Method and system for assessing pedicle screw positioning accuracy

Abstract

An orthopedic surgical system includes a processor configured to receive preoperative 3D image data of a planned pedicle screw placement in a patient's vertebra; obtain intraoperative or postoperative 3D image data of a pedicle screw placement in the patient's vertebra; and determine an accuracy measurement of the pedicle screw placement in the patient's vertebra based on the preoperative 3D image data and the intraoperative or postoperative 3D image data. The accuracy measurement of the pedicle screw placement may be based on a distance from a pedicle screw to a cortical bone of a patient's vertebra or a distance from a pedicle screw to region of interest of the patient.

Description

METHOD AND SYSTEM FOR ASSESSING PEDICLE SCREW POSITIONING ACCURACYFIELD OF THE INVENTION

[0001] The present invention relates to a method and system for assessing the accuracy of vertebral pedicle screw placement.BACKGROUND

[0002] Pedicle screws and other bone screws are the primary bone anchors for spine hardware constructs used in spine fusion and spine deformity correction surgery. Insertion of bone anchors in a safe manner is a primary goal of spine surgery. Conventional methods for determining pedicle screw accuracy and pedicle screw breach include visual analysis. Such conventional methods are fraught with systematic biases as well as observer biases that affect the reliability of the data. Further, during conventional methods accuracy and breach cannot be determined until after surgery, rather than during surgery where inaccurate placement may be corrected. Well-documented complications associated with bone anchor placement include neurological complications, vascular complications, and complications associated with pulmonary (pleura, lung, trachea) and gastrointestinal (esophagus) systems. Most of these complications relate to errors in the three-dimensional (3D) placement of the drill, tap or screw.SUMMARY OF THE DISCLOSURE

[0003] The present disclosure provides a system and method to compare a preoperative 3D image or tomography scan (e.g., CT, or MRI scan) with an intraoperative 3D image or tomography scan and / or a postoperative 3D image or tomography scan in order to determine a pedicle screw accuracy measurement and / or pedicle screw breach. The intraoperative image or tomography scan may display screws in place or the screws may be in the process of being placed. The system and method may guide correction of the pedicle screw position when indicated. Although the disclosure describes 3D images and tomographic scans, such embodiments are for illustration purposes only and should not be considered limiting. Exemplary embodiments of the methods described herein can include other preoperative, intraoperative, and postoperative images and scans, such as 2Dimages (X-rays, ultrasounds) and scans, 4D images and scans, 5D images and scans, and the like.

[0004] In an aspect of the present disclosure, the system may be an orthopedic surgical system (e.g., automated and / or semi-automated orthopedic surgical system). The automated orthopedic surgical system may include a processor configured to receive preoperative 3D image data of a planned pedicle screw placement in a patient’s vertebra; obtain intraoperative or postoperative 3D image data of a pedicle screw placement in the patient’s vertebra; determine an accuracy measurement of the pedicle screw placement in the patient’s vertebra based on the preoperative 3D image data and the intraoperative and / or postoperative 3D image data.

[0005] In an aspect of the present disclosure, the accuracy measurement is based on comparing a planned pedicle screw tip position and intraoperative or postoperative 3D image data of a pedicle screw tip; a planned pedicle screw midportion position and intraoperative or postoperative 3D image data of a pedicle screw midportion; and / or a planned pedicle screw tail position and intraoperative or postoperative 3D image data of a pedicle screw tail.

[0006] In an aspect of the present disclosure, the processor is configured to determine an accuracy measurement including a distance from a pedicle screw (e.g., the tip, midportion or tail of a pedicle screw) to a cortical bone of a patient’s vertebra.

[0007] In an aspect of the present disclosure, the processor is configured to determine an accuracy measurement including a distance from a pedicle screw to an extra-vertebral anatomic structure of the patient.

[0008] In an aspect of the present disclosure, the extra-vertebral anatomic structure can be a nerve, an artery, a vein, or a visceral organ such as, for example, a spinal cord, a phrenic nerve, a vagus nerve, an aorta, a vena cava, an esophagus, a lung, a trachea, and a bronchus.

[0009] In an aspect of the present disclosure, the processor is configured to determine an accuracy measurement based on geometric offsets of the pedicle screw based on data obtained from the preoperative 3D image data and the intraoperative or postoperative 3D image data.

[0010] In an aspect of the present disclosure, the processor is configured to determine an accuracy measurement based on a position of a pedicle wall of the patient’s vertebra obtained from the preoperative 3D image data and a position ofthe pedicle screw (e.g., a medial aspect of the pedicle screw or the tip of the pedicle screw) relative to the position of the pedicle wall obtained from the intraoperative or postoperative 3D image data.

[0011] In an aspect of the present disclosure, a method for assessing pedicle screw placement (e.g., assessing the accuracy of pedicle screw placement) in a patient includes receiving a preoperative 3D image of a vertebrae of a patient; determining a desired trajectory and position for insertion of the pedicle screw into the vertebrae of the patient; inserting the pedicle screw into the vertebral pedicle of the patient; receiving at least one of an intraoperative 3D image or postoperative 3D image of the vertebrae of the patient having the pedicle screw inserted therein; and determining an accuracy measurement of a pedicle screw position in the patient’s vertebra based on the desired trajectory and position for insertion of the pedicle screw into the vertebrae of the patient and the at least one intraoperative 3D image or postoperative 3D image.

[0012] In an aspect of the present disclosure, a method is provided for inserting a vertebral pedicle screw in a patient in need thereof. The method includes obtaining a preoperative 3D image of a vertebral pedicle in a patient in need thereof; planning a desired path for insertion of the screw into the patient’s vertebral pedicle; inserting a vertebral pedicle screw into the patient’s vertebral pedicle; obtaining an intraoperative or postoperative 3D image of the vertebral pedicle having the pedicle screw inserted therein; and determining, using a computer, an accuracy measurement of the pedicle screw compared to the planned desired path for insertion of the pedicle screw into the patient’s vertebral pedicle based on the preoperative 3D image and the intraoperative or postoperative 3D image.

[0013] In an aspect of the present disclosure, the method includes providing an accuracy measurement based on a distance from a pedicle screw to an extra- vertebral anatomic structure of the patient obtained from the intraoperative or postoperative 3D image.

[0014] The present disclosure includes the following aspect of the invention: 1 . An orthopedic surgical system comprising a processor, characterized by the processor being configured to: receive preoperative 3D image data of a planned pedicle screw placement in a patient’s vertebra; receive at least one of an intraoperative 3D image data of the pedicle screw placement in the patient’s vertebra or a postoperative 3D image data of the pedicle screw placement in the patient’svertebra; and determine an accuracy measurement of a pedicle screw position in the patient’s vertebra based on the preoperative 3D image data and the at least one of the intraoperative 3D image data or the postoperative 3D image data.2. The system of 1 , wherein the accuracy measurement of the pedicle screw position in the patient’s vertebra is based on a distance from the pedicle screw placement to a cortical bone of a patient’s vertebra obtained from the at least one of the intraoperative 3D image data or the postoperative 3D image data.3. The system of 1 , wherein the accuracy measurement of the pedicle screw position in the patient’s vertebra is based on a distance from a pedicle screw to a region of interest of the patient obtained from the at least one of the intraoperative 3D image data or the postoperative 3D image data.4. The system of 3, wherein the region of interest comprises at least one of a nerve, an artery, a vein, a visceral organ, a spinal cord, a phrenic nerve, a vagus nerve, an aorta, a vena cava, an esophagus, a lung, a trachea, or a bronchus.5. The system of 1 , wherein the accuracy measurement of the pedicle screw position in the patient’s vertebra is based on geometric offsets of a pedicle screw based on data obtained from the preoperative 3D image data and the at least one of the intraoperative 3D image data or the postoperative 3D image data.6. The system of 1 , wherein the accuracy measurement of the pedicle screw position in the patient’s vertebra is based on a position of a pedicle wall of the patient’s vertebra obtained from the preoperative 3D image data and a position of a pedicle screw relative to the position of the pedicle wall obtained from the at least one of the intraoperative 3D image data or the postoperative 3D image data.7. The system of 1 , wherein the accuracy measurement of the pedicle screw placement in the patient’s vertebra is based on comparing at least one of a group consisting of a planned pedicle screw tip position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw tip; a planned pedicle screw midportion position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw midportion; and a planned pedicle screw tail position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw tail.8. A method for assessing pedicle screw placement in a patient, characterized by: receiving, via a processor, a preoperative 3D image of a vertebrae of a patient; determining a desired trajectory and position for insertion of the pedicle screw intothe vertebrae of the patient; inserting the pedicle screw into the vertebral pedicle of the patient; receiving at least one of an intraoperative 3D image or a postoperative 3D image of the vertebrae of the patient having the pedicle screw inserted therein; and determining an accuracy measurement of a pedicle screw position in the patient’s vertebra based on the desired trajectory and position for insertion of the pedicle screw into the vertebrae of the patient and the at least one of the intraoperative 3D image or the postoperative 3D image.9. The method of 8, wherein the accuracy measurement of the pedicle screw position in the patient’s vertebrae is based on a distance from the pedicle screw to a cortical bone of a patient’s vertebrae.10. The method of 8, wherein the accuracy measurement of the pedicle screw position in the vertebrae of the patient is based on a distance from the pedicle screw to a region of interest of the patient obtained via at least one of the intraoperative 3D image or the postoperative 3D image.11 . The method of 10, wherein the region of interest comprises at least one of a nerve, an artery, a vein, a visceral organ, a spinal cord, a phrenic nerve, a vagus nerve, an aorta, a vena cava, an esophagus, a lung, a trachea, or a bronchus.12. The method of 8, wherein the accuracy measurement of the pedicle screw position in the patient’s vertebrae is based on geometric offsets of a pedicle screw based on data obtained from the preoperative 3D image data and at least one of the intraoperative or the postoperative 3D image data.13. The method of 8, wherein the accuracy measurement of the pedicle screw position in the patient’s vertebrae is based on a position of a pedicle wall of the vertebrae obtained from the preoperative 3D image data and a position of the pedicle screw relative to the position of the pedicle wall obtained from at least one of the intraoperative 3D image data or the postoperative 3D image data.14. The method of 8, further comprising adjusting a position of the pedicle screw if the pedicle screw breaches a predetermined position within the vertebrae of the patient or if the pedicle screw deviates from the determined desired trajectory or position.15. The method of 8, wherein the accuracy measurement of the pedicle screw placement in the patient’s vertebra is based on comparing at least one of a group consisting of a planned pedicle screw tip position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screwtip; a planned pedicle screw midportion position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw midportion; and a planned pedicle screw tail position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw tail.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The foregoing summary, as well as the following detailed description of the exemplary embodiments, will be better understood when read in conjunction with the appended drawing[s]. For the purpose of illustration, there are shown in the drawings exemplary embodiments of the subject disclosure. It should be understood, however, that the subject disclosure is not limited to the precise arrangements and instrumentalities shown.

[0016] FIGS. 1 A, 1 B illustrate example vertebrae with pedicle screws in place;

[0017] FIG. 2 is a flowchart illustrating a registration method of an orthopedic surgical system in accordance with an exemplary embodiment of the subject disclosure;

[0018] FIG. 3 is a flowchart illustrating an accuracy measurement method of an orthopedic surgical system in accordance with an exemplary embodiment of the subject disclosure;

[0019] FIG. 4 is a flowchart illustrating an accuracy measurement method of an orthopedic surgical system in accordance with an exemplary embodiment of the subject disclosure;

[0020] FIG. 5 is a flowchart illustrating a method of assessing pedicle screw position accuracy in accordance with an exemplary embodiment of the subject disclosure; and

[0021] FIG. 6 is a schematic diagram of an orthopedic surgical system in accordance with an exemplary embodiment of the subject disclosure.DETAILED DESCRIPTION

[0022] Reference will now be made in detail to the various exemplary examples of the subject disclosure illustrated in the accompanying drawingfs]. Wherever possible, the same or like reference numbers will be used throughout the drawings to refer to the same or like features. It should be noted that the drawings are in simplified form and are not drawn to precise scale. Certain terminology isused in the following description for convenience only and is not limiting. Directional terms such as top, bottom, left, right, above, below and diagonal, are used with respect to the accompanying drawings. The term “distal” shall mean away from the center of a body. The term “proximal” shall mean closer towards the center of a body and / or away from the “distal” end. The words “inwardly” and “outwardly” refer to directions toward and away from, respectively, the geometric center of the identified element and designated parts thereof. Such directional terms used in conjunction with the following description of the drawings should not be construed to limit the scope of the subject disclosure in any manner not explicitly set forth. Additionally, the term “a,” as used in the specification, means “at least one.” The terminology includes the words above specifically mentioned, derivatives thereof, and words of similar import.

[0023] “About” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, or ±0.1% from the specified value, as such variations are appropriate.

[0024] “Substantially” as used herein shall mean considerable in extent, largely but not wholly that which is specified, or an appropriate variation therefrom as is acceptable within the field of art. “Exemplary” as used herein shall mean serving as a non-limiting example.

[0025] Throughout this disclosure, various aspects of the subject disclosure can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the subject disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1 , 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.

[0026] Furthermore, the described features, advantages, and characteristics of the exemplary embodiments of the subject disclosure may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art willrecognize, in light of the description herein, that the present disclosure can be practiced without one or more of the specific features or advantages of a particular exemplary embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all exemplary embodiments of the subject disclosure.Definitions:

[0027] “Pre-Op 3D Imaging” is an image or volumetric image obtained preoperatively and containing at least multiple levels of the vertebral column (spine) involved in a surgery. Example Pre-Op 3D Imaging include a CT scan or an MRI scan.

[0028] “Pre-Op Plan” is a preoperative plan of screw trajectories defined in reference to the Pre-Op 3D imaging. Pre-Op Plans can include the dimensions (shaft length, shaft diameter, etc.) and intended position of each screw.

[0029] “Intra-Op 3D Imaging” is an intraoperative image or volumetric image containing at least multiple levels of the spine involved in a surgery. Examples include a CT scan, MRI scan, a fluoroscopy scan, 3D cone-beam CT, flat-panel X- ray detectors, and OEC 3D (GE Healthcare).

[0030] A “Binary Mask” is a voxel-wise segmentation mask generated for each bone for which screws are planned. This mask should correspond to the anatomical envelope of the bone. However, even significant segmentation errors will have minimal effect on the registration process (see FIG. 2), as long as the mask largely overlaps with the rigid structure of the relevant bone.

[0031] A “Fitted Screw” is a screw position stored as xyz coordinates of a screw tip and tail in a local coordinate system. Because screw structure is known a priori, only position is required for the screw detection technique of the present disclosure.

[0032] “Initial Alignment” is an alignment formulated as a rigid-body transform for each bone, stored as a 4x4 homogeneous transformation matrix with orthonormal rotational components (six degrees of freedom: three rotational and 3 translational).

[0033] “Final Alignment” is a rigid-body transformation for each bone stored as a 4x4 homogeneous transformation matrix with orthonormal rotational components (six degrees of freedom: three rotational and 3 translational).

[0034] “Translation / Rotation Error” is an accuracy defined in reference to the planned screw position in a screw-axis aligned coordinate system. After aligning the preoperative coordinate system with the postoperative volume, errors are computed as the translational and rotational offset between the preoperative planned screws and the postoperative Fitted Screws.

[0035] “Surface Label” means, for each bone’s Binary Mask, the surface envelope is extracted as a triangulated mesh in 3D space and all anatomical regions are identified. For breach analysis, the medial-inferior section of the pedicle is considered, as this is the portion of the pedicle adjacent to the spinal canal.

[0036] “Safety Measurements” and “Accuracy Measurements” according to the present disclosure may refer to one or more of the following: deviation of screw placement from the preoperative plan, the presence of medial pedicle breach, the maximal medial pedicle breach distance, the minimal distance to the pedicle cortical bone for non-breached screws, and the distance to extra-vertebral soft tissues.

[0037] Spinal fusion surgery is commonly used to treat degenerative disc disease. Spinal fusion typically involves distracting and / or decompressing one or more intervertebral spaces, removing any associated facet joints or discs, and joining or fusing two or more adjacent vertebra together. Fusion of vertebral bodies may involve fixation of two or more adjacent vertebrae, which may be accomplished through introduction of rods or plates, and screws (e.g., pedicle screws) or other devices into a vertebral joint to join various portions of a vertebra to a corresponding portion on an adjacent vertebra. Fusion may occur in the lumbar, thoracic or cervical spine region of a patient.

[0038] FIG. 1 A is a schematic representative of adjacent vertebrae 106a, 106b being affixed using two pedicle screws 102a, 102b and a rod 104. FIG. 1 B is a schematic representation of a lumbar vertebra 150. Starting from the anterior portion, the lumbar vertebra includes a vertebra body 152, a pedicle 154, a facet joint 156, a lamina 158, a transverse process 162, and a spinous process 160. As shown in FIG. 1 B, the pedicle 154 is a portion of a vertebra 150 between the vertebral body 152 and the lamina 158. Pedicle screws, such as pedicle screws 102a, 102b of FIG. 1 A, may be inserted through the pedicle 154 into the vertebral body 152 and are used to secure surgical hardware, such as the rod 104.

[0039] FIG. 6 illustrates a block diagram of a system 600 used to assess the accuracy of vertebral pedicle screw placement in accordance with an exemplaryembodiment of the subject disclosure The system 600 may be referred to as an orthopedic surgical system. The system 600 includes a processor 604 or other logic control, a memory 606, an input device 602, and a display 608 or other output. The processor 604 can include a memory, such as memory 606 or can be operatively connected to the memory 606. The memory 606 can be volatile memory, persistent memory, and the like.

[0040] Memory in the processor 604 can be allocated dynamically according to variables, variable states, static objects, and permissions associated with objects and variables in the system. Such memory allocation can be based on instructions stored in the memory 606. Memory resources can be conserved relative to other systems that do not associate variables and other objects with permission data for the specific device. The processor 604 can generate an output based on an input. For example, the processor 604 can receive an electronic and / or digital signal. The processor 604 can read the signal and perform one or more tasks with the signal, such as performing various functions with data in response to input received by the processing device.

[0041] The processor 604 can read information from the memory 606 that is needed to perform one or more functions. For example, the processor 604 can update a variable from static to dynamic based on a received input and a rule stored as data on the memory 606. The processor 604 can send an output signal to the memory 606, and the memory 606 can store data according to the signal output by the processor 604.

[0042] The processor 604 can include one or more processors, a microprocessors, a computer processing units (CPUs), graphics processing units (GPUs), neural processing units, physics processing units, digital signal processors, image signal processors, synergistic processing elements, field-programmable gate arrays (FPGAs), sound chips, multi-core processors, and the like. As used herein, “processor,” “processing component,” “processing device,” and / or “processing unit” can be used generically to refer to any or all of the aforementioned specific devices, elements, and / or features of the processor 504.

[0043] In accordance with an exemplary embodiment, the processor 604 is configured to receive preoperative 3D image data of a planned pedicle screw placement in a patient’s vertebra. The processor 604 is configured to obtain intraoperative or postoperative 3D image data of a pedicle screw placement in thepatient’s vertebra. The processor 604 is configured to determine an accuracy measurement of the pedicle screw placement in the patient’s vertebra based on the preoperative 3D image data and one or more of the intraoperative or postoperative 3D image data.

[0044] In an exemplary embodiment, the accuracy measurement of the pedicle screw placement in the patient’s vertebra can be based on comparing a planned pedicle screw tip position and at least one of an intraoperative 3D image data and / or a postoperative 3D image data of a pedicle screw tip. The accuracy measurement of the pedicle screw placement in the patient’s vertebra can be based on comparing a planned pedicle screw midportion position and at least one of an intraoperative 3D image data and / or a postoperative 3D image data of a pedicle screw midportion. The accuracy measurement of the pedicle screw placement in the patient’s vertebra can be based on comparing a planned pedicle screw tail position and at least one of an intraoperative 3D image data and / or a postoperative 3D image data of a pedicle screw tail. In embodiments, the accuracy measurement of the pedicle screw placement in the patient’s vertebra can be based on comparing a planned pedicle screw position of any one or more portions of the pedicle screw and at least one of an intraoperative 3D image data and / or a postoperative 3D image data of similar portion(s) of a pedicle screw.

[0045] In an exemplary embodiment, the processor 604 can be configured to determine if a pedicle screw breach is present within a patient. A pedicle screw breach can occur when a pedicle screw is malpositioned. A pedicle screw breach can weaken the stability of the spine of the patient and damage nerves of the patient. For example, a preoperative 3D CT (or MRI) image of a patient may be obtained and visualization / planning techniques can be used to plan pedicle screw placement, for example, to create a preoperative plan for pedicle screw placement. Screw position may be planned to avoid medial, superior and inferior breaches. The planned 3D screw position can be transferred into a robotic planning environment. A pedicle screw can be inserted in the patient during surgery according to the preoperative plan.

[0046] An intraoperative 3D scan (e.g., a CT scan, an MRI scan, a 3D conebeam CT, flat-panel X-ray detectors, OEC 3D (GE Healthcare), 3D fluoroscopy (O- Arm, FE, Ziehm, etc.) may be captured of the vertebra (e.g., of at least the vertebra) with the inserted screw. Automated segmentation techniques can be applied to thepreoperative image to determine an envelope of each vertebra having an inserted screw. As described herein, an envelope may include an outer boundary of a vertebra (e.g., segmented vertebra). In an embodiment, automated segmentation techniques can be applied to the preoperative image to label the voxels belonging to each vertebra.

[0047] Automatic rigid-body alignments can be performed for each bone (e.g., vertebral level) by maximizing mutual information with corresponding intraoperative vertebrae. As described herein, a vertebral level can be a single vertebra and / or a location of one or more vertebra within the spine. As used herein, maximizing mutual information can include a metric to determine the goodness of fit between two registered volumes based on comparing the voxel-wise intensity values.

[0048] Registration of an image (e.g., image alignment) can be performed. As used herein, a registration can include rotating and / or translating one 3D image until the image matches and / or aligns (e.g., best matches and / or aligns) with another image. For example, a registration can be performed that aligns one or more preoperative images, intraoperative images, and / or postoperative images. In an embodiment, a scan can be moved relative to another scan until the anatomy matches. For example, the preoperative scan can be considered an anchor or target position, and the postoperative (or intraoperative) scan can be moved until the bony anatomy lines up on top of the corresponding preoperative anatomy. In such example, each vertebra may be aligned (e.g., aligned separately) to allow for some differences in spine position between preoperative and postoperative scans. When two scans that were captured separately are to be compared, the scans should be aligned by aligning the scans into the same coordinate axes. For example, the preoperative scan can be aligned with the scan taken after screws were placed (e.g., intraoperative scans or scans taken several days later). After the scans are aligned, the preoperative plan can be compared to the actual postoperative screw position in a shared coordinate system.

[0049] Registrations can be performed which mask the preoperative image volume with the segmentation label. As used herein, masking may include a technique to discriminate geometries of dissimilar materials (e.g. discriminating a patient’s bone from a pedicle screw located within the patient). In an embodiment, masking may include disregarding one or more regions. For example, in a preoperative image, techniques may use (e.g., only use) a region of the vertebra inquestion and / or may ignore portions that are not associated with that bone. Such masking may be used so that nearby bones do not interfere with the registration, for example, in cases in which the spine is in a different position in the preoperative and postoperative scans.

[0050] The registration can be applied to bring the preoperative planned screw trajectory into alignment with intraoperative 3D imaging. Screw position can be determined (e.g., determined automatically) via one or more intraoperative 3D images and / or postoperative 3D images. For example, dimensions of the screws (e.g., via the preoperative plan) can be used to construct a geometric model of each screw. The geometric model can be fitted until the model matches (e.g., optimally matches) the CT data. Screw position (e.g., a position along the length of width of the screw) can be compared to the position in the preoperative plan using translations and angulations along one or more (e.g., all) three axes. A breach may be detected when the screw tip is detected outside of the pedicle envelope. For example, automated segmentation and computer vision techniques may align one or more (e.g., each) preoperative and postoperative vertebrae to compare positions along two or more (e.g., all three) axes. Registration may be necessary to compare positions defined in one image to another image, such as when comparing preoperative 3D images (target screw trajectories, drawn on preoperative CT scans) with intraoperative 3D images and / or postoperative 3D images.

[0051] The positions along two or more (e.g., all three) axes may be compared automatically. A pedicle screw breach may be calculated using 3D image data of the outer circumference of the screw implant and the pedicle walls. In another exemplary embodiment, a distance from the pedicle screw to the cortical bone of the patient’s vertebra may be determined. The position of the pedicle screw and the position of the cortical bone can be identified via intraoperative imaging and / or pre-operative images. Determining a distance from the pedicle screw to a cortical bone of the patient’s vertebra may be used to identify whether a pedicle screw breach is present.

[0052] An accuracy measurement can be a distance from a pedicle screw (e.g., a medial aspect of the pedicle screw) to one or more regions of interest within a patient’s body. For example, an accuracy measurement can be a distance from a pedicle screw to one or more portions of a patient’s spine, such as a cortical bone of a patient’s vertebra or the like. In an embodiment, an accuracy measurement can bea distance from a pedicle screw to one or more other devices implanted within a patient, such as another pedicle screw, a spinal implant, and the like. Screw accuracy may be defined as a three axis translation and angular deviation of intraoperative screw position versus planned trajectory. The steps of vertebral segmentation, image alignment, and geometric / error measurement may be automated.

[0053] As used herein, vertebral segmentation may refer to the use of 3D image processing techniques to identify the shape / geometry of a vertebra. In an embodiment, vertebral segmentation may include labeling each bone. For example, in a tomographic scan, vertebral segmentation may include marking each voxel that is part of a given structure. For spine scans vertebral segmentation may include each vertebra being labeled with a unique value. In an embodiment, segmentation may be defined as labeling regions in an image corresponding to the objects therein.

[0054] Geometric error measurement may refer to a 3D difference between one 3D object and another 3D object. For example, geometric error measurements may refer to a difference between a planned screw placement and / or trajectory and an actual pedicle screw placement. Geometric error measurements can include a distance (e.g., a distance between the planned screw tip and the actual screw tip) or an angle (e.g., an angle between planned screw axis and actual screw axis).

[0055] The distance from the screw to the medial-inferior pedicle wall may be calculated using the 3D outer circumference of the detected screw (e.g., approximated as a cylinder) and the 3D envelope of the pedicle wall as detected by the segmentation step. Pre-Op CT may be used by the segmentation step. In addition to distance measurements, the anatomic location of the relevant section of the pedicle wall may be determined. The anatomic location of the relevant section of the pedicle wall may include the most-breached point (for breached screws) or the point closest to being breached (for non-breached screws).

[0056] As described herein, accuracy measurement and safety measurement can be used interchangeably. In an exemplary embodiment of the subject disclosure, the accuracy measurement may be a distance from a pedicle screw to an extra-vertebral anatomic structure of the patient. For example, a preoperative 3D CT (or MRI) image may be obtained including at least a vertebra that will receive a pedicle screw and at least one extra-vertebral anatomic structure (e.g., a nerve, anartery, a vein, or a visceral organ such as a spinal cord, a phrenic nerve, a vagus nerve, an aorta, a vena cava, an esophagus, a lung, a trachea, and a bronchus).

[0057] Visualization / planning techniques can be used to plan pedicle screw placement. Screw position is planned to avoid medial, superior and inferior breach. The planned 3D screw position is transferred into a robotic planning environment. A pedicle screw is inserted in the patient during surgery according to the preoperative plan. An intraoperative 3D scan (e.g., a CT scan, an MRI scan, a 3D cone-beam CT, flat-panel X-ray detectors, OEC 3D (GE Healthcare), 3D fluoroscopy (O-Arm, FE, Ziehm, etc.)) of at least the vertebra with the inserted screw and the at least one extra-vertebral anatomic structure may be performed.

[0058] Segmentation techniques are applied to the preoperative image to determine the envelope of each vertebra having an inserted screw. The segmentation techniques may be based on neural networks. For example, segmentation techniques based on neural networks may use convolutional layers. The segmentation techniques may be trained on large datasets of spine scans that may be manually labeled and / or verified. Training techniques may include providing one or more algorithms a desired label map, such as each voxel being labeled with the correct value. The training techniques may consist of adjusting model weights until predictions match the ground truth. Upon the model being trained, a segmentation can be performed by providing a new image as input to the model. In such embodiment, predicted labels can be produced as an output of the model.

[0059] Automatic rigid-body alignments are performed for one or more (e.g., each) vertebral level by maximizing mutual information with corresponding intraoperative vertebrae. Registrations are performed to mask the preoperative image volume with the segmentation label. The registration is applied to bring the preoperative planned screw trajectory into alignment with intraoperative 3D imaging. Screw position is compared to the position in the preoperative plan, including translations and angulations along all three axes. Distance from the screw to the extra-vertebral anatomic structure is calculated using the 3D outer circumference of the detected screw (e.g., approximated as a cylinder) and the 3D envelope of the extra-vertebral anatomic structure.

[0060] In an exemplary embodiment of the subject disclosure, the accuracy measurement of a pedicle screw position (e.g., pedicle screw position in the patient’s vertebra) may be based on geometric offsets of the pedicle screw based on dataobtained from the preoperative 3D image data and the intraoperative or postoperative 3D image data. Non-limiting examples of a preoperative 3D imaging technology suitable for the present disclosure include a CT scan, and an MRI scan. Non-limiting examples of an intraoperative / postoperative 3D imaging technology suitable for the present disclosure include a CT scan, an MRI scan, a 3D cone-beam CT, flat-panel X-ray detectors, OEC 3D (GE Healthcare), and 3D fluoroscopy (O- Arm, FE, Ziehm, etc.). Accuracy can be defined in reference to the planned screw position, in a screw-axis aligned coordinate system. After aligning the coordinate space of the postoperative image with the coordinate space of the preoperative image, accuracy or errors can be computed as an amount of translational and / or rotational offset between the preoperative planned screw trajectory and the surgically placed screw position.

[0061] In an exemplary embodiment of the subject disclosure, the accuracy measurement is based on a position of a pedicle wall of the patient’s vertebra obtained from the preoperative 3D image data and a position of the pedicle screw relative to the position of the pedicle wall obtained from the intraoperative or postoperative 3D image data. For example, the accuracy measurement is based on a position of a medial wall the vertebral pedicle obtained from the preoperative 3D image data and a position of a medial aspect of a pedicle screw obtained from the intraoperative or postoperative 3D image data.

[0062] In an exemplary embodiment, the processor 604 may be configured to detect one or more regions of interest (e.g., anatomic structures, such as nerves, arteries, veins, visceral organs, spinal cords, phrenic nerves, vagus nerves, aortas, a vena cava, an esophagus, lungs, tracheas, bronchi, spinal cords, blood vessels, etc.) within a patient. For example, when inserting a screw, the processor 604 is configured to detect regions of interest within the patient. In such embodiments the processor 604 is configured to determine the accuracy measurement of pedicle screw placement based on the regions of interest detected within the patient via preoperative images.

[0063] In embodiments the processor 604 may be configured to detect one or more implants (e.g., other implants) in the patient. For example, when inserting a screw posteriorly, the processor 604 is configured to detect screws and / or other implants that may have been implanted within the patient via a different approach, such as via an anterior approach or from a past surgery. In such embodiments theprocessor 604 is configured to determine the accuracy measurement of the pedicle screw placement based on other implants detected within the patient via preoperative images.

[0064] In embodiments the processor 604 may be configured to detect one or more anatomical characteristics of the patient, such as the spinal cord of the patient. The processor 604 may detect the anatomical characteristics of the patient based on received 3D image data. In such embodiments the processor 604 may be configured to determine placement accuracy of the pedicle screws relative to the spinal cord of the patient. In embodiments, the processor 604 may be configured to determine placement of pedicle screws based on a combination of two or more of preoperative 3D image data of the patient, intraoperative 3D image data of the patient, and / or postoperative 3D image data of the patient. The processor 604 may be configured to determine placement of pedicle screws based other devices detected within the patient, anatomical characteristics of the patient, and the like.

[0065] FIGS. 2, 3, and 4 illustrate methods of determining an accuracy measurement of a pedicle screw placement in a patient’s vertebra according to exemplary embodiments. The steps shown in FIGS. 2, 3, and 4 may be shown in the order illustrated in the respective figures, or any other order useful for determining an accuracy measurement of a pedicle screw placement.

[0066] FIG. 2 shows a registration method 200 according to an exemplary embodiment of the present invention. Registration method 200 includes a segmentation step 204. Segmentation step 204 includes an input of a Pre-Op 3D Imaging 202 and an output of Binary Masks 206. The Segmentation step 204 can be a discrete, modular element. One or more segmentation techniques can be used with the Segmentation step 204. In an embodiment, the Segmentation step 204 can be treated as a black box that can receive raw CT as input and provide voxel-wise labels of one or more (e.g., all) of the vertebrae.

[0067] Regarding to the Segmentation step 204, Pre-Op 3D imaging 202 can include volumetric image data containing at least all levels of the spine of the patient involved in the surgery. Binary mask 206 can include voxel-wise segmentation masks that can be generated for one or more (e.g., each) bones for which screws are planned. The mask can correspond to the anatomical envelope of the bone of the patient and / or can overlap (e.g., largely overlap) with the rigid structure of the relevant bone of the patient.

[0068] A binary mask and vertebral segmentation can be used interchangeably herein. One or more (e.g., each) vertebra can be labeled with a binary mask. Voxels corresponding to the bone can be designated with a value (e.g., 1 ) and all other voxels can be designated with a different value (e.g., 0). Because tomographic scans are being used, the binary mask can result in a 3D shape of the patient’s vertebra being labeled. The binary mask (i.e., vertebral segmentation can be performed via one or more (e.g., two) steps. Step one includes a masking during registration (e.g., alignment) between preoperative and postoperative images. The masking performed in step one allows the algorithm to consider (e.g., only consider) the bone voxels when optimizing alignment. Step two includes computing a breach distance. In step two, the distance from the detected surgical screw position to the envelope of the segmentation can be computed as an accuracy measurement and / or safety measurement.

[0069] Registration method 200 can include Screw Detection step 220. Screw Detection step 220 can include input Post-Op CT 218 and Pre-Op Plan 208 as well as output Fitted Screws 222. Post-Op CT 218 can include Volumetric image data containing at least all levels of the spine of the patient involved in the surgery. PreOp Plan 208 can include planned screw trajectories e.g., screw trajectories inputted by a user such as a spine surgeon. Plans can include the dimensions (shaft length, shaft diameter, etc.) and intended position and insertion trajectory of each screw.

[0070] As described herein, one or more positions of screws implanted within a patient can be determined via an intraoperative 3D image and / or a postoperative 3D image. The positions of the screws may include the x, y, and z coordinates of the screws. When constructing a geometric model to determine the position of a pedicle screw, a headless screw can be identified as a single cylinder, although other types of screws (e.g., polyaxial screws) can be identified as two or more cylinders (e.g., one cylinder for the shaft of the screw and another cylinder for the head of the screw) that can rotate relative to one other about a fixed pivot point. The position of the geometric model may be optimized until the position aligns with the CT data of the screw, which may be provided via an intraoperative 3D image and / or a postoperative 3D image. The optimization may reposition the model to maximize the high-intensity (e.g., metal) voxels inside the model, which can result in a Fitted Screw model. In such embodiment, the position of the Fitted Screw model can be used as a detected screw position.

[0071] With respect to Fitted Screws 222, because the structure of the implant (screw) is known a priori, only the position may be required from the screw detection step 220. Screw positions may be stored as X-Y-Z coordinates of screw tip and tail in the local coordinate system (e.g., rotation about the screw axis may not be detectable or relevant).

[0072] Given an intraoperative 3D imaging (e.g., CT) volume, screws may be identified that correspond to the preoperative plan 208. Screw detection step 220 may be robust to imaging noise and extraneous metal implants and may include one or more of the following steps. At step 1 , the preoperative volume may be converted to a point cloud and threshold to keep only high-intensity voxels, corresponding to metal implants. At step 2, the preoperative planned screw trajectories may be aligned (e.g., rigidly aligned) to the thresholded point cloud using standard Iterative Closest Point (ICP) techniques. Randomly initialized alignments may account for sensitivity to initialization, keeping the best solution. The output of this step can include a single affine transformation mapping the preoperative plan to postoperative image space.

[0073] At step 3, a parametric, articulated form of ICP may be performed to improve the alignment between preoperative plan and thresholded point cloud. In step 3, the spine may be modeled as a series of coordinate systems corresponding to the segmented vertebrae of the preoperative spine. Techniques may allow neighboring vertebrae to move relative to each other to account for discrepancies in spine position between pre- and postoperative scans, but may penalize large deviations to enforce a prior that the spine should have approximately the same size and shape. The output of this step is an affine transformation for each vertebra, mapping from the preoperative plan to postoperative image space.

[0074] At step 4, screw positions may be optimized. For each screw, the dimensions and the approximate position from the matched cluster can be known. Screws may be modeled as a cylinder for the shaft and (if relevant) a cylinder for the head (for polyaxial screws these cylinders can rotate relative to each other). The goal of the optimization step is to align the screw model (cylinders) with the data (point cloud). The screw heads that had previously been masked out are included in the point cloud. A RANSAC search can be used to fit an initial line for the screw shaft, then an open-source nonlinear solver is employed to optimize the screwposition to maximize the number of points in the point cloud that are inside the envelope of the screw model.

[0075] At step 210, an Initialization can be performed. Initialization step 210 can include inputs of Pre-Op Plan 208 and Fitted Screws 222. The Initialization step 210 can include an output of initial alignment 212. A goal of the initialization step 210 can include obtaining an initial estimate of the alignment between preoperative and intraoperative 3D imaging (e.g., CT) volumes for each bone having a screw in it. Voxel intensities corresponding to cortical bone from pre- and postoperative volumes are converted to point clouds for alignment.

[0076] Each vertebra is allowed to rotate and translate relative to its neighbors in order to align the high-intensity voxels from the Pre-Op volume with the high- intensity voxels of the intraoperative volume by minimizing a cost function with three terms: data term, rotational regularization, and translational regularization. Translational regularization can be defined herein as differences in distances between neighboring vertebrae. In an embodiment, the intervertebral distances in the registration should match intervertebral distances in the preop segmentations. Data term can be used herein as a distance between aligned pre- and intraoperative point cloud. Rotational regularization can be used herein as a difference in rotations of neighboring vertebrae.

[0077] The output of the Initialization step 210 is a rigid-body homogeneous transformation matrix for each vertebra of the patient. The matrix maps the preoperative vertebrae onto the intraoperative vertebrae. The regularization terms reduce the effect of outliers by enforcing consistency in the relative positions of neighboring vertebrae in the preoperative and intraoperative scans, while still allowing for some articulated motion.

[0078] An Optimization step 214 can be performed. Inputs to the Optimization step 214 can include Pre-Op CT 202, Post-Op CT 218 (and / or an Intra-Op CT), Binary Mask 206, andjnitial Alignment 212. The Optimization step 214 can include an output Final Alignment 216.

[0079] Volumetric registration is a known problem in medical imaging. With initialization, such as the Initiation step 210, alignment can be reliably performed with a multi-scale rigid body optimization routine. An open source implementation that maximizes Mutual Information between the two volumes being registered can beused. Such techniques can also match between heterogeneous image contrast (e.g., CT to MRI or MRI-T1 to MRI-T2).

[0080] Because there may be some degree of movement between neighboring bones it may be necessary to exclude these regions from the calculation. This may be done by using the Binary Mask segmentations to exclude from the calculation any area outside of the relevant bone. An optimization, such as Optimization step 214, can be performed for one or more (e.g., each) vertebra yielding the Final Alignment 216 that can be used to merge the preoperative and intraoperative coordinate systems.

[0081] FIG. 3 shows an accuracy method 300 according to an exemplary embodiment of the present invention. As shown in FIG. 3, the accuracy method 300 may include inputs Pre-Op Plan 302, Fitted Screws 304, and Final Alignment 308, which may be substantially the same as Pre-Op Plan 208, Fitted Screws 222, and Final Alignment 216 described herein. Accuracy method 300 may include output Translation / Rotation Error 310. The Final Alignment 308 may be used to bring the Pre-Op Plan 302 and the Fitted Screws 304 into a shared coordinate system. Translation and rotation errors 310 may be based on geometric offsets. For example, with regard to Translation / Rotational Error 310, in a screw-axis aligned coordinate system, accuracy may be defined in reference to the planned screw position. After aligning the postoperative Fitted Screws 304 with the preoperative plan 302, errors can be computed as the translational and rotational offset.

[0082] FIG. 4 shows a safety method 400 according to an exemplary embodiment of the present invention. Safety measurements are exemplified in the method shown in FIG. 4 and may include (a) Template Matching 404 and (b) Breach Detection 410. As described herein, safety measurements and accuracy measurements may be used interchangeably.

[0083] The Template matching 404 may include an input Binary Mask 402 and an output Labeled Surface 406. Safety measurements and / or accuracy measurements may require an understanding of anatomical structure, since measurements can be made in reference to specific anatomical regions or landmarks. The volumetric voxelized Binary Mask 402 can be converted to a triangulated mesh via one or more techniques (e.g., the Marching Cubes technique). The conversion to the triangulated mesh can result in a manifold, watertight surface in 3D space representing the vertebral envelope.

[0084] Automated labeling of surface regions may be performed by finding pointwise correspondence with a template surface model. For each vertebral level, a generic surface model can be prepared with anatomical regions manually annotated. Upon correspondence with the template, the regions can be mapped onto the surface mesh under analysis.

[0085] Correspondence between the segmentation and the template can be found using an open source implementation of the Functional Maps framework. A goal can include minimizing geodesic distortion between the template surface and the corresponding points on the target surface. For example, points that are close together on the template can remain close together in the target. This method can be applied to surfaces as well as other domains (e.g. gridded volumes, tetrahedral meshes, etc.). Functional Maps can be used for the As Rigid As Possible alignment problem that is invariant to rotation / translation of the two surfaces to be aligned.

[0086] The Breach Detection step 410 can include inputs such as the Labeled Surface 406, the Fitted Screws 408, and the Final Alignment 414. The Breach Detection step 410 can include the output Safety Measurement 412 (i.e., accuracy measurement).

[0087] The Safety Measurements 412 can be performed for each screw in the intraoperative scan. As an example, the Safety Measurements 412 can include the following steps: At step 1 , determine if the screw violates the region of interest on the pedicle wall. A volumetric Boolean difference can be calculated between the cylinder model of the screw shaft and the vertebral envelope. Intersections can be compared to the labeled region to determine if a breach has occurred. At step 2, if the screw does not violate the region of interest on the pedicle wall: the distance-to- breach is computed as the closest distance between any point on the screw shaft (cylinder) and any point in the region of interest (medial-inferior pedicle wall). Distance is reported as a negative value. At step 3, if the screw does violate the region of interest on the pedicle wall: the breach distance is computed as the maximal distance from any point on the screw that protrudes into the spinal canal to any coplanar point on the intersected pedicle surface. Coplanar points are points that are in the same coronal plane. For example, breach distances are always perpendicular to the screw axis and reported as positive values.

[0088] The approach (e.g., automated approach) of the present disclosure can be used with one or more (e.g., any) 3D data sets including 3D Fluoroscopy (O-Arm,GE, Ziehm, etc.), CT scan, and / or MRL The method and system of the present disclosure can be used in any surgical intervention that involves a preoperatively planned instrumentation in which accuracy can be quantified by registration of preoperative and intraoperative scans.

[0089] In an exemplification of the present disclosure, preoperative screw planning may be accomplished using 3D CT visualization techniques. The planned screw tip and tail positions can be transferred to a robotic planning environment. An objective can include placing the screws along the mid-axis of the pedicles of a patient in the axial plane. A surgeon can insert the screw using robotic instrumentation. An intraoperative 3D scan of the relevant portion of the vertebral column is obtained. Using an automated segmentation tool, the patient’s preoperative 3D imaging (e.g., CT scan) can be labeled, and computer vision techniques are employed to align (e.g., automatically align) one or more vertebrae (e.g., each vertebra) with a corresponding intraoperative counterpart. Techniques according to the present disclosure can measure and compare intraoperative screw positions with the preoperative plan, including translations and angulations along all three axes. Calculation of predicted medial pedicle breach can be performed. As used within this disclosure, medial pedicle breach is defined as the point where the medial wall of the screw intersects with the preoperative medial wall of the pedicle.

[0090] In an exemplification of the present disclosure, method 500 may be performed to assess and / or plan pedicle screw placement within a patient. Pre-op 3D CT (or MRI) visualization / planning techniques may be used to assess and / or plan pedicle screw placement. The placement of one or more screws can be assessed and / or planned using methods for transpedicular mid-axis placement within pedicles attempting to avoid medial, superior and inferior breach. Planned 3D screw positions may be transferred into the robotic planning environment.

[0091] At step 502, bone anchors (e.g., pedicle screws or other screw types) may be inserted within a patient. The bone anchors may be inserted within the patient while the patient is in the operating room according to the preoperative plan. At step 504, one or more screw positions may be determined. In an example, intraoperative 3D scanning may be performed to determine the screw position(s). At step 506, the envelope of one or more (e.g., each) vertebra of the patient is determined. Segmentation techniques (e.g., automated segmentation techniques)may be applied to the preoperative image to determine the envelope of each vertebra.

[0092] At step 508, rigid-body alignments (e.g., automatic rigid-body alignments) can be performed. The rigid body alignments can be performed for each level of the spine of the patient by maximizing mutual information with corresponding intraoperative vertebrae. Registrations, such as the registration step 200, can be performed which may mask the preoperative image volume with the segmentation label. The registration can be applied to bring the preoperative planned screw trajectories into alignment with intraoperative scans.

[0093] At step 510, screw positions within the patient can be determined, and the determined positions of the screw can be compared to the preoperative plan. In embodiments, techniques can be used to provide the determination (e.g., automated determination) of screw positions and / or the comparisons of the positions to the preoperative plan, including translations and angulations along all three axes. As used within this disclosure, screw accuracy may be defined as three axis translation and angular deviation of intraoperative screw position versus planned trajectory. One or more steps of the analysis (e.g., vertebral segmentation, image alignment, and geometric / error measurements), including all of the analysis, can be automated. Segmentation (e.g., automated segmentation) and / or computer vision techniques can be employed to align each preoperative vertebra with its intraoperative counterpart. Positions along one or more (e.g., all three) axes may be compared.

[0094] At step 512, detection (e.g., automated detection) of predicted medial and inferior pedicle breach can be performed. Distance from the screw to the medial-inferior pedicle wall can be calculated, for example, by using the 3D outer circumference of the detected screw (approximated as a cylinder) and the 3D envelope of the preoperative pedicle wall as detected by a segmentation technique. The anatomic location of the relevant section of the pedicle wall can be determined and / or reported. For example, the most-breached point (for breached screws) and / or the point closest to being breached (for internal screws) can be determined and / or reported. The minimum pedicle diameter can be calculated as the minimum diameter of the pedicle measured perpendicular to the planned screw axis (e.g., the largest cylinder that could geometrically fit down the pedicle without intersecting the pedicle walls in any direction).

[0095] While the invention has been described with respect to specific examples including presently preferred modes of carrying out the invention, those skilled in the art will appreciate that there are numerous variations and permutations of the above described systems and techniques. It is to be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the present invention. Thus, the spirit and scope of the invention should be construed broadly as set forth in the appended claims.

Claims

l / We claim:1 . An orthopedic surgical system (600) comprising a processor (604), characterized by the processor (604) being configured to: receive preoperative 3D image data of a planned pedicle screw (102a) placement in a patient’s vertebra (106a); receive at least one of an intraoperative 3D image data of the pedicle screw (102a) placement in the patient’s vertebra (106a) or a postoperative 3D image data of the pedicle screw (102a) placement in the patient’s vertebra (106a); and determine an accuracy measurement of a pedicle screw (102a) position in the patient’s vertebra (106a) based on the preoperative 3D image data and the at least one of the intraoperative 3D image data or the postoperative 3D image data.

2. The system (600) of claim 1 , wherein the accuracy measurement of the pedicle screw (102a) position in the patient’s vertebra (106a) is based on a distance from the pedicle screw (102a) placement to a cortical bone of a patient’s vertebra(106a) obtained from the at least one of the intraoperative 3D image data or the postoperative 3D image data.

3. The system (600) of claim 1 , wherein the accuracy measurement of the pedicle screw (102a) position in the patient’s vertebra (106a) is based on a distance from a pedicle screw (102a) to a region of interest of the patient obtained from the at least one of the intraoperative 3D image data or the postoperative 3D image data.

4. The system (600) of claim 3, wherein the region of interest comprises at least one of a nerve, an artery, a vein, a visceral organ, a spinal cord, a phrenic nerve, a vagus nerve, an aorta, a vena cava, an esophagus, a lung, a trachea, or a bronchus.

5. The system (600) of claim 1 , wherein the accuracy measurement of the pedicle screw (102a) position in the patient’s vertebra (106a) is based on geometric offsets of a pedicle screw (102a) based on data obtained from the preoperative 3D image data and the at least one of the intraoperative 3D image data or the postoperative 3D image data.

6. The system (600) of claim 1 , wherein the accuracy measurement of the pedicle screw (102a) position in the patient’s vertebra (106a) is based on a position of a pedicle wall of the patient’s vertebra (106a) obtained from the preoperative 3Dimage data and a position of a pedicle screw (102a) relative to the position of the pedicle wall obtained from the at least one of the intraoperative 3D image data or the postoperative 3D image data.

7. The system (600) of claim 1 , wherein the accuracy measurement of the pedicle screw (102a) placement in the patient’s vertebra (106a) is based on comparing at least one of a group consisting of a planned pedicle screw (102a) tip position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw (102a) tip; a planned pedicle screw (102a) midportion position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw (102a) midportion; and a planned pedicle screw (102a) tail position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw (102a) tail.

8. A method for assessing pedicle screw (102a) placement in a patient, characterized by: receiving, via a processor (604), a preoperative 3D image of a vertebrae (106a) of a patient; determining a desired trajectory and position for insertion of the pedicle screw (102a) into the vertebrae (106a) of the patient; inserting the pedicle screw (102a) into the vertebral pedicle (154) of the patient; receiving at least one of an intraoperative 3D image or a postoperative 3D image of the vertebrae (106a) of the patient having the pedicle screw (102a) inserted therein; and determining an accuracy measurement of a pedicle screw (102a) position in the patient’s vertebra (106a) based on the desired trajectory and position for insertion of the pedicle screw (102a) into the vertebrae (106a) of the patient and the at least one of the intraoperative 3D image or the postoperative 3D image.

9. The method of claim 8, wherein the accuracy measurement of the pedicle screw (102a) position in the patient’s vertebrae (106a) is based on a distance from the pedicle screw (102a) to a cortical bone of a patient’s vertebrae (106a).

10. The method of claim 8, wherein the accuracy measurement of the pedicle screw (102a) position in the vertebrae (106a) of the patient is based on a distance from the pedicle screw (102a) to a region of interest of the patient obtained via at least one of the intraoperative 3D image or the postoperative 3D image.11 . The method of claim 10, wherein the region of interest comprises at least one of a nerve, an artery, a vein, a visceral organ, a spinal cord, a phrenic nerve, a vagus nerve, an aorta, a vena cava, an esophagus, a lung, a trachea, or a bronchus.

12. The method of claim 8, wherein the accuracy measurement of the pedicle screw (102a) position in the patient’s vertebrae (106a) is based on geometric offsets of a pedicle screw (102a) based on data obtained from the preoperative 3D image data and at least one of the intraoperative or the postoperative 3D image data.

13. The method of claim 8, wherein the accuracy measurement of the pedicle screw (102a) position in the patient’s vertebrae (106a) is based on a position of a pedicle wall of the vertebrae (106a) obtained from the preoperative 3D image data and a position of the pedicle screw (102a) relative to the position of the pedicle wall obtained from at least one of the intraoperative 3D image data or the postoperative 3D image data.

14. The method of claim 8, further comprising adjusting a position of the pedicle screw (102a) if the pedicle screw (102a) breaches a predetermined position within the vertebrae (106a) of the patient or if the pedicle screw (102a) deviates from the determined desired trajectory or position.

15. The method of claim 8, wherein the accuracy measurement of the pedicle screw (102a) placement in the patient’s vertebra (106a) is based on comparing at least one of a group consisting of a planned pedicle screw (102a) tip position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw (102a) tip; a planned pedicle screw (102a) midportion position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw (102a) midportion; and a planned pedicle screw (102a) tail position and at least one of an intraoperative 3D image data or a postoperative 3D image data of a pedicle screw (102a) tail.

Images