Patent Description:
Imaging may be used by a medical provider for diagnostic and/or therapeutic purposes. Patient anatomy can change over time, particularly following placement of a medical implant in the patient anatomy. Registration of one image to another enables changes in anatomical position to be identified and quantified.

According to <CIT>, a first and a second image are expressed in a common coordinate system by applying a geometric transformation to the second image so as to map a structure in the second image onto a corresponding structure in the first image in a common coordinate system. Starting from initial values, the parameters of the geometric transformation are updated taking into account the result of an evaluation of a cost function. Measurements are performed in the common coordinate system.

<CIT> discloses positioning of an X-ray imaging system. In order to provide an improved relative positioning of the X-ray imaging system for spine interventions, a device for positioning of an X-ray imaging system is provided. The device comprises a data storage unit, a processing unit and an output unit. The data storage unit is configured to store and provide 3D image data of a spine region of interest of a subject comprising a part of a spine structure, the spine structure comprising at least one vertebra. The processing unit is configured to select at least one vertebra of the spine structure as target vertebra; to segment at least the target vertebra in the 3D image data; wherein the segmentation comprises identifying at least one anatomic feature of the target vertebra; to define a position of a predetermined reference line based on a spatial arrangement of the at least one anatomic feature; and to determine a target viewing direction of an X-ray imaging system based on the reference line. The output unit is configured to provide the target viewing direction for an X-ray imaging system. <NPL> discloses aspects related to image registration techniques.

<CIT> discloses an image registration device, method, and program capable of performing registration between two images obtained by imaging a subject configured to include parts of a plurality of bones, such as the vertebral column, with high accuracy. The image registration device includes: a medical image acquisition unit that acquires first and second three-dimensional images by imaging a subject configured to include parts of a plurality of bones at different points in time; an identification unit that identifies the parts of the plurality of bones included in each of the first and second three-dimensional images; a matching unit that matches a part of each bone included in the first three-dimensional image with a part of each bone included in the second three-dimensional image; and a registration processing unit that performs registration processing between images of the matched parts of the bones.

The invention provides a method of correlating images taken at different times according to claim <NUM> and a system for correlating images taken at different times according to claim <NUM>.

It should also be understood that, depending on the example or embodiment, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, and/or may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the disclosed techniques according to different embodiments of the present disclosure). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a computing device and/or a medical device.

In one or more examples, the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof.

Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple A11, A12, A12X, A12Z, or A13 Bionic processors; or any other general purpose microprocessors), application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry.

The use of "including," "comprising," or "having" and variations thereof herein is meant to encompass the items listed thereafter as well as additional items.

Images taken at different points in time of a portion of a patient's anatomy may reflect considerable variability of the structure of the patient's anatomy. This is particularly true where the images are taken pre- and post-operation, and/or when the images are separated by a long period of time, including months or years. For example, the spine structure of a patient following insertion of a spinal rod may differ significantly from the spine structure of the patient prior to insertion of the rod. Moreover, the patient's spine may undergo significant deformation in the weeks, months, and years following insertion of the rod. There is a need to identify and quantify changes in the pose of one or more anatomical elements from a first time at which a first image is taken to a second time at which a second image is taken.

Using changes in the structure of a patient's spine as an example, several factors make regular measurements of such changes incomparable. Such factors may include a change in the pose of the camera(s) or other imaging devices that generate the first and second images; a change in the pose of the patient's anatomy when the first and second images are captured (e.g., a first image may be taken with the patient in a prone or supine position, and a second image may be taken with the patient in a standing position); noise in source labels due to noisy image and/or segmentation errors; and non-rigid transformation of the spine through time or before and after an operation.

Embodiments of the present disclosure utilize corresponding points along the perimeter of each vertebra depicted in the first and second images taken at times t1 and t2, respectively. For example, edge points of the vertebral end plates in AP or LT projections taken at any two times t1 and t2 may be used. The points may be identified manually or automatically.

Due to non-rigid transformation of the spine through time or before and after an operation, direct computation of a transformation between times t1 and t2 are not possible. In other words, because the vertebrae of the spine can move and rotate in different manners, simply comparing changes in the overall spinal structure from time t1 to time t2 does not provide accurate results. Instead, the problem may be treated in the vertebra scope, utilizing the piece-wise rigidity of the spine. Because the motion of each vertebra itself could be assumed to be rigid, a transformation may be computed for each vertebra. Moreover, since the vertebral perimeter can be represented as a plane (end plates, sides, in lateral or anterior projections, for example), a homography transformation H may be a sufficiently useful representation.

According to embodiments of the present disclosure, then, for each vertebra, at least four corresponding points in each image are used to compute homography H parameters. To reduce the noise in the computation, if more points are available, they can be used; classic computer vision methods may be used to automatically fix the labeled corner points; and interpolated points along the labeled lines may be used.

If the spine motion were rigid, then all of the computed homographies {H} would be more or less the same. But, because there is some movement of the individual vertebrae, the computed homographies are expected to be different. Also, noise in labeled points will add noisy homographies.

In light of the foregoing, the set of homographies {H} in the transformation space (whether a <NUM>-dimensional space or a reduced space) is clustered according to a predetermined characteristic. The most coherent cluster is selected, and the clusters/homographies may be filtered according to other criteria. The mean of the resulting cluster(s) is then taken as the homography H' between times t1, t2. All vertebra may then be projected from t2 onto t1 using H', and measurements/features may be computed in a more comparable way.

Embodiments of the present disclo sure rest on an assumption that bone structure changes over time are less predominant than soft tissue changes over time. Even so, compression fractures and bone osteophyte (spondylophyte) changes can interfere with successful utilization of embodiments of the present disclosure. Where one or more homographies are affected by compression fractures and/or bone osteophytes (and/or other changes to a rigid anatomical element's shape), such homographies may need to be filtered out before the others are averaged or otherwise utilized.

For registration of two-dimensional images, at least four corresponding points are needed, while for registration of three-dimensional images, at least eight corresponding points are needed. In some embodiments, implants themselves may be used instead of or in addition to vertebral endplates or other anatomical features as a source of corresponding points. For example, a rod may provide two corresponding points (e.g., one at each end of the rod), such that two rods, a rod and a screw, or even two screws may be utilized to obtain four corresponding points between the two images. Of course, where one or more implants are to be used to define one or more corresponding points, only pairs of images that both depict the one or more implants may be registered to each other. As a result, images taken before insertion of such implants cannot be used in these embodiments. Even so, using implants to define the corresponding points beneficially takes advantage of the fact that, unlike some anatomical elements, implant structure does not usually change over time.

Embodiments of the present disclosure beneficially enable long term registration-that is, registration of two images generated at times that are separated by a period of weeks, months, or even years. Embodiments of the present disclosure also beneficially utilize the piece-wise rigidity characteristic of the spine (and/or other anatomical elements comprised of a plurality of individual rigid elements) to overcome computational challenges of determining a transformation of the spine or other anatomical element directly. By combining a data science approach with classical computer vision methods, a potential set of transformations can be generated and then analyzed using clustering methods.

Embodiments of the present disclosure provide technical solutions to one or more of the problems of (<NUM>) generating and comparing accurate geometric measurements on X-ray images of the same patient, taken at different points in time separated by weeks, months, or even years; (<NUM>) during registration of two images taken at two different times and likely by two different imaging devices, (i) accounting for the effect of a change in camera pose relative to patient anatomy from one image to the other, (ii) accounting for the effect of a change in body pose and location from one image to the other, and (iii) accounting for noise in source labels due to noisy image or segmentation errors; (<NUM>) registering to each other two spinal images generated at different times despite non-rigid transformation of the spine during the period in between the generation of the first image and the generation of the second image; and (<NUM>) differentiating changes in pose of one or more rigid elements depicted in two images that are due to a change in camera pose from those that are due to a change in pose of the one or more rigid elements themselves.

Turning first to <FIG>, a block diagram of a system <NUM> according to at least one embodiment of the present disclosure is shown. The system <NUM> may be used to register two time-separated images to each other and/or carry out one or more other aspects of one or more of the methods disclosed herein. The system <NUM> comprises a computing device <NUM>, one or more imaging devices <NUM>, a navigation system <NUM>, a robot <NUM>, a database <NUM>, and/or a cloud <NUM>. Systems according to other embodiments of the present disclosure may comprise more or fewer components than the system <NUM>. For example, the system <NUM> may not include the navigation system <NUM>, the robot <NUM>, one or more components of the computing device <NUM>, the database <NUM>, and/or the cloud <NUM>.

The computing device <NUM> comprises a processor <NUM>, a memory <NUM>, a communication interface <NUM>, and a user interface <NUM>. Computing devices according to other embodiments of the present disclosure may comprise more or fewer components than the computing device <NUM>.

The processor <NUM> of the computing device <NUM> may be any processor described herein or any similar processor. The processor <NUM> may be configured to execute instructions stored in the memory <NUM>, which instructions may cause the processor <NUM> to carry out one or more computing steps utilizing or based on data received from the imaging device <NUM>, the robot <NUM>, the navigation system <NUM>, the database <NUM>, and/or the cloud <NUM>.

The memory <NUM> may be or comprise RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other tangible, non-transitory memory for storing computer-readable data and/or instructions. The memory <NUM> may store information or data useful for completing, for example, any step of the methods <NUM>, <NUM>, and/or <NUM> described herein, or of any other methods. The memory <NUM> may store, for example, one or more image processing algorithms <NUM>, one or more segmentation algorithms <NUM>, one or more transformation algorithms <NUM>, one or more homography algorithms <NUM>, and/or one or more registration algorithms <NUM>. Such instructions or algorithms may, in some embodiments, be organized into one or more applications, modules, packages, layers, or engines. The algorithms and/or instructions may cause the processor <NUM> to manipulate data stored in the memory <NUM> and/or received from or via the imaging device <NUM>, the robot <NUM>, the database <NUM>, and/or the cloud <NUM>.

The computing device <NUM> may also comprise a communication interface <NUM>. The communication interface <NUM> may be used for receiving image data or other information from an external source (such as the imaging device <NUM>, the navigation system <NUM>, the robot <NUM>, the database <NUM>, the cloud <NUM>, and/or any other system or component not part of the system <NUM>), and/or for transmitting instructions, images, or other information to an external system or device (e.g., another computing device <NUM>, the navigation system <NUM>, the imaging device <NUM>, the robot <NUM>, the database <NUM>, the cloud <NUM>, and/or any other system or component not part of the system <NUM>). The communication interface <NUM> may comprise one or more wired interfaces (e.g., a USB port, an ethernet port, a Firewire port) and/or one or more wireless transceivers or interfaces (configured, for example, to transmit and/or receive information via one or more wireless communication protocols such as <NUM>. 11a/b/g/n, Bluetooth, NFC, ZigBee, and so forth). In some embodiments, the communication interface <NUM> may be useful for enabling the device <NUM> to communicate with one or more other processors <NUM> or computing devices <NUM>, whether to reduce the time needed to accomplish a computing-intensive task or for any other reason.

The computing device <NUM> may also comprise one or more user interfaces <NUM>. The user interface <NUM> may be or comprise a keyboard, mouse, trackball, monitor, television, screen, touchscreen, and/or any other device for receiving information from a user and/or for providing information to a user. The user interface <NUM> may be used, for example, to receive a user selection or other user input regarding any step of any method described herein. Notwithstanding the foregoing, any required input for any step of any method described herein may be generated automatically by the system <NUM> (e.g., by the processor <NUM> or another component of the system <NUM>) or received by the system <NUM> from a source external to the system <NUM>. In some embodiments, the user interface <NUM> may be useful to allow a surgeon or other user to modify instructions to be executed by the processor <NUM> according to one or more embodiments of the present disclosure, and/or to modify or adjust a setting of other information displayed on the user interface <NUM> or corresponding thereto.

Although the user interface <NUM> is shown as part of the computing device <NUM>, in some embodiments, the computing device <NUM> may utilize a user interface <NUM> that is housed separately from one or more remaining components of the computing device <NUM>. In some embodiments, the user interface <NUM> may be located proximate one or more other components of the computing device <NUM>, while in other embodiments, the user interface <NUM> may be located remotely from one or more other components of the computer device <NUM>.

The imaging device <NUM> may be operable to image anatomical feature(s) (e.g., a bone, veins, tissue, etc.) and/or other aspects of patient anatomy to yield image data (e.g., image data depicting or corresponding to a bone, veins, tissue, etc.). The image data may be or comprise a pre-operative image, a post-operative image, or an image taken independently of any surgical procedure. In some embodiments, a first imaging device <NUM> may be used to obtain first image data (e.g., a first image) at a first time, and a second imaging device <NUM> may be used to obtain second image data (e.g., a second image) at a second time after the first time. The first time and the second time may be separated by a surgical procedure (e.g., one may be pre-operative, and the other may be post-operative), or by a period of time (e.g., days, weeks, months, or years). The imaging device <NUM> may be capable of taking a 2D image or a 3D image to yield the image data. "Image data" as used herein refers to the data generated or captured by an imaging device <NUM>, including in a machine-readable form, a graphical/visual form, and in any other form. In various examples, the image data may comprise data corresponding to an anatomical feature of a patient, or to a portion thereof. The imaging device <NUM> may be or comprise, for example, an ultrasound scanner (which may comprise, for example, a physically separate transducer and receiver, or a single ultrasound transceiver), a radar system (which may comprise, for example, a transmitter, a receiver, a processor, and one or more antennae), an O-arm, a C-arm, a G-arm, or any other device utilizing X-ray-based imaging (e.g., a fluoroscope, a CT scanner, or other X-ray machine), a magnetic resonance imaging (MRI) scanner, an optical coherence tomography scanner, an endoscope, a telescope, a thermographic camera (e.g., an infrared camera), or any other imaging device <NUM> suitable for obtaining images of an anatomical feature of a patient.

In some embodiments, the imaging device <NUM> may comprise more than one imaging device <NUM>. For example, a first imaging device may provide first image data and/or a first image, and a second imaging device may provide second image data and/or a second image. In still other embodiments, the same imaging device may be used to provide both the first image data and the second image data, and/or any other image data described herein. The imaging device <NUM> may be operable to generate a stream of image data. For example, the imaging device <NUM> may be configured to operate with an open shutter, or with a shutter that continuously alternates between open and shut so as to capture successive images. For purposes of the present disclosure, unless specified otherwise, image data may be considered to be continuous and/or provided as an image data stream if the image data represents two or more frames per second.

The navigation system <NUM> may provide navigation for a surgeon and/or a surgical robot during an operation. The navigation system <NUM> may be any now-known or future-developed navigation system, including, for example, the Medtronic StealthStation™ S8 surgical navigation system or any successor thereof. The navigation system <NUM> may include a camera or other sensor(s) for tracking one or more reference markers, navigated trackers, or other objects within the operating room or other room in which some or all of the system <NUM> is located. In various embodiments, the navigation system <NUM> may be used to track a position and orientation (i.e., pose) of the imaging device <NUM>, the robot <NUM> and/or robotic arm <NUM>, and/or one or more surgical tools (or, more particularly, to track a pose of a navigated tracker attached, directly or indirectly, in fixed relation to the one or more of the foregoing). The navigation system <NUM> may include a display for displaying one or more images from an external source (e.g., the computing device <NUM>, imaging device <NUM>, or other source) or for displaying an image and/or video stream from the camera or other sensor of the navigation system <NUM>. In some embodiments, the system <NUM> can operate without the use of the navigation system <NUM>. The navigation system <NUM> may be configured to provide guidance to a surgeon or other user of the system <NUM> or a component thereof, to the robot <NUM>, or to any other element of the system <NUM> regarding, for example, a pose of one or more anatomical elements, and/or whether or not a tool is in the proper trajectory (and/or how to move a tool into the proper trajectory) to carry out a surgical task according to a preoperative plan.

The robot <NUM> may be any surgical robot or surgical robotic system. The robot <NUM> may be or comprise, for example, the Mazor X™ Stealth Edition robotic guidance system. The robot <NUM> may be configured to position the imaging device <NUM> at one or more precise position(s) and orientation(s), and/or to return the imaging device <NUM> to the same position(s) and orientation(s) at a later point in time. The robot <NUM> may additionally or alternatively be configured to manipulate a surgical tool (whether based on guidance from the navigation system <NUM> or not) to accomplish or to assist with a surgical task. The robot <NUM> may comprise one or more robotic arms <NUM>. In some embodiments, the robotic arm <NUM> may comprise a first robotic arm and a second robotic arm, though the robot <NUM> may comprise more than two robotic arms. In some embodiments, one or more of the robotic arms <NUM> may be used to hold and/or maneuver the imaging device <NUM>. In embodiments where the imaging device <NUM> comprises two or more physically separate components (e.g., a transmitter and receiver), one robotic arm <NUM> may hold one such component, and another robotic arm <NUM> may hold another such component. Each robotic arm <NUM> may be positionable independently of the other robotic arm.

The robot <NUM>, together with the robotic arm <NUM>, may have, for example, at least five degrees of freedom. In some embodiments the robotic arm <NUM> has at least six degrees of freedom. In yet other embodiments, the robotic arm <NUM> may have less than five degrees of freedom. Further, the robotic arm <NUM> may be positioned or positionable in any pose, plane, and/or focal point. The pose includes a position and an orientation. As a result, an imaging device <NUM>, surgical tool, or other object held by the robot <NUM> (or, more specifically, by the robotic arm <NUM>) may be precisely positionable in one or more needed and specific positions and orientations.

In some embodiments, reference markers (i.e., navigation markers) may be placed on the robot <NUM> (including, e.g., on the robotic arm <NUM>), the imaging device <NUM>, or any other object in the surgical space. The reference markers may be tracked by the navigation system <NUM>, and the results of the tracking may be used by the robot <NUM> and/or by an operator of the system <NUM> or any component thereof. In some embodiments, the navigation system <NUM> can be used to track other components of the system (e.g., imaging device <NUM>) and the system can operate without the use of the robot <NUM> (e.g., with the surgeon manually manipulating the imaging device <NUM> and/or one or more surgical tools, based on information and/or instructions generated by the navigation system <NUM>, for example).

The system <NUM> or similar systems may be used, for example, to carry out one or more aspects of any of the methods <NUM>, <NUM>, and/or <NUM> described herein. The system <NUM> or similar systems may also be used for other purposes. In some embodiments, a system <NUM> may be used to generate and/or display a 3D model of an anatomical feature or an anatomical volume of a patient. For example, the robotic arm <NUM> (controlled by a processor of the robot <NUM>, the processor <NUM> of the computing device <NUM>, or some other processor, with or without any manual input) may be used to position the imaging device <NUM> at a plurality of predetermined, known poses, so that the imaging device <NUM> can obtain one or more images at each of the predetermined, known poses. Because the pose from which each image is taken is known, the resulting images may be assembled together to form or reconstruct a 3D model. The system <NUM> may update the model based on information (e.g., segmental tracking information) received from the imaging device <NUM>, as described elsewhere herein.

Turning now to <FIG>, embodiments of the present disclosure may be used, for example, to register two images <NUM> that are separated by time. For example, embodiments of the present disclosure may be used to register a pre-operative image 200A to a post-operative image 200B, an image 200C taken six months following the operation, and/or to an image 200D taken one year after the operation; to register a post-operative image 200B to an image 200C taken six months following the operation and/or to an image 200D taken one year following the operation; and/or to register an image 200C taken six months after an operation to an image 200D taken one year after the operation. Additionally, embodiments of the present disclosure may be used to obtain accurate geometric measurements regarding changes in the pose of one or more anatomical elements or medical implants depicted in the registered images, notwithstanding that the images cannot be directly registered to one another (e.g., by simply overlaying one image on another and lining up corresponding points) due to changes in the pose of the camera(s) used to take the images relative to the imaged patient anatomy; changes in the pose of the patient when the images were generated; noise in the images; and/or non-rigid transformation of the anatomy depicted in the images.

While <FIG> shows a pre-operative image 200A, a post-operative image 200B taken immediately following the surgical procedure used to implant the rod and screws depicted in image 200B, an image 200C taken six months after the same surgical procedure, and an image 200D taken one year after the same surgical procedure, embodiments of the present disclosure may be used to register two images that are separated by periods of time longer or shorter than from pre-operation to post-operation, six months, and/or one year. In some embodiments, the present disclosure may be utilized to register two images taken two, five, ten, or more years apart. In other embodiments, the present disclosure may be utilized to register two images taken one, two, three, four, five, seven, eight, nine, ten, or eleven months apart. In still other embodiments, the present disclosure may be utilized to register two images taken a period of weeks apart, or a period of days apart. While the benefits of embodiments of the present disclosure may be most pronounced when there is a significant transformation of an imaged anatomical element from one image to the other, those same embodiments may be used regardless of the degree of transformation of an anatomical element between the times at which two images of the anatomical element are taken.

<FIG> depicts a method <NUM> that may be used, for example, for long-term registration, short-term registration, updating a pre-operative model of a patient's anatomy, and/or updating a registration between any two or more of a robotic space, a navigation space, and/or a patient space. The term "long-term registration" is meant to convey that the method <NUM> may be used to register to time-separated images, including images that are taken days, weeks, months, or even years apart. Even so, the method <NUM> may also be used to register images taken in relatively close temporal proximity (e.g., preoperatively and intraoperatively).

The method <NUM> (and/or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor. The at least one processor may be the same as or similar to the processor(s) <NUM> of the computing device <NUM> described above. The at least one processor may be part of a robot (such as a robot <NUM>) or part of a navigation system (such as a navigation system <NUM>). A processor other than any processor described herein may also be used to execute the method <NUM>. The at least one processor may perform the method <NUM> by executing instructions stored in a memory such as the memory <NUM>. The instructions may correspond to one or more steps of the method <NUM> described below. The instructions may cause the processor to execute one or more algorithms, such as an image processing algorithm <NUM>, a segmentation algorithm <NUM>, a transformation algorithm <NUM>, a homography algorithm <NUM>, and/or a registration algorithm <NUM>.

The method <NUM> comprises receiving a first image of a patient's anatomy (step <NUM>). The first image is generated by an imaging device such as an imaging device <NUM>, and is generated at a first time. The first time may be one or more days, weeks, or months prior to a surgical procedure that affects the imaged anatomy, or the first time may be immediately prior to the surgical procedure (e.g., while the patient is positioned on an operating table and/or is within an operating room), or the first time may be after a surgical procedure. In some embodiments, the first image is taken independently of any surgical procedure.

The imaged anatomy may be, for example, a spine or portion thereof of the patient comprising a plurality of vertebrae. In other embodiments, the imaged anatomy may be any other anatomical object comprised of a plurality of rigid or substantially rigid sub-elements, or any other anatomical object that experiences non-rigid deformation and that can be analyzed on a subelement level.

The first image may be received, whether directly or indirectly, from an imaging device such as the imaging device <NUM>. The first image may be a two-dimensional image or a three-dimensional image. In some embodiments, the first image is an X-ray image or an image generated using X-rays, such as a CT image or a fluoroscopy image. The image may, however, be an image generated using any other imaging modality, such as ultrasound, magnetic resonance imaging, optical coherence tomography, or another imaging modality. Thus, the imaging device may be a CT scanner, a magnetic resonance imaging (MRI) scanner, an optical coherence tomography (OCT) scanner, an O-arm (including, for example, an O-arm 2D long film scanner), a C-arm, a G-arm, another device utilizing X-ray-based imaging (e.g., a fluoroscope or other X-ray machine), or any other imaging device.

The method <NUM> also comprises receiving a second image of the patient's anatomy (step <NUM>). The second image is also generated by an imaging device such as an imaging device <NUM>, although the imaging device used to generate the second image may be different than the imaging device used to generate the first image. Moreover, the second image is generated at a second time after the first time. The second time may be separated from the first time by a surgical procedure (e.g., the first image may be a preoperative image and the second image may be a postoperative image). The second time may be separated from the first time by one or more days, weeks, months, or years after the first time.

The second image generally corresponds to the same anatomical area or portion of the patient's anatomy as the first image, or a portion thereof. Thus, for example, if the first image depicts a spine or segment thereof of the patient, then the second image data also depicts the spine or segment thereof. As another example, if the first image depicts a knee or portion thereof of the patient, the second image also depicts the knee or a portion thereof.

The second image may be received, whether directly or indirectly, from an imaging device such as the imaging device <NUM>. The second image may have the same number of dimensions as the first image (e.g., two or three dimensions). The second image may be an image generated using the same imaging device as the first image or a different imaging device. The imaging device that generates the second image may be of the same imaging modality as the imaging device that generated the first image, or a related imaging modality. In some embodiments, the first and second images may be generated using different imaging modalities.

The method <NUM> also comprises determining a transformation from the first image to the second image for each of a plurality of rigid elements in the first image and the second image, to yield a set of transformations (step <NUM>). The step <NUM> may comprise, in some embodiments, preprocessing the first image and the second image using one or more image processing algorithms <NUM> to remove noise and/or artifacts therefrom, ensure that both images have the same scale, and otherwise prepare the image for other aspects of the step <NUM>. One or more image processing algorithms <NUM> may also be used to identify a plurality of rigid elements in each image, whether using feature recognition, edge detection, or other object-detection methods.

In some embodiments, the step <NUM> comprises segmenting the first and second images to identify and/or delineate individual rigid elements within each image. Such segmenting may be accomplished using one or more segmentation algorithms <NUM> and/or any other segmentation algorithm or process. The step <NUM> may further comprise utilizing an anatomical atlas, biomechanical model, or other reference to identify anatomical objects within the first and second images, determine which of those anatomical objects are rigid elements, and/or to determine a relationship (if any) between or among two or more identified rigid elements. Thus, for example, an anatomical atlas may be referenced to determine that two adjacent vertebra are connected by a vertebral disc, or a patient-specific biomechanical model may be referenced to determine that two adjacent vertebrae have been fused and should move within the patient's anatomy as one.

The plurality of rigid elements may comprise individual bones or other hard tissue anatomical objects. The plurality of rigid elements may further comprise one or more medical implants, such as pedicle screws, vertebral rods, surgical pins, and/or intervertebral bodies. Where a particular rigid element appears in the first image but not the second image, or vice versa, that particular rigid element may be excluded from the plurality of rigid elements. Similarly, the plurality of rigid elements may not include, in some embodiments, every rigid element depicted in one or both of the images. An element of bony anatomy or other hard tissue may be treated as rigid for purposes of the present disclosure even if the element has some degree of flexibility. At least one of the plurality of rigid elements is movable relative to at least one other of the plurality of rigid elements.

To determine the transformation, for each of the plurality of rigid elements, from the first image to the second image, one or more transformation algorithms <NUM> may be used. The determining may comprise overlaying the second image on the first image or defining any other relationship between the first and second images. In some embodiments, a "best-guess" alignment between the first and second images may be made automatically or manually, such as by aligning a prominent edge or surface in both images (e.g., a visible edge of the patient, such as the patient's back or side; one or more surfaces of a patient's hip or pelvis, or of another hard tissue element that is less likely to move over time than the rigid elements in question). For determining the transformation, a fixed relationship between the two images must be established; however, the fixed relationship need not be accurate, as the remaining steps of the method <NUM> will distinguish between aspects of each transformation that are attributable to a change in camera pose, patient position, or other parameter that affects the depiction of every rigid element in the same way, and aspects of each transformation that are attributable to movement of the rigid element.

The determined transformation for each of the plurality of rigid elements may be a homography. The homography relates a given rigid element as depicted in the first image to the same rigid element as depicted in the second image. For purposes of calculating the homography, a plurality of points on the rigid element (visible in both the first and second images) may be selected. For example, the points may be points along a perimeter of the rigid element in anterior-posterior (AP) or lateral (LT) projections. Where the rigid element is a vertebra, the points may be edge points of the vertebra end plates. Where the rigid element is a screw, the points may be at the two ends of the screw (e.g., at the top of the screw head and at the screw tip). Where the rigid element is a rod, the points may be at opposite ends of the rod. Multiple screws in a single anatomical element may be treated as a single rigid element for purposes of the present disclosure. The points may be designated manually (e.g., via a user interface such as the user interface <NUM>) or automatically (e.g., using an image processing algorithm <NUM>, a segmentation algorithm <NUM>, or any other algorithm). The homography may be calculated using a homography algorithm such as the homography algorithm <NUM>. Any known method for calculating homographies may be used.

In some embodiments, a homography may be calculated for adjacent rigid elements. Thus, for example, a homography may be calculated for each pair of adjacent vertebrae, using the determined transformations corresponding to each pair of adjacent vertebrae.

Where the first and second images are two-dimensional images, the plurality of points comprises at least four points. Where the first and second images are three-dimensional images, the plurality of points comprises at least eight points. Whether 2D or 3D images are being used, more points may be utilized than the minimum number of points. Additionally, the needed points may comprise points defined with referenced to the patient's anatomy, points defined with reference to one or more implants (e.g., screws, rods), or any combination thereof. The selected points may be connected by labeled lines, and one or more points may be interpolated along the labeled lines. Notably, noise in the points used to calculate the homographies (e.g., any discrepancy between the location of the point in each image relative to the corresponding rigid element in the image) will contribute to the calculation of noisy homographies.

Use of screws, rods, and/or other implants as rigid elements for purposes of the present disclosure is beneficial given that such implants may be less likely to change over time than anatomical elements.

The determination of a transformation (whether a homography or otherwise) for each of the plurality of rigid elements results in a set of transformations. Each transformation may comprise one or more distances, angles, and/or other measurements sufficient to describe a change in pose of the rigid element for which the transformation was determined. In some embodiments, each determined transformation may simply comprise a segmented image of the rigid element in a first position (e.g., as depicted in the first image) and in a second position (e.g., as depicted in the second image). In still other embodiments, the determined transformation may comprise an equation or a set of equations that describe the movement of the rigid element from a pose depicted in the first image to a pose depicted in the second image.

The method <NUM> also comprises calculating a homography for each transformation.

The method <NUM> also comprises identifying a common portion of each transformation attributable to a change in pose (step <NUM>). The identifying may be based, for example, on the calculated transformations. Based on an assumption that most of the transformations will be determined only by a change in camera pose (e.g., because the corresponding vertebrae or other rigid elements have not moved), or alternatively that identical or nearly identical transformations result from a change in camera pose (which affects each rigid element more or less equally, while the motion of an individual rigid element does not necessarily have any correlation to the motion of the other rigid elements), a clustering data science approach may be used to isolate the transformations that originate only from camera pose from those transformations that result from a combination of change in camera pose as well as from motion of the rigid element.

Where clustering is used, the clustering may be completed in the transformation space (e.g., a <NUM>-dimensional space) or a reduced space. The resulting clusters may be analyzed using silhouette measure, variance, size or another parameter useful for separating those transformations attributable to a change in camera pose from those attributable both to a change in camera pose and to motion of the rigid element. The most coherent cluster (or the cluster selected by application of the other parameter) may be averaged, with the mean of that cluster being taken as the transformation corresponding to the change in camera pose. The use of clustering beneficially accounts for noise in the transformations resulting from noise in the labeled points used to calculate the transformations.

The transformation corresponding to the change in camera pose may explain the motion of most individual rigid elements in the first image and the second image. Regardless of the number of rigid elements whose transformations are explained solely by a change in camera pose, the portion of each transformation that is explained solely by the change in camera pose constitutes the common portion of each transformation (e.g., because it affects each transformation equally).

Regardless of how the step <NUM> is completed, the result thereof is a determination of how a change in pose of the camera(s) used to take the first and second images contributes to the determined transformation of each rigid element.

The method <NUM> also comprises identifying an individual portion of each transformation attributable to a change in rigid element pose (step <NUM>). The identifying an individual portion of each transformation attributable to a change in rigid element pose may comprise, for example, utilizing the common portion of each transformation determined in the step <NUM> to project each rigid element from the second image onto the first image. The result of this projection will be to align the rigid elements depicted in the second image that did not move between the first time and the second time with the corresponding rigid element depicted in the first image. For rigid elements that did move between the first time and the second time, however, the result of the projection will be to remove the effect of the change in camera pose from the depiction of the rigid element. As a result, any misalignment between the rigid element as projected from the second image onto the first image and the corresponding rigid element as depicted in the first image is attributable to movement of the rigid element itself. In other words, any difference between the pose of the rigid element as projected from the second image onto the first image and the corresponding rigid element as depicted in the first image constitutes the individual portion of each transformation attributable to a change in rigid element pose.

In some embodiments, the identifying may not comprise projecting the rigid elements from the second image onto the first image using the common portion of each transformation determined in the step <NUM>. Instead, the identifying may comprise calculating a difference between the common portion of each transformation determined in the step <NUM> and the transformation calculated for the individual rigid element. In some embodiments, any such calculated difference that falls below a predetermined threshold may be discarded as attributable to noise or otherwise constituting immaterial movement. Other methods of identifying an individual portion of each transformation attributable to a change in rigid element pose may also be utilized according to embodiments of the present disclosure.

The method <NUM> also comprises registering the second image to the first image based on the identified common portion (step <NUM>). The step <NUM> may occur prior to the step <NUM> (among other steps) and may comprise projecting the rigid elements from the second image onto the first image using the common portion of each transformation identified in the step <NUM>. The registering may also comprise otherwise aligning the second image with the first image based on elements known not to have moved from the first time to the second time (e.g., elements that only appear to have moved due to the change in camera pose, but which in fact have not moved (or have not moved within a particular tolerance). The registering may utilize one or more registration algorithms, such as the registration algorithms <NUM>.

In some embodiments, the step <NUM> may alternatively comprise updating a preoperative model based on the individual portion of each transformation. The pre-operative model may have been generated, for example, based on a preoperative image, and the updating may comprise updating each rigid element depicted in the pre-operative model to reflect any changes in position of that rigid element from the time the preoperative image was taken to the time the second image was taken. In such embodiments, the second image may be an intraoperative image or a postoperative image.

Also in some embodiments, the step <NUM> may alternatively comprise updating a registration of one of a robotic space or a navigation space to an image space based on one of the common portion of each transformation or the individual portion of each transformation. The updating may facilitate maintenance of an accurate registration, which may in turn increase the accuracy of a surgical procedure.

The method <NUM> also comprises quantifying a change in pose of at least one rigid element (step <NUM>). The quantifying utilizes only the portion of each transformation attributable to a change in rigid element pose. In other words, the quantifying comprises quantifying one or more aspects of the change in pose of a particular rigid element resulting from movement of the rigid element from the first time to the second time (as opposed to an apparent change in pose of the particular rigid element attributable to the change in pose of the camera(s) used to image the rigid element at the first time and the second time).

The quantifying may comprise, for example, determining an angle of rotation of the rigid element from the first time to the second time, and/or determining a translation distance of the rigid element. The quantifying may comprise comparing pose of the rigid element at the second time to a desired change in pose, and may be expressed as a percentage of a desired change in pose (e.g., based on a comparison to a physical or virtual model of an ideal pose of the rigid element, whether in a surgical plan, a treatment plan, or otherwise). The quantifying may further comprise quantifying the change in pose of each of the rigid elements.

The present disclosure encompasses embodiments of the method <NUM> that comprise more or fewer steps than those described above, and/or one or more steps that are different than the steps described above.

<FIG> depicts a method <NUM> for correlating images taken at different times. The method <NUM> (and/or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor. The at least one processor may be the same as or similar to the processor(s) <NUM> of the computing device <NUM> described above. The at least one processor may be part of a robot (such as a robot <NUM>) or part of a navigation system (such as a navigation system <NUM>). A processor other than any processor described herein may also be used to execute the method <NUM>. The at least one processor may perform the method <NUM> by executing instructions stored in a memory such as the memory <NUM>. The instructions may correspond to one or more steps of the method <NUM> described below. The instructions may cause the processor to execute one or more algorithms, such as an image processing algorithm <NUM>, a segmentation algorithm <NUM>, a transformation algorithm <NUM>, a homography algorithm <NUM>, and/or a registration algorithm <NUM>.

The method <NUM> comprises segmenting each of a plurality of rigid elements in a first image and in a second image (step <NUM>). The first image is taken at a first time, and the second image is taken at a second time after the first time. The first image may be the same as or similar to any other first image described herein, and the second image may be the same as or similar to any other second image described herein. The first and second images each depict a common portion of an anatomy of a patient, although the first and second images may not be perfectly aligned (e.g., in addition to the common portion of the patient's anatomy depicted in both the first image and the second image, the first image may depict one or more portions of the anatomy of the patient not depicted in the second image and vice versa). The plurality of rigid elements may be or comprise, for example, one or more vertebrae and/or other bony anatomy or hard tissue elements, and/or one or more implants (e.g., pedicle screws, cortical screws, rods, pins, and/or other implants).

The segmenting may be accomplished using one or more segmentation algorithms <NUM> and/or any other segmentation algorithm or process. The step <NUM> may further comprise utilizing an anatomical atlas, biomechanical model, or other reference to identify anatomical objects within the first and second images, determine which of those anatomical objects are rigid elements, and/or to determine a relationship (if any) between or among two or more identified rigid elements. Thus, for example, an anatomical atlas may be referenced to determine that two adjacent vertebra are connected by a vertebral disc, or a patient-specific biomechanical model may be referenced to determine that two adjacent vertebrae have been fused and should move within the patient's anatomy as one. The segmenting enables determination of the perimeter of each rigid element in the first and second images, such that each rigid element can be individually analyzed.

The method <NUM> also comprises calculating a set of homographies correlating the depiction of each rigid element in the first image to the corresponding rigid element in the second image (step <NUM>). The homographies may be calculated using any known method. The calculating may utilize one or more homography algorithms <NUM>. Each calculated homography may describe a relationship between a rigid element in the first image and the corresponding rigid element in the second image. In other words, each homography relates a rigid element in the first image to a corresponding rigid element in the second image. Stated differently, using the calculated homography as well as a depiction of a rigid element in either the first image or the second image, the depiction of the rigid element in the other of the first image or the second image may be generated.

For purposes of calculating the homographies, a plurality of points on each rigid element (visible in both the first and second images) may be selected. For example, the points may be points along a perimeter of the rigid element in anterior-posterior (AP) or lateral (LT) projections. Where the rigid element is a vertebra, the points may be edge points of the vertebra end plates. Where the rigid element is a screw, the points may be at the two ends of the screw (e.g., at the top of the screw head and at the screw tip). Where the rigid element is a rod, the points may be at opposite ends of the rod. Multiple screws in a single anatomical element may be treated as a single rigid element for purposes of the present disclosure. The points may be designated manually (e.g., via a user interface such as the user interface <NUM>) or automatically (e.g., using an image processing algorithm <NUM>, a segmentation algorithm <NUM>, or any other algorithm). The homography may be calculated using a homography algorithm such as the homography algorithm <NUM>. Any known method for calculating homographies may be used.

The method <NUM> also comprises removing from the set of homographies any homographies affected by a physical change in shape of the rigid element (step <NUM>). The method <NUM> is based on an assumption that bone structure changes (and rigid element shape changes more generally) are less predominant than soft tissue changes, any rigid elements that have changed in shape will add undesirable noise. Changes in shape of rigid elements do sometimes occur, however. Such change in shape may result, for example, from compression fractures, bone osteophytes (spondylophytes), and/or other causes.

The removing of homographies affected by a physical change in shape may be completed manually or automatically. In some embodiments, changes in shape may be identified by a treating physician or other user (from the first and second images, for example) prior to calculation of any homographies. In other embodiments, a treating physician or other user may review the first and second images after the homographies have been calculated, and may identify one or more rigid elements that have changed in shape, based upon which identification the corresponding homographies may be discarded or ignored. In still other embodiments, changes in shape may be identified by a processor using one or more image processing algorithms <NUM> or other algorithms, whether prior to or after the segmenting of the step <NUM>. In such embodiments, changes in shape may be identified based on a rough comparison of the edges of each rigid element (e.g., as detected using an edge detection algorithm, a segmenting algorithm, or otherwise) in the first image and the second image.

The method <NUM> also comprises arranging the set of homographies into homography clusters (step <NUM>). The homographies may be clustered using any data science clustering approach. The purpose of the clustering is to identify those homographies that are most similar, which can be assumed to correspond to rigid elements that have not moved from the first time to the second time, but whose change in pose in the second image relative to the first image can be attributed entirely or almost entirely to a change in camera pose. Thus, any clustering approach may be used that results in similar homographies being grouped together. The clustering may be completed in the transformation space (e.g., a <NUM>-dimensional space) or a reduced space.

The method <NUM> also comprises selecting a homography cluster based on a parameter (step <NUM>). The parameter may be silhouette, variance, size or another parameter useful for separating those homographies attributable to a change in camera pose from those attributable both to a change in camera pose and to motion of the rigid element. The cluster may comprise a majority of the homographies utilized in the cluster analysis, or a minority of such homographies. Because a change in camera pose (e.g., from the first time to the second time) will affect each rigid element equally (while motion of each rigid element will not necessarily be correlated to motion of any other rigid element), the most coherent cluster is most likely to comprise homographies that reflect only perceived motion resulting from that change in camera pose. Even the most coherent cluster, however, is unlikely to have perfectly matching homographies, due to noise in the homographies resulting from noise in the labeled points used to calculate the homographies, the segmenting of each rigid element, and any other aspects of the method <NUM> that may lack <NUM>% accuracy.

The method <NUM> also comprises projecting each rigid element as depicted in the second image on the first image, using a mean of the selected homography cluster, to yield a projection image (step <NUM>). The mean of the selected homography is utilized to reduce the impact of any noise that affected the homographies in the most coherent (or other selected) cluster. The mean of the selected homography is then used to project the rigid elements from the second image onto the first image. Because the selected homography corresponds to the effect of the change in pose of the camera(s) from the first time (when the first image was captured) to the second time (when the second image was captured), the projecting results in any projected rigid elements that did not move from the first time to the second time being aligned with and overlapping the corresponding rigid element from the first image. For any rigid element that did move from the first time to the second time, the projecting of such rigid elements will remove any effect from the change in camera pose on the pose of the such projected rigid elements, such that the projection image depicts only the actual change in pose of such rigid elements from the first time to the second time.

The method <NUM> also comprises measuring a variation between the first and second poses of a rigid element as depicted in the projection image (step <NUM>). As described above, the projection image comprises images of each rigid element from the second image that have been projected onto the first image using a mean of the selected homography. As a result, any variation in pose between two corresponding anatomical elements in the projection image may be assumed to reflect an actual change in pose of the rigid element. Such change in pose may be measured, thus yielding one or more angles of rotation, distances of translation, and/or other parameters describing the movement of the rigid element from the first time to the second time. In some embodiments, the measured quantities may be compared to a desired quantity (as reflected, for example, in a treatment plan) to yield a percent of achievement or similar parameter. In other embodiments, the measured quantities may be compared to the amount of time separating the first time from the second time to yield a rate of change, which may be used to predict future changes in pose of the one or more rigid elements, to predict if and when additional surgery or other treatment will be needed, or for any other useful purpose.

Additionally, the measured quantity (and/or the results of any calculations completed using the measured quantity) may be displayed to a treating physician or other user on a user interface such as the user interface <NUM>. The measured quantity may be displayed as a number, or may be converted into an indicator (e.g., a red indicator where the quantity is within a predetermined range of unacceptable values, a yellow indicator where the quantity is within a predetermined range of unsatisfactory values, and a green indicator where the quantity is within a predetermined range of acceptable values).

<FIG> depicts a method <NUM> for comparing images. The method <NUM> (and/or one or more steps thereof) may be carried out or otherwise performed, for example, by at least one processor, which may be part of a system. The at least one processor may be the same as or similar to the processor(s) <NUM> of the computing device <NUM> described above. The at least one processor may be part of a robot (such as a robot <NUM>) or part of a navigation system (such as a navigation system <NUM>). A processor other than any processor described herein may also be used to execute the method <NUM>. The at least one processor may perform the method <NUM> by executing instructions stored in a memory such as the memory <NUM>. The instructions may correspond to one or more steps of the method <NUM> described below. The instructions may cause the processor to execute one or more algorithms, such as an image processing algorithm <NUM>, a segmentation algorithm <NUM>, a transformation algorithm <NUM>, a homography algorithm <NUM>, and/or a registration algorithm <NUM>.

The method <NUM> comprises identifying a plurality of elements in a first image (step <NUM>). The first image may be taken using any imaging device (e.g., an imaging device <NUM>), and is taken at a first time. The first image depicts a portion of a patient's anatomy. The identifying may utilize one or more image processing algorithms, such as the image processing algorithms <NUM>. Each of the elements is a rigid element, and may be an anatomical rigid element (e.g., a bony anatomy or hard tissue element) or a rigid implant (e.g., a screw, a rod, a pin). The plurality of elements may include both one or more anatomical rigid elements and one or more rigid implants. In some embodiments, the identifying the plurality of elements in the first image also comprises segmenting the plurality of elements in the first image, which may be accomplished in any manner described herein or in any other known manner of segmenting elements in an image.

The method <NUM> also comprises identifying the plurality of elements in a second image taken after the first image (step <NUM>). Like the first image, the second image may be taken using any imaging device (e.g., an imaging device <NUM>), and depicts the same portion of the patient's anatomy as the first image (or at least a substantially overlapping portion of the patient's anatomy). The second image is taken at a second time after the first time. As with other embodiments of the present disclosure, the second time may be days, weeks, months, or even years after the first time. The identifying may utilize one or more image processing algorithms, such as the image processing algorithms <NUM>. The plurality of elements identified in the second image is the same plurality of elements identified in the first image. In some embodiments, the identifying the plurality of elements in the second image also comprises segmenting the plurality of elements in the second image, which may be accomplished in any manner described herein or in any other known manner of segmenting elements in an image.

The method <NUM> also comprises calculating a homography for each one of the plurality of elements (step <NUM>). The step <NUM> is the same as or similar to the step <NUM> of the method <NUM> and/or the step <NUM> of the method <NUM>.

The method <NUM> also comprises determining, based on the homographies, a first change in pose attributable to a change in imaging device position, and a second change in pose not attributable to the imaging device position change (step <NUM>). The step <NUM> is the same as or similar to a combination of the steps <NUM> and <NUM> of the method <NUM>, and/or a combination of the steps <NUM>, <NUM>, and <NUM> of the method <NUM>.

The method <NUM> also comprises registering the second image to the first image based on the first change in pose (step <NUM>). The step <NUM> is the same as or similar to the step <NUM> of the method <NUM>.

As noted above, the present disclosure encompasses methods with fewer than all of the steps identified in <FIG>, <FIG>, and <FIG> (and the corresponding description of the methods <NUM>, <NUM>, and <NUM>), as well as methods that include additional steps beyond those identified in <FIG>, <FIG>, and <FIG> (and the corresponding description of the methods <NUM>, <NUM>, and <NUM>). The present disclosure also encompasses methods that comprise one or more steps from one method described herein, and one or more steps from another method described herein. Any correlation described herein may be or comprise a registration or any other correlation.

The foregoing is not intended to limit the disclosure to the form or forms disclosed herein. In the foregoing Detailed Description, for example, various features of the disclosure are grouped together in one or more aspects, embodiments, and/or configurations for the purpose of streamlining the disclosure. The features of the aspects, embodiments, and/or configurations of the disclosure may be combined in alternate aspects, embodiments, and/or configurations other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed aspect, embodiment, and/or configuration. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate preferred embodiment of the disclosure.

Claim 1:
A method (<NUM>) of correlating images taken at different times, comprising:
segmenting (<NUM>), in a first image of a plurality of rigid elements taken at a first time and in a second image of the plurality of rigid elements taken at a second time after the first time, each rigid element of the plurality of rigid elements;
calculating (<NUM>) a homography for each rigid element of the plurality of rigid elements to yield a set of homographies, each homography correlating the rigid element as depicted in the first image to the rigid element as depicted in the second image;
arranging (<NUM>) the set of homographies into homography clusters for identifying those homographies that are most similar, which correspond to rigid elements that have not moved from the first time to the second time, but whose change in pose in the second image relative to the first image can be attributed entirely to a change in camera pose;
selecting (<NUM>) a most coherent homography cluster; and
projecting (<NUM>) each of the plurality of rigid elements as depicted in the second image onto the first image using a mean of the selected homography cluster to yield a projection image.