed or registered to the other image using the derived motion parameters. The transformation can be a rigid or non-rigid / elastic transformation.

[0035] Compared to motion correction, parametric mapping relies on differences between images to extract information and generate estimates of parameters. That is, the changing contrast characteristics of images acquired for parametric mapping impair the performance of motion correction. Therefore, it is difficult to extract conclusions about the quality of parameter maps based on similarity metrics. In addition, inaccurate motion correction may be counterproductive for parameter mapping. Studies have shown that motion correction deteriorates the accuracy of parameter maps in approximately 12% of cases, and significantly deteriorates the accuracy in approximately 5% of cases. Parametric maps with reduced accuracy impair diagnostic and therapeutic accuracy and effectiveness. Visually inspecting images to see if motion correction improves the quality of parameter maps is subjective, time-consuming, and difficult. Therefore, there is a need for an objective or quantitative indication or metric that indicates whether motion correction improves or degrades the quality of the resulting parameter map.

[0036] The systems and methods disclosed herein provide metrics indicating the effectiveness of motion correction for parameter mapping. When motion correction improves the quality of the parameter mapping, the motion correction is verified to be effective. On the other hand, when motion correction reduces or degrades the quality of the parameter mapping, the motion correction is not verified to be effective or is indicated as a failure. The systems and methods allow a user to decide whether to skip or apply motion correction before generating a parameter map. Metrics can be incorporated into the parameter mapping to automatically determine whether to skip or apply motion correction in generating a parameter map. MR images and T1 maps are used herein as examples for illustrative purposes only. The systems and methods disclosed herein can be applied to other parameter mappings and / or use medical images acquired by other imaging modalities (such as computed tomography (CT)).

[0037] Figure 2A is a schematic diagram of an exemplary motion correction verification system 200. In an exemplary embodiment, the system 200 includes a motion correction verification computing device 202 configured to verify motion correction of MR images. The motion correction verification computing device 202 may be included in the workstation 12 of the MRI system 10, or may be included in a separate computing device that communicates with the workstation 12. In some embodiments, the motion correction verification computing device 202 is a computing device separate from the workstation 12 and receives MR images acquired by the workstation 12 through a portable storage device such as a flash drive or a thumb drive.

[0038] Figure 2B250 is a flow chart of an exemplary method 250. The method 250 may be implemented on a motion correction validation computing device 202. In an exemplary embodiment, the method includes receiving 252 a series of motion-corrected MR images of a series of MR images. The series of MR images are MR images of an anatomical region of an individual at different time points. The anatomical region may be a region including anatomical structures, such as the heart, brain, liver, or a portion of a phantom of an individual. The anatomical structures move during an MR scan. For example, the heart is constantly moving. In order to capture the motion, the heart is depicted at different time points, and / or a parameter map is derived, and a series of MR images of the heart are acquired at different time points. The motion-corrected MR images are a series of MR images corrected with a motion correction algorithm.

[0039] Figure 3 A series of sagittal MR images 302 of a human heart acquired at different time points using the same pulse sequence for T1 mapping is shown. The MR images are images of the same sagittal slice of the heart region. The different images in the series are acquired with different TIs. If the body does not move, the anatomical features in each of the MR images 302 will remain in the same position. Lines 304, 306 are used to indicate how much the body (particularly the heart 308) moves. As shown by arrows 310, 312, the periphery of the heart 308 moves over time, either below or above the lines 304, 306. As the heart 308 expands and contracts, different anatomical structures appear in or outside the image 302 as shown by arrows 314, 316.

[0040] Re-reference Figure 2B , the method 250 also includes, for each pixel of the plurality of pixels, generating 254 a time series for the pixel based on the series of motion-corrected images, fitting 256 the time series to a model of the parameters, and estimating 258 an indication of the goodness of fit of the time series to the model. Similar to the series of MR images, the series of motion-corrected images are images of the same anatomical structure at different points in time and can be represented in a 3D or 4D data set having a third or fourth dimension as the time dimension. The time series for each pixel includes data points at the pixel in the time dimension. As a result, the time series for each pixel are data points for the pixel at different points in time.

[0041] In an exemplary embodiment, the time series is fit 256 to a model of the parameter to be estimated. For example, for the T1 map, the time series for each pixel is fit to the model represented by equation (3). An indication of the goodness of fit of the time series to the model is estimated 258. For example, for the T1 map, the coefficient of determination (R 2 ) to indicate how well the time series fits the model represented by formula (3). 2 It can be calculated as:

[0042]

[0043] Among them, ss res is the sum of the squares of the residuals, ss tot is the sum of the squares, y i is the data point of the time series at time point i, is the mean of the time series, f i is the value corresponding to the data point y i The fitted data points of ss res and ss tot are summed over the time series points. Indicators such as model evaluation metrics such as root mean square error, residual standard error, mean absolute error, adjusted R 2 , Akaike's information criterion (AIC), AICc (AIC with correction for small sample size), Bayesian information criterion, and Mallows's Cp, or other indications that allow method 250 to be performed as described herein. Generating 254, fitting 256, and estimating 258 are repeated for each pixel in the plurality of pixels. The plurality of pixels can be all or some of the pixels in one of the series of motion-corrected images. In some embodiments, the plurality of pixels are pixels in an ROI in the motion-corrected images. An exemplary ROI is a region 318 covering only the heart (see Figure 3 ). Using ROIs is advantageous for some applications because, as mentioned above, some anatomical features (such as Figure 3 ) may appear in and outside the series of images, which affects the quality of the parameter mapping.

[0044] In an exemplary embodiment, method 250 also includes generating 260 a metric indicating the impact of motion correction based on the indication of the estimate of goodness of fit for the plurality of pixels. An exemplary metric is the percentage of pixels in the plurality of pixels having an indication of the estimate above a threshold for goodness of fit. For example, the metric is having an R greater than 0.98. 2 In other words, the number of pixels in the anatomical region of clinical interest that fit the signal evolution model is counted. Mismatch is one of the causes of non-conformity between the data and the model. Therefore, the metric indicates the performance of motion correction.

[0045] In an exemplary embodiment, method 250 also includes validating motion correction based on a metric. The metric can be used to infer whether motion correction has worked or failed, and the likelihood that motion correction is effective. The higher the metric, the more likely it is that motion correction is effective and has improved the quality of parameter mapping. For example, if a large percentage of pixels in the ROI (e.g., 90%) have an indication that the goodness of fit is greater than a threshold for the goodness of fit, then the image may be well aligned, and motion correction may have worked and improved the quality of parameter mapping. On the other hand, if a small percentage of pixels, such as 40% of the pixels, have an indication that the goodness of fit is greater than a threshold for the goodness of fit, then the image may not be well aligned, and motion correction may have failed and reduced the quality of parameter mapping. In some embodiments, the metric is compared to a metric threshold, and an inference is made based on the comparison. For example, the metric threshold is set to 90%. If more than 90% of the pixels in the ROI have an R 2 If the metric is greater than a threshold for goodness of fit, motion correction may have worked. If the metric is less than 90%, motion correction may have failed. Two different metric thresholds may be used for inference. In one example, a first metric threshold is 90% and a second metric threshold is 60%. If the metric is greater than 90%, motion correction may have worked. If the metric is less than 60%, motion correction may have failed. If the metric is between 60% and 90%, motion correction may have no effect on the alignment. When motion correction is inferred to have failed, the user may be warned that motion correction has failed and a recommendation to turn off motion correction in the parameter mapping.

[0046] In some embodiments, the metric is used to select among motion correction algorithms. For example, different motion correction algorithms are used to derive motion correction images, and corresponding metrics are estimated for each motion correction algorithm based on the derived motion correction images of the motion correction algorithms. The motion correction algorithm is selected based on the metric. For example, if the metric of the motion correction algorithm is above a metric threshold and is the highest, this motion correction algorithm is selected or the user may be advised to select this motion correction algorithm. The motion correction algorithm may be used to generate a parameter map based on the motion correction image. In another example, if all metrics are below a metric threshold, motion correction is skipped or a message is provided to the user that the parameter map should be generated without motion correction. In one example, when the metric indicates that all motion correction algorithms reduce the parameter map, the motion may be too severe in the series of MR images, and the user is warned that the image needs to be reacquired.

[0047] In some embodiments, verification 262 is automated and incorporated into the parameter map. When the metric indicates that motion correction has failed, motion correction is skipped and the uncorrected images are used to derive the parameter map. Alternatively, the metric is used to select the motion correction algorithm that will improve or best improve the parameter map. If the metrics for all motion correction algorithms indicate that motion correction degrades the parameter map, the uncorrected images can be used to derive the parameter map, or the user can be notified to acquire a new set of images.

[0048] In some embodiments, a goodness-of-fit map is generated. Figure 4 Example maps 402, 404, 406 of goodness of fit are shown. The maps 402, 404, 406 are R-fitted to the T1 model based on the cardiac image 302. 2 The mapping diagram (see Figure 3 ). The color bar 412 indicates R 2 The red color indicates R 2 Relatively high and blue indicates R 2 Figure 402 is the R for the uncorrected image. 2 Mapping diagram. Mapping diagram 404 is an R image of a motion corrected image using a first motion correction algorithm. 2 Mapping diagram. Mapping diagram 406 is an R image of a motion corrected image using a second motion correction algorithm. 2 Maps. Arrows 408, 410 indicate pixels with relatively low goodness of fit levels. More pixels in map 404 have relatively low goodness of fit compared to maps 402, 406, and more pixels in map 406 have relatively high goodness of fit compared to maps 402, 404. Comparing maps 402, 404, 406, the first motion correction algorithm deteriorates the T1 map, while the second motion correction algorithm improves the T1 map.

[0049] The workstation 12 and motion correction verification computing device 202 described herein may be any suitable computing device 800 and software implemented therein. Figure 5 800. In an exemplary embodiment, the computing device 800 includes a user interface 804 that receives at least one input from a user. The user interface 804 may include a keyboard 806 that enables the user to enter relevant information. The user interface 804 may also include, for example, a pointing device, a mouse, a stylus, a touch-sensitive panel (e.g., a touchpad and a touch screen), a gyroscope, an accelerometer, a position detector, and / or an audio input interface (e.g., including a microphone).

[0050] In addition, in an exemplary embodiment, the computing device 800 includes a display interface 817 that presents information (such as input events and / or verification results) to the user. The display interface 817 may also include a display adapter 808 coupled to at least one display device 810. More specifically, in an exemplary embodiment, the display device 810 may be a visual display device such as a cathode ray tube (CRT), a liquid crystal display (LCD), a light emitting diode (LED) display, and / or an "electronic ink" display. Alternatively, the display interface 817 may include an audio output device (e.g., an audio adapter and / or a speaker) and / or a printer.

[0051] The computing device 800 also includes a processor 814 and a memory device 818. The processor 814 is coupled to the user interface 804, the display interface 817, and the memory device 818 via the system bus 820. In an exemplary embodiment, the processor 814 communicates with the user, such as by prompting the user via the display interface 817 and / or by receiving user input via the user interface 804. The term "processor" generally refers to any programmable system, including system and microcontrollers, reduced instruction set computers (RISC), complex instruction set computers (CISC), application specific integrated circuits (ASICs), programmable logic circuits (PLCs), and any other circuits or processors capable of performing the functions described herein. The above examples are exemplary only and are therefore not intended to limit the definition and / or meaning of the term "processor" in any way.

[0052] In an exemplary embodiment, the memory device 818 includes one or more devices that enable information (such as executable instructions and / or other data) to be stored and retrieved. In addition, the memory device 818 includes one or more computer-readable media, such as but not limited to dynamic random access memory (DRAM), static random access memory (SRAM), solid state disk, and / or hard disk. In an exemplary embodiment, the memory device 818 stores but is not limited to application source code, application object code, configuration data, additional input events, application status, assertion statements, verification results, and / or any other type of data. In an exemplary embodiment, the computing device 800 may also include a communication interface 830 connected to the processor 814 via the system bus 820. In addition, the communication interface 830 is communicatively connected to a data acquisition device.

[0053] In an exemplary embodiment, processor 814 may be programmed by encoding operations using one or more executable instructions and providing the executable instructions in memory device 818. In an exemplary embodiment, processor 814 is programmed to select a plurality of measurements received from a data collection device.

[0054] In operation, a computer executes computer executable instructions embodied in one or more computer executable components stored on one or more computer readable media to implement various aspects of the present invention described and / or shown herein. Unless otherwise specified, the execution or implementation order of the operations in the embodiments of the present invention shown and described herein is not required. That is, unless otherwise specified, these operations can be performed in any order, and embodiments of the present invention may include more or fewer operations than those disclosed herein. For example, it is conceivable that performing or implementing a particular operation before, simultaneously with, or after another operation is within the scope of various aspects of the present invention.

[0055] At least one technical effect of the systems and methods described herein includes (a) a quantitative metric for indicating whether motion correction has improved parameter mapping; (b) automatic indication of the effectiveness of motion correction; and (c) a parameter mapping with automatic selection between a motion correction algorithm and / or no motion correction.

[0056] Exemplary embodiments of systems and methods for motion correction verification are described in detail above. These systems and methods are not limited to the specific embodiments described herein, but rather the components of the systems and / or operations of the methods may be used independently and separately from other components and / or operations described herein. In addition, the components and / or operations described may also be defined in other systems, methods, and / or devices, or used in conjunction with other systems, methods, and / or devices, and are not limited to practice with only the systems described herein.

[0057] Although specific features of various embodiments of the invention may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the invention, any feature of a drawing may be referenced and / or claimed in combination with any feature of any other drawing.

[0058] This written description uses examples to disclose the invention, including the best mode, and also to enable those skilled in the art to practice the invention, including making and using any devices or systems and performing any included methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insignificant differences from the literal language of the claims.

Claims

1. A computer-implemented method for verifying motion correction of magnetic resonance (MR) images using a quantized parameter map of parameters, comprising: receiving a series of motion-corrected images in a series of MR images, wherein the series of MR images are MR images of the anatomical region at different points in time; For each pixel of a plurality of pixels in one of the series of motion-corrected images, the method further comprises: generating a time series at the pixel based on the series of motion-corrected images; fitting the time series to a model of the parameters; and estimating an indication of the goodness of fit of the time series to the model; generating a metric indicative of effectiveness of the motion correction based on the indication of goodness of fit of the estimate of the plurality of pixels; and The motion correction is verified based on the metric. 2 . The method of claim 1 , wherein the metric is the percentage of pixels having an indication of a goodness of fit above a threshold value for goodness of fit.

3. The method of claim 1, wherein the indication of goodness of fit is the coefficient of determination (R 2 ).

4. The method according to claim 1, further comprising: When the metric is below a metric threshold, a user is alerted that the motion correction has failed.

5. The method according to claim 1, further comprising: A goodness-of-fit map is generated for the anatomical region based on the indication of the estimated goodness of fit.

6. The method according to claim 5, wherein generating a goodness-of-fit map further comprises: Indicates the locations of pixels where motion correction has failed.

7. The method according to claim 1, wherein: If the motion correction is verified to be effective, the method further comprises generating the parameter map based on the series of motion corrected images; and If the motion correction is not verified to be effective, the method further comprises generating the parametric map based on the series of MR images.

8. The method according to claim 1, further comprising: receiving a first series of motion-corrected images in the series of MR images and a second series of motion-corrected images in the series of MR images, wherein the first series of motion-corrected images are motion-corrected images of the series of MR images with a first motion correction algorithm, and the second series of motion-corrected images are motion-corrected images of the series of MR images with a second motion correction algorithm; For each pixel in the plurality of pixels, generating a first time series at the pixel based on the first series of motion-corrected images; fitting the first time series to the model; estimating a first indication of goodness of fit of the first time series to the model; generating a second time series at the pixels based on the second series of motion-corrected images; fitting the second time series to the model; as well as estimating a second indication of goodness of fit of the second time series to the model; generating a first metric based on the first indication and a second metric based on the second indication; selecting the first series of motion-corrected images and the second series of motion-corrected images in the series of MR images based on the first metric and the second metric; as well as Generate a parameter map using the selected series of images.

9. The method of claim 1, wherein generating a time series further comprises: A region of interest (ROI) in the series of MR images is determined, wherein the plurality of pixels are pixels in the ROI.

10. A motion correction verification system for verifying motion correction of magnetic resonance (MR) images using a quantized parameter map of a parameter, comprising a motion correction verification computing device, the motion correction verification computing device comprising at least one processor in communication with at least one memory device, and the at least one processor being programmed to: receiving a series of motion-corrected images of a series of MR images, wherein the series of MR images are MR images of the anatomical region at different points in time; For each pixel of a plurality of pixels in one of the series of motion-corrected images, the at least one processor is programmed to: generating a time series at the pixel based on the series of motion-corrected images; fitting the time series to a model of the parameters; as well as estimating an indication of the goodness of fit of the time series to the model; generating a metric indicative of effectiveness of the motion correction based on the indication of goodness of fit of the estimate of the plurality of pixels; as well as The motion correction is verified based on the metric.

11. The system of claim 10, wherein the metric is a percentage of pixels having an indication of a goodness of fit above a threshold for goodness of fit.

12. The system of claim 10, wherein the indication of goodness of fit is the coefficient of determination (R 2 ).

13. The system of claim 10, wherein the at least one processor is further programmed to: When the metric is below a metric threshold, a user is alerted that the motion correction has failed.

14. The system of claim 10, wherein the at least one processor is further programmed to: A goodness-of-fit map for the anatomical region is generated based on the indication of the estimated goodness of fit.

15. The system of claim 14, wherein the at least one processor is further programmed to: Indicates the locations of pixels where motion correction has failed.

16. The system of claim 10, wherein: The at least one processor is further programmed to generate the parameter map based on the series of motion corrected images if the motion correction is verified to be effective; and The at least one processor is further programmed to generate the parametric map based on the series of MR images if the motion correction is not verified to be effective.

17. The system of claim 10, wherein the at least one processor is further programmed to: receiving a first series of motion-corrected images in the series of MR images and a second series of motion-corrected images in the series of MR images, wherein the first series of motion-corrected images are motion-corrected images of the series of MR images with a first motion correction algorithm, and the second series of motion-corrected images are motion-corrected images of the series of MR images with a second motion correction algorithm; For each pixel in the plurality of pixels, generating a first time series at the pixel based on the first series of motion-corrected images; fitting the first time series to the model; estimating a first indication of goodness of fit of the first time series to the model; generating a second time series at the pixels based on the second series of motion-corrected images; fitting the second time series to the model; as well as estimating a second indication of goodness of fit of the second time series to the model; generating a first metric based on the first indication and a second metric based on the second indication; selecting the first series of motion-corrected images and the second series of motion-corrected images in the series of MR images based on the first metric and the second metric; as well as Generate a parameter map using the selected series of images.

18. A computer-implemented method for motion correction of medical images for validation of parameter quantization parameter maps, comprising: receiving a series of motion-corrected images in a series of medical images, wherein the series of medical images are medical images of an anatomical region at different points in time; For each pixel of a plurality of pixels in one of the series of motion-corrected images, the method further comprises: generating a time series at the pixel based on the series of motion-corrected images; fitting the time series to a model of the parameters; and estimating an indication of the goodness of fit of the time series to the model; generating a metric indicative of effectiveness of the motion correction based on the indication of goodness of fit of the estimate of the plurality of pixels; and The motion correction is verified based on the metric.

19. The method of claim 18, wherein the metric is the percentage of pixels having an indication of a goodness of fit above a threshold value for goodness of fit.

20. The method of claim 18, wherein: If the motion correction is verified to be effective, the method further comprises generating the parameter map based on the series of motion corrected images; and If the motion correction is not verified to be effective, the method further comprises generating the parameter map based on the series of medical images.

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