Systems and methods for automated diffusion tractography and lead placement confirmation

Abstract

A computer-implemented method for performing diffusion tractography of a subject's brain, includes generating, by a computer system that includes at least one processor in communication with at least one memory system, for each canonical fiber tract bundle in a canonical fiber tract model, subject specific regions of interest (ROIs), detecting, by the computer system, a canonical set of fiber tracts in magnetic resonance (MR) data of the subject's brain based on the canonical fiber tract model and the subject specific ROIs, and generating, by the computing system, a subject-specific brain map comprising the canonical set of fiber tracts detected in the MR data of the subject's brain.

Description

Ref. 169549.00076SYSTEMS AND METHODS FOR AUTOMATED DIFFUSION TRACTOGRAPHY AND LEAD PLACEMENT CONFIRMATIONCROSS-REFERENCE O RELATED APPLICATIONS

[0001] This application is based on, clams priority' to, and incorporates herein by reference in its entirety U.S. Serial No. 63 / 735,482 filed December 18, 2024, and entitled “System and Methods for Automated Diffusion Tractography and Lead Placement Confirmation.’7STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] N / ABACKGROUND

[0003] Psychiatric disorders are a common cause of severe and long-term disability and socioeconomic burden. In some patients, treatment modalities of drug therapy and psychotherapy do not produce sufficient therapeutic effects or induce intolerable side effects. For these patients, neuromodulation has been suggested as a potential treatment modality. Neuromodulation is one of the fastest-growing areas of medicine, and is the process of inhibition, stimulation, modification, regulation or therapeutic alteration of activity7, electrically or chemically, in the central, peripheral or autonomic nervous systems. Neuromodulation incudes deep brain stimulation, vagal nerve stimulation, and transcranial magnetic and electrical stimulation. Neuromodulation aims to treat chronic neurological or psychiatric diseases by surgically targeting deep brain nuclei and pathways involved in the mediation of the symptoms in order to stimulate, inhibit, or otherwise modify / modulate pathological activity.

[0004] Neural structures such as cortical and / or subcortical structures can be targeted using deep brain stimulation (DBS). DBS is a procedure in which a neurostimulation device (or neurostimulator) is surgically implanted into the brain for the purpose of treating neurological and psychiatric disorders, including, for example, essential tremor, Parkinson disease, dystonia, Tourette syndrome, obsessive compulsive disorder, and treatment-resistant depression. Surgical planning for implantation of a neurostimulation device (or neurostimulator) for DBS and planning for the ultimate performance of neuromodulation (e.g., DBS) can utilize a map of the subjects brain, for example, generated using tractography. However, various aspects of tractography such as. for example, diffusion tractography (DT). are ty pically performed “by hand” which can be tedious and prone to subjective user1QBX99709863.1Ref. 169549.00076 experience.SUMMARY OF THE DISCLOSURE

[0005] In accordance with an embodiment, a computer-implemented method for performing diffusion tractography of a subject’s brain, includes generating, by a computer system that includes at least one processor in communication with at least one memory system, for each canonical fiber tract bundle in a canonical fiber tract model, subject specific regions of interest (ROIs), detecting, by the computer system, a canonical set of fiber tracts in magnetic resonance (MR) data of the subject’s brain based on the canonical fiber tract model and the subject specific ROIs, and generating, by the computing system, a subject-specific brain map comprising the canonical set of fiber tracts detected in the MR data of the subject’s brain.

[0006] In accordance with another embodiment, a system for performing diffusion tractography of a subject’s brain includes a memory that stores one or more computer readable media that include instructions, and one or more processor devices configured to execute the instructions of the computer readable media to receive MR data of the subject’s brain, receive a canonical fiber tract model, generate, for each canonical fiber tract bundle in the canonical fiber tract model, subject specific regions of interest (ROIs), seed tractography from within the canonical fiber tract template, detect a canonical set of fiber tracts in the MR data of the subject's brain based on the canonical fiber tract model and the subject-specific ROI’s, and generate a subject-specific brain map comprising the canonical set of fiber tracts detected in the MR data of the subject’s brain.

[0007] In accordance with another embodiment, a computer-implemented method for confirming placement of one or more neurostimulation devices in a subject’s brain includes receiving, by a computing system that includes at least one processor in communication with at least one memory system, a post-operative CT image of the subject’s brain, receiving, by the computer system, pre-operative image data comprising MR data, registering, by the computer system, the post-operative CT image and the pre-operative MR data, identifying, by the computing system, a plurality of neurostimulation leads in the post-operative CT image, aligning, by the computer system, a predetermined lead model to the identified leads in the CT image, and generating, by the computing system, a report comprising a lead placement based on the identified leads.

[0008] The foregoing and other aspects and advantages of the present disclosure will appear from the following description. In the description, reference is made to the accompanying drawings that form a part hereof, and in which there is shown by way of illustration a2QBX99709863.1Ref. 169549.00076 preferred embodiment. This embodiment does not necessarily represent the full scope of the invention, however, and reference is therefore made to the claims and herein for interpreting the scope of the invention. Like reference numerals will be used to refer to like parts from Figure to Figure in the following description.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. 1 illustrates a method for performing diffusion fractography of a subject’s brain in accordance with an embodiment;

[0010] FIG. 2 is a block diagram of an example system for performing diffusion fractography for a subject's brain in accordance with an embodiment;

[0011] FIG. 3 illustrates a method for confirming placement of one or more neurostimulation devices in a subject’s brain in accordance with an embodiment;

[0012] FIG. 4 is a block diagram of an example system for confirming placement of one or more neurostimulation devices in accordance with an embodiment;

[0013] FIG. 5 is a block diagram of components that can implement the systems of FIGs. 3 and 4 in accordance with an embodiment; and

[0014] FIG. 6 is a schematic diagram of an example system for performing magnetic resonance imaging in accordance with an embodiment.DETAILED DESCRIPTION

[0015] Tractography is a technique that can be used to visually represent nerve tracts using data collected from magnetic resonance imaging (e.g., diffusion MRI). Diffusion tractography (DT) can be used to estimate the white matter pathways of the brain. DT is typically executed “by hand”, in that a user (e.g., a researcher, surgeon, or neuroradiologist) hand draws regions of interest (ROIs) that constrain where the tracts go. White matter tracts are then estimated using the diffusion signal (e.g., acquired using diffusion MRI) through each voxel, given many parameters, and a certain diffusion model. The parameters are commonly optimized for each subject by hand. After this is done, spurious white matter fibers are typically removed by hand as well. The present disclosure describes, in some embodiments, a system and method for automated diffusion tractography.

[0016] FIG. 1 illustrates a method for performing diffusion tractography for a subject’s brain in accordance with an embodiment. Although the blocks of the process of FIG. 1 are illustrated in a particular order, in some embodiments, one or more blocks may be executed in a different order than illustrated in FIG. 1, or may be bypassed. The method may be3QBX99709863.1Ref. 169549.00076 implemented by a processing system including at least one electronic processor, where the at least one electronic processor may be a processor (e.g., including one or more individual processor devices) as described herein.

[0017] For the purposes of this disclosure and accompanying claims, the term “real time” or related terms are used to refer to and define a real-time performance of a system, which is understood as performance that is subject to operational deadlines from a given event to a system’s response to that event. For example, a real-time extraction of data and / or displaying of such data based on empirically-acquired signals may be one triggered and / or executed simultaneously with and without interruption of a signal-data acquisition (e.g., pulse sequence) or imaging procedure.

[0018] At block 102, magnetic resonance (MR) data of a brain of a subject such as, for example, diffusion MRI data, can be received. The MR data of the brain of the subject can be acquired using an MRI system (e.g., MRI system 600 shown in FIG. 6) using known acquisition techniques and protocols. In some embodiments, the MR data of the brain of the subject can be acquired using, for example, diffusion imaging techniques (e.g., diffusion tensor imaging (DTI) or diffusion weighted imaging (DWI)). A subject may be a human, an animal, a phantom, or the like. In some embodiments, the MR data may be received from an MRI system (e.g., MRI system 600 shown in FIG. 6) in real time. In some embodiments, the MR data may be received or retrieved from data storage of an imaging system (e.g., disc storage 638 of MRI system 600 shown in FIG. 6). or data storage of other computer systems (e.g., memory 510 of computer device 550, or memory 520 of server 552 shown in FIG. 5).

[0019] At block 104, a tract model may be received, for example, the tract model can be received or retrieved, for example, from data storage of a computer system (e.g., memory' 510 of computer device 550, or memory 520 of server 552 shown in FIG. 5). The tract model can be a model of canonical fiber tracts (or canonical fiber tract model). The tract model (e.g., a model of canonical fiber tracts) can be used to represent target fiber tracts that can be searched for as discussed further below. In some embodiments, the model of canonical fiber tracts can be generated using existing data acquired from a plurality of healthy subjects to produce, for example, a group-based or group-averaged canonical fiber tract template. In some embodiments, the model of canonical fiber tracts can be generated using a machine learning model (or network) trained to create a model of canonical fiber tracts for the subject. An image of the subject (e.g. an MR image of the subject’s brain) can be input into the trained machine learning model which can then generate (or synthesize) one or more synthetic subject-specific tract models that match the anatomical characteristics of the input4QBX99709863.1Ref. 169549.00076 patient image. The trained machine learning model for generating a tract model of the subject can advantageously create anatomically realistic, patient-specific models rather than relying on a group-averaged template.

[0020] In some embodiments, the machine learning model for generating a canonical fiber tract model for the subject can be trained to learn a distribution of anatomical variations using, for example, a set of training data including a plurality of existing images from a plurality of subjects. In some embodiments, the machine learning model can be, for example, a generative adversarial network (GAN), a variational autoencoder, a diffusion model, etc. The machine learning model can be trained using known methods such as supervised learning, self-supervised learning, semi-supervised learning, etc. As one example, to perform supervised learning, the training data includes example inputs and corresponding desired (for example, actual) outputs, and the machine learning model progressively develops a model that maps inputs to the outputs included in the training data. As another example, to perform self-supervised learning, a model is trained on a task using the data itself to generate supervisory’ signals (e.g.. unlabeled training data), rather than relying on, e.g., external labels provided by a user (e.g., labeled training data). As yet another example, to perform semisupervised learning, the training data may include desired output values for a subset of the training data (e.g., labeled training data) while the remaining training data may be unlabeled or imprecisely labeled (e.g., unlabeled training data.

[0021] At block 106, a plurality of regions of interest (ROIs) can be generated. In some embodiments, where the tract model is a group-averaged canonical fiber tract template, for each canonical fiber tract bundle in the tract model (e.g., the model of canonical fiber tracts), a plurality' of regions of interest (ROIs) can be defined in a template space brain and can represent an average brain. In some embodiments, a plurality ROIs for a specific subject can then be generated by using, for example, non-linear warping and the diffusion signal from the acquired diffusion MR data of the subject, to place the ROIs in individual space for the subject. Accordingly, the generated ROIs can be optimized for each individual subject. In some embodiments, where a machine learning model has been trained to generate a tract model for the subject, at block 106 the tract model(s) for the subject generated by the trained machine learning model can be registered to the MR data of the subject using, for example, non-linear warping and the diffusion signal from the MR data of the subject. Subject-specific ROIs can then be extracted from the registered synthetic tract models. Accordingly, in such embodiments, the generated ROIs can also be optimized for each individual subject. In some embodiments, the generated subject-specific ROIs can be stored in data storage of a computer5QBX99709863.1Ref. 169549.00076 system (e.g., memory' 510 of computer device 550, or memory' 520 of server 552 shown in FIG. 5).

[0022] At block 108, the tractography process or algorithm can optionally be seeded from within the tract model (e.g., the model of canonical fiber tracts). At block 110, fiber tracts (e.g., a canonical set of fiber tracts) can be detected in the acquired MR data for the subject as constrained by the subject-specific ROIs (e.g.. the detection process can be restricted to tracts that pass through the subject-specific ROIs). In some embodiments, the detection process can be configured to reject and remove fiber tracts that are more than a predetermined distance from the tract model in Hausdorff distance space. Accordingly, the detection process can match the shape and size of the canonical model fiber tracts. In one example, if a tract is detected in the MR data, the tract can be stored in, for example, data storage. If a tract is not detected, the step size can be increased. If a tract is detected after step size increase, the tract can be stored in, for example, data storage. If a tract is not detected after the step size increase, the predetermined distance (e.g., the Hausdorff distance) from the tract model can be increased. If a tract is detected after the increase in Hausdorff distance, the tract can be stored in, for example, data storage. If a tract is not detected after the increase in Hausdorff distance, the process can return to increasing the step size and repeated. In some embodiments, the detection process can be performed using a classifier to identify or extract geometric parameters of the tracts and then determine if the geometric parameters are similar or close enough to the tract model (e.g., as defined by a predetermined threshold).

[0023] In some embodiments, parameters (e.g., ideal or optimized parameters) for the tractography process and a threshold for the Hausdorff space (e.g., the predetermined distance from the tract model) can be determined automatically. In some embodiments, a parameter sweep of the possible diffusion tractography parameters may be iterated alongside the Hausdorff distances, for example, using a simulated annealing paradigm, in order to find an optimal set of parameters for diffusion tractography that generates tracts that match the tract model, for example, a canonical fiber tract model. Accordingly, in some embodiments, the most conservative parameters that can still find fiber tracts that match the canonical fiber tracts can be found. In some embodiments, known anatomy from the subject-specific ROIs generated at block 106 can be used to constrain what fiber tracts are determined to be part of the canonical fiber tracts.

[0024] In some embodiments, locations where multiple canonical fiber tracts overlap in the detected set of fiber tracts, e.g., the maximal overlap of tracts, may also be determined automatically. In some embodiments, a searchlight (e.g., a moveable window' within a search6QBX99709863.1Ref. 169549.00076 space that can be used to examine localized patterns of information) may be used in the space that the fiber tracts are expected to overlap (e.g., the search space) based on subject-specific brain anatomy. At each voxel in the search space, tractography can be seeded from that voxel, and the resulting tracts can be compared to each of the canonical fiber tracts using, for example, spatial correlation. For each canonical fiber tract, that value represents how well that voxel overlaps with the canonical fiber tracts. Next, a combination of, for example, expectation maximization, entropy, and summation, can be used to calculate maximal overlap, using the values representing how well each voxel overlaps with the canonical fiber tracts as input. In some embodiments, a heatmap of every voxel in the search space, measuring how well these canonical fiber tracts overlap at that voxel, can be generated. In some embodiments, the process to determine locations where multiple canonical fiber tracts overlap in the detected set of fiber tracts can be highly tunable to balance the canonical fiber tracts in whatever way is required, as well as to center around known anatomical locations. Advantageously, this automated process can ensure a quantifiable maximization of the overlap, not depending on human subjectivity. In some embodiments, an anatomical weight can be included in the heatmap calculation. In some embodiments, this anatomical weight can be based on the standard space locations of the target along the upper bank of the cingulum.

[0025] At block 112, the detected set of fiber tracts for the subject (and, in some embodiments, the maximal overlap of tracts) can be stored in data storage of a computer system (e.g., memory 510 of computer device 550, or memory 520 of server 552 shown in FIG. 5). At block 114, a tractography subject-specific brain map can be generated based on the MR data of the subject and the detected canonical set of fiber tracts for the subject. At block 116, the generated subject-specific brain map (e.g., including the detected canonical set of fiber tracts for the subject) can be stored in data storage of a computer system (e.g., memory 510 of computer device 550, or memory 520 of server 552 shown in FIG. 5). At block 118, the subject-specific brain map may be displayed on a display (for example, displays 604, 636, 644 of MRI system 600 shown in FIG. 6, display 504 of computing device 550 shown in FIG. 5 or display 514 of server 552 shown in FIG. 5).

[0026] FIG. 2 is a block diagram of an example system for performing diffusion tractography for a subject’s brain in accordance in accordance with some embodiments of the systems and methods described in the present disclosure. As shown in FIG. 2, a computing device 250 can receive one or more types of data (e.g., MR data) from image source 202, which may be an MRI source. In some embodiments, computing device 250 can execute at least a portion of a system 204 for performing automated diffusion tractography for a subject’s brain to generate7QB\99709863.1Ref. 169549.00076 a canonical set of fiber tracts and a brain map.

[0027] Additionally or alternatively, in some embodiments, the computing device 250 can communicate information about data received from the image source 202 to a server 252 over a communication network 254, which can execute at least a portion of the system 204 for performing automated diffusion tractography for a subject’s brain to generate a canonical set of fiber tracts for the subject and a subject-specific brain map. In such embodiments, the server 252 can return information to the computing device 250 (and / or any other suitable computing device) indicative of an output of the system 204.

[0028] In some embodiments, computing device 250 and / or server 252 can 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 being executed by a physical computing device, and so on. The computing device 250 and / or server 252 can also reconstruct images from the data.

[0029] In some embodiments, image source 202 can 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 600 shown in FIG. 6), another computing device (e.g., a server storing image data), and so on. In some embodiments, image source 202 can be local to computing device 250. For example, image source 202 can be incorporated with computing device 250 (e.g., computing device 250 can be configured as part of a device for capturing, scanning, and / or storing images). As another example, image source 202 can be connected to computing device 250 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, image source 202 can be located locally and / or remotely from computing device 250, and can communicate data to computing device 250 (and / or server 252) via a communication network (e.g.. communication network 254).

[0030] In some embodiments, communication network 254 can be any suitable communication network or combination of communication networks. For example, communication network 254 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, a 5G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, and so on. In some embodiments, communication network 254 can be a local area network, a wide area network, a public netw ork (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. Communications links can each be any8QBX99709863.1Ref. 169549.00076 suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on.

[0031] In some embodiments, the detected canonical set of fiber tracts for the subject in the MR data of the subject and the subject-specific brain map can be used for interventional planning, for example, for an intervention such as neuromodulation. For example, the detected set of canonical fiber tracts for the subject and subject-specific brain map can be used for surgical planning for implantation of a neurostimulation device (e.g., leads) and even for the ultimate performance of neuromodulation directed at a target location. In some embodiments, the systems and methods disclosed herein may be used for interventional planning (e.g., surgical and treatment planning) for treatments of particular brain disorders and in particular structures of the brain. As used herein, the term brain disorders is used to refer to neurological and psychological disorders such as, for example, depression, motor stroke recovery, epilepsy, Tourette’s syndrome, disorders of consciousness (coma), essential tremor, and tremor predominant Parkinson’s.

[0032] Deep brain stimulation (DBS) is a form of neuromodulation in clinical use. DBS is a procedure in which a neurostimulator is surgically implanted into the brain for the purpose of treating brain disorders, such as Parkinson's disease, dystonia, essential tremor, obsessive compulsive disorder, epilepsy, depression, etc. In some embodiments, the determined canonical set of fiber tracts for the subject and subject-specific brain map can be used to guide planning for DBS lead placement. Once the neurostimulation device (e.g., leads) have been surgically placed, it can be advantageous to confirm the lead placement in the subject.

[0033] FIG. 3 illustrates a method for confirming placement of one or more neurostimulation devices in a subject’s brain in accordance with an embodiment. Although the blocks of the process of FIG. 3 are illustrated in a particular order, in some embodiments, one or more blocks may be executed in a different order than illustrated in FIG. 3, or may be bypassed.

[0034] At block 302, one or more computed tomography (CT) images of a brain of a subject acquired after one or more neurostimulation devices (e.g., leads) have been implanted (e.g., post-operative CT images) in a subject can be received. The one or more CT images of the brain of the subject can be acquired using a CT system using known acquisition techniques and protocols. A subject may be a human, an animal, a phantom, or the like. In some embodiments, the one or more CT images may be received from a CT system in real time. In some embodiments, the one or more CT images may be retrieved from data storage of a CT system, or data storage of other computer systems (e.g.. memory 510 of computer device 550, or memory 520 of server 552 shown in FIG. 5).9QBX99709863.1Ref. 169549.00076

[0035] At block 304, pre-operative image data, for example MR data, a brain map (e.g., a functional connectivity map. tractography), a set of fiber tracts, etc. can be received. In some embodiments, the pre-operative image data can be the diffusion MR data, canonical set of fiber tracts for the subject, and subject-specific brain map from the automated diffusion tractography technique described above with respect to FIGs. 1 and 2. The pre-operative image data can be received or retrieved from data storage of an imaging system (e.g., disc storage 638 of MRI system 600 shown in FIG. 6), or data storage of other computer systems (e.g., memory 510 of computer device 550, or memory 520 of server 552 shown in FIG. 5. At block 306, the acquired CT image(s) can be pre-processed. For example, in some embodiment, a check can be performed to determine data integrity and completeness (e.g., validation), and the CT image(s) can be converted to a predetermined format. For example, the CT image(s) can be converted from a DICOM format to a BIDS format. In some embodiments, the CT images can also be converted to a predetermined orientation (e.g., a RAS orientation).

[0036] At block 308, a post-operative CT image can be registered and aligned with the preoperative image data (e.g., MR data, MR images, brain map, etc.). At block 310, the leads (or neurostimulation devices) can be automatically identified in the registered / aligned CT image. In some embodiments, the leads can be identified in the registered / aligned CT image using segmentation, for example, known segmentation techniques. In some embodiments, the leads can be identified in the registered / aligned CT image using an object detection technique (e.g., identify the center of mass of a lead) or a point-based detection technique to identity the position of the leads. At block 312, a predetermined lead model can be aligned or registered with the identified leads in the CT image. At block 314, the aligned identified leads can be stored in data storage of a computer system (e.g., memory 510 of computer device 550, or memory 520 of server 552 shown in FIG. 5).

[0037] At block 316, a report can be generated that includes the lead placement based on the identified leads. In some embodiments, a three-dimensional visualization (e.g., a 3D rendering or image) of the lead placement can be generated and included in the report. The report can be stored in data storage of a computer system (e.g., memory 510 of computer device 550, or memory 520 of server 552 shown in FIG. 5). At block 318, the report can be displayed on a display (for example, display 504 of computing device 550 shown in FIG. 5 or display 514 of server 552 shown in FIG. 5). The displayed report, including lead placement, can be viewed by a user (e.g., a researcher, surgeon, or neuroradiologist) to confirm the lead placement (e.g., the registration and identified leads).10QBX99709863.1Ref. 169549.00076

[0038] FIG. 4 is a block diagram of an example system for confirming placement of one or more neurostimulation devices in accordance with some embodiments of the systems and methods described above. As shown in FIG. 4, a computing device 450 can receive one or more types of data (e.g., CT data, MR data) from image source(s) 402, which may be a CT source or an MRI source. In some embodiments, computing device 450 can execute at least a portion of a system 404 for confirming placement of one or more neurostimulation devices in a subject’s brain. In some embodiments, the computer system 450 may also be used to store pre-operative image data 406 (e.g., MR data, set of fiber tracts, brain map). The pre-operative imaging data 406 may be provided to the computer system from an image source (e.g., an MRI source), for example, the system for automated diffusion tractography described above with respect to FIGs. 1 and 2.

[0039] Additionally or alternatively, in some embodiments, the computing device 450 can communicate information about data received from the image source 402 to a server 452 over a communication network 454, which can execute at least a portion of the system 404 for confirming placement of one or more neurostimulation devices in a subject’s brain. In such embodiments, the server 452 can return information to the computing device 450 (and / or any other suitable computing device) indicative of an output of the system 404.

[0040] In some embodiments, computing device 450 and / or server 452 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a w earable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device 450 and / or server 452 can also reconstruct images from the data.

[0041] In some embodiments, image source 402 can 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 600 shown in FIG. 6), another computing device (e.g., a server storing image data), and so on. In some embodiments, image source 402 can be local to computing device 450. For example, image source 402 can be incorporated with computing device 450 (e.g., computing device 450 can be configured as part of a device for capturing, scanning, and / or stonng images). As another example, image source 402 can be connected to computing device 450 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, image source 402 can be located locally and / or remotely from computing device 450, and can communicate data to computing device 450 (and / or server 652) via a communication network (e.g.. communication network 454).

[0042] In some embodiments, communication network 454 can be any suitable11QBX99709863.1Ref. 169549.00076 communication network or combination of communication networks. For example, communication network 454 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., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, and so on. In some embodiments, communication network 454 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. Communications links can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links. Wi-Fi links, Bluetooth links, cellular links, and so on.

[0043] Referring now to FIG. 5, an example of hardware 500 that can be used to implement image source 202, computing device 250, and server 252 of in accordance with some embodiments discussed above with respect to FI\G. 2 and image source 402, computing device 450, and server 452 of in accordance with some embodiments discussed above with respect to FIG. 4 As shown in FIG. 5, in some embodiments, computing device 550 (e.g., computing device 202 in FIG. 2 and computing device 402 in FIG. 4) can include a processor 502, a display 504, one or more inputs 506, one or more communication systems 508, and / or memory 510. In some embodiments, processor 502 can 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, display 504 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 506 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0044] In some embodiments, communications systems 508 can include any suitable hardware, firmware, and / or software for communicating information over communication network 554 and / or any other suitable communication networks. For example, communications systems 508 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 508 can include hardware, firmware and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0045] In some embodiments, memory 510 can include any suitable storage device or12QBX99709863.1Ref. 169549.00076 devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 502 to present content using display 704, to communicate with server 652 via communications system(s) 708, and so on. Memory 510 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory7510 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, and so on. In some embodiments, memory 510 can have encoded thereon, or otherwise stored therein, a computer program for controlling operation of computing device 550. In such embodiments, processor 502 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server 552, transmit information to server 552, and so on.

[0046] In some embodiments, server 552 can include a processor 512, a display 514, one or more inputs 516, one or more communications systems 518, and / or memory 520. In some embodiments, processor 512 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, display 514 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 516 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0047] In some embodiments, communications systems 518 can include any suitable hardware, firmware, and / or software for communicating information over communication network 554 and / or any other suitable communication networks. For example, communications systems 518 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 518 can include hardware, firmware and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0048] In some embodiments, memory 520 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 512 to present content using display 514, to communicate with one or more computing devices 550, and so on. Memory7520 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 520 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, and so on. In some13QBX99709863.1Ref. 169549.00076 embodiments, memory 520 can have encoded thereon a server program for controlling operation of server 552. In such embodiments, processor 512 can execute at least a portion of the server program to transmit information and / or content (e.g., data, images, a user interface) to one or more computing devices 650, receive information and / or content from one or more computing devices 550, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on.

[0049] In some embodiments, image source 502 can include a processor 522, one or more image acquisition systems 524, one or more communications systems 526, and / or memory 528. In some embodiments, processor 522 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, the one or more image acquisition systems 524 are generally configured to acquire data, images, or both, and can include an MRI imaging system or a CT imaging system. Additionally or alternatively, in some embodiments, one or more image acquisition systems 524 can include any suitable hardware, firmware, and / or software for coupling to and / or controlling operations of an MRI system. In some embodiments, one or more portions of the one or more image acquisition systems 524 can be removable and / or replaceable.

[0050] Note that, although not shown, image source 502 can include any suitable inputs and / or outputs. For example, image source 502 can include input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, and so on. As another example, image source 502 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, etc., one or more speakers, and so on.

[0051] In some embodiments, communications systems 526 can include any suitable hardware, firmware, and / or software for communicating information to computing device 650 (and, in some embodiments, over communication network 554 and / or any other suitable communication networks). For example, communications systems 526 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 526 can include hardware, firmware and / or software that can be used to establish a wired connection using any suitable port and / or communication standard (e.g., VGA, DVI video, USB, RS-232, etc.), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0052] In some embodiments, memory' 528 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 522 to control the one or more image acquisition systems 524, and / or14QBX99709863.1Ref. 169549.00076 receive data from the one or more image acquisition systems 524; to reconstruct images from data; present content (e.g., images, a user interface) using a display; communicate with one or more computing devices 550; and so on. Memory 528 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 528 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, and so on. In some embodiments, memory 528 can have encoded thereon, or otherwise stored therein, a program for controlling operation of image source 502. In such embodiments, processor 522 can 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 550, receive information and / or content from one or more computing devices 550. receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), and so on.

[0053] In some embodiments, any suitable computer readable media can be used for storing instructions for performing the functions and / or processes described herein. For example, in some embodiments, computer readable media 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 media that is not fleeting or devoid of any semblance of permanence during transmission, and / or any suitable tangible media. As another example, transitory computer readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and / or any suitable intangible media.

[0054] In some embodiments, the methods described herein may be implemented by a system that includes 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 methods may automatically be performed by one or more processors or computing devices. In various additional aspects, the various acts illustrated in FIGs. 1 and 3 may be performed in the illustrated sequence, in other sequences, in parallel, or in some cases, may be omitted.15QBX99709863.1Ref. 169549.00076

[0055] In some aspects, the above described methods and processes may be implemented using a computing system, including one or more computers. The methods and processes described herein may be implemented as a computer application, computer service, computer API, computer library, and / or other computer program product.

[0056] Referring to FIG. 6, an example of an MRI system 600 that can implement the method for performing diffusion tractography of a subject’s brain described above with respect to FIG. 2 is illustrated. The MRI system 600 includes an operator workstation 602 that may include a display 604, one or more input devices 606 (e.g., a keyboard, a mouse), and a processor 608. The processor 608 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 602 provides an operator interface that facilitates entering scan parameters into the MRI system 600. The operator workstation 602 may be coupled to different servers, including, for example, a pulse sequence server 610, a data acquisition server 612, a data processing server 614, and a data store server 616. The operator workstation 602 and the servers 610, 612, 614, and 616 may be connected via a communication system 640, which may include wired or wireless network connections.

[0057] The pulse sequence server 610 functions in response to instructions provided by the operator workstation 602 to operate a gradient system 618 and a radiofrequency (“RF”) system 620. Gradient waveforms for performing a prescribed scan are produced and applied to the gradient system 618, which then excites gradient coils in an assembly 622 to produce the magnetic field gradients Gx, Gy, and Gzthat are used for spatially encoding magnetic resonance signals. The gradient coil assembly 622 forms part of a magnet assembly 624 that includes a polarizing magnet 626 and a whole-body RF coil 628.

[0058] RF waveforms are applied by the RF system 620 to the RF coil 628, or a separate local coil to perform the prescribed magnetic resonance pulse sequence. Responsive magnetic resonance signals detected by the RF coil 628, or a separate local coil, are received by the RF system 620. The responsive magnetic resonance signals may be amplified, demodulated, filtered, and digitized under direction of commands produced by the pulse sequence server 610. The RF system 620 includes an RF transmitter for producing a wide variety of RF pulses used in MRI pulse sequences. The RF transmitter is responsive to the prescribed scan and direction from the pulse sequence server 610 to produce RF pulses of the desired frequency, phase, and pulse amplitude waveform. The generated RF pulses may be applied to the wholebody RF coil 628 or to one or more local coils or coil arrays.

[0059] The RF system 620 also includes one or more RF receiver channels. An RF receiver16QBX99709863.1Ref. 169549.00076 channel includes an RF preamplifier that amplifies the magnetic resonance signal received by the coil 628 to which it is connected, and a detector that detects and digitizes the I and Q quadrature components of the received magnetic resonance signal. The magnitude of the received magnetic resonance signal may, therefore, be determined at a sampled point by the square root of the sum of the squares of the I and Q components:and the phase of the received magnetic resonance signal may also be determined according to the following relationship:

[0060] The pulse sequence server 610 may receive patient data from a physiological acquisition controller 630. By way of example, the physiological acquisition controller 630 may receive signals from a number of different sensors connected to the patient, including electrocardiograph (“ECG'’) signals from electrodes, or respiratory signals from a respiratory bellows or other respiratory monitoring devices. These signals may be used by the pulse sequence server 610 to synchronize, or “gate,” the performance of the scan with the subject’s heartbeat or respiration.

[0061] The pulse sequence server 610 may also connect to a scan room interface circuit 632 that receives signals from various sensors associated with the condition of the patient and the magnet system. Through the scan room interface circuit 632, a patient positioning system 634 can receive commands to move the patient to desired positions during the scan.

[0062] The digitized magnetic resonance signal samples produced by the RF system 620 are received by the data acquisition server 612. The data acquisition server 612 operates in response to instructions downloaded from the operator workstation 602 to receive the realtime magnetic resonance data and provide buffer storage, so that data is not lost by data overrun. In some scans, the data acquisition server 612 passes the acquired magnetic resonance data to the data processor server 614. In scans that require information derived from acquired magnetic resonance data to control the further performance of the scan, the data acquisition server 612 may be programmed to produce such information and convey it to the pulse sequence server 610. For example, during pre-scans, magnetic resonance data may be acquired and used to calibrate the pulse sequence performed by the pulse sequence server 610. As another example, navigator signals may be acquired and used to adjust the operating parameters of the RF system 620 or the gradient system 618, or to control the view order in which k-space is sampled. In still another example, the data acquisition server 612 may also17QBX99709863.1Ref. 169549.00076 process magnetic resonance signals used to detect the arrival of a contrast agent in a magnetic resonance angiography (“MRA”) scan. For example, the data acquisition server 612 may acquire magnetic resonance data and processes it in real-time to produce information that is used to control the scan.

[0063] The data processing server 614 receives magnetic resonance data from the data acquisition server 612 and processes the magnetic resonance data in accordance with instructions provided by the operator workstation 602. Such processing may include, for example, reconstructing two-dimensional or three-dimensional images by performing a Fourier transformation of raw k-space data, performing other image reconstruction algorithms (e.g., iterative or backproj ection reconstruction algorithms), applying filters to raw k-space data or to reconstructed images, generating functional magnetic resonance images, or calculating motion or flow images.

[0064] Images reconstructed by the data processing server 614 are conveyed back to the operator workstation 602 for storage. Real-time images may be stored in a database memory cache, from which they’ may be output to operator display 602 or a display 636. Batch mode images or selected real time images may be stored in a host database on disc storage 638. When such images have been reconstructed and transferred to storage, the data processing sen’ er 614 may notify the data store server 616 on the operator workstation 602. The operator workstation 602 may be used by an operator to archive the images, produce films, or send the images via a network to other facilities.

[0065] The MR1 system 600 may also include one or more networked workstations 642. For example, a networked workstation 642 may include a display 644, one or more input devices 646 (e.g., a keyboard, a mouse), and a processor 648. The networked workstation 642 may be located within the same facility as the operator workstation 602, or in a different facility, such as a different healthcare institution or clinic.

[0066] The networked workstation 642 may gain remote access to the data processing serv er 614 or data store server 616 via the communication system 640. Accordingly, multiple networked workstations 642 may have access to the data processing server 614 and the data store server 616. In this manner, magnetic resonance data, reconstructed images, or other data may be exchanged between the data processing server 614 or the data store server 616 and the networked workstations 642, such that the data or images may be remotely processed by a networked workstation 642.

[0067] The present disclosure has described one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside18QBX99709863.1Ref. 169549.00076 from those expressly stated, are possible and within the scope of the invention.19QB\99709863.1

Claims

Ref. 169549.00076CLAIMS1. A computer-implemented method for performing diffusion tractography of a subject’s brain, the method comprising: generating, by a computer system that includes at least one processor in communication with at least one memory system, for each canonical fiber tract bundle in a canonical fiber tract model, subject specific regions of interest (ROIs); detecting, by the computer system, a canonical set of fiber tracts in magnetic resonance (MR) data of the subject’s based on the canonical fiber tract model and the subjectspecific ROIs;; and generating, by the computing system, a subject-specific brain map comprising the canonical set of fiber tracts detected in the MR data of the subject’s brain.

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

3. The computer-implemented method according to claim 1, wherein the canonical fiber tract model is generated based on a plurality of subjects.

4. The computer-implemented method according to claim 1, further comprising displaying the subject-specific brain map on a display.

5. The computer-implemented method according to claim 1, wherein detecting a canonical set of fiber tracts in the MR data of the subject’s brain based on the canonical fiber tract model comprises removing, by the computer system, fiber tracts that are more than a predetermined distance away from the canonical fiber tract model in Hausdorff distance space.

6. The computer implemented method according to claim 1, wherein detecting a canonical set of fiber tracts in the MR data of the subject’s brain based on the canonical fiber tract model comprises determining, by the computer system, an optimal set of parameter for diffusion tractography that generate fiber tracts that match the canonical fiber tract model.20QBX99709863.1Ref. 169549.000767. The computer implemented method according to claim 1, wherein detecting a canonical set of fiber tracts in the MR data of the subject’s brain based on the canonical fiber tract model comprises determining, by the computer system, locations where multiple canonical fiber tracts overlap maximally.

8. The method according to claim 1, further comprising generating a report comprising the subject-specific brain map.

9. The method according to claim 8, wherein generating the report further comprises generating a three-dimensional visualization.

10. A system for performing diffusion fractography of a subject’s brain, the system comprising: a memory that stores one or more computer readable media that include instructions; and one or more processor devices configured to execute the instructions of the computer readable media to: receive MR data of the subject’s brain; receive a canonical fiber tract model; generate, for each canonical fiber tract bundle in the canonical fiber tract model, subject specific regions of interest (ROTs); seed fractography from within the canonical fiber tract template; detect a canonical set of fiber tracts in the MR data of the subject’s brain based on the canonical fiber tract model and the subject-specific ROTs; and generate a subject-specific brain map comprising the canonical set of fiber tracts detected in the MR data of the subject’s brain.

11. The system according to claim 10, wherein the MR data is diffusion MR data.

12. The computer-implemented method according to claim 10, wherein the canonical fiber tract model is generated based on a plurality of subjects.21QBX99709863.1Ref. 169549.0007613. The system according to claim 10, wherein the one or more processors are further configured to execute instructions of the computer readable media to display the subjectspecific brain map on a display.

14. The system according to claim 10. wherein detecting a canonical set of fiber tracts in the MR data of the subject’s brain based on the canonical fiber tract model comprises removing fiber tracts that are more than a predetermined distance away from the canonical fiber tract model in Hausdorff distance space.

15. The system according to claim 10, wherein the one or more processors are further configured to execute instructions of the computer readable media to determine an optimal set of parameters for diffusion tractography that generate fiber tracts that match the canonical fiber tract model.

16. The system according to claim 10, wherein the one or more processors are further configured to execute instructions of the computer readable media to determine locations where multiple canonical fiber tracts overlap maximally.

17. A computer-implemented method for confirming placement of one or more neurostimulation devices in a subject’s brain, the method comprising: receiving, by a computing system that includes at least one processor in communication with at least one memory system, a post-operative CT image of the subject’s brain; receiving, by the computer system, pre-operative image data comprising MR data; registering, by the computer system, the post-operative CT image and the preoperative MR data; identifying, by the computing system, a plurality of neurostimulation leads in the post-operative CT image; aligning, by the computer system, a predetermined lead model to the identified leads in the CT image; and generating, by the computing system, a report comprising a lead placement based on the identified leads.22QBX99709863.1Ref. 169549.0007618. The method according to claim 17, where identifying, by the computing systems, a plurality of neurostimulation leads in the post-operative CT image comprises segmenting the plurality of neurostimulation leads.

19. The method according to claim 17, where identify ing, by the computing systems, a plurality of neurostimulation leads in the post-operative CT image comprises performing an object detection technique.23QBX99709863.1

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