Systems and methods for interventional planning of treatment of brain disease

Abstract

A computer-implemented method for interventional planned brain mapping and target recognition using magnetic resonance imaging (MRI) includes receiving, by a computing system, MR data from an MRI system, the computing system including at least one processor in communication with at least one storage system, and the computing system communicates 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 an available MR dataset from the acquired MR data based on the identified motion, generating a map of the subject's brain based on the available MR dataset, and identifying a subcorpus callosum of the subject's brain based on the map of the subject's brain, sCC) in the region. The target location may be a point converged by a plurality of fiber bundles of the SCC region. The method may further include generating a report indicating the target location.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims priority to and incorporates by reference in its entirety U.S. Serial No. 63 / 375,454, filed on September 13, 2022, and titled "System and Method for Computer Aided Diagnosis (CAD) for Surgical Planning for the Treatment of Mental Disorders". Background Art

[0003] Mental disorders are a common cause of severe and long - term disability and socioeconomic burden. In some patients, forms of treatment such as pharmacotherapy and psychotherapy do not produce sufficient therapeutic effects or cause intolerable side effects. For these patients, neuromodulation is considered a potential form of treatment. Neuromodulation is one of the fastest - growing areas in medicine, and it is the process of inhibiting, stimulating, modifying, regulating, or therapeutically altering the activity in the central, peripheral, or autonomic nervous systems, either electrically or chemically. Neuromodulation includes deep brain stimulation, vagus nerve stimulation, transcranial magnetic stimulation, and transcranial electrical stimulation. Neuromodulation aims to treat chronic neurological or mental disorders by surgically targeting deep brain nuclei and pathways involved in symptom regulation to stimulate, inhibit, or otherwise alter / regulate pathological activity.

[0004] Using deep brain stimulation (DBS) to target neural structures such as the cortex and / or subcortical structures to treat neurological and mental disorders, including essential tremor, Parkinson's disease, dystonia, Tourette syndrome, obsessive - compulsive disorder, and treatment - resistant depression. However, the success rates for specific target structures vary. DBS of the ventral intermediate nucleus of the thalamus for treating essential tremor reduces tremors by more than 80% in all patients, while pallidal stimulation for treating dystonia only improves symptoms in 30% - 50% of all patients, and only 33% of patients have symptom improvement > 75%.

[0005] Body movements, such as head movements, are the biggest obstacle to collecting high-quality human brain magnetic resonance imaging (MRI) data. Head movements can distort structural (T1-weighted, T2-weighted, etc.), functional MRI (task-driven [fMRI] and resting-state functional connectivity [rs-fcMRI]), and diffusion MRI (e.g., diffusion tensor imaging (DTI)) data. In some cases, even submillimeter head movements (e.g., micromovements) can systematically alter structural, functional, and diffusion MRI data. Therefore, efforts have been made to develop post-acquisition methods to eliminate head movement distortion in MRI data.

[0006] Head movement from one MRI data frame (or slice) to the next, rather than absolute movement away from a reference frame, is considered to cause the most pronounced MRI signal distortion. Motion-related distortion is closely related to measurements of framewise displacement (FD, representing the sum of absolute head movement in all six rigid-body directions from frame to frame), zipper artifacts or abnormal slices, adjacent DWI correlations on the raw data, and DVARS (root mean square of the derivative of the differential time course of each voxel in the MRI image). Therefore, in various post hoc methods, measurements such as FD and DVARS that capture the global effects of subject movement during MRI data acquisition have been used to evaluate data quality. For example, post hoc frame censoring that removes all MRI data frames with FD values higher than a specific threshold (e.g., excluding data frames with FD values > 0.2 mm) has become a common method to improve the quality of functional MRI data.

[0007] Although frame censoring is necessary to reduce artifacts, it is costly. For example, depending on specific parameters and underlying data quality, frame censoring can exclude 50% or more of the rs-fcMRI data collected from a group. Since the accuracy of MRI measurements increases with the number of frames, a minimum number of data frames may be required to obtain reliable data. If the number of frames remaining after censoring is too small, the investigator may lose all data from the participant. To avoid this loss, investigators typically collect additional "buffer" data, which is an expensive practice that does not guarantee sufficient 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 greatly increases the cost and duration of brain MRI.

[0008] Recently developed structural MRI sequences with prospective motion correction use similar methods to reduce the deleterious effects of head motion. These MRI sequences pair each structural data acquisition with a rapid, low-resolution snapshot (echo planar image = EPI) of the entire brain, and then use that snapshot as a marker or navigator for head motion. These motion-corrected structural sequences calculate the relative motion between successive navigator images and use that information to flag linked structural data frames for exclusion and reacquisition. In this way, the structural data frames are "censored", increasing the duration and cost of structural MRI.

[0009] For structural, functional, and diffusion MRI, having access to real-time information about head motion within the scanner during scanning can significantly reduce the cost of MRI by eliminating the need for excessive scanning. Assessment of head motion obtained from real-time motion monitoring will allow the scanner operator to proceed with each scan until the desired number of low-motion data frames are obtained without the need for excessive buffered scans. Existing real-time motion monitoring methods use expensive cameras and lasers to measure surrogates for FD. Unfortunately, such surrogates for head motion have poor correlation with FD because these surrogates generally cannot distinguish motion of the face and scalp from motion of the brain. SUMMARY OF THE INVENTION

[0010] According to an embodiment, a computer-implemented method for brain mapping and target identification for interventional planning using magnetic resonance imaging (MRI) includes receiving, by a computing system, MR data from an MRI system, the computing system including at least one processor in communication with at least one storage system, and the computing system being communicatively coupled 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 an available MR data set from the acquired MR data based on the identified motion, generating a map of a subject's brain based on the available MR data set, and identifying a target location in a subcallosal cingulate (SCC) region of the subject's brain based on the map of the subject's brain. The target location can be a point at which multiple fiber bundles converge through the SCC region. The method can further include generating a report indicating the target location.

[0011] In some embodiments, the plurality of fiber bundles passing through the SCC region include the cingulum bundle (CM), the forceps minor (FM), the frontal striatal fiber (F-ST), and the uncinate fasciculus (UF). In some embodiments, the method further includes displaying a report on a display. In some embodiments, the received MR data is diffusion MR data. In some embodiments, the received diffusion MR data is acquired using one of diffusion tensor imaging (DTI) or diffusion weighted imaging (DWI). In some embodiments, the received diffusion MR data is acquired for 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 available MR data set. In some embodiments, the method further includes receiving, by a computer system, additional MR data acquired for the additional diffusion directions from an MRI system.

[0012] According to another embodiment, a system for brain mapping and target identification for interventional planning using magnetic resonance imaging (MRI) includes a computing device and a display. The computing device includes a processor programmed to receive MR data acquired using an MRI system, analyze the received MR data to monitor and identify motion in real time, determine an available MR data set from the acquired MR data based on the identified motion, generate a map of the subject's brain based on the available MR data set, and identify a target location in the subcallosal cingulate (SCC) region of the subject's brain based on the map of the subject's brain. The target location can be a point at which a plurality of fiber bundles passing through the SCC region converge. The processor is further programmed to generate a report indicating the target location. The display is coupled to the computing device and configured to display the report.

[0013] In some embodiments, the plurality of fiber bundles passing through the SCC region include the cingulum bundle (CM), the forceps minor (FM), the frontal striatal fiber (F-ST), and the uncinate fasciculus (UF). In some embodiments, the received MR data is diffusion MR data. In some embodiments, the received diffusion MR data is acquired using one of diffusion tensor imaging (DTI) or diffusion weighted imaging (DWI). In some embodiments, the received diffusion MR data is acquired for 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 available MR data set. In some embodiments, the processor is further programmed to receive additional MR data acquired for the additional diffusion directions from an MRI system.

[0014] The foregoing and other aspects and advantages of the present disclosure will become apparent from the following description. In this description, the reference drawings forming a part of this description illustrate preferred embodiments. However, these embodiments do not represent the full scope of the invention. Therefore, to interpret the scope of the invention, reference should be made to the claims and this text. In the following description, the same reference numerals will be used to refer to the same parts between the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Shows an example method for performing mapping and target location identification in a subject's brain for interventional planning according to an embodiment;

[0016] Figure 2 Shows an example display of a target location in the subcallosal cingulate (SCC) region of the brain according to an embodiment;

[0017] Figure 3 Is a flowchart showing an operation for aligning magnetic resonance imaging (MRI) data from an MRI scan to a selected frame during an MRI scan according to an embodiment;

[0018] Figure 4 Is a flowchart showing a method for generating a sensory feedback display to an operator of an MRI system and / or a patient within the MRI system during data acquisition according to an embodiment;

[0019] Figure 5 Is a schematic diagram of an example system for performing magnetic resonance imaging according to an embodiment;

[0020] Figure 6A Is a block diagram of an example of a system for brain mapping and target identification for interventional planning for the treatment of brain diseases according to an embodiment; and

[0021] Figure 6B Is a block diagram of components of a system for brain mapping and target identification for interventional planning for the treatment of brain diseases that can implement Figure 6A according to an embodiment. DETAILED DESCRIPTION

[0022] Figure 1 Shows an example method for performing mapping and target location identification in a subject's brain according to an embodiment. Although Figure 1 the boxes of the process are shown in a specific order, in some embodiments, one or more boxes may be in a different order than Figure 1executed in a different order than that shown, or can be bypassed. The method includes receiving magnetic resonance (MR) data of a subject's brain at block 102, such as, for example, structural (T1-weighted, T2-weighted) MRI data, functional MRI data, and diffusion MRI data. In some embodiments, MR data of the subject's brain can be acquired using, for example, diffusion imaging techniques (e.g., DTI or DWI) or functional magnetic resonance imaging (fMRI) techniques. The fMRI data of the subject's brain can include task-driven (fMRI) data, resting-state fMRI (rs-fMRI) data, or a combination thereof. The subject can be a human, an animal, a phantom, etc. In some embodiments, the MR data can be acquired in real time by an MRI system (e.g., Figure 5 the MRI system 500 shown in Figure 5 and received from the MRI system. In some embodiments, the MR data can be retrieved from the data storage of an imaging system (e.g., Figure 6B the disk storage 538 of the MRI system 500 shown in

[0023] or the data storage of another computer system (e.g., Figure 3 the memory 710 of the computer device 650 or the memory 720 of the server 652 shown in

[0024] For the 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 to be performance affected by the operational deadline from a given event to the system's response to that event. For example, such real-time extraction and / or display of data from a signal acquired empirically can be triggered and / or executed simultaneously, with or without interruption of the signal data acquisition (e.g., pulse sequence) or imaging process.

[0025] At block 108, in some embodiments, the MR data or images acquired at block 102 or the available MR data determined at block 106 may optionally be preprocessed for the mapping process. For example, in some embodiments, high-resolution T1 images may be preprocessed by performing skull stripping, image registration and normalization to a template, and tissue segmentation, e.g., estimating brain masks for gray matter (GM), white matter (WM), and cerebrospinal fluid (CBF). In some embodiments, diffusion-weighted imaging (DWI) data (or images) may be preprocessed by performing skull stripping, synchronous eddy current and distortion correction (e.g., registering diffusion-weighted (DW) images to B0 images using an affine transformation), image registration to the first acquired B0 image, image registration to the T1 image, and local tensor (DTI) fitting. In some embodiments, the transformation matrix between diffusion and T1 is cascaded with the non-linear normalization field between the previously computed T1 and the template, and this transformation matrix can be used to create a diffusion to the template transformation field.

[0026] At block 110, the method may include computing and generating a map (e.g., a functional connectivity map of the brain, tractography, etc.) based at least on the acquired MR data. At block 112, the method may then include identifying, based on the computed brain map, target locations in the subject's brain to be targeted by neuromodulation, for example. In some embodiments, methods capable of achieving personalized patient-specific targeting may be used for the identification of the target locations. In some embodiments, the identified regions may be in the subcallosal cingulate (SCC) region of the brain. Figure 2 An example display 200 of target locations in the subcallosal cingulate (SCC) region of the brain according to an embodiment is shown. Advantageously, in some embodiments, the target location 204 may be a convergence point of a plurality of fiber bundles passing through the SCC region 202. For example, as Figure 2 shown, the target location 204 may be a convergence point of four converging bundles including the cingulum bundle (CB) 206, forceps minor (FM) 208, frontostriatal fibers (F-St) 210, and uncinate fasciculus (UF) 212. The target location 204 may be defined as affecting the four bundles (e.g., a neuromodulator implanted at the target location would affect the four bundles). In some embodiments, the target location 204 may be automatically identified.

[0027] Return Figure 1, at block 112, in some embodiments, the target location can be, for example, the ventral capsule / ventral striatum (VC / VS), nucleus accumbens (NAcc), lateral habenula (LHb), inferior thalamic peduncle (ITP), medial forebrain bundle (MFB), bed nucleus of the stria terminalis (BNST), dentate nucleus, centromedian nucleus of the thalamus, ventral intermediate nucleus of the thalamus (VIM), or red nucleus. In some embodiments using diffusion MR data, the mapping operation and the target location identification operations 110 and 112 can respectively include diffusion tensor imaging (DTI) tractography techniques. For DTI fiber tractography techniques, typically more directions (diffusion gradients) are used for diffusion, and a higher resolution of individual fibers can be obtained. However, the more directions used, the longer the scan may take. In some embodiments, the operator can select a first number of diffusion directions for the scan. Then, based on feedback (or results) from real-time motion monitoring and determining the available MR data sets (e.g., blocks 104 and 106), the operator can determine whether additional directions are needed in an additional scan. For example, the operator can first select to scan three directions, and then based on the motion information from block 104, the operator can determine that a second scan with more than three directions should be performed. In another example, the operator can first select to scan three directions, and then based on the motion information from block 104, the operator can determine that another scan with additional different directions is not needed. In some embodiments, additional different diffusion directions can be automatically determined based on feedback (or results) from real-time monitoring of motion and determining the available MR data sets.

[0028] Regardless of the specific region of the brain being studied, a report can be generated at block 114 that at least indicates the target location. In some embodiments, the report can include a display that includes a visual indicator that identifies the target location, for example, on an image or atlas of the subject's brain. In some embodiments, the report can include the connectome of the subject's brain. At block 116, the generated report can be displayed on a display (e.g., Figure 5 the displays 504, 536, 544 of the MRI system 500 shown in Figure 6B the display 704 of the computing device 650 shown in Figure 6B or the display 714 of the server 652 shown in

[0029] In some embodiments, the systems and methods disclosed herein can be used for interventional planning (e.g., surgical and therapeutic planning) to treat specific brain diseases and specific structures of the brain. As used herein, the term brain disease is used to refer to neurological and psychiatric diseases. For example, various target locations (e.g., the SCC region, ventral capsule / ventral striatum (VC / VS), nucleus accumbens (NAcc), lateral habenula (LHb), inferior thalamic peduncle (ITP)), medial forebrain bundle (MFB), or bed nucleus of the stria terminalis (BNST)) can be used to treat depression, various target locations (e.g., the dentate nucleus) can be used for motor stroke recovery, various target locations (e.g., the centromedian nucleus of the thalamus, red nucleus) can be used to treat epilepsy, various target locations (e.g., the centromedian nucleus) can be used to treat Tourette syndrome, various target locations (e.g., the centromedian nucleus of the thalamus, red nucleus) can be used to treat disorders of consciousness (coma), various target locations (e.g., the ventral intermediate nucleus of the thalamus (VIM), red nucleus) can be used to treat essential tremor, and various target locations (e.g., the ventral intermediate nucleus of the thalamus (VIM)) can be used to treat tremor-dominant Parkinson's disease.

[0030] Deep brain stimulation (DBS) is a form of neuromodulation used clinically. DBS is a surgery in which a neurostimulator is surgically implanted into the brain to treat brain diseases such as Parkinson's disease, dystonia, essential tremor, obsessive-compulsive disorder, epilepsy, depression, etc. In some embodiments, the identified target locations can be used to guide the planning of DBS lead placement. For example, a doctor can view the proposed target location, e.g., on a display, and determine whether to select that target location for lead placement. In some embodiments, target locations in the subcallosal cingulate gyrus can be used for deep brain stimulation for depression. Other treatment methods include, e.g., transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and focused ultrasound. In some embodiments, the identified target locations can be used to guide the planning of neuromodulation by providing targets for TMS, tDCS, and focused ultrasound.

[0031] As described above, in some embodiments, at block 102, a frame-by-frame integrated real-time MRI monitoring (FIRMM) system, apparatus, and method can be advantageously used for real-time monitoring and prediction of the movement of a patient's body part (including but not limited to head movement during an MRI scan) to acquire MR data of a subject's brain. Examples of FIRMM systems and methods are described in U.S. Patent No. 11,181,599, issued November 23, 2021, and are incorporated herein by reference in their entirety. The FIRMM computer-implemented method can simultaneously improve the quality of MRI data and reduce the 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. The FIRMM method and system are generally described herein in the context of functional MRI data acquisition, but in various embodiments, the FIRMM methods and systems disclosed herein are suitable for real-time monitoring of head and body movement during other structural or anatomical MRI sequences, including but not limited to those FIRMM methods and systems that utilize motion navigation. Advantageously, the FIRMM system and method can provide real-time feedback to the scanner operator and the subject being scanned. More specifically, in some embodiments, the FIRMM system and method can provide sensory feedback to the subject during the scan based on real-time calculated data quality metrics and summary motion statistics, such that the subject can monitor and respond to the provided feedback to adjust their movement accordingly (e.g., remain still). In some embodiments, the FIRM system and method can provide stimulation conditions, such as viewing a fixation crosshair or a movie clip, to engage the subject while also providing real-time feedback to the subject.

[0032] In some embodiments, by way of non-limiting example, the FIRMM system and method can enable the scanner operator to continue each scan until a desired number of low-motion data frames have been acquired by (i) predicting the number of available data frames that will be available at the end of the scan; (ii) predicting the amount of time that a given subject may have to be scanned until a preset target time (number of minutes of low-motion FD data) has been acquired; and (iii) being able to include specific individual scans in the actual and predicted amounts of low-motion data.

[0033] Real-time information about head movement can be used to reduce head movement in a number of different ways, including but not limited to: 1) by influencing the behavior of the MRI scanner operator and 2) by influencing the behavior of the MRI scan subject. For any sudden or abnormal change in head movement, the scanner operator may be alerted, and the scanner operator can interrupt such a scan to investigate whether the subject has started moving more because they are feeling uncomfortable and whether they can be made to feel more comfortable by using the restroom, a blanket, repositioning, or other interventions. In some embodiments, the FIRMM method can further include an option to feed back information about head movement to the subject after and / or in real-time. In some embodiments, the FIRMM method can allow the scanner operator to find the optimal point that provides the required amount of low-motion data at the lowest cost. The scan can be stopped, the subject can be further instructed or reminded about how to try to stay still, and the scan can also be re-requested, etc., to address the movement.

[0034] Figure 3 An example FIRMM method 300 for processing an MRI frame set to align the frames with a reference image in the set to compensate for subject movement is shown. Although Figure 3 the boxes of the process are shown in a particular order, in some embodiments, one or more boxes can be performed in an order different from the Figure 3 order shown, or can be bypassed. The method 300 at block 302 can include receiving MR data in the form of MRI frames or images from a magnetic resonance imaging system. The MRI frames can be received by the computing device from the magnetic resonance imaging system via a network or by the computing device from a storage medium coupled to or communicating with the computing device.

[0035] At block 304, the method 300 can further include aligning the frames to a reference frame or reference image. In some embodiments, the reference image can be a single frame selected from the frames collected during the MRI scan, including but not limited to the first frame, a navigator frame, or any other suitable frame selected from the multiple frames collected during the MRI scan. In some embodiments, the reference image can be an image retrieved from an anatomical atlas. In some embodiments, the synthesis or combination of two or more frames collected during the MRI scan includes but is not limited to the average of two or more frames. In some embodiments, each current frame can be aligned with the immediately preceding previous frame that has been iteratively aligned with the reference image collected for a given MRI scan.

[0036] Starting from the second frame, each frame can be aligned with the reference image by a series of rigid body transformations Ti, where i indexes the spatial registration of frame i to reference frame 1. Each transformation is calculated by minimizing the registration error to an absolute minimum or below a selected cut-off value, or otherwise reaching a stopping condition with respect to the registration error, expressed as:

[0037]

[0038] wherein is the image intensity at the location, and s is a scalar factor used to compensate for fluctuations in the average signal intensity spatially averaged (angled brackets) over the entire brain. In some aspects, the frames may be realigned using the 4dfp cross_realign3d_4dfp (three-dimensional cross realignment 4dfp) algorithm (see Smyser, C.D. et al., 2010, Cerebral cortex, Vol. 20, pp. 2852-2862, (2010)), specifically incorporated herein by reference). Alternative alignment algorithms may also be used to align the frames.

[0039] In some embodiments, each transformation may be represented by a combination of rotation and displacement, as described below:

[0040]

[0041] where R i represents a 3x3 rotation matrix, including three basic rotations about each of the three axes, and d i represents a 3x1 displacement column vector. R i may include three basic rotations about each of the three axes, represented as: R i = R iα R iβ R iγ where

[0042]

[0043] At block 306, method 300 may further include calculating the relative motion of a body part (e.g., the head) between the frame and the previous frame. The relative motion of the body part (e.g., head motion) may be calculated based on a plurality of frame alignment parameters, including but not limited to x, y, z, θ x , θ y and θ z , where x, y, and z are translations along the three coordinate axes, and θ x , θ y and θ z are rotations about these axes.

[0044] At block 308, method 300 may also include calculating a data quality metric (e.g., total frame displacement) using multiple frame alignment parameters. In some embodiments, multiple displacement vectors of head movement may be used to determine the total frame displacement. As a non-limiting example, the total frame displacement may be calculated by summing the absolute displacements of a body part (e.g., the head) in six directions, treating the body part as a rigid body. In this non-limiting example, the head movement of the i-th frame may be converted to a scalar using the following formula:

[0045] Displacement i = |Δd ix | + |Δd iy | + |△d iz | + |Δα i | + |Δβ i | + |Δγ i | Equation 6

[0046] where Δd ix = d (t-1)x - d ix ; △d iy = d (i-1)y - d ty ; Δd iz = d (i-1)z - d iz ; and so on.

[0047] By calculating the displacements on the surface of a 3D volume representing the imaged body part, the rotational displacements |Δα i |, |△β i |, |△γ i | can be converted from degrees to millimeters. As a non-limiting example, if the head is imaged, the 3D volume selected to calculate the displacements may be a sphere (e.g., a sphere with a radius of 50 mm, which is approximately the average distance from the cerebral cortex to the center of the head in a healthy young adult). Since each data frame is realigned with a reference image, the frame displacement (FD) can be calculated by subtracting the displacement i (current frame) from the displacement i-1 (previous frame).

[0048] In some embodiments, method 300 may further include excluding frames having cutoffs with a total frame displacement higher than a pre-identified threshold at block 310. In some embodiments, the method may predict whether there will be at least a number n of available frames at the end of an MRI scan. In some embodiments, predicting the number of available frames includes applying a linear model (y = mx + b), where y is the number of good frames predicted at the end of the scan, x is the consecutive frame count, and m and b are estimated in real time for each subject. In some embodiments, if the relative object displacement for each frame is less than a given threshold (e.g., in millimeters), the frame may be marked as available using the position of the object on the previous frame as a reference. A non-limiting example of a cutoff threshold for available data frames is 0.2; however, in some embodiments, the scan operator may edit a settings file associated with the FIRMM software suite to select a different threshold as needed.

[0049] Once complete, method 300 may return to the start for each subsequent frame in the MRI scan. Display of data quality metrics and other motion monitoring information may be performed at block 312. In some embodiments, as discussed below with respect to Figure 4 the motion monitoring information may be provided to the operator and / or subject undergoing the MRI scan. In some embodiments, a visual display of the parameters for the scan may be presented to the operator. In some embodiments, FD may be provided to the operator in real time such that each time a new frame / scan / volume is acquired, a new data point is added to the FD versus frame # plot. In some embodiments, at the end of each scan, a summary of the counts for that scan may be displayed in a list that tabulates the summary head motion data for each scan individually and / or tabulates the summary head motion data for the sum of all data acquired so far in the active scan session. Prediction of the remaining time in the scan (e.g., until a preset target time (number of minutes of low motion FD data)) may be performed at block 314. For example, a plot of the actual amount of time (e.g., in minutes and seconds or as a percentage) elapsed to acquire “good quality” frames of the scan versus the amount of time for a preset criterion may be provided. Such information may be provided in a visual display, an audible signal, or any other known information-providing means, without limitation.

[0050] As described above, in some embodiments, the FIRMM method may generate a sensory feedback display that will be transmitted via a suitable feedback device to the operator and / or the subject undergoing the MRI scan. Any sensory feedback display may be provided via the FIRMM method via a feedback device, and the sensory feedback display includes, but is not limited to, a visual feedback display, an audible feedback display, or any other suitable sensory feedback display for any known sensory modality.

[0051] Figure 4 is a flow chart illustrating a method for providing sensory feedback to an operator of an MRI system and / or a patient within an MRI scanner of the MRI system during data acquisition according to an embodiment. Figure 4 The blocks of the process are shown in a particular order, but in some embodiments, one or more blocks may be arranged in a sequence similar to Figure 4 At block 402, method 400 may include calculating a data quality metric based on one or more motion components determined for a patient in an MRI device during a scan, as described above with respect to Figure 3 Any data quality metric may be calculated at block 402, not limited to that described herein, including but not limited to the above data quality metrics Figure 3 Any one or more of the described displacement components, other data quality metrics including DVARS (ie, the RMS of the derivative of the time history of each voxel of the MRI image), or any combination thereof.

[0052] At block 404, the method 400 may further include generating a visual display to an operator of the MRI system in real time based on at least one location of the data quality metric 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 to 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 a cumulative number of usable frames obtained in the current scan, a tabular or graphical element conveying an amount of time remaining in the current scan and / or a predicted amount of time remaining in the current scan to obtain a predefined number of usable scans, and any combination thereof. In some embodiments, elements of the visual feedback display may be updated at a preselected rate up to a real-time rate at which each display is updated as each relevant quantity is calculated, elements of the visual feedback display may be updated in response to a 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 plurality of factors including, but not limited to, a significant increase in monitored motion of the subject between frames, accumulated motion, or any other suitable criterion.

[0053] At block 406, method 400 may further include generating a sensory feedback display for the patient in the scanner during acquisition of MRI data. The sensory feedback display generated at block 406 may be updated at a variety of refresh rates ranging from a single update at the end of a scan to continuously updating in real time based on at least one of a number of factors including, but not limited to, the patient's age and condition.

[0054] At block 408, method 400 may further include determining a patient's total movement between a previous frame and a current frame in response to a sensory feedback display generated at block 406. In some embodiments, method 400 further includes 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 according to at least one of a plurality of termination criteria, the termination criteria including but not limited to one or more movements of unacceptably high magnitude, and a relatively low number of movements of unacceptably high quantity, determining that a suitable number of available frames have been obtained, predicting that a suitable number of available frames cannot be obtained within the remaining time in the scan, predicting that a suitable number of available frames will not be obtained within a reasonable cumulative scan time, and any combination thereof. If it is determined at block 410 to continue the scan, method 400 may transmit at least one feedback signal 412 for use, in part, in calculating a data quality metric at 402 to begin another iteration of method 400 for a subsequent frame.

[0055] As described above, in some embodiments, the mapping operations and target recognition operations 104 and 106 discussed above Figure 1 may include DTI fiber tracking techniques. For DTI fiber tracking techniques, more directions (diffusion gradients) are typically used for diffusion, and a higher resolution of individual fibers can be obtained. However, the more directions used, the longer the scan may take. In some embodiments in which the FIRMM method is used for data acquisition, an operator may select a first number of diffusion directions for the scan. Then, based on feedback (or results) from real-time monitoring and predictions of deteriorating data quality, the operator may determine whether additional directions (diffusion gradients) are needed in an additional scan. For example, the operator may first select to scan three directions, and then based on motion information from the FIRMM method, the operator may determine that a second scan with more than three directions should be performed. In another example, the operator may first select to scan three directions, and then based on motion information from the FIRMM method, the operator may determine that another scan with additional different directions is not needed. In some embodiments, additional different diffusion directions may be automatically determined based on feedback (or results) from real-time motion monitoring and determination of an available MR data set.

[0056] In some embodiments, the methods described herein may be implemented by a system that includes an MRI system and one or more processors or computing devices. In various aspects, one or more operations described herein may 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 may be automatically performed by one or more processors or computing devices. In various additional aspects, Figure 1 、 Figure 3 and Figure 4 the respective actions shown may be performed in the sequence shown, in other sequences in parallel, or in some cases may be omitted.

[0057] In some aspects, the above-described methods and processes may be implemented using a computing system that includes one or more computers. The methods and processes described herein may be implemented as a computer application, a computer service, a computer API, a computer library, and / or other computer program products.

[0058] Referring to Figure 5 , an example of an MRI system 500 that may implement 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., a keyboard, a 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 inputting 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.

[0059] The pulse sequence server 510 operates 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 prescribed scan are generated and applied to the gradient system 518, which then energizes gradient coils in a component 522 to generate magnetic field gradients G x 、G y and G z for spatially encoding magnetic resonance signals. The gradient coil assembly 522 forms part of a magnet assembly 524, which includes a polarized magnet 526 and a whole body RF coil 528.

[0060] An RF waveform is applied by the RF system 520 to the RF coil 528 or a separate local coil to perform a prescribed magnetic resonance pulse sequence. The response magnetic resonance signal detected by the RF coil 528 or a separate local coil is received by the RF system 520. The response magnetic resonance signal can be amplified, demodulated, filtered, and digitized under the guidance of commands generated by the pulse sequence server 510. The RF system 520 includes an RF transmitter for generating the various RF pulses used in the MRI pulse sequence. The RF transmitter responds to prescribed scans and directions from the pulse sequence server 510 to generate RF pulses of desired frequency, phase, and pulse amplitude waveforms. The generated RF pulses can be applied to the whole body RF coil 528 or to one or more local coils or coil arrays.

[0061] The RF system 520 also includes one or more RF receiver channels. The RF receiver channels include an RF preamplifier for amplifying the magnetic resonance signal received by the coil 528 to which the RF receiver channel is connected, and a detector for detecting and digitizing 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 the sampling point by the square root of the sum of the squares of the I and Q components:

[0062]

[0063] And the phase of the received magnetic resonance signal can also be determined according to the following relationship:

[0064]

[0065] The pulse sequence server 510 can receive patient data from the physiological acquisition controller 530. As an example, the physiological acquisition controller 530 can receive signals from a plurality of different sensors connected to the patient, including an electrocardiogram (“ECG”) signal from electrodes, or a respiration signal from a respiration bellows or other respiration monitoring device. These signals can be used by the pulse sequence server 510 to synchronize or “gate” the performance of the scan with the subject's heartbeat or respiration.

[0066] The pulse sequence server 510 can also be connected to the scan room interface circuit 532, which receives signals from various sensors associated with the condition of the patient and the magnet system. Through the scan room interface circuit 532, the patient positioning system 534 can receive commands to move the patient to a desired position during the scan.

[0067] 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 so that data is not lost due to data overflow. In some scans, the data acquisition server 512 transfers the acquired magnetic resonance data to the data processor server 514. In scans where information needs to be derived from the acquired magnetic resonance data to control further performance of the scan, the data acquisition server 512 can be programmed to generate such information and transfer it to the pulse sequence server 510. For example, during a prescan, magnetic resonance data can be acquired and used to calibrate the pulse sequence executed by the pulse sequence server 510. As another example, navigator signals can be acquired and used to adjust the operating parameters of the RF system 520 or the gradient system 518, or to control the view order of the sampled k-space. In yet another example, the data acquisition server 512 can also process magnetic resonance signals for detecting the arrival of a contrast agent in a magnetic resonance angiography (“MRA”) scan. For example, the data acquisition server 512 can acquire and process magnetic resonance data in real time to generate information for controlling the scan.

[0068] The data processor server 514 receives 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 can include, for example, reconstructing two-dimensional or three-dimensional images by performing a Fourier transform of the raw k-space data, performing other image reconstruction algorithms (e.g., iterative or back-projection reconstruction algorithms), applying filters to the raw k-space data or the reconstructed images, generating functional magnetic resonance images, or calculating motion or flow images.

[0069] Images reconstructed by the data processor server 514 are transferred back to the operator workstation 502 for storage. Real-time images can be stored in a database memory cache from which the real-time images can be output to the operator display 502 or the display 536. Batch-processed mode images or selected real-time images can be stored in a host database on the disk memory 538. When such images are reconstructed and transferred to the memory, the data processor 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, create movies, or send the images via a network to other facilities.

[0070] 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 within the same facility as the operator workstation 502 or in a different facility, such as a different medical institution or clinic.

[0071] The networked workstation 542 may obtain remote access to the data processing server 514 or the data storage server 516 via the communication system 540. Thus, multiple networked workstations 542 may access the data processing server 514 and the data storage server 516. In this way, 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 workstation 542 such that the data or images may be remotely processed by the networked workstation 542.

[0072] Now referring Figure 6A , an example of a system 600 in accordance with some embodiments of the systems and methods described in the present disclosure is shown. As Figure 6A shown, the computing device 650 may 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 may perform at least a portion of the system 604 for brain mapping and target identification for interventional planning for the treatment of brain diseases, which may include correcting for motion in the data received from the image source 602.

[0073] Additionally or alternatively, in some embodiments, the computing device 650 may transmit information regarding the data received from the image source 602 to a server 652 via a communication network 654, and the server 652 may perform at least a portion of the system 604 for brain mapping and target identification for interventional planning for the treatment of brain diseases. In such embodiments, the server 652 may return information indicative of the output of the system 604 to the computing device 650 (and / or any other suitable computing device).

[0074] 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 smart phone, a tablet computer, a wearable computer, a server computer, a virtual machine executed by a physical computing device, etc. The computing device 650 and / or the server 652 may also reconstruct images from the data.

[0075] In some embodiments, the image source 502 can be a source of any suitable image data (e.g., measurement data, images reconstructed from measurement data), such as a magnetic resonance imaging system (e.g., Figure 5 the MRI system 500 shown), another computing device (e.g., a server storing image data), and so on. In some embodiments, the image source 602 can be local 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 the device for capturing, scanning, and / or storing images). As another example, the image source 602 can be connected to the computing device 650 via a cable, a direct wireless link, etc. Additionally or alternatively, in some embodiments, the image source 602 can be local and / or remote to the computing device 650 and can transmit data to the computing device 650 (and / or the server 652) via a communication network (e.g., the communication network 654).

[0076] In some embodiments, the communication network 654 can be any suitable communication network or combination of communication networks. For example, the communication network 654 can include a Wi-Fi network (which can 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. compliant with any suitable standard such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, and so on. In some embodiments, the communication network 654 can 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 communication links can each be any suitable communication link or combination of communication links, such as a wired link, an optical fiber link, a Wi-Fi link, a Bluetooth link, a cellular link, and so on.

[0077] Now referring to Figure 6B , an example of the hardware 700 that can be used to implement the image source 602, the computing device 650, and the server 652 according to some embodiments of the systems and methods described in the present disclosure is shown. As Figure 6BAs shown, in some embodiments, the computing device 650 may include a processor 702, a display 704, one or more inputs 706, one or more communication systems 708, and / or a memory 710. In some embodiments, the processor 702 may be any suitable hardware processor or combination of processors, such as a central processing unit (“CPU”), a graphics processing unit (“GPU”), and so on. In some embodiments, the display 704 may include any suitable display device, such as a computer monitor, a touch screen, a television, and so on. In some embodiments, the input 706 may include any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, a mouse, a touch screen, a microphone, and so on.

[0078] In some embodiments, the communication system 708 may include any suitable hardware, firmware, and / or software for transmitting information over a communication network 654 and / or any other suitable communication network. For example, the communication system 708 may include one or more transceivers, one or more communication chips, and / or chip sets, and so on. In a more specific example, the communication system 708 may include the hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0079] In some embodiments, the memory 710 may include any suitable one or more storage devices that can be used to store instructions, values, data, and so on, which can be used, for example, by the processor 702 to present content using the display 704, to communicate with the server 652 via the (one or more) communication systems 708, and so on. The memory 710 may include any suitable volatile memory, non-volatile memory, storage device, 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, and so on. In some embodiments, the memory 710 may have encoded thereon, or otherwise stored therein, 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, and so on.

[0080] In some embodiments, the server 652 may include a processor 712, a display 714, one or more inputs 716, one or more communication systems 718, and / or a memory 720. In some embodiments, the processor 712 may be any suitable hardware processor or combination of processors, such as a CPU, a GPU, etc. In some embodiments, the display 714 may include any suitable display device, such as a computer monitor, a touch screen, a television, etc. In some embodiments, the input 716 may include any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, a mouse, a touch screen, a microphone, etc.

[0081] In some embodiments, the communication system 718 may include any suitable hardware, firmware, and / or software for transmitting information over a communication network 654 and / or any other suitable communication network. For example, the communication system 718 may include one or more transceivers, one or more communication chips, and / or chip sets, etc. In a more specific example, the communication system 718 may include the 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.

[0082] In some embodiments, the memory 720 may include any suitable one or more storage devices that can be used to store instructions, values, data, etc., which can be used, for example, by the processor 712 to present content using the display 714, to communicate with one or more computing devices 650, etc. The memory 720 may include any suitable volatile memory, non-volatile memory, storage device, or any suitable combination thereof. For example, the 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, a server program for controlling the operation of the server 652 may be encoded thereon. In such embodiments, the processor 712 may execute at least a portion of the server program to transmit information and / or content (e.g., data, images, user interfaces) to one or more computing devices 650, to receive information and / or content from one or more computing devices 650, to receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smart phone), etc.

[0083] 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 a memory 728. In some embodiments, the processor 722 may be any suitable hardware processor or combination of processors, such as a CPU, GPU, and the like. In some embodiments, 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, one or more image acquisition systems 724 may include any suitable hardware, firmware, and / or software for coupling to and / or controlling the operation of an MRI system. In some embodiments, one or more portions of one or more image acquisition systems 724 may be removable and / or replaceable.

[0084] Note that although not shown, the image source 602 may include any suitable input and / or output. For example, the image source 602 may include input devices and / or sensors that can be used to receive user input, such as a keyboard, mouse, touch screen, microphone, touchpad, trackball, and the like. As another example, the display 602 may include any suitable display device, such as a computer monitor, touch screen, television, etc., one or more speakers, and the like.

[0085] In some embodiments, the communication system 726 may include any suitable hardware, firmware, and / or software for transmitting information to the computing device 650 (and, in some embodiments, via the communication network 654 and / or any other suitable communication network). For example, the communication system 726 may include one or more transceivers, one or more communication chips, and / or chip sets, and the like. In a more specific example, the communication system 726 may include hardware, firmware, and / or software that can be used to establish a wired connection, Wi-Fi connection, Bluetooth connection, cellular connection, Ethernet connection, etc. using any suitable port and / or communication standard (e.g., VGA, DVI video, USB, RS-232, etc.).

[0086] In some embodiments, the memory 728 may include any suitable one or more storage devices that can be used to store instructions, values, data, etc., which can be used, for example, by the processor 722 to: control one or more image acquisition systems 724 and / or receive data from one or more image acquisition systems 724; image data; use a display to present content (e.g., images, user interfaces); communicate with one or more computing devices 650, etc. The memory 728 may include any suitable volatile memory, non-volatile memory, storage device, or any suitable combination thereof. For example, the 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, a computer program for controlling the operation of the image source 602 may be encoded on or otherwise stored in the memory 728. In such embodiments, the processor 722 may execute at least a portion of the 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, receive instructions from one or more devices (e.g., personal computers, laptops, tablets, smart phones, etc.), etc.

[0087] 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, the computer-readable medium may be transient or non-transient. For example, non-transitory computer-readable media may 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 is not transient or without any persistent appearance during transmission, and / or any suitable tangible medium. As another example, transient computer-readable media may include signals on a network, wires, conductors, optical fibers, in a circuit, or any suitable medium that is transient and without any persistent appearance during transmission, and / or any suitable intangible medium.

[0088] The present disclosure has described one or more preferred embodiments, and it should be understood that many equivalent, alternative, variations, and modifications other than those expressly stated are possible and within the scope of the present invention.

Claims

1. A computer-implemented method for performing interventional planning brain mapping and target identification using magnetic resonance imaging (MRI), the method comprising: Receiving MR data from an MRI system by a computing system, the computing system including at least one processor communicating with at least one storage system, and the computing system communicating to receive data acquired using the MRI system; Analyzing the received MR data by the computing system to monitor and identify motion in real time; Determining an available MR data set from the acquired MR data by the computing system based on the identified motion; Generating a map of the subject's brain by the computing system based on the available MR data set; Identifying a target location in the subcallosal cingulate (SCC) region of the subject's brain by the computing system based on the map of the subject's brain, wherein the target location is a point where multiple fiber bundles in the SCC region converge; And Generating a report indicating the target location by the computing system.

2. The computer-implemented method according to claim 1, wherein, The multiple fiber bundles passing through the SCC region include the cingulum bundle (CM), forceps minor (FM), frontostriatal fibers (F-ST), and uncinate fasciculus (UF).

3. The computer-implemented method according to claim 1, further comprising displaying the report on a display.

4. The computer-implemented method according to claim 1, wherein The received MR data is diffusion MR data.

5. The computer-implemented method according to claim 4, wherein The received diffusion MR data is acquired using one of diffusion tensor imaging (DTI) or diffusion weighted imaging (DWI).

6. The computer-implemented method according to claim 4, wherein Acquiring the received MR data for a first number of diffusion directions.

7. The computer-implemented method according to claim 6, further comprising determining additional diffusion directions different from the first number of diffusion directions based on the identified motion and the available MR data set.

8. The computer-implemented method according to claim 7, further comprising receiving, by the computer system, additional MR data acquired for the additional diffusion directions from the MRI system.

9. A system for performing interventional planning brain mapping and target identification using magnetic resonance imaging (MRI), the system comprising: A computing device including a processor programmed to: Receive MR data acquired using an MRI system; Analyze the received MR data to monitor and identify motion in real time; Determine an available MR data set from the acquired MR data based on the identified motion; Generate a map of the subject's brain based on the available MR data set; Identify a target location in the subcallosal cingulate (SCC) region of the subject's brain based on the map of the subject's brain, wherein the target location is a point where multiple fiber bundles in the SCC region converge; And Generate a report indicating the target location; And A display coupled to the computing device and configured to display the report.

10. The system according to claim 9, wherein The multiple fiber bundles passing through the SCC region include the cingulum bundle (CM), forceps minor (FM), frontostriatal fibers (F-ST), and uncinate fasciculus (UF).

11. The system according to claim 9, wherein The received MR data is diffusion MR data.

12. The system according to claim 11, wherein The received diffusion MR data is acquired using one of diffusion tensor imaging (DTI) or diffusion weighted imaging (DWI).

13. The system according to claim 11, wherein The received MR data is acquired for a first number of diffusion directions.

14. The system according to 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 available MR data sets.

15. The system according to claim 14, wherein The processor is further programmed to receive from the MRI system additional MR data acquired for the additional diffusion directions.

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