Systems and methods for intervention planning for the treatment of brain disorders

Abstract

A computer-implemented method for brain mapping and target identification for intervention planning using magnetic resonance imaging (MRI) includes receiving magnetic resonance (MR) data from an MRI system by a computing system having at least one processor in communication with at least one memory system and in communication to receive data acquired using the MRI system. The method further includes analyzing the received MR data to monitor and identify motion in real time, determining a set of usable MR data from the acquired MR data based on the identified motion, generating a brain map of the subject based on the set of usable MR data, and identifying a target location in the subcallosal cingulate cortex (SCC) region of the subject's brain based on the brain map of the subject. The target location is a convergence point of multiple fiber bundles passing through the SCC region. The method further includes generating a report indicating the target location.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Application No. 63 / 375,454, filed September 13, 2022, and entitled "Computer-Aided Diagnosis (CAD) System and Method for Surgical Planning for the Treatment of Psychiatric Disorders," the entire contents of which are incorporated herein by reference. [Background technology]

[0002] Psychiatric disorders are common causes of severe, long-term disability and socioeconomic burden. For some patients, treatments such as medication and psychotherapy may be ineffective or cause intolerable side effects. For these patients, neuromodulation therapy has been proposed as a potential treatment. Neuromodulation therapy, one of the fastest-growing fields in medicine, is the process of electrically or chemically inhibiting, stimulating, modifying, regulating, or therapeutically altering activity in the central, peripheral, and autonomic nervous systems. Neuromodulation therapies include deep brain stimulation, vagus nerve stimulation, and transcranial magnetic electrical stimulation. Neuromodulation therapy aims to treat chronic neurological and psychiatric disorders by surgically targeting deep brain nuclei and pathways involved in symptom relief and stimulating, inhibiting, or modifying / regulating pathological activity.

[0003] Deep brain stimulation (DBS) targets neural structures, such as the cortex and / or subcortical structures, in the treatment of psychiatric and neurological disorders, including essential tremor, Parkinson's disease, dystonia, Tourette's syndrome, obsessive-compulsive disorder, and treatment-resistant depression. However, the success rate varies depending on the specific target structure. DBS of the ventral intermedius thalamus for the treatment of essential tremor results in tremor reduction of 80% or more in all patients, whereas pallidal stimulation for the treatment of dystonia results in symptom improvement of only 30–50% in all patients, with improvement of >75% in only 33% of patients.

[0004] Body motion, such as head motion, is the greatest obstacle to collecting high-quality human brain magnetic resonance imaging (MRI) data. Head motion distorts structural MRI (e.g., T1-weighted, T2-weighted), functional MRI (e.g., task-driven MRI (fMRI) and resting-state functional connectivity MRI (rs-fcMRI)), and diffusion-weighted MRI (e.g., diffusion tensor imaging (DTI)) data. In some cases, even submillimeter head motion (e.g., minute movements) can systematically alter structural, functional, and diffusion-weighted MRI data. Therefore, significant efforts have been devoted to developing post-acquisition processing methods to remove head motion distortion from MRI data.

[0005] Head motion from one MRI data frame (or slice) to the next is thought to cause the most significant MRI signal distortion, rather than absolute motion from a reference frame. Motion-related distortions are strongly correlated with metrics such as framewise displacement (FD, representing the sum of absolute head motion in all six rigid-body directions between frames), zipper artifacts or outlier slices, adjacent DWI correlation in raw data, and DVARS (the root mean square of the derivative of the differential time course of each voxel in a single MRI image). Therefore, metrics such as FD and DVARS, which capture the overall effect of subject motion during MRI data acquisition, have been used to assess data quality in various post-hoc methods. For example, post-hoc frame censoring, which removes all MRI data frames with FD values ​​above a certain threshold (e.g., excluding data frames with FD values ​​above 0.2 mm), has become a common method for improving the quality of functional MRI data.

[0006] Frame censoring is necessary to reduce artifacts, but it comes at a significant cost. For example, frame censoring can exclude more than 50% of the rs-fcMRI data collected from a cohort. This depends on the individual parameters and the quality of the underlying data. Because the accuracy of MRI measurements improves with increasing frame number, a minimum number of data frames may be required to obtain reliable data. If too few frames remain after censoring, investigators may lose all of a participant's data. To avoid this loss, investigators typically collect additional "buffer" data, but this is a costly method that alone does not guarantee sufficiently high-quality MRI data for a given participant. The "overscanning" required to remove motion-distorted data while maintaining a sufficient sample size to achieve the desired data quality significantly increases the cost and duration of brain MRI.

[0007] Recently developed structural MRI sequences with predictive motion correction use a similar approach to mitigate the adverse effects of head motion. These MRI sequences combine each structural data acquisition with a high-speed, low-resolution snapshot of the entire brain (echoplanar image = EPI), which serves as a head motion marker or navigator. These motion-corrected structural sequences calculate the relative motion between successive navigator images and use this information to mark linked structural data frames for exclusion and reacquisition. In this way, structural data frames are "censored," increasing the time and cost of structural MRI.

[0008] For structural, functional, and diffusion-weighted MRI, access to real-time information about head motion within the scanner during scanning could eliminate the need for overscanning and significantly reduce MRI costs. Head motion assessments obtained from real-time motion monitoring allow scanner operators to continue each scan until the required number of low-motion data frames are acquired without overbuffering scans. Existing real-time motion monitoring methods use expensive cameras and lasers to measure proxies for FD. However, these head motion proxies typically correlate poorly with FD because they cannot distinguish between facial and scalp motion and brain motion. Summary of the Invention

[0009] According to one embodiment, a computer-implemented method for brain mapping and target identification for intervention planning using magnetic resonance imaging (MRI) includes receiving magnetic resonance (MR) data from an MRI system by a computing system having at least one processor in communication with at least one memory system and in communication to receive data acquired using the MRI system. The method further includes analyzing the received MR data to monitor and identify motion in real time, determining a set of usable MR data from the acquired MR data based on the identified motion, generating a brain map of the subject based on the set of usable MR data, and identifying a target location in a subcallosal cingulate (SCC) region of the subject's brain based on the brain map of the subject. The target location is a convergence point of multiple fiber bundles passing through the SCC region. The method further includes generating a report indicating the target location.

[0010] In some embodiments, the plurality of fiber bundles passing through the SCC region include the cingulum bundle (CM), forceps minor (FM), frontal striatal fibers (F-ST), and uncinate fasciculus (UF). In some embodiments, the method further includes displaying the report on a display. In some embodiments, the received magnetic resonance data is diffusion-weighted magnetic resonance data. In some embodiments, the received diffusion-weighted magnetic resonance data is acquired using one of diffusion tensor imaging (DTI) or diffusion-weighted imaging (DWI). In some embodiments, the received diffusion-weighted magnetic resonance data is acquired in a first number of diffusion directions. In some embodiments, the method further includes determining additional diffusion directions different from the first number of diffusion directions based on the identified motion and the set of available magnetic resonance data. In some embodiments, the method further includes receiving, by the computing system, additional magnetic resonance data acquired in the additional diffusion direction from the MRI system.

[0011] According to another embodiment, a system for brain mapping and target identification for intervention planning using magnetic resonance imaging (MRI) includes a computing system and a display. The computing system includes a processor, the processor being programmed to receive magnetic resonance data using an MRI system, analyze the received magnetic resonance data to monitor and identify motion in real time, determine a set of usable magnetic resonance data from the acquired magnetic resonance data based on the identified motion, generate a brain map of the subject based on the set of usable magnetic resonance data, and identify a target location in the subcallosal cingulate cortex (SCC) region of the subject's brain based on the brain map of the subject. The target location is a convergence point of multiple fiber bundles passing through the SCC region. The processor is programmed to generate a report indicating the target location. The display is coupled to the computing system and configured to display the report.

[0012] In some embodiments, the plurality of fiber bundles passing through the SCC region include the cingulate bundle (CM), forceps minor (FM), frontostriatal fibers (F-ST), and uncinate bundle (UF). In some embodiments, the received magnetic resonance data is diffusion-weighted magnetic resonance data. In some embodiments, the received diffusion-weighted magnetic resonance data is acquired using one of diffusion tensor imaging (DTI) or diffusion-weighted imaging (DWI). In some embodiments, the received diffusion-weighted magnetic resonance data is acquired in a first number of diffusion directions. In some embodiments, the processor is further programmed to determine additional diffusion directions different from the first number of diffusion directions based on the identified motion and the set of available magnetic resonance data. In some embodiments, the processor is further programmed to receive additional magnetic resonance data acquired in the additional diffusion directions from the MRI system.

[0013] These and other aspects and advantages of the present disclosure will become apparent from the following description, in which reference is made to the accompanying drawings, which form a part hereof, and in which a preferred embodiment is shown by way of example. However, this embodiment does not necessarily represent the full scope of the invention, and reference should be made to the claims and this specification for interpreting the scope of the invention. In the following description, like parts in the various figures are designated by like reference numerals. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 illustrates an exemplary method for performing mapping and identification of target locations in a subject's brain for intervention planning according to one embodiment. [Figure 2] FIG. 10 illustrates an example display of a target location in the subcallosal cingulate cortex (SCC) region of the brain, according to one embodiment. [Figure 3] 1 is a flowchart illustrating operations for aligning magnetic resonance imaging (MRI) data from an MRI scan to a selected frame in the MRI scan, according to one embodiment. [Figure 4] 1 is a flowchart illustrating a method for generating sensory feedback displays to an operator of an MRI system and / or a patient within an MRI system during data acquisition according to one embodiment. [Figure 5] FIG. 1 is a schematic diagram illustrating an example of a system for performing magnetic resonance imaging according to an embodiment. [Figure 6A] FIG. 1 is a block diagram of an example of a system for brain mapping and target identification for intervention planning for the treatment of brain disorders, according to one embodiment. [Figure 6B] FIG. 6B is a block diagram of components that may implement the system for brain mapping and target identification for intervention planning for the treatment of brain disorders of FIG. 6A according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0015] FIG. 1 illustrates an example method for mapping and identifying a target location in a subject's brain, according to one embodiment. While the process blocks in FIG. 1 are shown in a predetermined order, in some embodiments, one or more blocks may be ordered differently than shown in FIG. 1 or may be bypassed. The method begins in block 102 with receiving magnetic resonance imaging (MR) data of the subject's brain, such as structural MRI (T1-weighted, T2-weighted) data, functional MRI data, and diffusion-weighted MRI data. In some embodiments, the MR data of the subject's brain may be acquired using, for example, a diffusion imaging technique (e.g., DTI or DWI) or a functional magnetic resonance imaging (fMRI) technique. The fMRI data of the subject's brain may include task-driven (fMRI) data, resting-state fMRI (rs-fMRI) data, or a combination thereof. The subject may be a human, an animal, a phantom, or the like. In some embodiments, the MR data may be acquired and received in real time by an MRI system (e.g., MRI system 500 shown in FIG. 5). In some embodiments, the MR data can be obtained from data storage in the imaging system (e.g., disk storage 538 of MRI system 500 in FIG. 5) or from data storage in another computer system (e.g., memory 710 of computer device 650 or memory 720 of server 652 shown in FIG. 6B).

[0016] In block 104, the MR data is analyzed to identify motion in real time. In block 106, a set of usable MR data (e.g., non-corrupted motion MR data) can be determined based on the motion identified in block 104. In some embodiments, blocks 104 and 106 can be performed as part of the MR data acquisition in block 102. For example, blocks 102, 104, and 106 can include systems, devices, and methods for real-time monitoring and prediction of motion of a patient's body part, including, but not limited to, head motion, during an MRI scan. In some embodiments, acquiring the MR data can include a Framewise Integrated Real-time MRI Monitoring (FIRMM) system, device, and method, further described with reference to FIG. 3 below, for highlighting motion artifacts. Real-time monitoring and prediction of degraded data quality includes, but is not limited to, patient motion (e.g., head motion) during the scan.

[0017] 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, understood as performance that conforms to an operational deadline from a particular event to the system's response to that event. For example, real-time data extraction based on empirically acquired signals and / or display of such data can be triggered and / or performed simultaneously with or without interruption of signal data acquisition (e.g., pulse sequence) or imaging procedure.

[0018] In block 108, the MR data or images acquired in block 102 or the available MR data determined in block 106 can optionally be preprocessed for the mapping process. For example, in some embodiments, high-resolution T1 images can be preprocessed by, for example, skull removal, image registration and normalization to a template, and tissue segmentation, such as estimating brain masks for gray matter (GM), white matter (WM), and cerebrospinal fluid (CBF). In some embodiments, diffusion-weighted imaging (DWI) data (or images) can be preprocessed by performing skull removal, simultaneous eddy current and distortion correction (e.g., by registering the diffusion-weighted (DW) image to the B0 image with an affine transformation), image registration to the B0 image of the initial acquisition, image registration to the T1 image, and local tensor (DTI) fitting. In some embodiments, a transformation matrix between diffusion weighting and T1 concatenated with a previously calculated nonlinear normalized field between T1 and the template can be used to create a diffusion weighting to template transformation field.

[0019] At block 110, the method may calculate and generate a map (e.g., a brain functional connectivity map, a tractography, etc.) based at least on the acquired MR data. The method may include identifying a target location in the target subject's brain, e.g., by neuromodulation based on the calculated brain map, at block 112. In some embodiments, the target location may be identified using a method that allows for patient-specific individual targeting. In some embodiments, the identified region may be in the subcallosal cingulate cortex (SCC) region of the brain. FIG. 2 shows an example of a representation 200 of a target location in the subcallosal cingulate cortex (SCC) region of the brain, according to one embodiment. Advantageously, in some embodiments, the target location 204 may be a convergence point of multiple fiber bundles passing through the SCC region 202. For example, as shown in Figure 2, the target location 204 can be the convergence point of four fiber bundles, including the cingulate bundle (CB) 206, the forceps minor bundle (FM) 208, the frontostriatal fibers (F-St) 210, and the uncinate bundle (UF) 212. The target location 204 can be defined to affect the four fiber bundles (e.g., a neuromodulation device implanted at the target location will affect the four fiber bundles). In some embodiments, the target location 204 can be identified automatically.

[0020] Returning to FIG. 1 , in block 112, in some embodiments, the target location can be, for example, the ventral tegmentum / ventral striatum (VC / VS), nucleus accumbens (NAcc), habenula (LHb), inferior thalamic bundle (ITP), medial forebrain bundle (MFB), bed nucleus of the stria terminalis (BNST), dentate nucleus, centromedian thalamic nucleus, ventrointermediate thalamic nucleus (VIM), or red nucleus. In some embodiments using diffusion MR data, the mapping operation 110 and the target location identification operation 112 can each include a diffusion-weighted tensor imaging tracking technique. For DTI fiber tracking techniques, typically, using more directions of diffusion (diffusion gradients) allows for improved resolution of individual fibers. However, using more directions results in longer scan times. In some embodiments, an operator can select a first number of diffusion directions for a scan. Then, based on feedback (or results) from real-time monitoring of motion and determining a set of available MR data (e.g., blocks 104 and 106), the operator can determine whether additional directions are needed for additional scans. For example, the operator may initially select to perform a scan in three directions, and based on the motion information from block 104, the operator may determine that a second scan in three more directions is needed. In another example, the operator may initially select to perform a scan in three directions, and based on the motion information from block 104, the operator may determine that another scan with additional different directions is not needed. In some embodiments, the additional different diffusion directions can be automatically determined based on feedback (or results) from real-time monitoring of motion and determining a set of available MR data.

[0021] Regardless of the particular region of the brain being studied, a report indicating at least the target location is generated in block 114. In some embodiments, the report can have a display including, for example, a visual indicator identifying the target location on an image or map of the subject's brain. In some embodiments, the report can include the connectome of the subject's brain. In block 116, the generated report can be displayed on a display (e.g., display 504, 536, 544 of MRI system 500 shown in FIG. 5, display 704 of computing device 650 shown in FIG. 6B, or display 714 of server 652 shown in FIG. 6B). In a non-limiting example, the target location may be a target location for an intervention, such as neuromodulation. This method can further facilitate surgical planning, for example, for implantation of a neuromodulation device, and ultimately the administration of neuromodulation directed to the identified target location. In some embodiments, these reports, images, and maps generated by the systems and methods described herein can be used to implement intervention planning, for example, surgery (e.g., tumor resection) or treatment planning.

[0022] In some embodiments, the systems and methods described herein can be used for intervention planning (e.g., surgical planning or treatment planning) for the treatment of certain brain disorders, particularly brain structures. As used herein, the term brain disorder refers to neurological and psychological disorders. For example, various target locations (e.g., SCC region, ventral capsule / ventral striatum (VC / VS), nucleus accumbens (NAcc), habenula (LHb), inferior thalamic bundle (ITP), medial forebrain bundle (MFB), or bed nucleus of the stria terminalis (BNST)) may be used to treat depression, various target locations (e.g., dentate nucleus) may be used to treat motor recovery after stroke, various target locations (e.g., centromedian thalamic nucleus, red nucleus) may be used to treat epilepsy, and various A single target location (e.g., centromedian nucleus) may be used to treat Tourette's syndrome, various target locations (e.g., centromedian nucleus of the thalamus, red nucleus) may be used to treat disorders of consciousness (coma), various target locations (e.g., ventral intermediate nucleus of the thalamus (VIM), red nucleus) may be used to treat essential tremor, and various target locations (e.g., ventral intermediate nucleus of the thalamus (VIM)) may be used to treat tremor-dominant Parkinson's disease.

[0023] Deep brain stimulation (DBS) is a type of neuromodulation therapy used clinically. DBS is a procedure in which a neurostimulator is surgically implanted in the brain to treat brain disorders such as Parkinson's disease, dystonia, essential tremor, obsessive-compulsive disorder, epilepsy, and depression. In some embodiments, the identified target location can be used to guide planning for DBS lead placement. For example, a physician may review the proposed target location, e.g., on a display, and decide whether to select that target location for lead placement. In some embodiments, a target location in the subcallosal cingulate cortex could be used for deep brain stimulation therapy for depression. Other treatments include, for example, transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and focused ultrasound. In some embodiments, the identified target location can be used to guide planning for neuromodulation therapy by providing targets for TMS, tDCS, and focused ultrasound.

[0024] As described above, according to some embodiments, in block 102, MR data of a subject's brain may be advantageously acquired using frame-wise integrated real-time MRI monitoring (FIRMM) systems, devices, and methods for monitoring and predicting the motion of a patient's body parts, including but not limited to, head motion, during an MRI scan in real time. One example of a FIRMM system and method is described in U.S. Patent No. 1,181,599, issued November 23, 2021, and incorporated herein by reference in its entirety. FIRMM computer-implemented methods can improve the quality of MRI data while reducing costs associated with MRI data acquisition. In some embodiments, the FIRMM method can be implemented in the form of a software suite that calculates and displays data quality metrics and / or summary motion statistics in real time during MRI data acquisition. While the FIRMM method and system are described herein in the context of functional MRI data acquisition, according to various embodiments, the FIRMM method and system disclosed herein are suitable for real-time monitoring of head and body motion during other structural or anatomical MRI sequences, including but not limited to those utilizing motion navigation. Advantageously, FIRMM systems and methods can provide real-time feedback to both the scanner operator and the subject undergoing the scan. More specifically, in some embodiments, FIRMM systems and methods can provide sensory feedback to the subject during the scan based on data quality metrics and summary motion statistics calculated in real time, thereby allowing the subject to monitor their movements and adjust their movements accordingly (e.g., remain still) in response to the provided feedback. In some embodiments, FIRMM systems and methods can also provide stimulus conditions, such as a fixation crosshair or the display of a movie clip, to simultaneously engage the subject while providing real-time feedback to the subject.

[0025] In some embodiments, the FIRMM system and method can, by way of non-limiting example, (i) predict the number of data frames that will be available at the end of a scan; (ii) predict the amount of time that a particular subject will likely need to be scanned before a pre-set reference time (a fraction of low-motion FD data) is acquired; and (iii) allow for individual selection and deselection of particular scans for inclusion in the actual and predicted amounts of low-motion data, thereby enabling the scanner operator to continue each scan until the required number of low-motion data frames are acquired.

[0026] Real-time information about head motion can be used to reduce head motion in several different ways, including, but not limited to, 1) influencing the behavior of the MRI scanner operator and 2) influencing the behavior of the subject undergoing the MRI scan. The scanner operator can be alerted to sudden or unusual changes in head motion and can interrupt the scan to determine whether the subject is moving due to discomfort, or whether a bathroom break, blanket, repositioning, or other intervention can make the subject more comfortable. In some embodiments, the FIRMM method can further include the option of providing feedback about head motion to the subject after the scan and / or in real time. In some embodiments, the FIRMM method allows the scanner operator to find the optimal conditions for obtaining the required amount of low-motion data at the lowest cost. The scan can be stopped, and the subject can be addressed by re-acquiring the scan after further instructions or reminders to remain still.

[0027] FIG. 3 illustrates an example of a FIRMM method 300 for processing a set of MRI frames and aligning the frames to a set of reference images to correct for subject motion. While the blocks of the process in FIG. 3 are shown in a particular order, in some embodiments, one or more blocks may be performed in a different order than shown in FIG. 3 or may be bypassed. The method 300 may include, at block 302, receiving MR data in the form of MRI frames or images from a magnetic resonance imaging system. The MRI frames may be received by the computing device from the magnetic resonance imaging system over a network or from a memory connected to or in communication with the computing device.

[0028] At block 304, the method 300 may also include aligning the frame to a reference frame or image. In some embodiments, the reference image may be a single frame selected from frames acquired from the MRI scan (including, but not limited to, the first frame, a navigator frame, or other suitable frame selected from multiple frames acquired during the MRI scan). In some embodiments, the reference image may be an image obtained from an anatomical atlas. In some embodiments, a composite or combination of two or more frames acquired during the MRI scan includes, but is not limited to, an average of two or more frames. In some embodiments, each current frame may be aligned to the immediately preceding previous frame, which is iteratively aligned with the reference image acquired for a given MRI scan.

[0029] Each frame is a sequence of rigid transformations T iwhere i indexes the spatial registration of frame i with respect to reference frame 1, starting from the second frame. Each transformation is computed by minimizing the registration error to an absolute minimum or below a selected cutoff value, or reaching a stopping criterion for the registration error, and is expressed as:

number

[0030] In some embodiments, each transformation can be expressed by a combination of a rotation and a translation according to the following equations:

number

number

number

number

[0031] At block 306, the method 300 may also include calculating the relative movement of a body part (e.g., head) between the current frame and the previous frame. The relative movement of a body part (e.g., head movement) is calculated in x, y, z, θ x , θ y , θ z The alignment parameters can be calculated from multiple frame alignment parameters, including, but not limited to, where x, y, and z are translations in the three coordinate axes, and θ x , θ y , θ z is a rotational motion around these axes.

[0032] At block 308, method 300 may also include calculating a data quality metric (e.g., total frame displacement) using the multiple frame alignment parameters. In some embodiments, the total frame displacement may be determined using multiple displacement vectors of head motion. As a non-limiting example, the total frame displacement may be calculated by adding the absolute displacements of a body part (e.g., head) in six directions, thereby treating the body part as a rigid body. In this non-limiting example, the head motion in the ith frame may be converted to a scalar quantity using the following equation:

number

number

[0033] Multiple rotational displacements |Δα i |, |Δβ i |, |Δγ i | can be converted from degrees to millimeters by calculating the displacement on the surface of a 3D volume representing the part of the body being imaged. As a non-limiting example, if the head is being imaged, the 3D volume selected to calculate the displacement may be a sphere (e.g., a sphere with a radius of 50 mm, which corresponds approximately to the average distance from the cerebral cortex to the center of the head in a healthy young adult). Each frame of data is realigned to a reference image, so that the frame displacement (FD) is calculated by multiplying the displacement i Displacement from (current frame) i-1 It can be calculated by subtracting (previous frame).

[0034] In some embodiments, method 300 further includes filtering out frames with a cutoff value of total frame displacement greater than a predetermined threshold at block 310. In some embodiments, the method can also predict whether there will be at least n usable frames at the end of the MRI scan. In some embodiments, predicting the number of usable frames includes applying a linear model (y = mx + b), where y is the predicted number of good frames at the end of the scan, x is the number of consecutive frames, and m and b are estimated for each subject. In some embodiments, a frame can be declared usable if the relative object displacement in each frame is less than a predetermined threshold (e.g., in millimeters) using the object's position in the previous frame as a reference. One non-limiting example of a cutoff threshold for usable data frames is 0.2, although in some embodiments, the scan operator can edit a configuration file associated with the FIRMM software suite to select a different desired threshold.

[0035] Upon completion, method 300 may return to the beginning for each subsequent frame in the MRI scan. Displaying data quality metrics and other motion monitoring information may occur in block 312. In some embodiments, motion monitoring information may be provided to the operator and / or subject during the MRI scan, as described with reference to FIG. 4 . In some embodiments, a visual display of the parameters for the scan may be shown to the operator. In some embodiments, the FD may be provided to the operator in real time, with new data points added to the “FD-vs-frame # graph” each time a new frame / scan / volume is required. In some embodiments, at the end of each scan, a summary of the counts for that scan may be displayed, along with a summary of each scan's individual head motion data and / or a list tabulating the totals of all data acquired so far in the active scanning session. A prediction of remaining time in the scan (e.g., up to a preset reference time (a fraction of low-motion FD data)) may occur in block 314. For example, a graph of the actual amount of time (e.g., in minutes, seconds, or as a percentage) taken to scan a "high quality" frame relative to a pre-set reference amount of time can be provided. Such information can be provided in the form of a visual display, an audible signal, or any other existing means of providing information, without limitation.

[0036] As mentioned above, in some embodiments, the FIRMM method can generate sensory feedback indications that are communicated via a suitable feedback device to an operator and / or a subject during an MRI scan. Any sensory feedback indication can be provided by the FIRMM method via a feedback device, including, but not limited to, a visual feedback display, an auditory feedback display, or a suitable sensory feedback display for any other existing sensory modality.

[0037] FIG. 4 is a flowchart illustrating a method for providing sensory feedback about an MRI system to an operator of the MRI system and / or a patient within an MRI scanner of the MRI system during data acquisition, according to one embodiment. While the blocks in the process of FIG. 4 are shown in a particular order, in some embodiments, one or more blocks may be performed in a different order than that shown in FIG. 4 or may be bypassed. At block 402, method 400 may include calculating a data quality metric based on one or more motion components determined for the patient within the MRI device during the scan, as described with reference to FIG. 3 above. Any data quality metric may be calculated at block 402, including, without limitation, any one or more displacement components described with reference to FIG. 3, DVARS (i.e., RMS of the derivative of the time course of each voxel in the MRI image), or a combination thereof.

[0038] At block 404, method 400 may further include generating a visual display for an operator of the MRI system in real time based at least on the location of the data quality metrics calculated at block 402. Non-limiting examples of suitable visual feedback displays include at least a portion of a GUI, a light bar, a video, an image, etc. In some embodiments, the visual feedback display for the operator of the MRI system may include visual elements including, but not limited to, one or more graphs displaying data quality metrics for all frames received in a scan, a table of summary statistics regarding the quality of the current and previous scans, a graphical or tabular element conveying the cumulative number of usable frames acquired in the current scan, a tabular or graphical element conveying the time remaining in the current scan and / or the projected time remaining in the current scan to acquire a predetermined number of usable scans, and any combination thereof. In some embodiments, the elements of the visual feedback display can be updated at a preselected rate, up to a real-time rate that updates each display as each relevant quantity is calculated; the elements of the visual feedback display can be updated in response to a request from an operator of the MRI system; and the elements of the visual feedback display can be dynamically updated in response to at least one of a number of factors, including, but not limited to, a significant increase in monitored motion of the subject between frames, cumulative motion, or other suitable criteria.

[0039] The method 400 may further include generating a sensory feedback display for the patient in the scanner while acquiring the MRI data at block 406. The sensory feedback display generated at block 406 may be updated at various 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 several factors, including, but not limited to, the age and condition of the patient.

[0040] At block 408, method 400 may further include determining the total patient motion between the previous frame and the current frame in response to the sensory feedback indication generated at block 406. In some embodiments, method 400 may further include evaluating at least one of a plurality of factors to determine whether the current MRI scan should be terminated at block 410. In some embodiments, the scan may be terminated based on at least one of a plurality of termination criteria, including, but not limited to, one or more unacceptably large amplitude motions and an unacceptably large number of relatively small amplitude motions, a determination that an adequate number of usable frames have been acquired, a prediction that an adequate number of usable frames will not be acquired within the time remaining for the scan, a prediction that an adequate number of usable frames will not be acquired within a reasonable cumulative scan time, and any combination thereof. If it is determined to continue the scan at block 410, method 400 may communicate at least one feedback signal 412, used in part to calculate the data quality metric at block 402, to initiate another iteration of method 400 for a subsequent frame.

[0041] As mentioned above, in some embodiments, the mapping and target identification operations 104 and 106 can include DTI fiber tracking technology, as described above with reference to FIG. 1 . For DTI fiber tracking technology, generally, using more directions for diffusion (diffusion gradients) allows for better resolution of individual fibers. However, the more directions used, the longer the scan takes. In some embodiments using the FIRMM method for data acquisition, an operator can select a first number of diffusion directions for the scan. Then, based on feedback (or results) from real-time monitoring and prediction of degraded data quality, the operator can determine whether additional directions (additional gradients) are needed in additional scans. For example, the operator may initially select three directions, and then, based on motion information from the FIRMM method, the operator may determine that a second scan with three more directions is needed. In other examples, the operator may initially perform three directions, and then, based on motion information from the FIRMM method, the operator may determine that no further scans with additional directions are needed. In some embodiments, additional different diffusion directions may be determined automatically based on feedback (or results) from real-time monitoring of motion and determination of the set of available MR data.

[0042] In some embodiments, the methods described herein can be implemented by a system having 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 those operations. In various aspects, one or more steps of the methods can be performed automatically by one or more processors or computing devices. In various aspects, various operations shown in Figures 1, 3, and 4 may be performed in the illustrated sequence, in other sequences, in parallel, or in other cases omitted.

[0043] In some embodiments, the methods and processes described above may be implemented using a computer system having 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.

[0044] Referring to FIG. 5, an example of an MRI system 500 capable of implementing the methods described herein is shown. The MRI system 500 has 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 device 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 coupled to different 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.

[0045] 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 then generate the magnetic field gradients G , which are used to spatially encode magnetic resonance signals. x , G y , G z Gradient coil assembly 522 forms part of a magnet assembly 524 which includes a magnetizing magnet 526 and a whole-body RF coil 528.

[0046] RF waveforms are applied by the RF system 520 to the RF coil 528 or another local coil to perform a predetermined magnetic resonance pulse sequence. Response magnetic resonance signals detected by the RF coil 528 or another local coil are received by the RF system 520. The response magnetic resonance signals may be amplified, demodulated, filtered, and digitized as directed by commands generated by the pulse sequence server 510. The RF system 520 includes an RF transmitter for generating various RF pulses used in MRI pulse sequences. The RF transmitter generates RF pulses of desired frequency, phase, and pulse amplitude waveforms in response to predetermined scans and directions from the pulse sequence server 510. The generated RF pulses are applied to the whole-body RF coil 528 or one or more local coils or coil arrays.

[0047] The RF system 520 also has one or more RF receiver channels. An RF receiver channel includes an RF preamplifier that amplifies the magnetic resonance signals received by the connected coil 528 and a detector that detects and digitizes the quadrature I and Q components of the received magnetic resonance signals. The amplitude of the received magnetic resonance signals is therefore determined at sample points determined by the square root of the sum of the squares of the I and Q components.

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[0048] The pulse sequence server 510 can receive patient data from a physiological acquisition controller 530. As an example, the physiological acquisition controller 530 can 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 can be used by the pulse sequence server 510 to synchronize or "gate" the performance of the scan to the subject's heartbeat or breathing.

[0049] The pulse sequence server 510 may also connect to a scan room interface circuit 532, which receives signals from various sensors associated with the patient's condition and the magnet system. It is through the scan room interface circuit 532 that a patient positioning system 534 may receive commands to move the patient to the desired position during the scan.

[0050] Digitized magnetic resonance signal samples generated by the RF system 520 are received by the data acquisition server 512. The data acquisition server 512 receives real-time magnetic resonance data and operates according to instructions downloaded from the operator workstation 502 to provide buffer storage so that data is not lost due to data overrun. For some scans, the data acquisition server 512 passes acquired magnetic resonance data to the data processing server 514. For scans that require information derived from the acquired magnetic resonance data to control further performance of the scan, the data acquisition server 512 can be programmed to generate and send such information to the pulse sequence server 510. For example, in a pre-scan, magnetic resonance data can be acquired and used to calibrate the pulse sequence performed by the pulse sequence server 510. As another example, navigator signals can be acquired and used to adjust operating parameters of the RF system 520 or gradient system 518 or to control the view order for sampling k-space. As a further example, the data acquisition server 512 may 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 acquires magnetic resonance data and processes it in real time to generate information used to control the scan.

[0051] 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 on 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 reconstructed images, generating functional magnetic resonance images, or calculating motion or flow images to reconstruct two-dimensional or three-dimensional images.

[0052] Images reconstructed by the data processing server 514 are sent back to the operator workstation 502 for storage. Real-time images can be stored in a database memory cache from which they can be output to the operator display 502 or display 536. Batch mode images or selected real-time images may be stored in a host database on disk storage 538. Thus, as images are reconstructed and transferred to storage, the data processing server 514 can notify the data storage server 516 on the operator workstation 502. The operator workstation 502 can be used by the operator to archive images, produce film, and transmit images over a network to other facilities.

[0053] 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 a different facility, such as a different medical institution or clinic.

[0054] The networked workstations 542 can remotely access the data processing server 514 or the data storage server 516 via the communication system 540. Thus, multiple networked workstations 542 can access the data processing server 514 and the data storage server 516. In this manner, magnetic resonance data, reconstructed images, or other data is exchanged between the data processing server 514 or the data storage server 516 and the networked workstations 542, such that the data or images can be processed remotely by the networked workstations 542.

[0055] 6A, an example of a system 600 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., MR 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 system 604 for brain mapping and target identification for intervention planning for the treatment of brain disorders, which may include correcting for motion in the data received from the image source 602.

[0056] Additionally or alternatively, in some embodiments, computing device 650 can transmit information regarding the data received from image source 602 to server 652 via communications network 654. Server 652 can execute at least a portion of system 604 for brain mapping and target identification for intervention planning for the treatment of brain disorders. In such embodiments, server 652 can return information indicative of the output of system 604 to computing device 650 (and / or other suitable computing device).

[0057] In some embodiments, the computing device 650 and / or 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, a virtual machine running on a physical computing device, etc. The computing device 650 and / or server 652 may also reconstruct an image from the data.

[0058] In some embodiments, image source 602 may be any suitable source of image data (e.g., measurement data, images reconstructed from measurement data), such as a magnetic resonance imaging system (e.g., MRI system 500 shown in FIG. 5 ), another computing device (e.g., a server that stores image data), etc. In some embodiments, image source 602 may be local to computing device 650. For example, image source 602 may be incorporated into computing device 650 (e.g., computing device 650 may be configured as part of a device for capturing, scanning, and / or storing images). As another example, image source 602 may be connected to computing device 650 by a cable, a direct wireless link, etc. Additionally or alternatively, in some embodiments, image source 602 may be located locally and / or remotely from computing device 650 and communicate data to computing device 650 (and / or server 652) via a communications network (e.g., communications network 654).

[0059] In some embodiments, communications network 654 may be any suitable communications network or combination of communications networks. For example, 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, 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 link may 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.

[0060] Referring to FIG. 6B , an example of hardware 700 that can be used to implement image source 602, computing device 650, and server 652 in accordance with some embodiments of the systems and methods described in this disclosure is shown. As shown in FIG. 6B , in some embodiments, 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, 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, display 704 can include any suitable display device, such as a computer monitor, a touchscreen, a television, etc. In some embodiments, input 706 can include any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, etc.

[0061] In some embodiments, communications system 708 may include any suitable hardware, firmware, and / or software for communicating information over communications network 654 and / or other suitable communications networks. For example, communications system 708 may include one or more transceivers, one or more communications chips and / or chipsets, etc. In more specific examples, communications system 708 may include hardware, firmware, and / or software usable to establish a Wi-Fi® connection, a Bluetooth® connection, a cellular connection, an Ethernet connection, etc.

[0062] In some embodiments, memory 710 may include any suitable storage device or devices that may be used to store instructions, values, data, etc., that may be used by processor 702, for example, to present content using display 704 or to communicate with server 652 via communication system 708. Memory 710 may include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, 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, memory 710 may have encoded or otherwise stored thereon a computer program for controlling the operation of computing device 650. In such embodiments, 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 server 652, and transmit information to server 652.

[0063] 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 be any suitable display device, such as a computer monitor, a touchscreen, a television, etc. In some embodiments, input 716 may be any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, etc.

[0064] In some embodiments, communications system 718 may include any suitable hardware, firmware, and / or software for communicating information over communications network 654 and / or other suitable communications networks. For example, communications system 718 may include one or more transceivers, one or more communications chips and / or chipsets, etc. In more particular examples, communications system 718 may include hardware, firmware, and / or software usable to establish Wi-Fi® connections, Bluetooth® connections, cellular connections, Ethernet connections, etc.

[0065] In some embodiments, memory 720 may include any suitable storage device or devices that may be used to store instructions, values, data, etc., that 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 is encoded with 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 interface) to one or more computing devices 650, receive information and / or content from one or more computing devices 650, and receive instructions from one or more devices (e.g., personal computers, laptop computers, tablet computers, smartphones).

[0066] 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 MRI 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 MRI system. In some embodiments, one or more portions of the one or more image acquisition systems 724 may be removable and / or replaceable.

[0067] Although not shown, image source 602 may include any suitable input and / or output devices. For example, image source 602 may include input devices and / or sensors usable to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, etc. Image source 602 may also include any suitable display device, such as a computer monitor, a touchscreen, a television, one or more speakers, etc.

[0068] In some embodiments, communications system 726 may include any suitable hardware, firmware, and / or software for communicating information to computing device 650 (and, in some embodiments, via communications network 654 and / or other suitable communications networks). For example, communications system 726 may include one or more transceivers, one or more communications chips and / or chipsets, etc. In more particular examples, communications system 726 may include hardware, firmware, and / or software that can be used 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.

[0069] In some embodiments, memory 728 can include any suitable storage device or devices that can be used to store instructions, values, data, etc. that can be used by processor 722 to, for example, control one or more image acquisition systems 724 and / or receive data from one or more image acquisition systems 724, create images from the data, present content (e.g., images, user interfaces) using a display, communicate with one or more computing devices 650, etc. Memory 728 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 728 can 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 has encoded or otherwise stored thereon a program for controlling the operation of image source 602. In such an embodiment, the processor 722 may execute at least a portion of a program to generate images, transmit 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, and receive instructions from one or more devices (e.g., personal computers, laptop computers, tablet computers, smartphones, etc.).

[0070] In some embodiments, any suitable computer-readable medium can be used to store instructions for performing the functions and / or processes described herein. For example, in some embodiments, the computer-readable medium can be transitory or non-transitory. For example, non-transitory computer-readable media can include media such as magnetic media (e.g., hard disks, floppy disks), optical media (e.g., compact discs, digital video discs, Blu-ray discs), semiconductor media (e.g., random access memory (“RAM”), flash memory, electrically programmable read-only memory (“EPROM”), electrically erasable programmable read-only memory (“EEPROM”)), any suitable medium that does not lose its transitory or permanent appearance during transmission, and / or any suitable tangible medium. As another example, transitory computer-readable media include signals on a network, wires, conductors, optical fibers, circuits, or any suitable medium that is transitory and has no permanent nature during transmission, and / or any suitable intangible medium.

[0071] While this disclosure has described one or more preferred embodiments, it should be understood that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and fall within the scope of the invention.

Claims

1. 1. A computer-implemented method for brain mapping and target identification for interventional planning using magnetic resonance imaging (MRI), said method comprising: receiving magnetic resonance (MR) data from an MRI system by a computing system having at least one processor in communication with at least one memory system and in communication to receive data acquired using the MRI system; analyzing, by the computing system, the received magnetic resonance data to monitor and identify motion in real time; determining, by the computing system, a set of usable magnetic resonance data from the acquired magnetic resonance data based on the identified movement; generating, by the computing system, a brain map of the subject based on the set of available magnetic resonance data; Identifying, by the computing system, a target location in a subcallosal cingulate cortex (SCC) region of the brain of the subject based on the brain map of the subject, the target location being a convergence point of a plurality of fiber bundles passing through the SCC region; generating, by the computing system, a report indicating the target location; method.

2. 2. The method of claim 1, wherein the plurality of fiber bundles passing through the SCC region include the cingulate fasciculus (CM), forceps minor fasciculus (FM), frontostriatal fibers (F-ST), and uncinate fasciculus (UF).

3. The method of claim 1 , further comprising displaying the report on a display.

4. The method of claim 1 , wherein the received magnetic resonance data is diffusion-weighted magnetic resonance data.

5. The method of claim 4 , wherein the received diffusion-weighted magnetic resonance data is acquired using one of diffusion tensor imaging (DTI) or diffusion-weighted imaging (DWI).

6. The method of claim 4 , wherein the received magnetic resonance data is acquired in a first number of diffusion directions.

7. The method of claim 6 , further comprising determining additional diffusion directions different from the first number of diffusion directions based on the identified motion and the set of available magnetic resonance data.

8. The method of claim 7 further comprising receiving, by the computing system, additional magnetic resonance data acquired in the additional diffusion direction from the MRI system.

9. 1. A system for brain mapping and target identification for interventional planning using magnetic resonance imaging (MRI), the system comprising:

1. A computing system comprising a processor, the processor comprising: receiving magnetic resonance data using an MRI system; analyzing the received magnetic resonance data to monitor and identify motion in real time; determining a set of usable magnetic resonance data from the acquired magnetic resonance data based on the identified movement; generating a brain map of the subject based on the set of available magnetic resonance data; identifying a target location in a subcallosal cingulate cortex (SCC) region of the subject's brain based on the brain map of the subject, the target location being a convergence point of a plurality of fiber bundles passing through the SCC region; a computing system programmed to generate a report indicating the target location; a display coupled to the computing system and configured to display the report.

10. 10. The system of claim 9, wherein the plurality of fiber bundles passing through the SCC region include the cingulate fasciculus (CM), forceps minor fasciculus (FM), frontostriatal fibers (F-ST), and uncinate fasciculus (UF).

11. The system of claim 9 , wherein the received magnetic resonance data is diffusion-weighted magnetic resonance data.

12. The system of claim 11 , wherein the received diffusion-weighted magnetic resonance data is acquired using one of diffusion tensor imaging (DTI) or diffusion-weighted imaging (DWI).

13. The system of claim 11 , wherein the received magnetic resonance data is acquired in a first number of diffusion directions.

14. 14. The system of claim 13, wherein the processor is further programmed to determine additional diffusion directions different from the first number of diffusion directions based on the identified motion and the set of available magnetic resonance data.

15. 15. The system of claim 14, wherein the processor is further programmed to receive additional magnetic resonance data acquired in the additional diffusion direction from the MRI system.

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