System, method, and computer program product for generating pruned tractograms of neural fiber bundles

Disclosed are a system, method, and computer program product for generating pruned tractograms of neural fiber bundles. The method includes receiving scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device. The method also includes generating an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data. The method further includes generating a density map using a set of tracts from the initial tractogram, identifying each tract that passes through a segment of the density map more than once, and setting a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value. The method further includes generating a pruned tractogram by identifying a segment having a unique tract count less than or equal to the threshold pruning value and excluding the segment from the pruned tractogram.

BACKGROUND

Brain tumor removal is difficult, and often leads to severe post-operative complications. Neuronavigation is a magnetic resonance imaging (MRI) technique used during the brain tumor removal operation to assist the surgeon in identifying the critical (e.g., healthy) tracts of the brain to be avoided. The accuracy of current neuronavigation units is poor.

In the United States, around 25,000 adults are diagnosed with a brain tumor every year (males 5.8, females 4.1 per 100,000). At the University of Pittsburgh Medical Center (UPMC), the Neurological Surgery Department treats an average of 500 brain tumor patients per year. Surgical removal of the tumor is offered in 95% of cases, but favorable outcomes to the surgical approach are affected by limitations in imaging modalities during surgery. Patients who undergo brain tumor surgery often have significant postsurgical complications, including motor deficits and cognitive impairment, and do not recover to their full pre-surgical extent. Therefore there is a need for an improved tractogram generation solution providing a safer approach to prevent these complications.

SUMMARY

Non-limiting embodiments port HAFT data to surgical navigation systems using a super-resolution conversion from track file format to voxel format. Described systems and methods re-grid the HAFT space into smaller voxel elements to obtain high quality and super-resolution quality of the maps, which allow neurosurgeons to perform brain tumor surgery with high definition visualization of fiber tracts involved and surrounding the lesion. Non-limiting embodiments generate more accurate tractograms by pruning false connections (e.g., singular tracts) from segments of density maps of scanned MRI data. Accordingly, systems, devices, products, apparatus, and/or methods for generating pruned tractograms of neural fiber bundles are provided herein.

According to a non-limiting embodiment or aspect, provided is a computer-implemented method for generating pruned tractograms of neural fiber bundles. The method includes receiving, with at least one processor, scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device. The method also includes generating, with the at least one processor, an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data. The method further includes generating, with the at least one processor, a density map of the at least a portion of the brain using a set of tracts from the initial tractogram. The method further includes identifying, with the at least one processor, each tract of the set of tracts that passes through a segment of a plurality of segments of the density map more than once, and setting a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value. The method further includes generating, with the at least one processor, a pruned tractogram from the initial tractogram by identifying at least one segment of the plurality of segments having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment from the pruned tractogram. The method further includes communicating, with the at least one processor, the pruned tractogram for display on a computing device.

In non-limiting embodiments or aspects, the computing device may include a neuronavigator device programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

In non-limiting embodiments or aspects, the density map may be a three-dimensional (3D) histogram and the plurality of segments may be a plurality of voxels of the 3D histogram. The pruned tractogram may include only voxels having unique tract counts greater than the threshold pruning value, and the threshold pruning value may be one.

In non-limiting embodiments or aspects, the method may further include generating, with the at least one processor, a second density map of the at least a portion of the brain using a second set of tracts from the initial tractogram. The method may further include identifying, with the at least one processor, each tract of the second set of tracts that passes through a segment of a plurality of segments of the second density map more than once, and setting a contribution of said tract to a unique tract count of the segment of the plurality of segments of the second density map equal to the threshold pruning value. The method may further include generating, with the at least one processor, an intermediate tractogram from the initial tractogram by identifying at least one segment of the plurality of segments of the second density map having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment of the plurality of segments of the second density map from the intermediate tractogram. The pruned tractogram may be generated at least partially from the intermediate tractogram.

In non-limiting embodiments or aspects, the method may further include displaying, with the at least one processor, the pruned tractogram in a graphical user interface. The method may further include receiving, with the at least one processor, input data from a user including additional tracts for removal from the pruned tractogram. The method may further include modifying, with the at least one processor, the pruned tractogram based on the input data. The computing device may include a neuronavigator device programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

According to a non-limiting embodiment or aspect, provided is a computer program product for generating pruned tractograms of neural fiber bundles. The computer program product includes at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to receive scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device. The program instructions also cause the at least one processor to generate an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data. The program instructions further cause the at least one processor to generate a density map of the at least a portion of the brain using a set of tracts from the initial tractogram. The program instructions further cause the at least one processor to identify each tract of the set of tracts that passes through a segment of a plurality of segments of the density map more than once, and set a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value. The program instructions further cause the at least one processor to generate a pruned tractogram from the initial tractogram by identifying at least one segment of the plurality of segments having a unique tract count less than equal to the threshold pruning value and excluding the at least one segment from the pruned tractogram. The program instructions further cause the at least one processor to communicate the pruned tractogram for display on a computing device.

In non-limiting embodiments or aspects, the computing device may include a neuronavigator device programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

In non-limiting embodiments or aspects, the density map may be a three-dimensional (3D) histogram and the plurality of segments are a plurality of voxels of the 3D histogram. The pruned tractogram may include only voxels having unique tract counts greater than the threshold pruning value, and the threshold pruning value may be one.

In non-limiting embodiments or aspects, the program instructions may further cause the at least one processor to generate a second density map of the at least a portion of the brain using a second set of tracts from the initial tractogram. The program instructions may further cause the at least one processor to identify each tract of the second set of tracts that passes through a segment of a plurality of segments of the second density map more than once, and setting a contribution of said tract to a unique tract count of the segment of the plurality of segments of the second density map equal to the threshold pruning value. The program instructions may further cause the at least one processor to generate an intermediate tractogram from the initial tractogram by identifying at least one segment of the plurality of segments of the second density map having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment of the plurality of segments of the second density map from the intermediate tractogram. The pruned tractogram may be generated at least partially from the intermediate tractogram.

In non-limiting embodiments or aspects, the program instructions may further cause the at least one processor to display the pruned tractogram in a graphical user interface. The program instructions may further cause the at least one processor to receive input data from a user including additional tracts for removal from the pruned tractogram. The program instructions may further cause the at least one processor to modify the pruned tractogram based on the input data. The computing device may include a neuronavigator device programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

According to a non-limiting embodiment or aspect, provided is a system for generating pruned tractograms of neural fiber bundles. The system includes a neuronavigator device including at least one processor that is programmed and/or configured to receive scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device. The at least one processor is also programmed and/or configured to generate an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data. The at least one processor is further programmed and/or configured to generate a density map of the at least a portion of the brain using a set of tracts from the initial tractogram. The at least one processor is further programmed and/or configured to identify each tract of the set of tracts that passes through a segment of a plurality of segments of the density map more than once, and set a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value. The at least one processor is further programmed and/or configured to generate a pruned tractogram from the initial tractogram by identifying at least one segment of the plurality of segments having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment from the pruned tractogram. The at least one processor is further programmed and/or configured to display the pruned tractogram.

In non-limiting embodiments or aspects, the at least one processor may be further programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

In non-limiting embodiments or aspects, the density map may be a three-dimensional (3D) histogram and the plurality of segments may be a plurality of voxels of the 3D histogram. The pruned tractogram may include only voxels having unique tract counts greater than the threshold pruning value, and the threshold pruning value may be one.

In non-limiting embodiments or aspects, the at least one processor may be further programmed and/or configured to generate a second density map of the at least a portion of the brain using a second set of tracts from the initial tractogram. The at least one processor may be further programmed and/or configured to identify each tract of the second set of tracts that passes through a segment of a plurality of segments of the second density map more than once, and setting a contribution of said tract to a unique tract count of the segment of the plurality of segments of the second density map equal to the threshold pruning value. The at least one processor may be further programmed and/or configured to generate an intermediate tractogram from the initial tractogram by identifying at least one segment of the plurality of segments of the second density map having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment of the plurality of segments of the second density map from the intermediate tractogram. The pruned tractogram may be generated at least partially from the intermediate tractogram.

In non-limiting embodiments or aspects, the at least one processor may be further programmed and/or configured to display the pruned tractogram in a graphical user interface. The at least one processor may be further programmed and/or configured to receive input data from a user including additional tracts for removal from the pruned tractogram. The at least one processor may be further programmed and/or configured to modify the pruned tractogram based on the input data. The at least one processor may be further programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

Other non-limiting embodiments or aspects are set forth in the following numbered clauses:

Clause 1: A computer-implemented method comprising: receiving, with at least one processor, scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device; generating, with the at least one processor, an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data; generating, with the at least one processor, a density map of the at least a portion of the brain using a set of tracts from the initial tractogram; identifying, with the at least one processor, each tract of the set of tracts that passes through a segment of a plurality of segments of the density map more than once, and setting a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value; generating, with the at least one processor, a pruned tractogram from the initial tractogram by identifying at least one segment of the plurality of segments having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment from the pruned tractogram; and communicating, with the at least one processor, the pruned tractogram for display on a computing device.

Clause 2: The computer-implemented method of clause 1, wherein the computing device comprises a neuronavigator device programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

Clause 3: The computer-implemented method of clause 1 or 2, wherein the density map is a three-dimensional (3D) histogram and the plurality of segments are a plurality of voxels of the 3D histogram.

Clause 4: The computer-implemented method of any of clauses 1-3, wherein the pruned tractogram comprises only voxels having unique tract counts greater than the threshold pruning value, and wherein the threshold pruning value is one.

Clause 5: The computer-implemented method of any of clauses 1-4, further comprising: generating, with the at least one processor, a second density map of the at least a portion of the brain using a second set of tracts from the initial tractogram; identifying, with the at least one processor, each tract of the second set of tracts that passes through a segment of a plurality of segments of the second density map more than once, and setting a contribution of said tract to a unique tract count of the segment of the plurality of segments of the second density map equal to the threshold pruning value; and generating, with the at least one processor, an intermediate tractogram from the initial tractogram by identifying at least one segment of the plurality of segments of the second density map having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment of the plurality of segments of the second density map from the intermediate tractogram, wherein the pruned tractogram is generated at least partially from intermediate tractogram.

Clause 6: The computer-implemented method of any of clauses 1-5, further comprising: displaying, with the at least one processor, the pruned tractogram in a graphical user interface; receiving, with the at least one processor, input data from a user comprising additional tracts for removal from the pruned tractogram; and modifying, with the at least one processor, the pruned tractogram based on the input data.

Clause 7: The computer-implemented method of any of clauses 1-6, wherein the computing device comprises a neuronavigator device programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

Clause 8: A computer program product comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to: receive scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device; generate an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data; generate a density map of the at least a portion of the brain using a set of tracts from the initial tractogram; identify each tract of the set of tracts that passes through a segment of a plurality of segments of the density map more than once, and set a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value; generate a pruned tractogram from the initial tractogram by identifying at least one segment of the plurality of segments having a unique tract count equal to the threshold pruning value and excluding the at least one segment from the pruned tractogram; and communicate the pruned tractogram for display on a computing device.

Clause 9: The computer program product of clause 8, wherein the computing device comprises a neuronavigator device programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

Clause 10: The computer program product of clause 8 or 9, wherein the density map is a three-dimensional (3D) histogram and the plurality of segments are a plurality of voxels of the 3D histogram.

Clause 11: The computer program product of any of clauses 8-10, wherein the pruned tractogram comprises only voxels having unique tract counts greater than the threshold pruning value, and wherein the threshold pruning value is one.

Clause 12: The computer program product of any of clauses 8-11, wherein the program instructions further cause the at least one processor to: generate a second density map of the at least a portion of the brain using a second set of tracts from the initial tractogram; identify each tract of the second set of tracts that passes through a segment of a plurality of segments of the second density map more than once, and setting a contribution of said tract to a unique tract count of the segment of the plurality of segments of the second density map equal to the threshold pruning value; and generate an intermediate tractogram from the initial tractogram by identifying at least one segment of the plurality of segments of the second density map having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment of the plurality of segments of the second density map from the intermediate tractogram, wherein the pruned tractogram is generated at least partially from the intermediate tractogram.

Clause 13: The computer program product of any of clauses 8-12, wherein the program instructions further cause the at least one processor to: display the pruned tractogram in a graphical user interface; receive input data from a user comprising additional tracts for removal from the pruned tractogram; and modify the pruned tractogram based on the input data.

Clause 14: The computer program product of any of clauses 8-13, wherein the computing device comprises a neuronavigator device programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

Clause 15: A system comprising a neuronavigator device comprising at least one processor that is programmed and/or configured to: receive scan data produced by diffusion imaging of at least a portion of a brain from a magnetic-resonance imaging (MRI) device; generate an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data; generate a density map of the at least a portion of the brain using a set of tracts from the initial tractogram; identify each tract of the set of tracts that passes through a segment of a plurality of segments of the density map more than once, and set a contribution of said tract to a unique tract count of the segment equal to a threshold pruning value; generate a pruned tractogram from the initial tractogram by identifying at least one segment of the plurality of segments having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment from the pruned tractogram; and display the pruned tractogram.

Clause 16: The system of clause 15, wherein the at least one processor is further programmed and/or configured to use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

Clause 17: The system of clause 15 or 16, wherein the density map is a three-dimensional (3D) histogram and the plurality of segments are a plurality of voxels of the 3D histogram.

Clause 18: The system of any of clauses 15-17, wherein the pruned tractogram comprises only voxels having unique tract counts greater than the threshold pruning value, and wherein the threshold pruning value is one.

Clause 19: The system of any of clauses 15-18, wherein the at least one processor is further programmed and/or configured to: generate a second density map of the at least a portion of the brain using a second set of tracts from the initial tractogram; identify each tract of the second set of tracts that passes through a segment of a plurality of segments of the second density map more than once, and setting a contribution of said tract to a unique tract count of the segment of the plurality of segments of the second density map equal to the threshold pruning value; and generate an intermediate tractogram from the initial tractogram by identifying at least one segment of the plurality of segments of the second density map having a unique tract count less than or equal to the threshold pruning value and excluding the at least one segment of the plurality of segments of the second density map from the intermediate tractogram, wherein the pruned tractogram is generated at least partially from the intermediate tractogram.

Clause 20: The system of any of clauses 15-19, wherein the at least one processor is further programmed and/or configured to: display the pruned tractogram in a graphical user interface; receive input data from a user comprising additional tracts for removal from the pruned tractogram; modify the pruned tractogram based on the input data; and use the pruned tractogram in a computer-aided surgical process of the brain to avoid affecting a critical neural pathway of the brain.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

As used herein, the term “computing device” may refer to one or more electronic devices configured to process data. A computing device may, in some examples, include the necessary components to receive, process, and output data, such as a processor, a display, a memory, an input device, a network interface, and/or the like. A computing device may be a mobile device. As an example, a mobile device may include a cellular phone (e.g., a smartphone or standard cellular phone), a portable computer, a wearable device (e.g., watches, glasses, lenses, clothing, and/or the like), a personal digital assistant (PDA), and/or other like devices. A computing device may also be a desktop computer or other form of non-mobile computer. A computing device may include a computer program product, e.g., one or more non-transitory computer-readable data storage media programmed and/or configured to store program instructions that, when executed by one or more processors, cause one or more processors to execute a computer function.

As used herein, the term “server” may refer to or include one or more computing devices that are operated by or facilitate communication and processing for multiple parties in a network environment, such as the Internet, although it will be appreciated that communication may be facilitated over one or more public or private network environments and that various other arrangements are possible. Further, multiple computing devices (e.g., servers, mobile devices, etc.) directly or indirectly communicating in the network environment may constitute a “system.” Reference to “a server” or “a processor,” as used herein, may refer to a previously-recited server and/or processor that is recited as performing a previous step or function, a different server and/or processor, and/or a combination of servers and/or processors. For example, as used in the specification and the claims, a first server and/or a first processor that is recited as performing a first step or function may refer to the same or different server and/or a processor recited as performing a second step or function.

As used herein, the term “graphical user interface” (GUI) refers to a generated display, such as one or more displays with which a user may interact, either directly or indirectly (e.g., through a keyboard, mouse, touchscreen, etc.).

Every brain tumor surgery offers a unique challenge. The surgical approach that the surgeon takes in a brain tumor surgery depends on the location of the tumor and the displacement of critical neuronal pathways around the tumor. To avoid damaging important fiber pathways (e.g., motor, sensory, language, etc.), neurosurgeons have used surgical navigators, e.g., neuronavigators, to inform brain structure in the operation room. However, current surgical navigators use conventional structural imaging, which does not provide trajectories for important fiber pathways. Neurosurgeons also use diffusion tensor imaging (DTI), but that approach is limited by its accuracy, known to exhibit 40% error in mapping fiber trajectories.

DTI is the MRI-based technology available in current surgical navigators. DTI has several limitations, which impacts the accuracy of surgery and safety of patients. DTI acquires only 30 or 60 sampling directions at only one diffusion sensitization, and a weak gradient coil only at 2.5 mm. The existing DTI methods model diffusion patterns as simple Gaussian distributions. DTI requires 30-60 diffusion gradient samplings to calculate the diffusion tensor, and the axonal direction can be determined by the principal direction of the tensor. Based on this principal direction, the trajectories of the axonal connections can be tracked by diffusion fiber tracking. However, the Gaussian model is considered inadequate in real-world applications because more than 90% of the human brain volume has complicated fiber geometry. Results derived from DTI miss a substantial number of important crossing structures and sometimes create false connections due to tracking failure in the crossing regions, which can lead to even worse consequences.

High Accuracy Fiber Tracking (HAFT) is an MRI neuronavigation software platform and method used for intraoperative MRI neuronavigation that has significantly greater imaging accuracy than other solutions (e.g., DTI: ˜40% error, HAFT: ˜8% error), allowing for the critical pathways of the brain to be left undamaged leading to reduced post-operative complications during brain tumor removal surgery. Reduced patient complications leads to reduced health care costs. HAFT is a technology that implements a high-sampling-diffusion MRI scan that is modeled by a nonparametric diffusion distribution that guides a fiber tracking algorithm. The approach that this technology uses improves the mapping accuracy of neuronavigators used for brain tumor surgeries.

HAFT pushes the limitation of diffusion MRI to acquire 258 diffusion sampling directions at 23 different sensitization strengths. This high angular resolution diffusion spectrum acquisition provides a means of acquiring the most detailed diffusion data from tumor patients.

The superiority of HAFT over the commonly used DTI approach has already been demonstrated in several publications. Studies have shown that HAFT (a tractogram therefrom shown inFIG.1B) is a major improvement over DTI tractography (a tractogram therefrom shown inFIG.1A) and correlated well with histology and cadaver microdissection in mapping several fiber pathways.

A preliminary study has shown that HAFT can resolve multiple fiber directions, overcome the complexity of the tissue geometry and provide quantitative data, such as track volume and anisotropic diffusion and the total volume of white matter pathways. This white matter information cannot be detected using conventional diffusion tensor imaging (DTI) (compare HAFT, shown inFIG.1B, to DTI, shown inFIG.1A). Notwithstanding, tractograms produced by diffusion MRI fiber tracking may include false connections. Fiber bundles may include tracts exhibiting premature termination or false continuity. There is a need in the art for a technical solution to produce enhanced tractograms, such as tractograms having false connections pruned from the generated tractography.

Described systems and methods provide a technical solution referred to herein as topology-informed pruning (TIP), a method that automatically identifies singular tracts and eliminates them to improve neural tracking accuracy. The accuracy of the tractography with and without TIP was evaluated by a team of 6 neuroanatomists in a blinded setting to examine whether TIP could improve the accuracy. The results showed that TIP improved the tracking accuracy by 11.93% in the single-shell scheme and by 3.47% in the grid scheme. The improvement is statistically significantly different from a random pruning (p-value<0.001). The diagnostic agreement between TIP and neuroanatomists was comparable to the agreement between neuroanatomists. The proposed TIP algorithm can be used to automatically clean up noisy fibers in deterministic tractography, with a potential to confirm the existence of a fiber connection in basic neuroanatomical studies or clinical neurosurgical planning.

To understand the improvements in accuracy, it is noted that deterministic tractography may identify false connections. There are two major causes for false connections: premature termination and false continuity. Premature terminations can be partly addressed by using a white matter mask. The mask allows for automatic checking of the fiber trajectory endpoints, and then false endpoints are rejected to achieve a better accuracy. Based on this paradigm, demarcation between white and gray matter boundaries derived from T1-weighted images have been used as a tract-termination benchmark to cope with premature termination problem. False tracts were determined as those prematurely terminating in white rather than gray matter, while real tracts were defined as those which terminated in gray matter, as determined by the overlap of T1 and diffusion-weighted images. However, delineating an accurate white matter mask can be complicated by image distortion in addition to the resolution mismatch between the diffusion-weighted and T1-weighted images. It is possible that this termination check may introduce another error due to an imperfect white matter mask.

Although premature termination can be detected using a white matter mask, there is no effective strategy to detect false continuity using structural images or diffusion data. This is due to the limitation of diffusion MRI techniques in resolving the exact configuration of crossing fibers, such as bending, fanning, or interdigitating. Diffusion signals cannot alone differentiate these configurations within the voxel space. Described systems and methods relieve the requirement for a priori knowledge of white matter anatomy to validate tractography results against the false continuity issue.

By separating whole-brain trajectories into clusters based on their neighboring distance, it may be shown that clusters with fewer neighboring tracts are more likely to be false connections with false continuity. A track with few neighboring tracts may have a high likelihood of being a false continuity. One potential explanation of this phenomenon is that the false tracts arise between the overlapping boundaries of two real, adjacent fiber bundles. Since this overlapping boundary only forms a touching surface or line between two fiber pathways, it will only allow for a limited number of trajectories to pass through and cause false continuity. Moreover, errors of fiber tracking will accumulate during the tracking process. As a result, the trajectories that pass through the overlapping boundaries tend to have very diverse propagation routes due to the perturbation around the boundaries.

These two unique conditions combined will greatly reduce the chance of a false continuity tract to find a neighboring fiber trajectory. Accordingly, the described systems and methods operate to make use of the concept that a singular tract (e.g., a trajectory with no neighboring tract) has a higher likelihood of being a false continuity connection. This concept underlies the topology-informed pruning (TIP) algorithm, which uses the topology of a tractogram itself to identify candidate false connections for removal. In non-limiting embodiments, false connections may be identified by constructing a 3D tract density histogram to single out voxels with only one track passing through them, then subsequently eliminating those singular tracts to improve the accuracy.

One effect provided by the technology is the decrease of incidence of postsurgical morbidities, such as cognitive and motor deficits, as well as the increase of patient survival. The technology may be implemented with a neuronavigator device (e.g., a computing device used by neurosurgeons to plan and guide the surgical approach allowing access to the brain tumor, also called neuronavigators herein). To this end, patients may benefit in undergoing a surgical procedure that provides a more accurate technology that improves surgical outcomes, and hospitals may reduce costs that are currently directed towards the care of patients that develop postsurgical complications. The improvement in survival outcomes is expected to be directly proportional to the improved capacity of neurosurgeons in accessing distant and complex areas to address existing lesions within the white matter.

Described systems and methods combine HAFT with neuronavigators and new modeling techniques to bridge a technical gap in prior neural pathway tracking solutions. HAFT uses diffusion MRI scans to model the diffusion patterns of critical fiber pathways and maps their trajectories. The trajectory information taken by HAFT is then supplied to the surgical navigator, allowing neurosurgeons to know where the critical pathways are during the surgery.

Referring now toFIG.2, shown is a process diagram depicting a topology-informed pruning (TIP) model according to non-limiting embodiments. The input data202are a set of trajectories (e.g., discrete curves of nerve fibers, also called tracts or traces) obtained from the tractogram of a target fiber bundle (e.g., set of nerve fibers). Tractography is a three-dimensional (3D) modeling technique used to visually represent nerve tracts using data collected by diffusion magnetic resonance imaging (MRI). The results are presented in two- or three-dimensional images called tractograms. Diffusion imaging with an MRI device produces in vivo magnetic resonance images of biological tissues sensitized with the local characteristics of molecular diffusion, e.g., water. The tractography of a fiber bundle can be obtained from a region-of-interest or any track selection approach. The number of trajectories should be high enough to produce a 3D histogram, e.g., as shown in a first density map204. Density maps may be 2D or 3D visual representations of densities (e.g., number of item in an area) of neural fibers based on location in a fiber bundle. A 3D histogram may represent an intensity of a value (e.g., number of unique nerve fibers) on a 2D grid or plane. A tract may pass through the same voxel more than one time, but it is only counted once in the histogram. The second step is to identify voxels with a track count less than or equal to a threshold pruning value (e.g., a value of one) to generate a second density map206. The threshold pruning value may be any value (e.g., one, two three, etc.) representative of a number of unique nerve tracts that, when a segment of a density map has a unique tract count less than or equal to the threshold pruning value, the segment likely contains one or more stray neural tracts or false connections, or no tracts at all. For example, the threshold pruning value may be one, and segments having a unique tract count of one may represent a segment containing a false connection. These voxels are nearby white matter regions where tracts “go astray” via an overlapping boundary. A set of tracts208are identified and excluded from the input data bundle202to produce a pruned tractogram210(e.g., a tractogram having one or more tracts removed from an initially generated tractogram). The whole process can be repeated until no more stray tracts are found. The computation complexity of TIP is O(N) (linear time complexity) where N is the number of trajectories. This TIP algorithm is fully automatic and requires no manual intervention to eliminate false connections.

Referring toFIG.3, depicted is an input-output diagram depicting the topology-informed pruning (TIP) process according to non-limiting embodiments. Shown are tractograms of an evaluated 60-year-old female patient diagnosed with glioblastoma. The patient was scanned on a Siemens 3T Tim Trio scanner using a twice-refocused spin-echo diffusion sequence to acquire the diffusion data. A total of 514 diffusion directions were sampled with a maximum b-value of 7000 s/mm2. The in-plane resolution was 2.4 mm. The slice thickness was 2.4 mm. The diffusion data were reconstructed using generalized q-sampling imaging with a diffusion sampling length ratio of 1.25. The restricted diffusion was quantified using restricted diffusion imaging to differentiate between tumor regions, edematous area, and normal white matter tissue. The tumor region and peritumoral edematous area were manually delineated to facilitate tracking the peritumoral fiber pathways. A peritumoral tractogram was generated using a deterministic fiber tracking algorithm with a region of interest placed at the peritumoral edematous region. A total of 10,000 tracts were calculated, and the tractograms with and without TIP were compared to examine whether TIP could improve quality.

With further reference toFIG.3, depicted are tractograms of arcuate fasciculus before and after being pruned by the TIP algorithm. The tractogram generated using the angular gyms region-of-interest (ROI) method shows “noisy” fibers, especially using the single-shell scheme, shown in a first pre-pruning tractogram302. The second pre-pruning tractogram306from the grid sampling scheme appears to be cleaner, but deviant trajectories are still visible. A first post-pruning tractogram304and second post-pruning tractogram308illustrate the removal of likely false connections by the TIP algorithm, to produce cleaner, more accurate tractograms. TIP effectively removes noisy fibers while retaining the main topology structure. The improvement is particularly striking in the single-shell scheme, as shown in the comparison of the first pre-pruning tractogram302relative to the first post-pruning tractogram304. The pruned tractograms appear to be consistent with microdis section results.

With reference toFIGS.4A and4B, depicted are evaluations of the accuracy improvements provided by described systems and methods of TIP according to non-limiting embodiments. The tractogram from a single-shell scheme is shown inFIG.4A, whereas the tractogram from the grid scheme is shown inFIG.4B. A total of 6 different evaluations were conducted independently by 6 neuroanatomists. The lines connect evaluations from the same neuroanatomists. In bothFIG.4AandFIG.4B, all evaluations unanimously showed improved accuracy after TIP was applied to the arcuate fasciculus tractography. The average improvement in the single-shell scheme was 11.93%, whereas the average improvement in the grid scheme was 3.47%. Improvement is most obvious in the single-shell dataset. The lower improvement in the grid scheme could be due to its lower sensitivity for demonstrating branching fibers, as shown in the original tractogram before pruning, inFIG.3.

Referring toFIG.5, depicted are tractograms of arcuate fasciculus using the single-shell and grid schemes, comparing manual pruning by experts to computer-driven pruning by TIP, according to non-limiting embodiments. Depicted are tractograms produced when false tracts are removed by a representative expert (e.g., neuroanatomist), compared with the same tracts pruned by TIP. The neuroanatomist chosen for illustration had the highest diagnostic agreement with the other 5 neuroanatomists and thus can be viewed as the representative neuroanatomist of the group. In both the single-shell and grid schemes, the TIP-processed tractogram was highly consistent with the neuroanatomist-pruned tractogram, though differences can still be observed at minor branches. The first expert-pruned tractogram502is comparatively similar to the first TIP-processed tractogram504, where a single-shell scheme was employed. The second expert-pruned tractogram506is comparatively similar to the second TIP-processed tractogram508, where a grid scheme was employed.

Referring toFIGS.6A,6B,7A, and7B, depicted is a stepwise process illustrating how TIP can be used to remove false connections in peritumoral tractography according to non-limiting embodiments. Shown inFIG.6Ais the T1-weighted image502of a brain scan by MRI. Shown inFIG.6Bis a 3D reconstruction504depicting the tumor (flesh color,506) located at frontoparietal region and its peritumoral edematous area (light-blue color,508).FIG.7Ashows the peritumoral tractogram generated directly from the fiber tracking algorithm after placing the region of interest at the peritumoral edematous region. The red arrows point to possible false connections that cross the several sulci due to false continuity. These pathways are false connections because there should be no pathway crossing a sulcus between two nearby gyri.FIG.7Bshows the same tractogram processed automatically by two consecutive TIP runs to eliminate false connections. The false connections pointed by red arrows were eliminated without any manual invention, suggesting that TIP is an effective tool for improving the accuracy of diffusion MRI tractography.

Referring toFIG.8, depicted is a system800for high-accuracy fiber tracking and eliminating false connections in tractograms, according to non-limiting embodiments. The system may include a magnetic resonance imaging (MRI) device802(e.g., an MRI machine) to acquire scan data (e.g., magnetic resonance image data) of at least a portion of a subject brain, e.g., a brain of a patient with a tumor, such as by diffusion magnetic resonance imaging. The MRI device802may communicate with a model processor808via a communication interface804(e.g., a local communication network, a cloud server system, a wired connection, and/or the like). Further disclosure of exemplary communication interfaces are described in connection withFIG.9, in the context of a computing device. The model processor808may be a computing device programmed and/or configured to generate tractograms of neural fiber bundles, such as by executing a HAFT process. The model processor808may have a dedicated modeling engine810for generating tractograms from scan data of MRI devices802. The scan data may be communicated to the model processor808via the communication interface804. The scan data may also be separately acquired from the MRI device802and delivered to the model processor808via a data input port (e.g., internet port, USB port, serial port, etc.) of the computing device.

The system800may further include a neuronavigator806. The neuronavigator806may be the same computing device comprising the model processor808. The neuronavigator806may be programmed and/or configured to execute a computer-aided surgical process of the brain. The neuronavigator806may include a display807configured to show one or more tractograms of a scanned brain. The neuronavigator806may further include a graphical user interface (GUI)809for receiving input from a user, e.g., a neuroanalyst, a surgeon, and/or the like. The GUI809may be programmed and/or configured to display a tractogram and accept user input (e.g., identification, selection, etc.) of one or more neural tracts for removal from a displayed fiber bundle.

Referring now toFIG.9, shown is a diagram of example components of a device900according to non-limiting embodiments. Device900may correspond to the MRI device802, model processor808, or neuronavigator806inFIG.8, as an example. In some non-limiting embodiments, such systems or devices may include at least one device900and/or at least one component of device900. The number and arrangement of components shown are provided as an example. In some non-limiting embodiments, device900may include additional components, fewer components, different components, or differently arranged components than those shown inFIG.8. Additionally, or alternatively, a set of components (e.g., one or more components) of device900may perform one or more functions described as being performed by another set of components of device900.

As shown inFIG.9, device900may include a bus902, a processor904, memory906, a storage component908, an input component910, an output component912, and a communication interface914. Bus902may include a component that permits communication among the components of device900. In some non-limiting embodiments, processor904may be implemented in hardware, firmware, or a combination of hardware and software. For example, processor904may include a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed to perform a function. Memory906may include random access memory (RAM), read only memory (ROM), and/or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, etc.) that stores information and/or instructions for use by processor904.

With continued reference toFIG.9, storage component908may store information and/or software related to the operation and use of device900. For example, storage component908may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, etc.) and/or another type of computer-readable medium. Input component910may include a component that permits device900to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, etc.). Additionally, or alternatively, input component910may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, etc.). Output component912may include a component that provides output information from device900(e.g., a display, a speaker, one or more light-emitting diodes (LEDs), etc.). Communication interface914may include a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, etc.) that enables device900to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communication interface914may permit device900to receive information from another device and/or provide information to another device. For example, communication interface914may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi® interface, a cellular network interface, and/or the like.

Device900may perform one or more processes described herein. Device900may perform these processes based on processor904executing software instructions stored by a computer-readable medium, such as memory906and/or storage component908. A computer-readable medium may include any non-transitory memory device. A memory device includes memory space located inside of a single physical storage device or memory space spread across multiple physical storage devices. Software instructions may be read into memory906and/or storage component908from another computer-readable medium or from another device via communication interface914. When executed, software instructions stored in memory906and/or storage component908may cause processor904to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software. The term “programmed or configured,” as used herein, refers to an arrangement of software, hardware circuitry, or any combination thereof on one or more devices.

Referring toFIG.10, depicted is a method1000of generating pruned tractograms of neural fiber bundles, according to non-limiting embodiments. One or more steps of the method1000may be executed by an MRI device802, a model processor808, and/or a neuronavigator806. A computing device comprising one or more of an MRI device802, a model processor808, and/or a neuronavigator806may execute one or more steps of method1000. In step1002, a model processor may receive scan data (e.g., communicated from another computing device) produced by diffusion imaging of at least a portion of a brain from an MRI device. In step1004, the model processor may generate an initial tractogram by mapping (e.g., tracing) neuronal fiber pathways (e.g., nerve fiber trajectories) of a target fiber bundle (e.g., set of neural fibers to be evaluated) of the scan data, such as by using a high accuracy fiber tracking (HAFT) process. In step1006, the model processor may generate a density map of the scanned portion of the brain using a set of tracts from the initial tractogram. The set of tracts may be some or all of the tract from the initially generated tractogram.

In step1008, the model processor may identify each tract that passes through a segment (e.g., two- or three-dimensional section, such as a unit of area by which a density map is constructed) of the density map more than once. The density map may be a three-dimensional (3D) histogram, and an evaluated segment may be a voxel (e.g., a unit of graphic information that defines a point in three-dimensional space). For each tract that passes through a same segment more than once, the tract's contribution to a unique tract count of the segment (e.g., the number of times counted in the total count) may be set to a threshold pruning value (e.g., one). For example, the model processor may identify a same tract passing through a particular voxel three times. Instead of the tract contributing to the tract count of the voxel three times, the contribution of the tract to the voxel's tract count may be set to one. In step1010, the model processor may determine one or more segments having unique tract counts less than or equal to the threshold pruning value (e.g., one) and exclude the segment from the density map and corresponding evaluated tractogram. In doing so, the model processor may generate a pruned tractogram where one or more (or all) segments having unique tract counts of one or less are excluded from the tractogram. In step1012, the model processor may communicate the pruned tractogram for display on a computing device (e.g., a neuronavigator).

Referring toFIG.11, depicted is a method1100of generating pruned tractograms of neural fiber bundles, according to non-limiting embodiments. One or more steps of the method1100may be executed by an MRI device802, a model processor808, and/or a neuronavigator806. A computing device comprising one or more of an MRI device802, a model processor808, and/or a neuronavigator806may execute one or more steps of method1100. Method1100illustrates the use of topology informed pruning (TIP) in multiple passes. In step1004, a model processor may generate an initial tractogram by mapping neuronal fiber pathways of a target fiber bundle of the scan data, such as by using a HAFT process. In step1102, the model processor may generate a second density map of at least a portion of the brain using a second set of tracts from the initial tractogram. The second set of tracts may be the same set of tracts used to generate the initial tractogram. In step1104, the model processor may identify each tract that passes through a segment of the second density map more than once. The second density map may be a three-dimensional (3D) histogram, and an evaluated segment may be a voxel. For each tract that passes through a same segment more than once, the tract's contribution to a unique tract count of the segment (e.g., number of unique neural fiber trajectories passing through a segment) may be set to a threshold pruning value (e.g., one). For example, the model processor may identify a same tract passing through a particular voxel three times. Instead of the tract contributing to the tract count of the voxel three times, the contribution of the tract to the voxel's tract count may be set to one.

In step1106, the model processor may determine one or more segments having unique tract counts less than or equal to the threshold pruning value (e.g., equal to one) and exclude the segment from the second density map and corresponding evaluated tractogram. In doing so, the model processor may generate a pruned tractogram where one or more (or all) segments having unique tract counts of the threshold pruning value or less are excluded from the tractogram. In step1108, the final pruned tractogram may be generated based on pruning from the first density map of the initial tractogram and the second density map of an intermediate tractogram (e.g., exclude voxels pruned in the first or second pass). The second density map may exclude one or more pruned voxels from the first pass of the first density map. One or more intermediate tractograms may be produced to generate the final pruned tractogram (e.g., more than two passes of a TIP method may be employed).

Referring toFIG.12, depicted is a method1200of generating pruned tractograms of neural fiber bundles, according to non-limiting embodiments. One or more steps of the method1200may be executed by an MRI device802, a model processor808, and/or a neuronavigator806. A computing device comprising one or more of an MRI device802, a model processor808, and/or a neuronavigator806may execute one or more steps of method1200. In step1202, a neuronavigator may display a pruned tractogram in a GUI. In step1204, the neuronavigator may receive input data from a user including additional tracts for removal from the pruned tractogram. For example, a user may navigate a three-dimensional model of the pruned tractogram using computer controls and select one or more portions of fiber tracts for removal from the pruned tractogram. In doing so, the user (e.g., a neuroanalyst, a surgeon, etc.) may be presented with a pre-pruned tractogram, which may be critical in a time-sensitive procedure. The neuroanalyst may then focus on removing tracts that may have survived one or more TIP passes. In step1206, the neuronavigator may modify the pruned tractogram based on the input data from the user. In step1208, the neuronavigator may facilitate a computer-aided surgical process of the brain, using the pruned tractogram as a reference model of the patient's brain, to avoid affecting a critical neural pathway of the brain (e.g., neural pathways that, if removed, would result in a loss of function in the brain). Step1208may be executed after any version of a generated pruned tractogram is produced, such as after step1010or step1012inFIG.10, after step1108inFIG.11, and/or the like.

With further reference to the foregoing figures, it will be appreciated that changing parameter settings for the TIP algorithm could expand its potential applications. For example, the threshold for defining low-density voxels can be adjusted to adapt to a different seeding density. A high threshold will yield highly confirmative results to justify the existence of a fiber pathway. As more and more fiber tracking studies have been proposed to discover human brain pathways and their segmentation, TIP can strengthen the results by boosting the accuracy of tractography. The number of recursive iterations can be limited to a small number to allow for different pruning effects. For studies correlating structural connectivity with neuropsychological measures, a single iteration of TIP can complement group connectometry analysis to achieve better false discovery rates. In neurosurgical applications, TIP can assist mapping of peritumoral pathways to help neurosurgeons organize a detailed surgical plan.