Systems and methods for determining a pose of an object relative to an imaging device

Abstract

Systems and methods for positioning an imaging device are provided. A first image dataset may be received from an imaging device at a first pose. A second image dataset may be received from the imaging device at a second pose. A difference between the first pose and the second pose may be measured and a scaling factor may be determined. A pose of a target portion of an object depicted in the first image dataset and the second image dataset may be determined relative to an iso-center of the imaging device based on the scaling factor and the measured difference.

Description

SYSTEMS AND METHODS FOR DETERMINING A POSE OF AN OBJECT RELATIVE TO AN IMAGING DEVICEBACKGROUND

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 563,174, filed 8 March 2024, the entire content of which is incorporated herein by reference.

[0002] The present disclosure is generally directed to determining a pose of an object, and relates more particularly to determining a pose of an object relative to an imaging device.

[0003] Surgical robots may assist a surgeon or other medical provider in carrying out a surgical procedure, or may complete one or more surgical procedures autonomously. 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.BRIEF SUMMARY

[0004] Example aspects of the present disclosure include:

[0005] A system for positioning an imaging device according to at least one embodiment of the present disclosure comprises an imaging device comprising a source configured to emit a wave and a detector configured to receive a signal indicative of the emitted wave; at least one processor; and a memory storing data for processing by the processor, the data, when processed, causing the processor to: receive a first image dataset from the imaging device at a first pose, the first image dataset comprising a first image depicting at least a portion of an object; receive a second image dataset from the imaging device at a second pose, the second image dataset comprising a second image depicting the at least a portion of the object; measure a difference between the first pose and the second pose; determine a scaling factor based on the first image dataset and the second image dataset; and determine a pose of a target portion of the object relative to an iso-center of the imaging device based on the scaling factor and the measured difference.

[0006] Any of the aspects herein, wherein the memory saves further data configured to cause the processor to: move at least one of the object or the imaging device to position the target portion of the object at the iso-center of the imaging device.

[0007] Any of the aspects herein, wherein the memory saves further data configured to cause the processor to: determine an offset between the target portion of the object and the iso-center of the imaging device, wherein the at least one of the object or the imaging device is moved based on the offset.

[0008] Any of the aspects herein, wherein determining the scaling factor comprises registering the first image and the second image and iterating a scaling factor based on the registered first image and second image.

[0009] Any of the aspects herein, wherein the memory saves further data configured to cause the processor to: scale the second image using the scaling factor; fuse the scaled second image with the first image; compare the fused image to determine if the scaled second image and the first image overlap at the target portion of the object to validate the scaling factor.

[0010] Any of the aspects herein, wherein the first source to object height and the second source to object height are obtained from the same orientations.

[0011] Any of the aspects herein, wherein determining the pose is also based on a distance between the iso-center and the source of the imaging device.

[0012] Any of the aspects herein, wherein the imaging device is an X-ray imaging device.

[0013] Any of the aspects herein, wherein the memory saves further data configured to cause the processor to: determine a distance per pixels in the first image based on the pose of the target portion of the object; and obtain a measurement based on the distance per pixels.

[0014] Any of the aspects herein, wherein the measurement comprises at least one of a length, a distance between two points, a width, a depth, or a combination thereof.

[0015] Any of the aspects herein, wherein determining the pose of the target portion of the object includes determining the first source to object height and the second source to object height based on the measured difference between the first pose and the second pose.

[0016] Any of the aspects herein, wherein determining the first source to object height includes adding one to the measured difference and dividing by the scaling factor.

[0017] A system for determining a pose of an object relative to an imaging device according to at least one embodiment of the present disclosure comprises an imaging device; at least one processor; and a memory storing data for processing by the processor, the data, when processed, causing the processor to: receive a first image dataset from the imaging device at a first pose corresponding to a first distance between the imaging device and an object, the first image dataset comprising a first image depicting at least a portion of the object; receive a second image dataset from the imaging device at a second pose corresponding to a second distance between the imaging device and the object, the second image dataset comprising a second image depicting at least the portion of the object; determine a scaling factor based on the first image dataset, the first distance, the second image dataset, and the second distance; and determine a pose of a target portion of the object relative to a pose of the imaging device based on the scaling factor.

[0018] Any of the aspects herein, wherein the memory saves further data configured to cause the processor to: position the imaging device based on the pose of the target portion of the object relative to the pose of the imaging device.

[0019] Any of the aspects herein, wherein the memory saves further data configured to cause the processor to: determine an offset between the target portion of the object and the iso-center of the imaging device, wherein the at least one of the object or the imaging device is moved based on the offset.

[0020] Any of the aspects herein, wherein the imaging device comprises an ultrasound imaging device.

[0021] Any of the aspects herein, wherein determining the pose of the target portion of the object includes determining the first source to object height and the second source to object height based on the measured difference between the first pose and the second pose.

[0022] Any of the aspects herein, wherein determining the first source to object height includes adding one to the measured difference and dividing by the scaling factor.

[0023] Any of the aspects herein, wherein the memory saves further data configured to cause the processor to: determine a distance per pixels in the first image based on the pose of the target portion of the object; and obtain a measurement based on the distance per pixels.

[0024] A system for determining a pose of an object relative to an imaging device according to at least one embodiment of the present disclosure comprises an imaging device; at least one processor; and a memory storing data for processing by the processor, the data, when processed, causing the processor to: receive a first image dataset from the imaging device at a first pose corresponding to a first distance between the imaging device and an object, the first image dataset comprising a first image depicting at least a portion of the object; receive a second image dataset from the imaging device at a second pose corresponding to a second distance between the imaging device and the object, the second image dataset comprising a second image depicting at least the portion of the object; determine a scaling factor based on the first image dataset, the first distance, the second image dataset, and the second distance; determine a distance per pixels in the first image based on the pose of the target portion of the object; and obtain a measurement based on the distance per pixels.

[0025] Any aspect in combination with any one or more other aspects.

[0026] Any one or more of the features disclosed herein.

[0027] Any one or more of the features as substantially disclosed herein.

[0028] Any one or more of the features as substantially disclosed herein in combination with any one or more other features as substantially disclosed herein.

[0029] Any one of the aspects / features / embodiments in combination with any one or more other aspects / features / embodiments .

[0030] Use of any one or more of the aspects or features as disclosed herein.

[0031] It is to be appreciated that any feature described herein can be claimed in combination with any other feature(s) as described herein, regardless of whether the features come from the same described embodiment.

[0032] The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims.

[0033] The phrases “at least one”, “one or more”, and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together. When each one of A, B, and C in the above expressions refers to an element, such as X, Y, and Z, or class of elements, such as XI -Xn, Yl-Ym, and Zl-Zo, the phrase is intended to refer to a single element selected from X, Y, and Z, a combination of elements selected from the same class (e.g., XI and X2) as well as a combination of elements selected from two or more classes (e.g., Y1 and Zo).

[0034] The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising”, “including”, and “having” can be used interchangeably.

[0035] The preceding is a simplified summary of the disclosure to provide an understanding of some aspects of the disclosure. This summary is neither an extensive nor exhaustive overview of the disclosure and its various aspects, embodiments, and configurations. It is intended neither to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure but to present selected concepts of the disclosure in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other aspects, embodiments, and configurations of the disclosure are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.

[0036] Numerous additional features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the embodiment descriptions provided hereinbelow.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0037] The accompanying drawings are incorporated into and form a part of the specification to illustrate several examples of the present disclosure. These drawings, together with the description, explain the principles of the disclosure. The drawings simply illustrate preferred and alternative examples of how the disclosure can be made and used and are not to be construed as limiting the disclosure to only the illustrated and described examples. Further features and advantages will become apparent from the following, more detailed, description of the various aspects, embodiments, and configurations of the disclosure, as illustrated by the drawings referenced below.

[0038] Fig. 1 is a block diagram of a system according to at least one embodiment of the present disclosure;

[0039] Fig. 2 is a schematic illustration of a system according to at least one embodiment of the present disclosure;

[0040] Fig. 3A is an example image according to at least one embodiment of the present disclosure;

[0041] Fig. 3B is an example image according to at least one embodiment of the present disclosure;

[0042] Fig. 4A is an example image according to at least one embodiment of the present disclosure;

[0043] Fig. 4B is an example image according to at least one embodiment of the present disclosure;

[0044] Fig. 5 is a flowchart according to at least one embodiment of the present disclosure;

[0045] Fig. 6 is a flowchart according to at least one embodiment of the present disclosure; and

[0046] Fig. 7 is a flowchart according to at least one embodiment of the present disclosure.DETAILED DESCRIPTION

[0047] It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. 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 maynot 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.

[0048] In one or more examples, the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Alternatively or additionally, functions may be implemented using machine learning models, neural networks, artificial neural networks, or combinations thereof (alone or in combination with instructions). Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0049] 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 Al l, A12, A12X, A12Z, or A13 Bionic processors; or any other general purpose microprocessors), graphics processing units (e.g., Nvidia GeForce RTX 2000-series processors, Nvidia GeForce RTX 3000-series processors, AMD Radeon RX 5000-series processors, AMD Radeon RX 6000-series processors, or any other graphics processing units), application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0050] Before any embodiments of the disclosure are explained in detail, it is to be understood that the disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or“having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. Further, the present disclosure may use examples to illustrate one or more aspects thereof. Unless explicitly stated otherwise, the use or listing of one or more examples (which may be denoted by “for example,” “by way of example,” “e.g.,” “such as,” or similar language) is not intended to and does not limit the scope of the present disclosure.

[0051] The terms proximal and distal are used in this disclosure with their conventional medical meanings, proximal being closer to the operator or user of the system, and further from the region of surgical interest in or on the patient, and distal being closer to the region of surgical interest in or on the patient, and further from the operator or user of the system.

[0052] In conventional methods for centering a target anatomy with an isocenter of an imaging device such as a computed tomography (CT) imaging device, an X-ray image is taken at two different orientations. For each orientation, the target anatomy is moved to a center of the screen. This process may be repeated to obtain an accurate positioning of the target anatomy, thus exposing the patient to radiation over several images.

[0053] In embodiments according to the present disclosure, systems and methods are provided for determining a relative pose of the target anatomy (or object) to the imaging device using two images. The relative pose can then be used to position the target anatomy at the isocenter of the imaging device. The two images are taken at the same orientation and at two different heights. A relative scaling factor of the target anatomy in the two images is obtained and a difference between the two different heights is measured, which can be used to calculate the relative pose of the target anatomy to the imaging device (or more specifically and in some instances, the isocenter of the imaging device). More specifically, the scaling factor can be obtained using imaging processing (e.g., registration) and / or visualization techniques (e.g., fusion imaging). The scaling factor can be used to estimate three-dimensional (3D) coordinates of the target anatomy and the target anatomy and / or the imaging device can be moved to position the target anatomy at the isocenter. Such systems and methods only obtain two images to determine the relative pose of the target anatomy to the imaging device and / or the isocenter of the imaging device, beneficially reducing the amount of radiation exposure to the patient and reducing the time needed to locate the target anatomy relative to the imaging device.

[0054] Embodiments of the present disclosure provide technical solutions to one or more of the problems of (1) increasing the working efficiency of an operator of an imaging device; (2) decreasing radiation exposure to a patient; and (3) increasing patient and medical team safety.

[0055] Turning first to Fig. 1, a block diagram of a system 100 according to at least one embodiment of the present disclosure is shown. The system 100 may be used to determine a pose of an object relative to an imaging device and / or carry out one or more other aspects of one or more of the methods disclosed herein. The system 100 comprises a computing device 102, one or more imaging devices 112, a robot 114, a navigation system 118, a database 130, and / or a cloud or other network 134. Systems according to other embodiments of the present disclosure may comprise more or fewer components than the system 100. For example, the system 100 may not include the imaging device 112, the robot 114, the navigation system 118, one or more components of the computing device 102, the database 130, and / or the cloud 134.

[0056] The computing device 102 comprises a processor 104, a memory 106, a communication interface 108, and a user interface 110. Computing devices according to other embodiments of the present disclosure may comprise more or fewer components than the computing device 102.

[0057] The processor 104 of the computing device 102 may be any processor described herein or any similar processor. The processor 104 may be configured to execute instructions stored in the memory 106, which instructions may cause the processor 104 to carry out one or more computing steps utilizing or based on data received from the imaging device 112, the robot 114, the navigation system 118, the database 130, and / or the cloud 134.

[0058] The memory 106 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 106 may store information or data useful for completing, for example, any step of the methods 500, 600, and / or 700 described herein, or of any other methods. The memory 106 may store, for example, instructions and / or machine learning models that support one or more functions of the robot 114. For instance, the memory 106 may store content (e.g., instructions and / or machine learning models) that, when executed by the processor 104, enable image processing 120, scaling 122, and / or registration 124.

[0059] The image processing 120 enables the processor 104 to process image data of an image (obtained from, for example, the imaging device 112) for the purpose of, for example, identifying information about anatomical elements and / or objects depicted in the image. The information may comprise, for example, identification of hard tissue and / or soft tissues, a boundary between hard tissue and soft tissue, a boundary of hard tissue and / or soft tissue, identification of a surgical tool, etc. The image processing 120 may, for example, identify hard tissue, soft tissue, and / or a boundary of the hard tissue and / or soft tissue by determining a difference in or contrast between colors orgrayscales of image pixels. For example, a boundary between the hard tissue and the soft tissue may be identified as a contrast between lighter pixels and darker pixels.

[0060] The scaling 122 enables the processor 104 to determine a scaling factor based on a first image data set and a second image data set and to apply the scaling factor to an image data set (whether the first image data set, the second image data set, or any other image data set).

[0061] The registration 124 enables the processor 104 to correlate an image with another image. The registration 124 may enable the processor 104 to also correlate identified anatomical elements and / or individual objects in one image with identified anatomical elements and / or individual objects in another image.

[0062] Such content, if provided as in instruction, may, in some embodiments, be organized into one or more applications, modules, packages, layers, or engines. Alternatively or additionally, the memory 106 may store other types of content or data (e.g., machine learning models, artificial neural networks, deep neural networks, etc.) that can be processed by the processor 104 to carry out the various method and features described herein. Thus, although various contents of memory 106 may be described as instructions, it should be appreciated that functionality described herein can be achieved through use of instructions, algorithms, and / or machine learning models. The data, algorithms, and / or instructions may cause the processor 104 to manipulate data stored in the memory 106 and / or received from or via the imaging device 112, the robot 114, the database 130, and / or the cloud 134.

[0063] The computing device 102 may also comprise a communication interface 108. The communication interface 108 may be used for receiving image data or other information from an external source (such as the imaging device 112, the robot 114, the navigation system 118, the database 130, the cloud 134, and / or any other system or component not part of the system 100), and / or for transmitting instructions, images, or other information to an external system or device (e.g., another computing device 102, the imaging device 112, the robot 114, the navigation system 118, the database 130, the cloud 134, and / or any other system or component not part of the system 100). The communication interface 108 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 802.11a / b / g / n, Bluetooth, NFC, ZigBee, and so forth). In some embodiments, the communication interface 108 may be useful for enabling the device 102 to communicate with one or more other processors 104 or computing devices 102, whether to reduce the time needed to accomplish a computing-intensive task or for any other reason.

[0064] The computing device 102 may also comprise one or more user interfaces 110. The user interface 110 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 110 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 100 (e.g., by the processor 104 or another component of the system 100) or received by the system 100 from a source external to the system 100. In some embodiments, the user interface 110 may be useful to allow a surgeon or other user to modify instructions to be executed by the processor 104 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 110 or corresponding thereto.

[0065] Although the user interface 110 is shown as part of the computing device 102, in some embodiments, the computing device 102 may utilize a user interface 110 that is housed separately from one or more remaining components of the computing device 102. In some embodiments, the user interface 110 may be located proximate one or more other components of the computing device 102, while in other embodiments, the user interface 110 may be located remotely from one or more other components of the computer device 102.

[0066] The imaging device 112 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.). “Image data” as used herein refers to the data generated or captured by an imaging device 112, 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 image data may be or comprise a preoperative image, an intraoperative image, a postoperative image, or an image taken independently of any surgical procedure. In some embodiments, a first imaging device 112 may be used to obtain first image data (e.g., a first image) at a first time, and a second imaging device 112 may be used to obtain second image data (e.g., a second image) at a second time after the first time. The imaging device 112 may be capable of taking a 2D image or a 3D image to yield the image data. The imaging device 112 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), an 0-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 (OCT) scanner, an endoscope, a microscope, an optical camera, athermographic camera (e.g., an infrared camera), a radar system (which may comprise, for example, a transmitter, a receiver, a processor, and one or more antennae), or any other imaging device 112 suitable for obtaining images of an anatomical feature of a patient. The imaging device 112 may be contained entirely within a single housing, or may comprise a transmitter / emitter and a receiver / detector that are in separate housings or are otherwise physically separated.

[0067] In some embodiments, the imaging device 112 may comprise more than one imaging device 112. 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 112 may be operable to generate a stream of image data. For example, the imaging device 112 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.

[0068] In some embodiments, the imaging device 112 may comprise a source 112A and a detector 112B. In some embodiments, the source 112A and the detector 112B may be in separate housings or are otherwise physically separated. In such embodiments, the source 112A and the detector 112B may be oriented a fixed distance away from each other on, for example, a gantry. As such, a distance between the source 112A and the detector 112B is known as a source detector distance (SDD) and a distance from the source 112A to an isocenter of the imaging device 112 is known as a source to isocenter distance (SID).

[0069] In other embodiments, the source 112A may be oriented by a first robotic arm and the detector 112B may be oriented by a second robotic arm. In still other embodiments, the source 112A and the detector 112B may be in the same housing. The source 112A may be configured to emit a wave and the detector 112B may be configured to receive a signal indicative of the emitted wave. The detector 112B may also be configured to save a plurality of image datasets to, for example, the memory 106. The wave may be, for example, an X-ray wave.

[0070] The robot 114 may be any surgical robot or surgical robotic system. The robot 114 may be or comprise, for example, the Mazor X™ Stealth Edition robotic guidance system. The robot 114 may be configured to position the imaging device 112 at one or more precise position(s) and orientation(s), and / or to return the imaging device 112 to the same position(s) and orientation(s) at a later point in time. The robot 114 may additionally or alternatively be configured to manipulate asurgical tool (whether based on guidance from the navigation system 118 or not) to accomplish or to assist with a surgical task. In some embodiments, the robot 114 may be configured to hold and / or manipulate an anatomical element during or in connection with a surgical procedure. The robot 114 may comprise one or more robotic arms 116. In some embodiments, the robotic arm 116 may comprise a first robotic arm and a second robotic arm, though the robot 114 may comprise more than two robotic arms. In some embodiments, one or more of the robotic arms 116 may be used to hold and / or maneuver the imaging device 112. In embodiments where the imaging device 112 comprises two or more physically separate components (e.g., a transmitter and receiver), one robotic arm 116 may hold one such component, and another robotic arm 116 may hold another such component. Each robotic arm 116 may be positionable independently of the other robotic arm. The robotic arms 116 may be controlled in a single, shared coordinate space, or in separate coordinate spaces.

[0071] The robot 114, together with the robotic arm 116, may have, for example, one, two, three, four, five, six, seven, or more degrees of freedom. Further, the robotic arm 116 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 112, surgical tool, or other object held by the robot 114 (or, more specifically, by the robotic arm 116) may be precisely positionable in one or more needed and specific positions and orientations.

[0072] The robotic arm(s) 116 may comprise one or more sensors that enable the processor 104 (or a processor of the robot 114) to determine a precise pose in space of the robotic arm (as well as any object or element held by or secured to the robotic arm).

[0073] In some embodiments, reference markers (e.g., navigation markers) may be placed on the robot 114 (including, e.g., on the robotic arm 116), the imaging device 112, or any other object in the surgical space. The reference markers may be tracked by the navigation system 118, and the results of the tracking may be used by the robot 114 and / or by an operator of the system 100 or any component thereof. In some embodiments, the navigation system 118 can be used to track other components of the system (e.g., imaging device 112) and the system can operate without the use of the robot 114 (e.g., with the surgeon manually manipulating the imaging device 112 and / or one or more surgical tools, based on information and / or instructions generated by the navigation system 118, for example).

[0074] The navigation system 118 may provide navigation for a surgeon and / or a surgical robot during an operation. The navigation system 118 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 118 may include one or more cameras orother 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 100 is located. The one or more cameras may be optical cameras, infrared cameras, or other cameras. In some embodiments, the navigation system 118 may comprise one or more electromagnetic sensors. In various embodiments, the navigation system 118 may be used to track a position and orientation (e.g., a pose) of the imaging device 112, the robot 114 and / or robotic arm 116, 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 118 may include a display for displaying one or more images from an external source (e.g., the computing device 102, imaging device 112, or other source) or for displaying an image and / or video stream from the one or more cameras or other sensors of the navigation system 118. In some embodiments, the system 100 can operate without the use of the navigation system 118. The navigation system 118 may be configured to provide guidance to a surgeon or other user of the system 100 or a component thereof, to the robot 114, or to any other element of the system 100 regarding, for example, a pose of one or more anatomical elements, 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 or other surgical plan.

[0075] The database 130 may store information that correlates one coordinate system to another (e.g., one or more robotic coordinate systems to a patient coordinate system and / or to a navigation coordinate system). The database 130 may additionally or alternatively store, for example, one or more surgical plans (including, for example, pose information about a target and / or image information about a patient’s anatomy at and / or proximate the surgical site, for use by the robot 114, the navigation system 118, and / or a user of the computing device 102 or of the system 100); one or more images useful in connection with a surgery to be completed by or with the assistance of one or more other components of the system 100; and / or any other useful information. The database 130 may be configured to provide any such information to the computing device 102 or to any other device of the system 100 or external to the system 100, whether directly or via the cloud 134. In some embodiments, the database 130 may be or comprise part of a hospital image storage system, such as a picture archiving and communication system (PACS), a health information system (HIS), and / or another system for collecting, storing, managing, and / or transmitting electronic medical records including image data.

[0076] The cloud 134 may be or represent the Internet or any other wide area network. The computing device 102 may be connected to the cloud 134 via the communication interface 108, using a wired connection, a wireless connection, or both. In some embodiments, the computingdevice 102 may communicate with the database 130 and / or an external device (e.g., a computing device) via the cloud 134.

[0077] The system 100 or similar systems may be used, for example, to carry out one or more aspects of any of the methods 500, 600, and / or 700 described herein. The system 100 or similar systems may also be used for other purposes.

[0078] Fig. 2 is a schematic diagram 200 for obtaining a first image dataset at a first source to object height (e.g., a first height Hl) 202 and obtaining a second image dataset at a second source to object height (e.g., a second height H2) 204 using an imaging device 212 having a source 212A and a detector 212B. The imaging device 212 may be the same as or similar to the imaging device 112; the source 212A may be the same as or similar to the source 112A; and the detector 212B may be the same as or similar to the detector 112B. In the illustrated embodiment, the source 212A and the detector 212B may be fixed distance from each other and as such, an isocenter 210 of the imaging device 112 is known, as well as a SID 206 and a SDD 208. It will be appreciated that in other embodiments, the source 212A and the detector 212B may not be a fixed distance from each other and may be positioned independently of each other.

[0079] In the illustrated embodiment, the source 212A may be positioned at a first relative pose to an object 211 and the distance from the source 212A to the object 211 may be defined as the first source to object height Hl 202. The source 212A may then be positioned at a second relative pose to an object 211 and the distance from the source 212A to the object 211 may be defined as the second source to object height H2 204. The object 211 may be, for example, a surgical tool, a surgical instrument, an implant, a target anatomical element, a target portion of an anatomical element, or any other target area to image. As will be described in more detail in Figs. 5-7, the first image dataset may be obtained with the imaging device 212 at the first height Hl and the second image dataset may be obtained with the imaging device 212 at the second height H2. In such instances, the source 212A and the detector 212B remain at the same orientation (which may be, for example, an anterior-posterior (AP) view, posterior-anterior (PA) view, left-lateral (LLAT), right-lateral (RLAT), or a lateral (LAT) view). Based on the known distances defined such as, for example, the SID 208, the SDD 208, and by measuring a distance between Hl and H2 to yield a measured difference 216 based on movement of the imaging device 212, a scaling factor can be determined. The scaling factor, the first image dataset, and the second image dataset can be used to estimate a pose or three- dimensional (3D) coordinates of the object 211 relative to the imaging device 112, 212. The imaging device 112, 212 or the object 211, or both, can then be moved to position the object 211 at the isocenter of the imaging device 112, 212 using only two images as opposed to more than two imagesas is typically done to center a target object at an isocenter of an imaging device. Thus, patient exposure is beneficially limited to two images using the systems and methods of the present disclosure.

[0080] It will be appreciated that once the pose or 3D coordinates of the object 211 are determined, the pose or 3D coordinates of the object 211 can be used as input for other imaging devices such as, for example, an ultrasound imaging device. In other words, a depth of the object 211 (as determined from the pose or 3D coordinates determined as described above) can be used to optimize and / or improve image quality of the ultrasound imaging device.

[0081] Figs. 3A-4B are images obtained from experiments to illustrate the systems and methods for determining the pose of an object or anatomical element relative to an imaging device. In such experiments, two images are taken at two heights (e.g., Hl and H2) and a scaling factor is obtained based on the two images using image registration. The the scaling factor may be determined by iterating a scaling factor based on the registered first image and second image. The scaling factor is used to scale the image taken at H2 and the scaled image is fused with the image taken at Hl. As shown, certain features align in the fused image using different scaling factors. Hl and H2 can then be calculated based on target object(s) being aligned, which can be used to calculate a distance from the target object(s) to isocenter of the imaging device 112, 212.

[0082] Figs. 3A and 3B are a first image 300 and a second image 302 taken in the AP view, respectively. The first image 300 is obtained from an imaging device such as the imaging device 112, 212 at a first height Hl (e.g., a first source to object height Hl) and the second image 302 is obtained from the imaging device 112, 212 at a second height H2 (e.g., a second source to object height H2). As shown, the first image 300 and the second image 302 reflect the same field of view at different magnifications. In some instances, the first image 300 may be expressed as the second image 302 scaled by a scaling factor of H2 / H1. The scaling factor can be obtained, for example, from registering the first image 300 and the second image 302. In the illustrated examples, the first image 300 and the second image 302 are registered using, for example, a registration such as the registration 124 from which a scaling factor of 1.2 is obtained. The second image 302 may be scaled by the scaling factor of 1.2 and the scaled second image may be fused with the first image 300, as shown in Figs. 4 A and 4B.

[0083] Figs. 4A and 4B illustrate a first fused image 400 and a second fused image 402, respectively. Figs. 4A and 4B show that the alignment of target regions of interest can be used to measure the depth of different target objects or target anatomical elements. For example, in Fig. 4A,the second image 302 is scaled by the scaling factor of 1.2 and fused with the first image 300. As shown, a first target region of interest 404 correlating to boney anatomy in the first image 300 and the scaled second image 302 are aligned and a second target region of interest 406 correlating to an implanted structure in the first image 300 and the scaled second image 302 are not aligned. In such example, the depth of the first target region of interest 404 can be measured. However, in Fig. 4B, the second image 302 is scaled by a scaling factor of 1.17 and fused with the first image 300. As shown, the first target region of interest 404 in the first image 300 and the scaled second image 302 are not aligned and the second region of interest 406 in the first image 300 and the scaled second image 302 are aligned. In such example, the depth of the second target region of interest 406 can be measured. Thus, different scaling factors can be used for different regions of interest to measure the depth of different target objects or anatomies.

[0084] Fig. 5 depicts a method 500 that may be used, for example, for determining a relative pose of a target object and / or anatomical element to an imaging device.

[0085] The method 500 (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) 104 of the computing device 102 described above. The at least one processor may be part of a robot (such as a robot 114) or part of a navigation system (such as a navigation system 118). A processor other than any processor described herein may also be used to execute the method 500. The at least one processor may perform the method 500 by executing elements stored in a memory such as the memory 106. The elements stored in memory and executed by the processor may cause the processor to execute one or more steps of a function as shown in method 500. One or more portions of a method 500 may be performed by the processor executing any of the contents of memory, such as an image processing 120, a scaling 122, and / or a registration 124.

[0086] The method 500 comprises receiving a first image dataset (step 504). The first image may be obtained from an imaging device such as the imaging device 112, 212 at a first pose. The first pose may correlate to a first source to object height or Hl, which may be the same as or similar to Hl 202. The imaging device may be any imaging device such as an MRI scanner, a CT scanner, any other X- ray based imaging device, or an ultrasound imaging device. The imaging device may have a source such as the source 112A, 212A and a detector such as the detector 112B, 212B. The first image dataset may also be generated by and / or uploaded to any other component of a system such as the system 100. In some embodiments, the first image dataset may be indirectly received via any other component of the system or a node of a network to which the system is connected.

[0087] The first image dataset may correspond to a first image that may be a 2D image or a 3D image or a set of 2D and / or 3D images. The first image may depict a target region of interest having, for example, one or more target object(s) which may be the same as or similar to the object 211. The object may be, for example, a surgical tool, a surgical instrument, an implant, a target anatomical element, a target portion of an anatomical element, or any other target area to image. In some embodiments, the first image may depict multiple anatomical elements associated with the patient anatomy, including incidental anatomical elements (e.g., ribs or other anatomical objects on which a surgery or surgical procedure will not be performed) in addition to target anatomical elements (e.g., vertebrae or other anatomical objects on which a surgery or surgical procedure is to be performed). The first image may comprise various features corresponding to the patient’s anatomy and / or anatomical elements (and / or portions thereof), including gradients corresponding to boundaries and / or contours of the various depicted anatomical elements, varying levels of intensity corresponding to varying surface textures of the various depicted anatomical elements, combinations thereof, and / or the like. The first image may depict any portion or part of patient anatomy and may include, but is in no way limited to, one or more vertebrae, ribs, lungs, soft tissues (e.g., skin, tendons, muscle fiber, etc.), a patella, a clavicle, a scapula, combinations thereof, and / or the like.

[0088] The first image may be processed using image processing such as the image processing 120 to identify objects such as anatomical elements and / or any components in the first image. In some embodiments, feature recognition may be used to identify a feature of the anatomical element or the tracking device. For example, a contour of a vertebrae, femur, or other bone may be identified in the first image. In other embodiments, the image processing may use artificial intelligence or machine learning to identify the anatomical element and / or the tracking device.

[0089] The method 500 also comprises receiving a second image dataset (step 508). The step 508 is the same as or similar to the step 504 except that the second image dataset corresponding to a second image is obtained from the imaging device at a second pose different from the first pose. It will be appreciated that the imaging device is maintained at the same orientation and at different heights (e.g., Hl and H2). As will be described below, the imaging device at the second pose correlates to a second source to object height or H2, which may be the same as or similar to H2 204, which can be used with Hl to determine the relative pose of the object to the imaging device.

[0090] The method 500 also comprises measuring a difference between the first pose and the second pose (step 510). The measured difference may be the same as or similar to the measured difference 216. The first pose of the imaging device and the second pose of the imaging device can be obtained from, for example, a navigation system such as the navigation system 118. The first poseand the second pose may also be determined by, for example, encoders in the imaging device. The difference can be automatically measured by, for example, the processor. The difference between the first pose and the second pose correlates to a difference between Hl and H2.

[0091] The method 500 also comprises determining a scaling factor (step 512). Determining the scaling factor may include registering the first image obtained in, for example, the step 504 and the second image obtained in, for example, the step 508 using a registration such as the registration 124. The registration enables a processor such as the processor 104 to correlate an image with another image such as the first image with the second image. The scaling factor can then be determined from the registered first image and the second image using a scaling such as the scaling 122. The scaling enables the processor to determine a scaling factor based on a first image data set and a second image data set.

[0092] The method 500 also comprises determining an offset between the target object and the imaging device (step 516). The offset can be determined by first determining a first source to object distance and a second source to object distance. The first source to object distance and the second source to object distance may be determined from the scaling factor and the measured difference between the first pose and the second pose. More specifically, the first source to object distance may be obtained by adding one to the measured difference and dividing by the scaling factor. The second source to object distance may be obtained by multiplying the measured difference by the scaling factor and dividing by the scaling factor minus one.

[0093] After the first source to object distance and the second source to object distance are determined, a relative pose of the target object or anatomical element to the imaging device can be obtained. In some instances (such as where the imaging device has a source and a detector), a relative pose of the target object or anatomical element to an isocenter of the imaging device may be determined. For example, in embodiments where the source and the detector have a fixed SDD and SID, the relative pose of the target object or anatomical element to the isocenter can be determined based on Hl, H2, and the SID. More specifically, the relative pose of the target object or anatomical element to the isocenter can be calculated by subtracting the SDD 208 from the second source to object height H2 204. An offset between the target object and the imaging device can then be determined based on the pose of the target object or anatomical element and the isocenter of the imaging device.

[0094] The method 500 also comprises moving the object and / or the imaging device (step 520). The target object or anatomical element may be moved to position the target object or anatomical element at the isocenter of the imaging device. In other embodiments, the imaging device may bemoved to position the target object or anatomical element at the isocenter of the imaging device. In still other embodiments, the imaging device and the target object or anatomical element may both be moved to position the target object or anatomical element at the isocenter of the imaging device

[0095] The present disclosure encompasses embodiments of the method 500 that comprise more or fewer steps than those described above, and / or one or more steps that are different than the steps described above.

[0096] Fig. 6 depicts a method 600 that may be used, for example, for determining a scaling factor.

[0097] The method 600 (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) 104 of the computing device 102 described above. The at least one processor may be part of a robot (such as a robot 114) or part of a navigation system (such as a navigation system 118). A processor other than any processor described herein may also be used to execute the method 600. The at least one processor may perform the method 600 by executing elements stored in a memory such as the memory 106. The elements stored in memory and executed by the processor may cause the processor to execute one or more steps of a function as shown in method 600. One or more portions of a method 600 may be performed by the processor executing any of the contents of memory, such as an image processing 120, a scaling 122, and / or a registration 124.

[0098] The method 600 comprises receiving a first image dataset (step 604). The step 604 may be the same as or similar to the step 504 of the method 500 described above.

[0099] The method 600 also comprises receiving a second image dataset (step 608). The step 608 may be the same as or similar to the step 508 of the method 500 described above.

[0100] The method 600 also comprises measuring a difference between the first pose and the second pose (step 610). The step 610 may be the same as or similar to the step 510 of the method 500 described above.

[0101] The method 600 also comprises determining a scaling factor (step 612). The step 612 may be the same as or similar to the step 512 of the method 500 described above.

[0102] The method 600 also comprises scaling a second image using the scaling factor (step 616). The scaling factor may be automatically applied to the second image by, for example, the processor. The scaling factor may be obtained in the step 612, for example. Alternatively or additionally, the scaling factor may be received as user input from a user such as, for example, a surgeon or other medical provider.

[0103] The method 600 also comprises fusing the scaled second image with a first image (step 620). The scaled second image and the first image may be fused automatically by, for example, the processor. The fused image may be then displayed on a user interface such as the user interface 110.

[0104] The method 600 also comprises analyzing the fused image (step 624). The fused image may be analyzed to determine target region(s) of interest that are aligned in the first image and the scaled second image. If a desired target region of interest is not aligned in the first image and the scaled second image, then the scaling factor may be adjusted in the step 628 (described below), the second image may be rescaled in the step 616, and the first image and the rescaled second image may be refused in the step 620. Thus, the steps 628, 614, 620, and 624 may be repeated until a desired target region of interest is aligned. When such target region of interest is aligned, the scaling factor may be used in, for example, the method 500 described above or the method 700 described below.

[0105] The method 600 also comprises adjusting the scaling factor (step 628). The scaling factor may be adjusted if, for example, the desired target region of interest is not aligned in the first image and the scaled second image as described above. The scaling factor may be adjusted manually by a user such as, for example, a surgeon or other medical provider. In such embodiments, the scaling factor may be adjusted based on user input received through, for example, a user interface such as the user interface 110. In other embodiments, the scaling factor may be adjusted automatically by, for example a processor such as the processor 104. In such embodiments, the automatically adjusted scaling factor may be further adjusted or approved by the user.

[0106] The present disclosure encompasses embodiments of the method 600 that comprise more or fewer steps than those described above, and / or one or more steps that are different than the steps described above.

[0107] Fig. 7 depicts a method 700 that may be used, for example, for enabling measurements in an image.

[0108] The method 700 (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) 104 of the computing device 102 described above. The at least one processor may be part of a robot (such as a robot 114) or part of a navigation system (such as a navigation system 118). A processor other than any processor described herein may also be used to execute the method 700. The at least one processor may perform the method 700 by executing elements stored in a memory such as the memory 106. The elements stored in memory and executed by the processor may cause the processor to execute one or more steps of a function as shown inmethod 700. One or more portions of a method 700 may be performed by the processor executing any of the contents of memory, such as an image processing 120, a scaling 122, and / or a registration 124.

[0109] The method 700 comprises receiving a first image dataset (step 704). The step 704 may be the same as or similar to the step 504 of the method 500 described above.

[0110] The method 700 also comprises receiving a second image dataset (step 708). The step 708 may be the same as or similar to the step 508 of the method 500 described above.

[0111] The method 700 also comprises determining a scaling factor (step 712). The step 712 may be the same as or similar to the step 512 of the method 500 described above.

[0112] The method 700 also comprises determining a distance per pixels in the first image (step 716). Determining the distance per pixels in the first image first includes determining a first source to object distance and a second source to object distance. As previously described, the first source to object distance and the second source to object distance may be determined from the scaling factor and the measured difference between the first pose and the second pose. More specifically, the first source to object distance may be obtained by adding one to the measured difference and dividing by the scaling factor. The second source to object distance may be obtained by multiplying the measured difference by the scaling factor and dividing by the scaling factor minus one.

[0113] After the first source to object distance and the second source to object distance are determined, a relative pose of the target object or anatomical element to the imaging device can be obtained. The relative pose of the target object and the scaling factor can then be used to determine a distance per pixels in the first image. More specifically, the number of pixels in the target object or anatomical element is determined and converted to number of pixels to distance using the scaling factor.

[0114] The method 700 also comprises obtaining at least one measurement (step 720). The at least one measurement may be based on the distance per pixels determined in the step 716. The measurement may include a length, a distance between two points, a width, a depth, or a combination thereof.

[0115] The present disclosure encompasses embodiments of the method 700 that comprise more or fewer steps than those described above, and / or one or more steps that are different than the steps described above.

[0116] As noted above, the present disclosure encompasses methods with fewer than all of the steps identified in Figs. 5, 6, and 7 (and the corresponding description of the methods 500, 600, and 700), as well as methods that include additional steps beyond those identified in Figs. 5, 6, and 7(and the corresponding description of the methods 500, 600, and 700). 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.

[0117] 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.

[0118] Moreover, though the foregoing has included description of one or more aspects, embodiments, and / or configurations and certain variations and modifications, other variations, combinations, and modifications are within the scope of the disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative aspects, embodiments, and / or configurations to the extent permitted, including alternate, interchangeable and / or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and / or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.

[0119] The following statements provide non-limiting examples of systems and methods for setting and fixing one or more anatomical elements of the present disclosure:

[0120] Example 1. A system for positioning an imaging device, the system comprising:

[0121] an imaging device comprising a source configured to emit a wave and a detector configured to receive a signal indicative of the emitted wave; at least one processor; and a memory storing data for processing by the processor, the data, when processed, causing the processor to: receive a first image dataset from the imaging device at a first pose, the first image dataset comprising a first image depicting at least a portion of an object; receive a second image dataset from the imaging device at a second pose, the second image dataset comprising a second imagedepicting the at least a portion of the object; measure a difference between the first pose and the second pose; determine a scaling factor based on the first image dataset and the second image dataset; and determine a pose of a target portion of the object relative to an iso-center of the imaging device based on the scaling factor and the measured difference.

[0122] Example 2. The system of example 1, wherein the memory saves further data configured to cause the processor to: move at least one of the object or the imaging device to position the target portion of the object at the iso-center of the imaging device.

[0123] Example 3. The system of example 2, wherein the memory saves further data configured to cause the processor to: determine an offset between the target portion of the object and the iso-center of the imaging device, wherein the at least one of the object or the imaging device is moved based on the offset.

[0124] Example 4. The system of any of examples 1-3, wherein determining the scaling factor comprises registering the first image and the second image and iterating a scaling factor based on the registered first image and second image.

[0125] Example 5. The system of any of examples 1-4, wherein the memory saves further data configured to cause the processor to: scale the second image using the scaling factor; fuse the scaled second image with the first image; compare the fused image to determine if the scaled second image and the first image overlap at the target portion of the object to validate the scaling factor.

[0126] Example 6. The system of any of examples 1-5, wherein the first source to object height and the second source to object height are obtained from the same orientations.

[0127] Example 7. The system of any of examples 1-6, wherein determining the pose is also based on a distance between the iso-center and the source of the imaging device.

[0128] Example 8. The system of any of examples 1-7, wherein the imaging device is an X-ray imaging device.

[0129] Example 9. The system of any of examples 1-8, wherein the memory saves further data configured to cause the processor to: determine a distance per pixels in the first image based on the pose of the target portion of the object; and obtain a measurement based on the distance per pixels.

[0130] Example 10. The system of example 9, wherein the measurement comprises at least one of a length, a distance between two points, a width, a depth, or a combination thereof.

[0131] Example 11. The system of any of examples 1-10, wherein determining the pose of the target portion of the object includes determining the first source to object height and the second source to object height based on the measured difference between the first pose and the second pose.

[0132] Example 12. The system of Example 11, wherein determining the first source to object height includes adding one to the measured difference and dividing by the scaling factor.

[0133] Example 13. A system for determining a pose of an object relative to an imaging device, the system comprising: an imaging device; at least one processor; and a memory storing data for processing by the processor, the data, when processed, causing the processor to: receive a first image dataset from the imaging device at a first pose corresponding to a first distance between the imaging device and an object, the first image dataset comprising a first image depicting at least a portion of the object; receive a second image dataset from the imaging device at a second pose corresponding to a second distance between the imaging device and the object, the second image dataset comprising a second image depicting at least the portion of the object; determine a scaling factor based on the first image dataset, the first distance, the second image dataset, and the second distance; and determine a pose of a target portion of the object relative to a pose of the imaging device based on the scaling factor.

[0134] Example 14. The system of example 13, wherein the memory saves further data configured to cause the processor to: position the imaging device based on the pose of the target portion of the object relative to the pose of the imaging device.

[0135] Example 15. The system of examples 13 or 14, wherein the memory saves further data configured to cause the processor to: determine an offset between the target portion of the object and the iso-center of the imaging device, wherein the at least one of the object or the imaging device is moved based on the offset.

[0136] Example 16. The system of any of examples 13-15, wherein the imaging device comprises an ultrasound imaging device.

[0137] Example 17. The system of any of examples 13-16, wherein determining the pose of the target portion of the object includes determining the first source to object height and the second source to object height based on the measured difference between the first pose and the second pose.

[0138] Example 18. The system of any of examples 13-17, wherein determining the first source to object height includes adding one to the measured difference and dividing by the scaling factor.

[0139] Example 19. The system of any of examples 13-18, wherein the memory saves further data configured to cause the processor to: determine a distance per pixels in the first image based on the pose of the target portion of the object; and obtain a measurement based on the distance per pixels.

[0140] Example 20. A system for determining a pose of an object relative to an imaging device, the system comprising: an imaging device; at least one processor; and a memory storing data for processing by the processor, the data, when processed, causing the processor to: receive a first imagedataset from the imaging device at a first pose corresponding to a first distance between the imaging device and an object, the first image dataset comprising a first image depicting at least a portion of the object; receive a second image dataset from the imaging device at a second pose corresponding to a second distance between the imaging device and the object, the second image dataset comprising a second image depicting at least the portion of the object; determine a scaling factor based on the first image dataset, the first distance, the second image dataset, and the second distance; determine a distance per pixels in the first image based on the pose of the target portion of the object; and obtain a measurement based on the distance per pixels.

Claims

CLAIMSWhat is claimed is:

1. A system (100) for positioning an imaging device (112, 212) , the system comprising: an imaging device (112, 212) comprising a source (112A, 212A) configured to emit a wave and a detector (112B, 212B) configured to receive a signal indicative of the emitted wave; at least one processor (104); and a memory (106) storing data for processing by the processor, the data, when processed, causing the processor to: receive a first image dataset from the imaging device at a first pose, the first image dataset comprising a first image depicting at least a portion of an object (211); receive a second image dataset from the imaging device at a second pose, the second image dataset comprising a second image depicting the at least a portion of the object; measure a difference between the first pose and the second pose; determine a scaling factor based on the first image dataset and the second image dataset; and determine a pose of a target portion of the object relative to an iso-center of the imaging device based on the scaling factor and the measured difference.

2. The system of claim 1, wherein the memory saves further data configured to cause the processor to: move at least one of the object or the imaging device to position the target portion of the object at the iso-center of the imaging device.

3. The system of claim 2, wherein the memory saves further data configured to cause the processor to: determine an offset between the target portion of the object and the iso-center of the imaging device, wherein the at least one of the object or the imaging device is moved based on the offset.

4. The system of any of claims 1-3, wherein determining the scaling factor comprises registering the first image and the second image and iterating a scaling factor based on the registered first image and second image.

5. The system of any of claims 1-4, wherein the memory saves further data configured to cause the processor to: scale the second image using the scaling factor; fuse the scaled second image with the first image; compare the fused image to determine if the scaled second image and the first image overlap at the target portion of the object to validate the scaling factor.

6. The system of any of claims 1-5, wherein the first source to object height and the second source to object height are obtained from the same orientations.

7. The system of any of claims 1-6, wherein determining the pose is also based on a distance between the iso-center and the source of the imaging device.

8. The system of any of claims 1-7, wherein the imaging device is an X-ray imaging device.

9. The system of any of claims 1-8, wherein the memory saves further data configured to cause the processor to: determine a distance per pixels in the first image based on the pose of the target portion of the object; and obtain a measurement based on the distance per pixels.

10. The system of claim 9, wherein the measurement comprises at least one of a length, a distance between two points, a width, a depth, or a combination thereof.

11. The system of any of claims 1-10, wherein determining the pose of the target portion of the object includes determining the first source to object height and the second source to object height based on the measured difference between the first pose and the second pose.

12. The system of claim 11, wherein determining the first source to object height includes adding one to the measured difference and dividing by the scaling factor.

13. A system (100) for determining a pose of an object relative to an imaging device, the system comprising:an imaging device (112, 212); at least one processor (104); and a memory (106)storing data for processing by the processor, the data, when processed, causing the processor to: receive a first image dataset from the imaging device at a first pose corresponding to a first distance between the imaging device and an object, the first image dataset comprising a first image depicting at least a portion of the object (211); receive a second image dataset from the imaging device at a second pose corresponding to a second distance between the imaging device and the object, the second image dataset comprising a second image depicting at least the portion of the object; determine a scaling factor based on the first image dataset, the first distance, the second image dataset, and the second distance; and determine a pose of a target portion of the object relative to a pose of the imaging device based on the scaling factor.

14. The system of claim 13, wherein the memory saves further data configured to cause the processor to: position the imaging device based on the pose of the target portion of the object relative to the pose of the imaging device.

15. The system of claims 13 or 14, wherein the memory saves further data configured to cause the processor to: determine an offset between the target portion of the object and the iso-center of the imaging device, wherein the at least one of the object or the imaging device is moved based on the offset.

Images