System and method for rapidly reconstructing functional magnetic resonance images - Patents.com

Abstract

Systems and methods for generating resting-state functional magnetic resonance imaging (rs-fMRI) images are provided. The methods can include receiving functional magnetic resonance imaging (fMRI) data acquired from a subject while the subject is exposed to at least one of performing a task or experiencing a stimulus, and reconstructing the fMRI data acquired while the subject is exposed to at least one of performing a task or experiencing a stimulus using a resting-state fMRI (rs-fMRI) reconstruction process without considering the at least one of performing the task or experiencing the stimulus to generate an rs-fMRI image. The methods can also include displaying the rs-fMRI image and / or using the rs-fMRI image to determine subject movement during acquisition of the fMRI data.

Description

[Technical field]

[0001] <CROSS REFERENCE TO RELATED APPLICATIONS> This application is based on and supported by U.S. Provisional Application No. 63 / 123,302, filed December 9, 2020, which is incorporated herein by reference. [Background technology]

[0002] Patient motion of any kind (bulk rigid motion or motion with complex distortions) is the biggest obstacle in medical imaging. Some clinical applications require accurate acquisition of small signals with high spatial resolution, and even small amounts of motion can significantly impair the clinical value of the information. For example, magnetic resonance imaging (MRI) of the brain is a highly valuable clinical application, and even small amounts of motion can significantly impair the clinical value of the images. For example, head motion can impair the value of anatomical or structural (T1-weighted, T2-weighted, etc.) images, further impairing the clinical usefulness of so-called functional MRI data (fMRI). Even sub-millimeter head movements (e.g., micrometer movements) can potentially totally alter the structural and functional MRI data. Therefore, much effort has been put into developing post-acquisition methods to remove head motion distortion from MRI data.

[0003] Head movement from one MRI data frame to the next is believed to induce the most significant MRI signal distortion, rather than absolute movement from a reference frame. Motion-related distortion correlates strongly with measures of framewise displacement (FD), which represents the sum of absolute head movements from frame to frame in all six rigid directions, and DVARS, which is the root mean square of the derivatives of the differentiated time course of each voxel in the MRI image. Thus, measures such as FD and DVARS, which capture the global effects of subject movement during MRI data acquisition, have been used to assess data quality in various a posteriori methods. For example, a posteriori frame censoring, such as removing all MRI data frames with FD values ​​above a predefined threshold (e.g., excluding data frames with FD values ​​above 0.2 mm), has become a commonly used method to improve the quality of functional MRI data.

[0004] Frame censoring is necessary to reduce artifacts, but it is expensive. For example, frame censoring may exclude more than 50% of the data in some studies. For example, so-called resting-state functional connectivity MRI (rs-fcMRI) data may be particularly susceptible to motion problems because the tests are, by definition, long in length and focus on small signals caused by blood oxygen level-dependent (BOLD) contrast mechanisms. Because the accuracy of MRI measurements improves with the number of frames, a minimum number of data frames may be required to obtain reliable data. If too few frames remain after censoring, examiners may lose all data from a patient. To avoid this loss, clinicians typically acquire additional "buffer" data, but this is an expensive practice that, by itself, does not guarantee sufficiently high-quality MRI data for a given participant. The overscans required to remove motion distortion data significantly increase the cost and duration of brain MRI, while still maintaining a sufficient sample size to achieve the desired data quality. Of course, in some ways this solution only exacerbates the problem. That is, because the likelihood of patient motion increases with scan duration, extending the scan to acquire additional data only increases the likelihood of patient motion.

[0005] Recently developed structural MRI sequences with predictive motion correction use a similar approach to reduce the deleterious effects of head motion. These MRI sequences combine each structural data acquisition with an acquisition with a high-speed, low-resolution snapshot of the entire brain (e.g., echo planar image, EPI), which is used as a marker or navigator for head motion. These motion-corrected structural sequences calculate the relative motion between successive navigator images and use this information to mark connected structural data frames for exclusion and reacquisition. In this way, the structural data frames are "censored," thereby increasing the duration and cost of structural MRI.

[0006] These challenges with motion correction in the general context of fMRI are considerable. However, in some specific tests that require long lengths and are inherently motion intolerant, the challenges are even greater. For example, rs-fMRI, by definition, requires extended acquisitions that also extend the duration of the scan, thereby increasing the chance of motion corruption of the data and the cost of reacquisition. Furthermore, rs-fMRI focuses on extracting small signals from the brain, and fractions of a millimeter can lead to large variations in clinical information. Summary of the Invention

[0007] The present disclosure addresses the above-mentioned shortcomings by providing a system and method for controlling the effects of motion on images generated using functional magnetic resonance imaging (fMRI). As one non-limiting example, as provided herein, a system and method for real-time motion identification can be used to improve and minimize acquisition time. Additionally or alternatively, according to the present disclosure, a system and method for reconstruction of any form of fMRI data (i.e., BOLD contrast data), whether acquired as task-based fMRI data or resting-state fMRI (rs-fMRI) data, can be reconstructed using the rs-fMRI reconstruction process.

[0008] In one configuration, a system is provided for performing resting-state functional magnetic resonance image (rs-fMRI) reconstruction of a functional magnetic resonance imaging (fMRI) dataset, the system including a magnet system configured to generate a polarizing magnetic field around at least a portion of a subject, a magnetic field gradient system including a plurality of magnetic field gradient coils configured to apply at least one magnetic field gradient to the polarizing magnetic field, and a radio frequency (RF) system configured to apply an RF magnetic field to the subject and receive magnetic resonance signals from the subject using the coil array. The system further includes a computer system programmed to control the magnetic field gradient system and the RF system to acquire an fMRI dataset using at least one of task-based fMRI data acquisition or rs-fMRI data acquisition, and to reconstruct the fMRI dataset using an rs-fMRI reconstruction process during the at least one of the task-based fMRI data acquisition or rs-fMRI data acquisition to generate at least one resting-state (rs) image. The computer system is further programmed to compare the at least one rs image with a reference image during at least one of task-based fMRI data acquisition or rs-fMRI data acquisition to determine subject movement during the at least one of task-based fMRI data acquisition or rs-fMRI data acquisition and to determine a subject displacement corresponding to the subject movement. The computer system is further configured to generate at least one of an alert or a real-time indication of displacement communicated to an operator of the MRI system during the at least one of task-based fMRI data acquisition or rs-fMRI data acquisition.

[0009] In one configuration, a computer-implemented method for resting-state functional magnetic resonance image (rs-fMRI) reconstruction of a functional magnetic resonance imaging (fMRI) dataset is provided. The computer-implemented method includes receiving an fMRI dataset from a magnetic resonance imaging (MRI) system while the MRI system is performing at least one of task-based fMRI data acquisition or rs-fMRI data acquisition, using a computing device including at least one processor in communication with at least one memory device. The computer-implemented method further includes performing an rs-fMRI reconstruction of the fMRI dataset using the computing device to generate an rs-fMRI image, and comparing the rs-fMRI image to at least one reference image during the at least one of the task-based fMRI data acquisition or the rs-fMRI data acquisition, using the computing device. The computer-implemented method further includes determining, using the computing device, subject motion based on comparing the rs-fMRI image to the at least one reference image, and communicating, using the computing device, an alert to an operator of the MRI system indicative of motion detected during the at least one of the task-based fMRI data acquisition or the rs-fMRI data acquisition.

[0010] In another configuration, a system for performing resting-state functional magnetic resonance imaging (rs-fMRI) is provided, the system including a magnet system configured to generate a polarizing magnetic field around at least a portion of a subject, a magnetic field gradient system including a plurality of magnetic field gradient coils configured to apply at least one magnetic field gradient to the polarizing magnetic field, and a radio frequency (RF) system configured to apply an RF magnetic field to the subject and receive magnetic resonance signals from the subject using the coil array to form an rs-fMRI data set according to fMRI data acquisition. The system further includes a computer system programmed to receive the rs-fMRI data set, compare the rs-fMRI data set to a reference data set to determine subject motion, determine subject displacement corresponding to subject motion, and generate at least one of an alert or a real-time indication of the displacement communicated to an operator of the MRI system during fMRI data acquisition.

[0011] In yet another configuration, a method for generating a resting-state functional magnetic resonance imaging (rs-fMRI) image is provided. The method includes receiving functional magnetic resonance imaging (fMRI) data acquired from a subject while the subject is exposed to at least one of performing a task or experiencing a stimulus. The method further includes reconstructing the fMRI data acquired while the subject is exposed to at least one of performing a task or experiencing a stimulus using a resting-state fMRI (rs-fMRI) reconstruction process without taking into account the at least one of performing the task or experiencing the stimulus, and displaying the rs-fMRI image.

[0012] These and other aspects and advantages of the present disclosure will become apparent from the following description. In this specification, reference is made to the accompanying drawings, which show preferred embodiments forming a part hereof. The embodiments do not necessarily represent the full scope of the invention, and therefore reference should be made to the claims and this specification for interpreting the scope of the invention. In the following description, like reference numerals are used for like parts between the drawings. [Brief description of the drawings]

[0013] [Figure 1] 1 is a flowchart of a non-limiting example of a method for processing a set of fMRI datasets according to the present disclosure. [Diagram 2] 1 is a flowchart illustrating a non-limiting exemplary method for providing sensory feedback to an operator of an MRI system and / or a patient within an MRI system during fMRI data acquisition. [Diagram 3] 4 is a flow chart illustrating a non-limiting exemplary method for removing artifacts associated with detected motion data using an Nth order filter. [Figure 4] 4 is a flow chart illustrating an adaptive filtering method as a non-limiting example. [Diagram 5] 1 is a schematic diagram of a system for performing magnetic resonance imaging in accordance with the present disclosure; [Figure 6A] FIG. 1 is a block diagram of an example functional mapping guided intervention targeting system. [Figure 6B] FIG. 6B is a block diagram of components that may implement the functional mapping guided intervention targeting system of FIG. 6A. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Systems and methods are provided for processing fMRI data, such as resting-state fMRI (rs-fMRI) data and / or task-based fMRI data, to reduce motion artifacts and the scan time required to acquire all required data without motion corruption. Real-time monitoring and prediction of patient body part motion can include, but is not limited to, head motion during fMRI scanning. Methods, computer-readable storage devices, and systems are described for aligning fMRI data, such as a data set acquired from an MRI scanner, to a reference data set to monitor the motion of a patient body part during fMRI scanning. In various embodiments, the reference data set provides a common reference from which the displacements or motions of all data sets may be acquired and compared.

[0015] The fMRI data used according to the present disclosure may include task-based fMRI data, rs-fMRI data, or a combination thereof, and may also be referred to as blood oxygen level dependent (BOLD) data or BOLD activity data when an activity or task is performed. Task-based fMRI includes MRI acquisitions in which the subject performs a task or responds to stimuli while imaging is taking place. Resting-state fMRI (rs-fMRI) is when the subject does not perform a task or respond to (or is not exposed to) stimuli while imaging is taking place. Instead, the subject lies in the MR scanner for a period of time while BOLD data is acquired. rs-fMRI shows highly correlated low-frequency (<0.1 Hz) changes in the BOLD signal between different regions of the brain, which reveals the brain's functional connectivity. rs-fMRI presents a unique challenge in that data must be acquired over an extended period of time, and any movement by the subject can cause detrimental errors or artifacts in the resulting images, thereby invalidating any diagnostic capabilities of the rs-fMRI scan.

[0016] Conventional fMRI image reconstruction involves acquisition of fMRI data that includes both periods during which the subject is at rest and periods during which the subject is performing a task or receiving a stimulus. The acquired data is then processed using conventional fMRI reconstruction, or as used herein, "fMRI reconstruction." In fMRI reconstruction, fMRI is reconstructed based on periods of task / stimulation and periods of no task / stimulation. In this manner, the reconstruction is specifically designed around distinguishing between periods of task or stimulation and periods of no task or stimulation. For example, during reconstruction, the data can be weighted to select periods during which the subject is performing a task and to mitigate any errors in the reconstructed image from periods during which the subject is at rest.

[0017] In some aspects, systems and methods according to the present disclosure provide for reconstructing a subject's fMRI data, which may include data acquired while performing a task or while receiving a stimulus (i.e., an fMRI data set or a task-based fMRI data set), using an rs-fMRI reconstruction process. In contrast to conventional approaches that weight task fMRI periods, the fMRI data can be reconstructed using only the rs-fMRI process. That is, according to the present disclosure, the rs-fMRI process can be used without ignoring or considering the distinction between tasks or stimuli. In this way, even when "conventional" or "task-based" fMRI data is acquired, a single robust image set can be generated even when motion may be present during part of the acquisition. Furthermore, by ignoring the distinction or weighting for task and non-task or stimulus and non-stimulus data, images can be rapidly reconstructed. This rapid reconstruction without considering the distinction or weighting for task and non-task or stimulus and non-stimulus data is referred to herein as rs-fMRI reconstruction because the reconstruction treats the data as if it was all acquired while the patient was at rest (whether this is true or not). In this way, rapid reconstruction of images can be performed as described above. This can be advantageous, for example, in a motion management process according to the present disclosure, in that these images reconstructed using rs-fMRI reconstruction allow for reconstruction of images during the fMRI process (e.g., without having to wait for the entire process of tasks and stimuli or for an extended period of time for rs-fMRI acquisition to complete), and these images generated during the fMRI data acquisition process can then be used to identify movements by the patient.

[0018] In various aspects, the systems and methods described herein can improve the quality of fMRI data and can reduce costs associated with fMRI data acquisition. In one aspect, a method according to the present disclosure is implemented in the form of software that calculates and displays a data quality matrix and / or summary motion statistics in real time during fMRI data acquisition (whether resting-state fMRI (rs-fMRI) or task-based fMRI). In a non-limiting example, the display can be in the form of a GUI that is generated and communicated to the clinician and / or patient during fMRI data acquisition.

[0019] The systems and methods provided herein overcome one or more of at least some of the drawbacks of previous systems. To address the drawbacks associated with overscanning by previous systems to compensate for data distorted by motion, the systems and methods provided herein can provide real-time feedback to the scanner operator and / or the subject undergoing the scan. The operator can receive feedback in the form of a display that quantifies the amount of motion experienced by the subject during the scan. Sensory feedback can be provided to the subject during the scan based on the real-time calculated data quality metrics and summary motion statistics, whereby the subject can respond to the provided feedback by monitoring and adjusting their own motion accordingly (e.g., remaining still). The systems and methods may include providing stimulus conditions, such as a fixed crosshair or viewing a video clip, to engage the subject while simultaneously providing real-time feedback to the subject.

[0020] For purposes of this disclosure and the appended claims, the term "real-time" or related terms are used to refer to and define the real-time performance of a system, which is understood as performance that depends on operational deadlines from a given event to the system's response to that event. For example, real-time extraction of data based on empirically acquired signals and / or display of such data may be triggered and / or performed simultaneously with or without interruption of the signal acquisition procedure.

[0021] The shortcomings of conventional systems such as those described above can be addressed in accordance with the present disclosure by allowing the scanner operator to continue each scan until the desired number of low motion data sets are acquired. Non-limiting examples of arriving at the desired number of low motion data sets include (i) predicting the number of data sets that will be available at the end of the scan, (ii) predicting the amount of time a given subject will likely have to be scanned until a pre-set time-to-criterion (minutes of low motion FD data) is obtained, (iii) allowing the selection and deselection of specific individual scans for inclusion in the actual and predicted amounts of low motion data, etc.

[0022] Until now, motion estimates for brain MRIs have typically been analyzed offline after data acquisition has been completed for a given subject, or more commonly in large batches for entire cohorts. Delaying head motion analysis can be expensive and risky, especially when scanning previously untested patient populations and after changes in data collection protocols or personnel.

[0023] Real-time information about head motion can be used to reduce head motion by several different methods, including, but not limited to, by influencing the behavior of the MRI scanner operator and by influencing the behavior of the subject being MRI scanned. The scanner operator can be alerted about any sudden or unusual changes in head motion and can interrupt such scans if the subject begins to move more because they are beginning to feel uncomfortable to see if a bathroom break, providing a blanket, changing position, or other interventions will make them more comfortable. In some aspects, the methods provided herein further include an option to feed information about head motion back to the subject after the scan and / or in real time. The disclosed methods allow the scanner operator to find the "sweet spot" that provides the required amount of low-motion data at the lowest cost. Following the method, the scan can be stopped, for example, to address the motion, the subject can be further instructed or reminded how to try to remain still, and the scan can be reacquired.

[0024] Referring to FIG. 1, a non-limiting exemplary method 100 for processing a set of fMRI data sets to align the data sets to a reference data set in the set to compensate for subject movement is shown. The method 100 includes receiving fMRI data in the form of fMRI data sets or images / frames from a magnetic resonance imaging system at step 102. The fMRI data sets may be received by a computing device, from the magnetic resonance imaging system over a network, or from a storage device coupled to or in communication with the computing device. The fMRI data sets may be reconstructed using an rs-fMRI process, as described above, to emphasize data acquired over periods of rest as opposed to data acquired over periods in which the subject is performing a task. The rs-fMRI reconstruction process may also be used to prepare the data for motion correction, as described below.

[0025] The method 100 may also include comparing the dataset to a reference dataset at step 104. Each dataset or image is subjected to a set of rigid transformations T i The data may be aligned to a reference dataset via, where i indexes the spatial registration of dataset i to the reference dataset I, starting from the second dataset. Each transformation may be calculated by minimizing the registration error to an absolute minimum or below a selected cutoff value.

[0026]

number

[0027] where I(x) is the image intensity at locus x, and s is a scalar factor that compensates for the variation in the mean signal intensity, spatially averaged over the entire brain, as indicated by the brackets. In some embodiments, the dataset can be realigned using the 4dfp cross_realign3d_4dfp algorithm (see Smyser, CD et al. 2010, Cerebral cortex 20, 2852-2862, (2010), specifically incorporated herein by reference). Alternative alignment algorithms can also be used to align the dataset.

[0028] In various aspects, each transformation may be represented by a combination of a rotation and a translation, as described below.

[0029]

number

[0030] In the formula, R i represents a 3×3 rotation matrix containing three elementary rotations about each of the three axes (see example 1 below), and d i represents a 3×1 column vector of displacements. R i is R i =R iα R iβ R iγ The rotation may include three basic rotations on each of the three axes, as represented by:

number

[0031] Method 100 may also include determining a relative motion of the body part between the data set or image and the previous data set or image, as shown in step 106. In one non-limiting example, method 100 may calculate the relative motion of the body part between the current image frame and the previous image frame in step 106. The relative motion of the body part (e.g., head motion) may be calculated using x, y, z, θ χ ,θ y ,θ ζ The six alignment parameters can be calculated from the following: χ ,θ y ,θ ζ are the rotations around those axes.

[0032] Optionally, method 100 may also include determining a data quality metric, such as total displacement, at step 108, for example to generate multiple displacement vectors for head motion. In a non-limiting example, the total displacement is calculated by adding the absolute displacements of a body part (e.g., head) in six directions, whereby the body part can be treated as a rigid body. Head motion for an I th data set, such as the I th frame, can be converted to a scalar quantity using the following formula:

[0033]

number

[0034] Rotational displacement,

number

[0035] In some aspects, method 100 may further include filtering out data sets that have above a predetermined threshold cutoff for overall displacement, step 110. Once completed, method 100 may return to the beginning for each subsequent data set in the MRI scan. A display of data quality metrics may be provided, step 112, and a prediction of time remaining in the scan may be provided, step 114.

[0036] 2, a flow chart is shown illustrating a non-limiting exemplary method 200 for providing sensory feedback to an operator of an MRI system and / or a patient in an MRI system during data acquisition. The method 200 may include, at step 202, calculating a data quality metric based on one or more components of motion determined for a patient in an MRI device during a scan. At 202, any data quality metric may be calculated, including, but not limited to, any one or more of the displacement components as described above (e.g., frame-wise displacement, per slice displacement / motion), total displacement as described above, other data quality metrics including DVARS as described above, and any combination thereof, without the limitations described herein.

[0037] Method 200 includes, at step 204, generating a visual display to an operator of the MRI system in real time based at least in part on the data quality metrics calculated at step 202. Non-limiting examples of suitable visual feedback displays include at least a portion of a GUI, a light bar, a video, an image, and the like. In various embodiments, visual elements that may be included in the visual feedback display to the operator of the MRI system may include, but are not limited to, one or more graphs displaying data quality metrics for all data sets received in the scan, as described herein, a table of summary statistics regarding the quality of the current and previous scans, a graphical or table element conveying a cumulative number of available data sets obtained in the current scan, a graphical or table element conveying an amount of time remaining in the current scan, a graphical or table element conveying a predicted amount of time remaining in the current scan to obtain a predetermined number of available scans, and any combination thereof. In various embodiments, the elements of the visual feedback display may be updated at any preselected rate equal to or less than the real-time rate at which each display is updated as each associated quantity is calculated. Elements of the visual feedback display may be updated upon request from an operator of the MRI system, and elements of the visual feedback display may be dynamically updated in response to at least one of a number of factors, including, but not limited to, a significant increase in the monitored motion of the subject between data sets, cumulative motion, or any other suitable criteria.

[0038] Method 200 may optionally include generating a sensory feedback display for the patient in the scanner during acquisition of the fMRI data, at step 206. As described in more detail below, the sensory feedback display generated at step 206 may be updated at a variety of refresh rates ranging from a single update at the end of the scan to continuous updates in real time based on at least one of a number of factors including, but not limited to, the age and condition of the patient.

[0039] In various aspects, the method 200 may further include, at step 208, determining the overall patient motion between the previous data set and the current data set in response to the sensory feedback indication generated at step 206. In one aspect, the method 200 further includes, at step 210, evaluating at least one of a plurality of factors to determine whether the current fMRI scan should be terminated. In various aspects, the scan may be terminated according to at least one of a plurality of termination criteria, including, but not limited to, one or more of unacceptably large motion and an unacceptably large number of relatively small motions, a determination that an adequate number of usable data sets have been obtained, a prediction that an adequate number of usable data sets will not be obtained within the time remaining for the scan, a prediction that an adequate number of usable data sets will not be obtained within a reasonable cumulative scan time, and any combination thereof. If it is determined to continue the scan at step 210, the method 200 may communicate at least one feedback signal 212 that is used in part to calculate a data quality metric at step 202 to initiate another iteration of the method 200 for a subsequent data set.

[0040] In one aspect, the systems and methods provided herein can provide a visual feedback display to a subject undergoing an MRI scan. In this aspect, the characteristics of the visual feedback display may be varied to communicate the occurrence of a subject's movement based on the detected movement of the subject obtained using the methods as described above. Any characteristic of one or more elements of the visual feedback display may be selected to be varied to communicate the occurrence of movement, including, but not limited to, size, shape, color, texture, brightness, focus, position, blink rate, any other suitable visual element characteristic, and any combination thereof.

[0041] In another embodiment, an auditory feedback display can be provided to a subject undergoing an MRI scan. The characteristics of the auditory feedback display may be changed to signal the occurrence of a subject movement based on the detected movement of the subject obtained using the method as described above. Any characteristics of one or more elements of the auditory feedback display may be selected to signal the occurrence of a movement, including, but not limited to, a pause in the playing of the musical selection, a restart of the playing of the musical selection, a verbal cue, a volume of a sound, a pitch of a sound, a duration of each sound in a series of sounds, a repeat rate of a series of sounds, a stability or oscillation of the pitch or volume of a sound, any other suitable auditory feedback characteristic, and any combination thereof.

[0042] In various embodiments, the characteristics of the sensory feedback display may be varied based on the degree or magnitude of detected motion of the subject within the MRI scanner. In one embodiment, the characteristics of the sensory feedback display may be varied continuously in proportion to the degree of detected motion of the subject. In another embodiment, the characteristics of the sensory feedback display may be varied within a discrete set of features, where each feature within the discrete set is configured to convey the occurrence of one level of motion, including, but not limited to, no motion, small motion, moderate or intermediate level motion, and large motion.

[0043] In various embodiments, the sensory feedback display can change in response to a change in a single component of the motion, such as translation in a single x-, y-, or z-direction, or rotation about a single x-, y-, or z-direction; the sensory feedback display can change in response to a change in a combination of two or more components of the motion; or the sensory feedback display can change in response to an overall motion metric, such as the displacement described above. In one embodiment, one feature of the sensory feedback display is changed to communicate the occurrence of motion to the subject. In another embodiment, two or more features of the sensory feedback are changed independently to communicate the occurrence of motion to the subject, where each feature is changed based on a subset of the motion components. As a non-limiting example, the sensory feedback display can include a first feature that changes based on the motion of the subject in the x-direction, and a second feature that changes independently based on the combined motion of the subject in the y- and z-directions. In a non-limiting example, the sensory feedback display can include a color-coded indication that is displayed to the patient, e.g., red is used to indicate that motion has occurred in the x-direction, green is used for motion in the y-direction, and blue is used for motion in the z-direction.

[0044] In various embodiments, the frequency at which the features of the sensory feedback display are updated may range from a single feedback display at the end of the scan to communicate whether sufficiently small motion was maintained during the scan, to a frequency equal to the real-time frequency at which motion is monitored by the method, and any intermediate frequency, without limitation. In various embodiments, the frequency at which the features of the sensory feedback display are updated may be selected based on at least one characteristic of the subject being imaged in the MRI scanner, including, but not limited to, the subject's age, the subject's condition, such as attention deficit disorder or learning disability, and any other relevant subject characteristics, without limitation. In various embodiments, the method provides feedback based on motion values ​​from a single data set or a combination of motion values ​​across multiple data sets. In various other embodiments, the method provides real-time feedback and time-delayed feedback. As a non-limiting example, when using a high update frequency for the sensory feedback display for a very young child, the display may prompt the child to increase movement in the MRI scanner as a way to provide a more entertaining dynamic sensory feedback experience. In various embodiments, the frequency at which the features of the sensory feedback display are updated may be specified to be a constant update rate throughout the MRI scan, or the update rate may vary dynamically based on instantaneous and / or cumulative assessments of the subject's motion.

[0045] In a non-limiting example of sensory feedback, a subject undergoing an fMRI scan may be instructed to look at a stationary crosshair (e.g., a target). The crosshair may be color-coded based on the subject's detected movement (e.g., head movement), and the subject may be instructed to keep the crosshair a constant color (e.g., a first color) by remaining stationary during the scan. As a result of a detected change in the subject's movement, the crosshair may change to a second color (e.g., to represent moderate movement) or a third color (e.g., to represent large movement), thereby allowing the subject to monitor and adjust their movement during the scan.

[0046] In a non-limiting example of sensory feedback, a subject undergoing an MRI scan may be instructed to watch a video clip. Based on the subject's level of movement (small, medium, large), visual obstructions on the video clip may prevent the subject from viewing a portion of the video clip. For example, the subject may be instructed to remain still during the scan to see an unobstructed view of the video clip. Based on the subject's level of movement, the video clip may be obstructed by rectangular blocks of a certain size (e.g., a small yellow rectangle for medium movement and a large red rectangle for large movement). In this way, the subject can monitor and adjust their own movement during the scan based on real-time visual feedback.

[0047] Fixed and adaptive feedback conditions may be provided for real-time visual display or sensory feedback. In one aspect of fixed feedback conditions, thresholds for small, medium and large movements may be kept constant during the fMRI scan. In another aspect of adaptive feedback conditions, thresholds for small, medium and large movements may be varied and replaced with more stringent (e.g., lower) thresholds during the fMRI scan. With adaptive feedback conditions, the MRI scanner can adapt to the subject's ability to remain still and, for example, increase the difficulty of keeping crosshairs a first color or keeping a video clip visually unobstructed.

[0048] In some aspects, modifications in MRI acquisition procedures, including but not limited to multi-band imaging, can improve temporal and spatial resolution compared to previous fMRI acquisition procedures. However, the improvement in temporal and spatial resolution may be accompanied by artifacts in the motion estimation from post-acquisition dataset alignment procedures, which are believed to be primarily caused by chest movement during breathing. Without being limited to any particular theory, chest movement related to breathing changes the static magnetic field (B0) during MRI data acquisition, which "tricks" any inter-dataset alignment procedures used in real-time motion monitoring into correcting for "head movement" even in the absence of actual head movement. In one aspect, an optional band-stop (or notch) filter is provided to remove breathing-related artifacts from the motion estimation, thereby improving the accuracy of the real-time representation of motion.

[0049] A notch filter (e.g., a band-stop filter) can be applied to the motion measurements to remove artifacts from the motion estimate caused by the subject's respiration. Subject respiration can degrade the quality of the motion estimate in fMRI, thereby distorting the quality of the obtained fMRI data. Some embodiments utilize a general notch filter to capture most of the respiration peaks of the sample population relative to the power. Subject-specific filters based on filter parameters specific to the subject's respiration belt data can be used.

[0050] A band-stop (e.g., a notch filter) may be implemented to remove spurious signals in the motion estimate that correspond to the aliased respiration rate. This filter can remove unwanted frequency components while leaving other components unaffected. The notch filter may include design parameters of a median cutoff frequency and a bandwidth or range of frequencies to be removed. To establish the median cutoff frequency and bandwidth parameters, the distribution of respiration rates obtained from various subjects in fMRI during data acquisition may be analyzed and the median of the distribution may be used as the cutoff frequency, and the quartiles 2 and 3 of the distribution may be used to determine the bandwidth of the notch filter. After establishing these parameters, the notch filter may be designed using the MR notch filter function. For a given sampling rate (1 / TR), the respiration rate may not be aliased. In other cases, when the combination of TR and respiration rate results in aliasing, the aliased respiration rate may be used instead.

[0051] The designed filter may include a difference equation. When applied to a sequence representing motion estimation, this difference equation may recursively weight two previous samples to provide an instantaneous filtered signal. This procedure may start from the third sample, weighting the previous two points, and continue until the last time point is filtered. The filtered signal in such an implementation may have a phase delay with respect to the original signal. This phase delay may be compensated by applying the filter twice, once in the forward direction and once in the reverse direction, so that the opposite phase shifts cancel each other. After the filter is applied to the entire sequence, the same filter (difference equation) may be reapplied in the reverse direction, where the last time point of the forward filtered sequence is used as the first point for the reverse application of the filter, and the recursive process may continue until the first time point of the forward filtered sequence is filtered. In various aspects, designed notch filters (general and subject-specific) may be applied to the sequence of motion estimation post-processing to improve data quality.

[0052] 3, a flow chart is shown illustrating a non-limiting exemplary method 300 for removing artifacts associated with detected motion data, such as respiration, using an Nth order filter. In one aspect, the method 300 includes receiving 2N+1 data sets from an MRI system, which are used to generate an Nth order filter, at step 302. In addition to the 2N+1 data sets, the method 300 may further include generating an Nth order filter based on minimum and maximum respiration rates, at step 304. In various aspects, the subject's respiration rate may be obtained using a variety of devices and methods, including, but not limited to, a respiratory monitor belt worn by the subject configured to extract respiration rate information from MRI signals acquired from the patient in an MRI scanner, and any other suitable non-limiting method.

[0053] The method 300 may further include, at step 306, calculating the motion of the patient's body part using the methods described herein. The method may further include, at step 308, applying the Nth order filter generated at step 302 to the current data set in both forward and reverse directions with respect to data acquisition time. The Nth order filter in both directions may be used to eliminate phase lag from the filtered data. At step 310, the filtered motion estimate may be used to calculate a data quality metric including, but not limited to, displacement. If an additional data set is acquired at step 312, the method may begin a subsequent iteration of the method 300 by replacing the earliest data set of the 2N+1 data sets previously received at step 302 with the data set received at step 312.

[0054] The designed filter can also be applied in real time since each instantaneous estimate of motion can be filtered out by weighting the previous estimate according to the difference equation of the notch filter. In one aspect, the filter is performed in pseudo real time to minimize the resulting time phase lag. In this aspect, once a specified number of samples are obtained, such as five samples in a non-limiting example, the filter can be applied twice and the best estimate will be the value corresponding to the third sample. This delayed signal has no phase delay. Each time a new sample is obtained, the filter can be applied twice to the entire sequence and the process can be repeated. Each time a new sample is measured, the filtered sequence converges to approach the optimal output obtained when the filter is applied twice to the entire sequence. In the final data set of a given run, the filtered sequence is identical to the filtered sequence obtained during post-processing. Thus, the designed notch filter may be used in real time to improve the accuracy of real-time estimation of motion using the motion prediction method described above.

[0055] In various embodiments, adaptive filtering methods, including least squares adaptive filtering, can be applied in real time to identify and remove signal content related to undesired numbers from subject motion data, such as heart rate and respiration rate, from measured subject displacement data, including but not limited to displacement data, without simultaneously introducing phase lags to these data. In one embodiment, real-time adaptive filters can be used to remove respiration-related artifacts from fMRI data.

[0056] Referring to FIG. 4, a flow chart illustrating a non-limiting example adaptive filtering method 400 is shown. The method utilizes an unfiltered signal at step 402, including but not limited to displacement (e.g., frame-wise displacement (FD), per-slice displacement, etc.) data derived from a data set obtained from a subject in an MRI scanner using the methods described above, and a best estimate of a noise signal at step 404, which is removed using an adaptive filter at step 406. The adaptive filter method 400 can minimize the contribution of undesired signals to the measured signal in real time by a steepest descent method at step 408, thereby providing an optimally filtered sequence. The method can be repeated at step 410. Non-limiting examples of suitable noise signals input to the adaptive filter include a real-time measurement of the breathing rate of a subject in an MRI scanner, a sum of multiple sinusoidal signals at different phases having a frequency corresponding to the subject's breathing rate, and any other suitable estimate of the subject's breathing rate. In one aspect, the participant's breathing rate is measured while the T1w or previous sequence is acquired and used as the signal noise input.

[0057] In one aspect, the adaptive filter method 400 applies a filter to each of the directions (i.e., x, y, z, θ) determined using the methods described above. χ ,θ y ,θ ζ) of the head motion. This first estimate of head motion includes both the actual head motion (s) and the undesired artifact (n0). These two signals (s) and (n0) can be assumed to be independent and uncorrelated. An additional input may be used for a best estimate of the received undesired artifact (n1=n0). If the undesired artifact n0 corresponds to a respiration rate, this signal may be provided as a real-time measurement of the respiration rate. In another aspect, if a real-time measurement of respiration is not available, a sinusoidal signal may be generated that includes a sum of multiple sinusoidal signals, with the most likely respiration rate corresponding to the subject in the scanner. This error signal may be filtered out by an adaptive filter to generate an optimized estimate of the error signal (y(T)). In this aspect, the goal of the adaptive filter may be to maximize the correlation between the optimized estimate of the error signal (y(T)) and the measured estimate of the head motion (d(T)). When using the first data set, the adaptive filter may not affect the signal (n0). Also in this aspect, an optimized estimate of the error signal (y(T)) can be subtracted from the head movement measurement estimate (d(T)) to calculate an error signal (i.e., e(T)=s+n0-y(T)). This error may be used as a feedback signal to modify the parameters of the adaptive filter to correlate the signal (y(t)) to the measurement (d(T)) as closely as possible. Since the actual head movement (s) and the actual artifact (n0) are uncorrelated, maximizing the correlation between n0 and d(T) can be achieved by matching between n0 and n0. Thus, subtracting these signals (d(T) and y(T)) removes the undesired artifacts. In one aspect, the adaptive filter method may be implemented using well-established methods in which the parameters of a second-order difference equation are optimized to maximize the estimate of the undesired artifacts.

[0058] In various aspects, the methods according to the present disclosure can be implemented by a system including an MRI system and one or more processors or computing devices. In various aspects, one or more operations described herein can be implemented by one or more processors having physical circuitry programmed to perform the operations. In various other aspects, one or more steps of the method can be performed automatically by one or more processors or computing devices. In various further aspects, the various acts illustrated can be performed in the sequence illustrated, in other sequences, in parallel, or in some cases omitted.

[0059] In some aspects, the methods and processes described above may be implemented using a computing system including one or more computers. The methods and processes described herein may be implemented as computer applications, computer services, computer APIs, computer libraries, and / or other computer program products.

[0060] Referring to FIG. 5, an example of an MRI system 500 capable of performing the methods described herein is shown. The MRI system 500 includes an operator workstation 502, which may include a display 504, one or more input devices 506 (e.g., keyboard, mouse), and a processor 508. The processor 508 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 502 provides an operator interface that facilitates input of scan parameters into the MRI system 500. The operator workstation 502 may be connected to various servers, including, for example, a pulse sequence server 510, a data acquisition server 512, a data processing server 514, and a data storage server 516. The operator workstation 502 and the servers 510, 512, 514, and 516 may be connected via a communication system 540, which may include a wired or wireless network connection.

[0061] The pulse sequence server 510 functions in response to instructions provided by the operator workstation 502 to operate a gradient system 518 and a radio frequency ("RF") system 520. Gradient waveforms for performing a given scan are generated and applied to the gradient system 518, which in turn excite gradient coils in an assembly 522 to produce magnetic field gradients G x ,G y and G z , which is used to spatially encode the magnetic resonance signals. The gradient coil assembly 522 forms part of a magnet assembly 524 which includes a polarizing magnet 526 and a whole-body RF coil 528.

[0062] RF waveforms are applied by the RF system 520 to the RF coil 528 or to a separate local coil to perform a prescribed magnetic resonance pulse sequence. Responsive magnetic resonance signals detected by the RF coil 528 or a separate local coil are received by the RF system 520. The responsive magnetic resonance signals may be amplified, demodulated, filtered, and digitized under the direction of instructions generated by the pulse sequence server 510. The RF system 520 includes an RF transmitter for generating a wide variety of RF pulses used in MRI pulse sequences. The RF transmitter generates RF pulses having desired frequency, phase, and pulse amplitude waveforms in response to prescribed scans and instructions from the pulse sequence server 510. The generated RF pulses may be applied to the whole-body RF coil 528 or to one or more local coils or coil arrays.

[0063] The RF system 520 also includes one or more RF receiver channels, each of which includes an RF preamplifier that amplifies the magnetic resonance signal received by the coil 528 to which it is connected, and a detector that detects and digitizes the I and Q quadrature components of the received magnetic resonance signal. Thus, the magnitude of the received magnetic resonance signal can be determined at a sampled point by the square root of the sum of the squares of the I and Q components as follows:

[0064]

number

[0065] Also, the phase of the received magnetic resonance signal can be determined according to the following relationship:

[0066]

number

[0067] The pulse sequence server 510 may receive patient data from a physiological acquisition controller 530. In one embodiment, the physiological acquisition controller 530 may receive signals from a number of different sensors connected to the patient, including electrocardiograph ("ECG") signals from electrodes or respiratory signals from a respiratory bellows or other respiratory monitoring device. These signals may be used by the pulse sequence server 510 to synchronize or "gate" the performance of the scan with the subject's heartbeat or breathing.

[0068] The pulse sequence server 510 may also be connected to a scan room interface circuit 532, which receives signals from various sensors related to the condition of the patient and the magnet system. It is also through the scan room interface circuit 532 that a patient positioning system 534 may receive commands to move the patient to a desired position during a scan.

[0069] The digitized magnetic resonance signal samples generated by the RF system 520 are received by the data acquisition server 512. The data acquisition server 512 operates in response to instructions downloaded from the operator workstation 502 to receive real-time magnetic resonance data and provide buffer storage to ensure that data is not lost due to data overruns. In some scans, the data acquisition server 512 passes acquired magnetic resonance data to the data processor server 514. In scans in which information derived from the acquired magnetic resonance data is needed to control further performance of the scan, the data acquisition server 512 may be programmed to generate such information and communicate it to the pulse sequence server 510. For example, during a prescan, magnetic resonance data may be acquired and used to calibrate the pulse sequence executed by the pulse sequence server 510. As another example, a navigator signal may be acquired and used to adjust operating parameters of the RF system 520 or the gradient system 518 or to control the view order in which k-space is sampled. In yet another embodiment, the data acquisition server 512 may also process magnetic resonance signals used to detect the arrival of contrast agents in a magnetic resonance angiography ("MRA") scan. For example, the data acquisition server 512 may acquire magnetic resonance data and process it in real time to generate information used to control a scan.

[0070] The data processing server 514 receives the magnetic resonance data from the data acquisition server 512 and processes the magnetic resonance data according to instructions provided by the operator workstation 502. Such processing may include, for example, performing a Fourier transform of the raw k-space data, performing other image reconstruction algorithms (e.g., iterative or backprojection reconstruction algorithms), applying filters to the raw k-space data or the reconstructed images, generating functional magnetic resonance images, or reconstructing two- or three-dimensional images by calculating motion or flow images.

[0071] Images reconstructed by the data processing server 514 are returned to the operator workstation 502 for storage. Real-time images are stored in a database cache memory from which they are output to the operator display 502 or to a display 536. Batch mode images or selected real-time images may be stored in a host database on disk storage 538. Once such images have been reconstructed and sent for storage, the data processing server 514 may notify the data storage server 516 of the operator workstation 502. The operator workstation 502 may be used by the operator to archive images, create film, or send images over a network to other facilities.

[0072] The MRI system 500 may also include one or more networked workstations 542. For example, the networked workstation 542 may include a display 544, one or more input devices 546 (e.g., keyboard, mouse), and a processor 548. The networked workstation 542 may be located in the same facility as the operator workstation 502 or may be located in another facility, such as another medical facility or clinic.

[0073] The networked workstations 542 may remotely access the data processing server 514 or the data storage server 516 via the communication system 540. Thus, a plurality of networked workstations 542 may access the data processing server 514 and the data storage server 516. In this manner, magnetic resonance data, reconstructed images or other data may be exchanged between the data processing server 514 or the data storage server 516 and the networked workstations 542, and the data or images may be processed remotely by the networked workstations 542.

[0074] 6A, an example of a system 600 for functional mapping guided intervention targeting (e.g., determining anatomical targets for brain stimulation) is shown, in accordance with some embodiments of the systems and methods described in this disclosure. As shown in FIG. 6A, a computing device 650 can receive one or more types of data (e.g., fMRI, task-based fMRI, and / or rs-fMRI data) from an image source 602, which may be an MRI source. In some embodiments, the computing device 650 can execute at least a portion of a functional mapping guided intervention targeting system 604 to generate intervention targets from the data received from the image source 602.

[0075] Additionally or alternatively, in some embodiments, computing device 650 may communicate information regarding the data received from image source 602 to a server 652 via a communications network 654 that may implement at least a portion of functional mapping guided intervention targeting system 604. In such embodiments, server 652 may return information indicative of the output of functional mapping guided intervention targeting system 604 to computing device 650 (and / or any other suitable computing device).

[0076] In some embodiments, the computing device 650 and / or the server 652 may be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, or a virtual machine run by a physical computing device. The computing device 650 and / or the server 652 may also reconstruct an image from the data.

[0077] In some embodiments, the image source 602 can be any suitable source of image data (e.g., measurement data, an image reconstructed from the measurement data), such as a magnetic resonance imaging system, another computing device (e.g., a server that stores image data), etc. In some embodiments, the image source 602 can be located locally with respect to the computing device 650. For example, the image source 602 can be incorporated with the computing device 650 (e.g., the computing device 650 can be configured as part of an apparatus for capturing, scanning, and / or storing images). As another example, the image source 602 can be connected to the computing device 650 by a cable, a wireless link, etc. Additionally or alternatively, in some embodiments, the image source 602 can be located locally and / or remotely with respect to the computing device 650 and can communicate data to the computing device 650 (and / or the server 652) via a communications network (e.g., the communications network 654).

[0078] In some embodiments, the communications network 654 may be any suitable communications network or combination of communications networks. For example, the communications network 654 may include a Wi-Fi network (which may include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., conforming to any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, etc. In some embodiments, the communications network 654 may be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. The communications links may each be any suitable communications link or combination of communications links, such as a wired link, an optical fiber link, a Wi-Fi link, a Bluetooth link, a cellular link, etc.

[0079] 6B, an example of hardware 700 that can be used to implement the image source 602, computing device 650, and server 652 according to some embodiments of the systems and methods described in this disclosure is shown. As shown in FIG. 6B, in some embodiments, the computing device 650 can include a processor 702, a display 704, one or more inputs 706, one or more communication systems 708, and / or memory 710. In some embodiments, the processor 702 can be any suitable hardware processor or combination of processors, such as a central processing unit ("CPU"), a graphics processing unit ("GPU"), etc. In some embodiments, the display 704 can include any suitable display device, such as a computer monitor, a touch screen, a television, etc. In some embodiments, the input 706 can include any suitable input device and / or sensor that can be used to receive user input, such as, for example, a keyboard, a mouse, a touch screen, a microphone, etc.

[0080] In some embodiments, communications system 708 may include any suitable hardware, firmware, and / or software for communicating information over communications network 654 and / or any other suitable communications network. For example, communications system 708 may include one or more transmitters, one or more communications chips and / or sets of chips, etc. In more particular examples, communications system 708 may include hardware, firmware, and / or software that can be used to establish a Wi-Fi® connection, a Bluetooth® connection, a cellular connection, an Ethernet® connection, etc.

[0081] In some embodiments, the memory 710 may include any suitable storage device or devices that may be used to store instructions, values, data, etc., which may be used by the processor 702, for example, to present content using the display 704 or to communicate with the server 652 via the communication system 708. The memory 710 may include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, the memory 710 may include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, etc. In some embodiments, the memory 710 may be encoded with or have stored thereon a computer program for controlling the operation of the computing device 650. In such embodiments, the processor 702 may execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from the server 652, transmit information to the server 652, etc.

[0082] In some embodiments, server 652 may include a processor 712, a display 714, one or more inputs 716, one or more communication systems 718, and / or memory 720. In some embodiments, processor 712 may be any suitable hardware processor or combination of processors, such as a CPU, a GPU, etc. In some embodiments, display 714 may include any suitable display device, such as a computer monitor, a touch screen, a television, etc. In some embodiments, input 716 may include any suitable input device and / or sensor that may be used to receive user input, such as, for example, a keyboard, a mouse, a touch screen, a microphone, etc.

[0083] In some embodiments, communications system 718 may include any suitable hardware, firmware, and / or software for communicating information over communications network 654 and / or any other suitable communications network. For example, communications system 718 may include one or more transmitters, one or more communications chips and / or sets of chips, etc. In more particular examples, communications system 718 may include hardware, firmware, and / or software that can be used to establish a Wi-Fi® connection, a Bluetooth® connection, a cellular connection, an Ethernet® connection, etc.

[0084] In some embodiments, memory 720 may include any suitable storage device or devices that may be used to store instructions, values, data, etc., which may be used by processor 712, for example, to present content using display 714 or to communicate with one or more computing devices 650. Memory 720 may include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 720 may include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, etc. In some embodiments, memory 720 may be encoded with or have stored thereon a server program for controlling the operation of server 652. In such embodiments, processor 712 may execute at least a portion of the server program to send information and / or content (e.g., data, images, user interfaces) to one or more computing devices 650, receive information and / or content from one or more computing devices 650, receive instructions from one or more devices (e.g., personal computers, laptop computers, tablet computers, smartphones), etc.

[0085] In some embodiments, the image source 602 may include a processor 722, one or more image acquisition systems 724, one or more communication systems 726, and / or memory 728. In some embodiments, the processor 722 may be any suitable hardware processor or combination of processors, such as a CPU, a GPU, etc. In some embodiments, the one or more image acquisition systems 724 are generally configured to acquire data, images, or both, and may include an MR imaging system. Additionally or alternatively, in some embodiments, the one or more image acquisition systems 724 may include any suitable hardware, firmware, and / or software for connecting to and / or controlling the operation of an MR imaging system. In some embodiments, one or more portions of the one or more image acquisition systems 724 may be removable and / or replaceable.

[0086] Although not shown, it should be noted that image source 602 may include any suitable inputs and / or outputs. For example, image source 602 may include input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touch screen, a microphone, a track pad, a track ball, etc. As another example, image source 602 may include any suitable display device, such as a computer monitor, a touch screen, a television, etc., and one or more speakers.

[0087] In some embodiments, communications system 726 may include any suitable hardware, software, and / or firmware for communicating information to computing device 650 (and in some embodiments via communications network 654 and / or any other suitable communications network). For example, communications system 726 may include one or more transmitters, one or more communications chips and / or sets of chips, etc. In more particular examples, communications system 726 may include hardware, firmware, and / or software usable to establish a wired connection using any suitable port and / or communications standard (e.g., VGA, DVI video, USB, RS-232, etc.), a Wi-Fi® connection, a Bluetooth® connection, a cellular connection, an Ethernet® connection, etc.

[0088] In some embodiments, memory 728 may include any suitable storage device or devices that may be used to store instructions, values, data, etc., which may be used by processor 722, for example, to control one or more image acquisition systems 724 and / or receive data from one or more image acquisition systems 724; to generate images from the data; to present content (e.g., images, user interfaces) using a display; to communicate with one or more computing devices 650, etc. Memory 728 may include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 728 may include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, etc. In some embodiments, memory 728 may be encoded with or have stored therein programs for controlling the operation of image source 602. In such embodiments, the processor 722 may execute at least a portion of a program to generate images, send information and / or content (e.g., data, images) to one or more computing devices 650, receive information and / or content from one or more computing devices 650, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), etc.

[0089] In some embodiments, any suitable computer-readable medium may be used to store instructions for performing the functions and / or processes described herein. For example, in some embodiments, computer-readable media may be transitory or non-transitory. For example, non-transitory computer-readable media may include media such as magnetic media (hard disks, floppy disks, etc.), optical media (compact disks, digital video disks, Blu-ray® disks, etc.), semiconductor media (e.g., random access memory ("RAM"), flash memory, electrically programmable read-only memory ("EPROM"), electrically erasable programmable read-only memory ("EEPROM"), etc.), any suitable media that does not lack a semblance of non-transitory or persistence in transmission, and / or any suitable tangible media. As another example, transitory computer-readable media may include signals over networks, wires, conductors, optical fibers, circuits, or any suitable media that lack a semblance of transitory and persistence in transmission, and / or any suitable intangible media.

[0090] While one or more preferred embodiments have been described in this disclosure, it should be recognized that, except as expressly stated, many equivalents, alternatives, variations and modifications are possible and within the scope of the invention.

Claims

1. A magnet system configured to generate a polarizing magnetic field around at least a portion of a subject; a magnetic field gradient system including a plurality of magnetic field gradient coils configured to apply at least one magnetic field gradient to the polarizing magnetic field; a radio frequency (RF) system configured to apply an RF magnetic field to the subject and receive magnetic resonance signals from the subject using a coil array; A system comprising: a computer system, The computer system includes: controlling the magnetic field gradient system and the RF system to acquire a functional magnetic resonance imaging (fMRI) data set using at least task-based fMRI data acquisition; reconstructing the fMRI dataset using a resting-state functional magnetic resonance image (rs-fMRI) reconstruction process of the fMRI dataset to generate at least one resting-state (rs) image during the task-based fMRI data acquisition; comparing the at least one rs image to a reference image during the task-based fMRI data acquisition to determine movement of the subject during the task-based fMRI data acquisition; determining a displacement of the object corresponding to the movement of the object; The system is programmed to generate at least one of an alert communicated to an operator or a real-time display of the displacement during the task-based fMRI data acquisition.

2. 2. The system of claim 1, wherein the computer system is further programmed to indicate at least one of an amount of the fMRI data set affected by the displacement or a portion of the task-based fMRI data acquisition affected by the displacement.

3. The computer system is further programmed to determine the movement of the object using six alignment parameters; The six alignment parameters are x, y, z, θ χ , θ ｙ , θ ζ The system of claim 1 .

4. The system of claim 1 , wherein the reference image comprises a prior rs image reconstructed from the fMRI data set using the rs-fMRI reconstruction process.

5. The computer system is further programmed to predict an amount of the fMRI data set that is below a predetermined threshold for displacement; 10. The system of claim 1, further comprising a display configured to display in real time a predicted amount of the fMRI data set below the threshold during the task-based fMRI data acquisition.

6. Comparing the at least one rs-fMRI image with the reference image comprises a series of rigid body transformations T ｉ Calculating where i indexes the spatial registration of the at least one rs-fMRI image to at least one prior image reconstructed from the fMRI dataset; Each of the series of rigid transformations determines a registration error, [0010] It is calculated by minimizing 2. The system of claim 1, wherein I(x) represents the image intensity at location x and s represents a scalar factor that compensates for variations in average signal intensity.

7. Each of the series of rigid transformations [0025] It is expressed as a combination of rotation and displacement given by In the formula, R ｉ represents a 3 × 3 rotation matrix, and d ｉ represents a 3×1 column vector of displacements, R ｉ 7. The system of claim 6, wherein x, y, z, y, z represent three basic rotations about each axis.

8. 2. The system of claim 1, wherein determining an overall displacement comprises subtracting a displacement for a previous one of the at least one rs-fMRI images from a displacement for a current image of the at least one rs-fMRI image.

9. Further comprising a sensory feedback system; The system of claim 1 , wherein the sensory feedback system is configured to provide sensory feedback to the subject based on the displacement to prompt a reduction in the displacement or possible future displacements.

10. A method of receiving a subject's task-based functional magnetic resonance imaging (fMRI) data set from a magnetic resonance imaging (MRI) system using a computing device including at least one processor in communication with at least one memory device, while the MRI system is performing at least task-based fMRI data acquisition; performing resting-state functional magnetic resonance imaging (rs-fMRI) reconstruction of the task-based fMRI dataset to generate an rs-fMRI image with the computing device; comparing the rs-fMRI image with at least one reference image during the task-based fMRI data acquisition using the computing device; determining, with the computing device, motion of the subject based on comparing the rs-fMRI image to the at least one reference image; and using the computing device to communicate an alert to an operator of the MRI system indicating movement detected during the task-based fMRI data acquisition.

11. Determining the movement of the object includes using six alignment parameters; The six alignment parameters are x, y, z, θ χ , θ ｙ , θ ζ The method of claim 10, wherein

12. The method of claim 11 , wherein the six alignment parameters include at least one of a frame-wise alignment or a slice-wise alignment.

13. The method of claim 10 , wherein the reference image comprises a preceding image for the rs-fMRI image.

14. predicting, using the computing device, an amount of the task-based fMRI data set below a predetermined threshold for a displacement of the subject corresponding to a movement of the subject; and communicating, in real time during the task-based fMRI data acquisition, a predicted amount of the task-based fMRI data set that is below the threshold, using the computing device.

15. The step of comparing the rs-fMRI image with at least one reference image comprises the step of: ｉ Calculating where i indexes the spatial registration of the rs-fMRI image to at least one reference image corresponding to a preceding sub-image for the rs-fMRI image; Each of the series of rigid transformations determines a registration error, [0030] It is calculated by minimizing 11. The method of claim 10, wherein I(x) represents the image intensity at location x and s represents a scalar factor that compensates for variations in the average signal intensity.

16. Each of the series of rigid transformations [0045] It is expressed as a combination of rotation and displacement given by In the formula, R ｉ represents a 3 × 3 rotation matrix, and d ｉ represents a 3×1 column vector of displacements, R ｉ The method of claim 15, wherein x, y, z, z represent three basic rotations about each axis.

17. The method of claim 10, wherein the step of determining the overall displacement comprises subtracting a displacement for a preceding image for the rs-fMRI image from a displacement for the rs-fMRI image.

18. 11. The method of claim 10, further comprising using the computing device to provide sensory feedback to the subject based on a displacement of the subject corresponding to a movement of the subject to prompt a reduction in the displacement or possible future displacement.

19. A magnet system configured to generate a polarizing magnetic field around at least a portion of a subject; a magnetic field gradient system including a plurality of magnetic field gradient coils configured to apply at least one magnetic field gradient to the polarizing magnetic field; a radio frequency (RF) system configured to apply an RF magnetic field to the subject using a coil array and to receive magnetic resonance signals from the subject; A computer system, The computer system includes: controlling the magnetic gradient system and the RF system to perform a task-based functional magnetic resonance imaging (fMRI) acquisition to acquire a task-based fMRI data set from the subject; The method is programmed to reconstruct the task-based fMRI data set using a resting-state functional magnetic resonance image (rs-fMRI) reconstruction process to generate an rs-fMRI image from the task-based fMRI data set.

20. A method of detecting a subject's brain activity, comprising: receiving functional magnetic resonance imaging (fMRI) data obtained from the subject while the subject is exposed to at least one of performing a task or experiencing a stimulus; reconstructing the fMRI data acquired while the subject is exposed to at least one of performance of a task or experience of a stimulus using a resting-state fMRI (rs-fMRI) reconstruction process without taking into account the at least one of performance of the task or experience of the stimulus; and displaying the rs-fMRI image.

Images