Artificial intelligence extended field of view

Abstract

A system may use Artificial Intelligence (AI) to enhance / enlarge the field of view (FOV) of an image. The system includes an imaging device, a processor, and a memory. The memory stores data that, when processed by the processor, enables the processor to receive image data associated with an image captured by the imaging device, wherein the image data includes data for a fully sampled region and partial data for a region beyond the fully sampled region. The data further enables the processor to reconstruct the partial data for the region beyond the fully sampled region to generate reconstruct data and enhance, using an AI model, the reconstruct data for the region beyond the fully sampled region to generate an enhanced image. The enhanced image may be display, used for preoperative registration, and / or used for artifact correction.

Description

A0012955ARTIFICIAL INTELLIGENCE EXTENDED FIELD OF VIEW

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 722,482, filed 19 November 2024, the entire content of which is incorporated herein by reference.FIELD OF INVENTION

[0002] The present disclosure is generally directed to imaging, and relates more particularly to surgical imaging.BACKGROUND

[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 including a processor and a memory storing data thereon that, when processed by the processor, enables the processor to: receive image data associated with an image captured by an imaging device, wherein the image data includes data for a fully sampled region and partial data for a region beyond the fully sampled region; generate a second image by reconstructing the partial data for the region beyond the fully sampled region, wherein the second image includes reconstruct data for a reconstructed portion beyond the fully sampled region; and display the second image.

[0006] A method including receiving image data associated with an image captured by an imaging device, wherein the image data includes data for a fully sampled region and partial data for a region beyond the fully sampled region, reconstructing the partial data for the region beyond the fully sampled region to generate reconstruct data, enhancing, using an Al model, the reconstruct data for the region beyond the fully sampled region to generate an enhanced image, and outputting the enhanced image.

[0007] A system including an imaging device, a processor, and a memory storing data thereon that, when processed by the processor, enable the processor to: receive image data associated with an image captured by the imaging device, wherein the image data includes data for a fully sampled region and partial data for a region beyond the fully sampled region; reconstruct the partial data for the region beyond the fully sampled region to generate reconstruct data; enhance,A0012955 using an Al model, the reconstruct data for the region beyond the fully sampled region to generate an enhanced image; and output the enhanced image.

[0008] Any of the aspects herein, wherein the data further enables the processor to: receive second image data associated with the second image, wherein the second image data includes the reconstruct data for the reconstructed portion beyond the fully sampled region; enhance, using an Artificial Intelligence (Al) model, the reconstruct data for the reconstructed portion of the second image to generate an enhanced image; and output the enhanced image.

[0009] Any of the aspects herein, wherein the Al model is trained using simulated computed tomography (CT) data.

[0010] Any of the aspects herein, wherein the CT data is simulated using a simulated 0-arm with a 50 cm length detector.

[0011] Any of the aspects herein, wherein the CT data is used to generate a cone beam computed tomography (CBCT) dataset.

[0012] Any of the aspects herein, wherein the Al model is trained using reconstructions from cropped CBCT detector data.

[0013] Any of the aspects herein, wherein the enhanced image is used for registration to a preoperative image.

[0014] Any of the aspects herein, wherein the registration comprises image merge or stereotactic frame registration.

[0015] Any of the aspects herein, wherein the fully sampled region of the image comprises a field of view (FOV) of 20 cm, and wherein the enhanced image comprises a FOV of 25 cm.

[0016] Any of the aspects herein, wherein the enhanced image is used for artifact correction.

[0017] Any of the aspects herein, wherein the artifact correction includes bone beam-hardening correction or truncation artifact correction.

[0018] Any of the aspects herein, wherein the imaging device comprises an 0-arm or a C-arm.

[0019] Any of the aspects herein, wherein the Al model is trained using simulated CT data, wherein the simulated CT data is generated using a simulated 0-arm with a 50cm length detector, and wherein the enhanced image comprises a field of view (FOV) of 25 cm.

[0020] Any of the aspects herein, wherein the image data comprises a CBCT dataset, and wherein CT data is used to generate the CBCT dataset.

[0021] Any aspect in combination with any one or more other aspects.

[0022] Any one or more of the features disclosed herein.

[0023] Any one or more of the features as substantially disclosed herein.

[0024] Any one or more of the features as substantially disclosed herein in combination with any one or more other features as substantially disclosed herein.A0012955

[0025] Any one of the aspects / features / implementations in combination with any one or more other aspects / features / implementations.

[0026] Use of any one or more of the aspects or features as disclosed herein.

[0027] 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 implementation.

[0028] 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.

[0029] 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, implementations, 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, implementations, 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.

[0030] Numerous additional features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the implementation descriptions provided hereinbelow.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0031] 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, implementations, and configurations of the disclosure, as illustrated by the drawings referenced below.

[0032] Figs. 1 A through IB illustrate examples of a system that support aspects of the present disclosure.

[0033] Fig. 2A through 2C illustrates examples of a system that supports by aspects of the present disclosure.A0012955

[0034] Figs. 3 A-E are examples of images and image data in accordance with aspects of the present disclosure.

[0035] Fig. 4 illustrates an example of a process flow in accordance with aspects of the present disclosure.DETAILED DESCRIPTION

[0036] 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 implementation, 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 implementations 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.

[0037] 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). 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).

[0038] 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),A0012955 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.

[0039] Before any implementations 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 implementations 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.

[0040] 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.

[0041] Fine positioning of the 0-arm is desired, especially in cranial cases, where a matter of an inch or two can be crucial to a successful outcome. An Artificial Intelligence (Al) method may provide a larger and / or enhanced field of view (FOV) for visualization and registration tasks without needing to alter the image acquisition protocol or sacrifice image quality in the center of the image.

[0042] Cone beam computed tomography (CBCT), is a medical imaging technique including X-ray computed tomography where the X-rays are divergent, forming a cone. In CBCT imaging, the Axial field of view (FOV) is often limited by the length of the detector in the spin-axis direction. For example in 0-arm imaging, a 40cm length detector yields an axial diameter FOV of approximately 20cm. Techniques exist to shift the detector position in an off-center manner to extend the reconstruction FOV (as in the 0-arm 40cm FOV mode), though this may come at the cost of increased dose and / or reduced image quality. However, using Al on the dataset generated by the 40cm length detector, the same dataset may be reconstructed with a larger FOV, such asA001295525 cm. Although the image quality is slightly diminished as the region is not fully sampled, there is still valuable anatomical information in that region.

[0043] The present disclosure leverages Al to improve the reconstruction quality of this larger field of view data (e.g., 25 cm), thereby extending the FOV without using modified detector positions or increasing dose. The Al may be trained using CT to CBCT simulations, in this manner we can create simulated CBCT projection data using a 40 cm length detector to produce O-arm-like image data that can mimic the images when reconstructed. The same data is then simulated with a 50 cm length detector, to achieve a 25cm FOV reconstruction without artifacts. The Al model will then be trained using the 25 cm reconstruction from a 40 cm length detector as input, and using the 25 cm reconstruction from 50 cm length detector as the ground-truth output that the Al model learns to mimic. Non-simulated clinical and cadaver data can further be leveraged for training by cropping true 40 cm length detector data to smaller lengths (e.g., 30 cm, or 35 cm), and similarly using the reconstructions from the smaller detector data as input during training, and the larger detector data reconstructions as ground-truth.

[0044] Some imaging systems may support capturing images of a patient anatomy using an imaging device (e.g., an 0-arm, etc.), The O-Arm® imaging system provided by Medtronic Navigation, Inc. supports providing an enhanced / enlarged field of view (FOV) feature (also referred to herein as an enhanced FOV representation).

[0045] Aspects of the present disclosure may support imaging in association with cranial procedures, for example, for endoscopic cranial surgery and deep brain stimulation procedures. In embodiments, a cross-section on bone may be needed to perform a correction, an increased FOV allows bone that was not visible in an original image to be visible and used for performing the hone bean-hardening correction. For example, the imaging techniques described herein may support the generation or capture of 2D images and extended 2D images of the cranium. As will be described and illustrated herein, 2D images and extended 2D images may include images captured an imaging device (e.g., an 0-arm, etc.).

[0046] Implementations of the present disclosure provide technical solutions to one or more of the problems of radiation exposure to operators, surgeons, and patients. X-ray exposure can be quantified by dose, or the amount of energy deposited by radiation in tissue. Ionizing radiation can cause debilitating medical conditions. The enhanced FOV imaging techniques described herein reduce the risk of additional radiation exposure due to generating multiple images. Implementations of the present disclosure provide technical solutions to one or more of the problems of modifying the imaging device to achieve a larger FOV.

[0047] Figs. 1A and IB illustrate examples of a system 100 that support aspects of the present disclosure.A0012955

[0048] Referring to Fig. 1 A, the system 100 includes a computing device 102, one or more imaging devices 112, a robot 114, a navigation system 118, a database 130, and / or a cloud network 134 (or other network). Systems according to other implementations of the present disclosure may include more or fewer components than the system 100. For example, the system 100 may omit and / or include additional instances of one or more components of the computing device 102, the imaging device(s) 112, the robot 114, navigation system 118, the database 130, and / or the cloud network 134. In an example, the system 100 may omit any instance of the computing device 102, the imaging device(s) 112, the robot 114, navigation system 118, the database 130, and / or the cloud network 134. The system 100 may support the implementation of one or more other aspects of one or more of the methods disclosed herein.

[0049] The computing device 102 includes a processor 104, a memory 106, a communication interface 108, and a user interface 110. Computing devices according to other implementations of the present disclosure may include more or fewer components than the computing device 102. The computing device 102 may be, for example, a control device including electronic circuitry associated with controlling any of the imaging device 112, the robot 114, and the navigation system 118.

[0050] 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 devices 112, the robot 114, the navigation system 118, the database 130, and / or the cloud network 134.

[0051] The memory 106 may be or include 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 associated with completing, for example, any step of the process flows 300 and 400 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 imaging devices 112, the robot 114, and the navigation system 118. 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, segmentation 122, transformation 124, registration 128, and / or FOV module 129. Such content, if provided as in instruction, may, in some implementations, be organized into one or more applications, modules, packages, layers, or engines.

[0052] 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.A0012955Thus, 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 devices 112, the robot 114, the navigation system 118, the database 130, and / or the cloud network 134.

[0053] The computing device 102 may also include a communication interface 108. The communication interface 108 may be used for receiving data or other information from an external source (e.g., the imaging devices 112, the robot 114, the navigation system 118, the database 130, the cloud network 134, and / or any other system or component separate from the system 100), and / or for transmitting instructions, data (e.g., image data, etc.), or other information to an external system or device (e.g., another computing device 102, the imaging devices 112, the robot 114, the navigation system 118, the database 130, the cloud network 134, and / or any other system or component not part of the system 100). The communication interface 108 may include 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 802.1 la / b / g / n, Bluetooth, NFC, ZigBee, and so forth). In some implementations, the communication interface 108 may support communication between the device 102 and 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.

[0054] The computing device 102 may also include one or more user interfaces 110. The user interface 110 may be or include 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 implementations, the user interface 110 may support user modification (e.g., by a surgeon, medical personnel, etc.) of instructions to be executed by the processor 104 according to one or more implementations of the present disclosure, and / or to user modification or adjustment of a setting of other information displayed on the user interface 110 or corresponding thereto.

[0055] In some implementations, 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.A0012955In some implementations, the user interface 110 may be located proximate one or more other components of the computing device 102, while in other implementations, the user interface 110 may be located remotely from one or more other components of the computer device 102.

[0056] 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 include data corresponding to an anatomical feature of a patient, or to a portion thereof. The image data may be or include a preoperative image, an intraoperative image, a postoperative image, or an image taken independently of any surgical procedure. In some implementations, an imaging device 112 may be used to obtain first image data (e.g., a first image) at a first time, and the imaging device 112 may be used to obtain second image data (e.g., a second image) at a second time after the first time.

[0057] 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 include, for example, an ultrasound scanner (which may include, for example, a physically separate transducer and receiver, or a single ultrasound transceiver), 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 (OCT) scanner, an endoscope, a microscope, an optical camera, a thermographic camera (e.g., an infrared camera), a radar system (which may include, 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 148. The imaging device 112 may be contained entirely within a single housing, or may include a transmitter / emitter and a receiver / detector that are in separate housings or are otherwise physically separated.

[0058] In some implementations, the imaging device 112 may include 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 implementations, 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 presentA0012955 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.

[0059] The imaging device 112 may include a source 138, a detector 140, and a collimator 144, example aspects of which are later described with reference to Fig. IB.

[0060] The robot 114 may be any surgical robot or surgical robotic system. The robot 114 may be or include, 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 a surgical tool (whether based on guidance from the navigation system 118 or not) to accomplish or to assist with a surgical task. In some implementations, 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 include one or more robotic arms 116. In some implementations, the robotic arm 116 may include a first robotic arm and a second robotic arm, though the robot 114 may include more than two robotic arms. In some implementations, one or more of the robotic arms 116 may be used to hold and / or maneuver the imaging device 112. In implementations where the imaging device 112 includes 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.

[0061] 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.

[0062] The robotic arm(s) 116 may include 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).

[0063] In some implementations, 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 implementations, the navigation system 118 can be usedA0012955 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).

[0064] 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 Stealth Station™ S8 surgical navigation system or any successor thereof. The navigation system 118 may include one or more cameras 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 100 is located. The one or more cameras may be optical cameras, infrared cameras, or other cameras. In some implementations, the navigation system 118 may include one or more electromagnetic sensors. In various implementations, 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).

[0065] 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 implementations, 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.

[0066] The processor 104 may utilize data stored in memory 106 as a neural network. The neural network may include a machine learning architecture. In some aspects, the neural network may be or include one or more classifiers. In some other aspects, the neural network may be or include any machine learning network such as, for example, a deep learning network, a convolutional neural network, a reconstructive neural network, a generative adversarial neural network, or any other neural network capable of accomplishing functions of the computing device 102 described herein. Some elements stored in memory 106 may be described as or referred to as instructions or instruction sets, and some functions of the computing device 102 may be implemented using machine learning techniques.A0012955

[0067] For example, the processor 104 may support machine learning model(s) which may be trained and / or updated based on data (e.g., training data) provided or accessed by any of the computing device 102, the imaging device 112, the robot 114, the navigation system 118, the database 130, and / or the cloud network 134. The machine learning model(s) may be built and updated by the system 100 based on the training data (also referred to herein as training data and feedback).

[0068] In some examples, based on the data, the neural network may generate one or more algorithms (e.g., processing algorithms) supportive of the FOV module 129.

[0069] The database 130 may store information that correlates one coordinate system to another (e.g., imaging coordinate systems, robotic coordinate systems, a patient coordinate system, a navigation coordinate system, etc.). 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 imaging device 112, 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 or analyzed; and / or any other useful information. The database 130 may additionally or alternatively store, for example, images captured or generated based on image data provided by the imaging device 112.

[0070] 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 network 134. In some implementations, the database 130 may be or include 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.

[0071] In some aspects, the computing device 102 may communicate with a server(s) and / or a database (e.g., database 130) directly or indirectly over a communications network (e.g., the cloud network 134). The communications network may include any type of known communication medium or collection of communication media and may use any type of protocols to transport data between endpoints. The communications network may include wired communications technologies, wireless communications technologies, or any combination thereof.

[0072] Wired communications technologies may include, for example, Ethernet-based wired local area network (LAN) connections using physical transmission mediums (e.g., coaxial cable, copper cable / wire, fiber-optic cable, etc.). Wireless communications technologies may include, for example, cellular or cellular data connections and protocols (e.g., digital cellular, personalA0012955 communications service (PCS), cellular digital packet data (CDPD), general packet radio service (GPRS), enhanced data rates for global system for mobile communications (GSM) evolution (EDGE), code division multiple access (CDMA), single-carrier radio transmission technology (I xRTT), evolution-data optimized (EVDO), high speed packet access (HSPA), universal mobile telecommunications service (UMTS), 3G, long term evolution (LTE), 4G, and / or 5G, etc.), Bluetooth®, Bluetooth® low energy, Wi-Fi, radio, satellite, infrared connections, and / or ZigBee® communication protocols.

[0073] The Internet is an example of the communications network that constitutes an Internet Protocol (IP) network consisting of multiple computers, computing networks, and other communication devices located in multiple locations, and components in the communications network (e.g., computers, computing networks, communication devices) may be connected through one or more telephone systems and other means. Other examples of the communications network may include, without limitation, a standard Plain Old Telephone System (POTS), an Integrated Services Digital Network (ISDN), the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a wireless LAN (WLAN), a Session Initiation Protocol (SIP) network, a Voice over Internet Protocol (VoIP) network, a cellular network, and any other type of packet-switched or circuit-switched network known in the art. In some cases, the communications network may include any combination of networks or network types. In some aspects, the communications network may include any combination of communication mediums such as coaxial cable, copper cable / wire, fiber-optic cable, or antennas for communicating data (e.g., transmitting / receiving data).

[0074] The computing device 102 may be connected to the cloud network 134 via the communication interface 108, using a wired connection, a wireless connection, or both. In some implementations, the computing device 102 may communicate with the database 130 and / or an external device (e.g., a computing device) via the cloud network 134.

[0075] The system 100 or similar systems may be used, for example, to carry out one or more aspects of any of the methods 300 and / or 400 described herein. The system 100 or similar systems may also be used for other purposes.

[0076] Fig. IB illustrates an example of the system 100 that supports aspects of the present disclosure. Aspects of the system 100 previously described with reference to Figs. 1 A and descriptions of like elements are omitted for brevity.

[0077] With reference to Fig. IB, features of the system 100 may be described in conjunction with a coordinate system 101. The coordinate system 101, as shown in Figs. IB, and 2A through 2C, includes three-dimensions including an X-axis, a Y-axis, and a Z-axis. Additionally or alternatively, the coordinate system 101 may be used to define planes (e.g., the XY-plane, theA0012955XZ-plane, and the YZ-plane) of the system 100. These planes may be disposed orthogonal, or at 90 degrees, to one another. While the origin of the coordinate system 101 may be placed at any point on or near the components of the system 100 (e.g., components of the imaging device 112, for the purposes of description, the axes of the coordinate system 101 are always disposed along the same directions from figure to figure, whether the coordinate system 101 is shown or not. In some examples, reference may be made to dimensions, angles, directions, relative positions, and / or movements associated with one or more components of the system 100 (e.g., imaging device 112) with respect to the coordinate system 101.

[0078] The system 100 may be used to initiate scans of a patient, adjust imaging components to capture images of the patient, set or identify target coordinates associated with the images, etc.

[0079] The imaging device 112 is illustrated to include an upper wall or member 152, a lower wall 161 (also referred to herein as member 161), and a pair of sidewalls 156-a and 156-b (also referred to herein as members 156-a and 156-b). In some embodiments, the imaging device 112 is fixed securable to an operating room surface 168 (such as, for example, a ground surface of an operating room or other room). In other embodiments, the imaging device 112 may be releasably securable to the operating room wall 168 or may be a standalone component that is simply supported by the operating room wall 168.

[0080] A table 150 configured to support the patient 148 may be positioned orthogonally to the imaging device 112, such that the table 150 extends in a first direction from the imaging device 112. In some embodiments, the table 150 may be mounted to the imaging device 112. In other embodiments, the table 150 may be releasably mounted to the imaging device 112. In still other embodiments, the table 150 may not be attached to the imaging device 112. In such embodiments, the table 150 may be supported and / or mounted to an operating room wall, for example. In embodiments where the table 150 is mounted to the imaging device 112 (whether detachably mounted or permanently mounted), the table 150 may be mounted to the imaging device 112 such that a pose of the table 150 relative to the imaging device 112 is selectively adjustable. The patient 148 may be positioned on the table 150 in a supine position, a prone position, a recumbent position, and the like.

[0081] The table 150 may be any suitable operating table configured to support the patient 148 during a surgical procedure. The table 150 may include any accessories mounted to or otherwise coupled to the table 150 such as, for example, a bed rail, a bed rail adaptor, an arm rest, an extender, or the like. The table 150 may be stationary or may be operable to maneuver the patient 148 (e.g., the table 150 may be moveable). In some embodiments, the table 150 has two positioning degrees of freedom and one rotational degree of freedom, which allows positioning of the specific anatomy of the patient anywhere in space (within a volume defined by the limitsA0012955 of movement of the table 150). For example, the table 150 may slide forward and backward and from side to side, tilt (e.g., around an axis positioned between the head and foot of the table 150 and extending from one side of the table 150 to the other) and / or roll (e.g., around an axis positioned between the two sides of the table 150 and extending from the head of the table 150 to the foot thereof). In other embodiments, the table 150 may be bendable at one or more areas (which bending may be possible due to, for example, the use of a flexible surface for the table 150, or by physically separating one portion of the table 150 from another portion of the table 150 and moving the two portions independently). In at least some embodiments, the table 150 may be manually moved or manipulated by, for example, a surgeon or other user, or the table 150 may include one or more motors, actuators, and / or other mechanisms configured to enable movement and / or manipulation of the table 150 by a processor such as a processor 104 of the computing device 102.

[0082] The imaging device 112 may include a gantry. The gantry may be or include a substantially circular, or “O-shaped,” housing that enables imaging of objects placed into an isocenter thereof. In other words, the gantry may be positioned around the object being imaged. In some embodiments, the gantry may be disposed at least partially within the member 152, the sidewall 156-a, the sidewall 156-b, and the lower wall 161 of the imaging device 112.

[0083] The imaging device 112 also includes a source 138 and a detector 140. The source 138 may be a device configured to generate and emit radiation, and the detector 140 may be a device configured to detect the emitted radiation. In some embodiments, the source 138 and the detector 140 may be or include an imaging source and an imaging detector (e.g., the source 138 and the detector 140 are used to generate data useful for producing images). The source 138 may be positioned in a first position and the detector 140 may be positioned in a second position opposite the source 138. In some embodiments, the source 138 may include an X-ray source (e.g., a thermionic emission tube, a cold emission x-ray tube, or the like). The source 138 may project a radiation beam that passes through the patient 148 and onto the detector 140 located on the opposite side of the imaging device 112. The detector 140 may be or include one or more sensors that receive the radiation beam (e.g., once the radiation beam has passed through the patient 148) and transmit information related to the radiation beam to one or more other components (e.g., processor 104) of the system 100 for processing.

[0084] In some embodiments, the detector 140 may include an array of detection sensors. For example, the detector 140 may include three 2D flat panel solid-state detectors arranged side-by- side, and angled to approximate the curvature of the imaging device 112. It will be understood, however, that various detectors and detector arrays can be used with the imaging device 112, including any detector configurations used in typical diagnostic fan-beam or cone-beam CTA0012955 scanners. For example, the detector 140 may include a 2D thin-film transistor X-ray detector using scintillator amorphous-silicon technology.

[0085] The source 138 may be or include a radiation tube (e.g., an x-ray tube) capable of generating the radiation beam. In some embodiments, the source 138 and / or the detector 140 may include a collimator 144 configured to confine or shape the radiation beam emitted from the source 138 and received at the detector 140. Once the radiation beam passes through patient tissue and received at the detector 140, the signals output from the detector 140 may be processed by the processor 104 to generate a reconstructed image of the patient tissue. In this way, the imaging device 112 can effectively generate reconstructed images of the patient tissue imaged by the source 138 and the detector 140.

[0086] The source 138 and the detector 140 may be attached to the gantry and configured to rotate 360 degrees around the patient 148 in a continuous or step-wise manner so that the radiation beam can be projected through the patient 148 at various angles. In other words, the source 138 and the detector 140 may rotate, spin, or otherwise revolve about an axis that passes through the top and bottom of the patient 148, with the patient anatomy that is the subject of the imaging positioned at the isocenter of the imaging device 112. The rotation may occur through a drive mechanism that causes the gantry to move such that the source 138 and the detector 140 encircle the patient 148 on the table 150 in a substantially coordinated motion.

[0087] The imaging device 112 may be included in the O-Arm® imaging system sold by Medtronic Navigation, Inc. having a place of business in Louisville, Colo., USA. The imaging device 112, including the O-Arm® imaging system, or other appropriate imaging systems supportive of aspects of the present disclosure can be found in U.S. Pat. Nos. 7,188,998, 7,108,421, 7,106,825, 7,001,045 and 6,940,941, each of which is incorporated herein by reference. The O-Arm ® imaging system can include a mobile cart (not illustrated) that supports movement of the imaging device 112 from one operating theater or room to another, and the gantry may move relative to the mobile cart.

[0088] Turning to Figs. 2A-2C, aspects of the system 200 are shown according to at least one embodiment of the present disclosure. Fig. 2A depicts a stereotactic frame 204 attached to a patient 208. The stereotactic frame 204 comprises a stereotactic frame base ring 212 and a localizer 216.

[0089] In some embodiments, reference may be made to dimensions, angles, directions, relative positions, and / or movements associated with one or more components of the system 200 with respect to a coordinate system 202. The coordinate system 202, as shown in the accompanying figures, includes three dimensions comprising an X-axis, a Y-axis, and a Z-axis. Additionally or alternatively, the coordinate system 202 may be used to define planes (e.g., theA0012955XY-plane, the XZ-plane, and the YZ-plane) when describing the system 200. These planes may be disposed orthogonal, or at 90 degrees, to one another. While the origin of the coordinate system 202 may be placed at any point on or near any one or more components of the system 200, for the purposes of description, the axes of the coordinate system 202 are disposed along the same directions from figure to figure. Additionally or alternatively, the directionality of the X- axis, Y-axis, and Z-axis may be flipped, as noted with negative directionality (i.e., the negative X-axis direction is the opposite direction of the X-axis direction illustrated by the direction of the associated arrow).

[0090] The stereotactic frame base ring 212 (also referred to herein as the “frame base ring 212” or the “base ring 212”) may provide a support structure for the localizer 216 and, more generally, to other components of the stereotactic frame 204. The base ring 212 may be rigidly attached to the patient 208, such that the base ring 212 maintains a fixed pose relative to the patient 208. For example, the base ring 212 may include slots, grips, or the like that attach to the shoulders, neck, and / or head of the patient 208 to secure the base ring 212 relative to the patient 208. In one example, the base ring 212 may include three or four pointed screws / pins that are pressed into the skull of the patient 208 to connect the base ring 212 to the patient 208. Typically, the base ring 212 is attached to a surgical table for stability. In such embodiments, the patient 208 may be positioned on the table and the base ring 212 may be secured to the table and / or to the head of the patient 208, such that the base ring 212 is fixed relative to the patient 208 or, more specifically, relative to the head of the patient 208.

[0091] The base ring 212 includes a navigation marker 218 that is capable of being tracked by the navigation system 118. The navigation marker 218 may be or comprise, for example, a radiopaque marker capable of being detected in images captured by the imaging device 112. The navigation marker 218 may be disposed on or otherwise connected to the base ring 212, or may alternatively be disposed in a predetermined pose relative to the base ring 212 (e.g., attached to a surgical bed or other linkage to the surgical bed). The navigation marker 218 may remain in the predetermined pose relative to the base ring 212 throughout the surgery or surgical procedure. The use of the navigation marker 218 may enable registration of the surgical bed to the base ring 212, such that the surgical bed can be moved after the imaging device 112 performs imaging. In some instances, the geometry of the navigation marker 218 may be such that the navigation system 118 can track the base ring 212 with up to six degrees of freedom.

[0092] The localizer 216 may include rods 216A-216B connectable and detachable from the base ring 212 that can be used to determine the pose of the stereotactic frame 204. The pose of the stereotactic frame 204 may be used by the navigation system 118 to navigate one or more surgical tools, imaging equipment, or other instruments relative to the stereotactic frame 204. InA0012955 one example, the localizer 216 comprises a first rod 216A and a second rod 216B. It is to be understood that, while two rods are depicted and discussed herein, additional rods may be used. For example, the localizer 216 may comprise three or four planes of rods that are placed around the head of the patient 208. The navigation system 118 may be able to identify these rods in image data generated by the imaging device 112 (e.g., the rods appear in generated images as circles or ellipses around the patient’s head). The navigation system 118 may then be able to use the identified rods along with the known geometry of the localizer 216 relative to the base ring 212 to define one or more coordinate systems, as discussed in further detail below.

[0093] The localizer 216 may be attached to the base ring 212 in a predetermined and / or known orientation. For example, the base ring 212 may have openings (e.g., apertures, slots, etc.) into which the localizer 216 can be inserted. In this example, the rods 216A-216B encircle the head of the patient 208. One or more portions of the localizer 216 may be radiopaque (e.g., the rods 216A-216B), such that depictions of the localizer 216 appear and are identifiable in one or more types of surgical images (e.g., CT images, fluoroscopic images, etc.).

[0094] The localizer 216 may enable the navigation system 118 to determine a pose of the base ring 212 and, more generally, of the stereotactic frame 204 based on the geometry of the rods 216A-216B. The pose may be based on the determined pose of the rods 216A-216B relative to the base ring 212 and the predetermined or known pose of the localizer 216 relative to the base ring 212. The navigation system 118 may then determine a coordinate system relative to the base ring 212. For example, the imaging device 112 may capture one or more images or volumetric scans (e.g., a CT scan, an MRI scan, etc.) of the patient 208 and the localizer 216. In some embodiments, the computing device 102 may then use segmentation 122 to identify the first rod 216A and the second rod 216B in the image(s) (which may be or comprise 0-arm images, CT images, MRI images, and / or the like), and may use one or more transformations 124 to transform the different views of the first rod 216A and the second rod 216B into a common reference frame. The computing device 102 may then use registration 128 to register the coordinates associated with the first rod 216A and / or the second rod 216B into a coordinate system associated with the base ring 212. In some cases, the coordinate system associated with the base ring 212 may be or comprise a global coordinate system (e.g., a coordinate system shared by all other surgical tools, imaging device, and other components in a surgical space). The computing device 102 may also use registration 128 to register the navigation marker 218 to the coordinate system associated with the base ring 212 based on the predetermined or known pose of the navigation marker 218 relative to the base ring 212.

[0095] Once the coordinates associated with the localizer 216 has been determined, the navigation system 118 may register the coordinates associated with the localizer 216 into aA0012955 coordinate system associated with the base ring 212 based on the expected geometry of the localizer 216. In some embodiments, the navigation system 118 may use the computing device 102 to register the coordinates associated with the localizer 216 into the coordinate system associated with the base ring 212. In some cases, the computing device 102 may further use registration 128 to merge additional scans and / or images (e.g., scans and / or images used in planning the target or trajectory of the surgical implant, scans and / or images stored in the database 130, etc.) to the images captured by the imaging device 112, enabling the operator to view the scans and / or images in a coordinate system associated with the base ring 212.

[0096] The localizer 216 may be disconnected from the base ring 212 once the imaging device 112 captures the images depicting the localizer 216, and other components may be attached to the stereotactic frame 204. As depicted in Figs. 2B and 2C, the localizer 216 may be removed and a set of arcs 220 may be attached or connected to the base ring 212. The set of arcs 220 comprises frame reticles 224A-224B, a tool apparatus 228, and an arc 232. In some cases, the frame reticles 224A-224B, the tool apparatus 228, and the arc 232 may be an integrated apparatus connectable to the base ring 212 to enable a user to align a surgical tool along a trajectory to reach a target location.

[0097] The tool apparatus 228 may comprise one or more surgical instruments or other components capable of carrying out one or more tasks associated with a surgery or surgical procedure, and a mounting mechanism that can mechanically couple the one or more surgical instruments to the arc 232. For example, the tool apparatus 228 may have mounting equipment designed to permit a surgical instrument 236 (e.g., a surgical drill) to be mounted thereto and moved relative to the patient 208. The surgical instrument 236 may perform one or more surgical tasks associated with the surgery or surgical procedure, such as a drilling operation, an implant operation, or the like.

[0098] The tool apparatus 228 is shown to be moveable along the arc 232 to position the surgical instrument 236 relative to the patient 208. In some embodiments, the arc 232 may be moveable relative to the base ring 212 and / or relative to the patient 208, and / or the tool apparatus 228 may be movable along another arc mounted to the stereotactic frame 204, such that the tool apparatus 228 can be maneuvered throughout a 3D space surrounding the target surgical site. For example, the arc 232 may be moveable up or down (e.g., in the Z-axis or negative Z-axis direction of the coordinate system 202), may be moveable right or left (e.g., in the X-axis or negative X-axis direction), may be pivotable relative to the base ring 212 (e.g., pivotable about the X-axis of the coordinate system 202), and / or the like to align the tool apparatus 228 relative to the target surgical site. In some embodiments, the tool apparatus 228 may enable multiple different surgical instruments to be connected to the tool apparatus 228 such that differentA0012955 surgical instruments can be connected and utilized at various steps in the surgery or surgical procedure.

[0099] The frame reticles 224A-224B may be or comprise components connected to the stereotactic frame 204 that provide reference structures that enable imaging of a surgical site. The frame reticles 224A-224B comprise a first frame reticle 224A and a second frame reticle 224B, each of which may be disposed on either side of the set of arcs 220. As shown in Figs. 2B-2C, the first frame reticle 224A may be positioned on the right hand side of the patient 208, while the second frame reticle 224B may be positioned on the left hand side of the patient 208, such that the first frame reticle 224A and the second frame reticle 224B are in predetermined and known locations relative to the set of arcs 220. It is to be understood that the frame reticles 224A-224B may be positioned at different locations than those illustrated.

[0100] The first frame reticle 224A and second frame reticle 224B may be adjustable relative to the base ring 212 or other components of the stereotactic frame 204 (e.g., the frame reticles 224A-224B may each be rotatable in the YZ-plane, the frame reticles 224A-224B may be movable in a circle around the patient 208 in the XY-plane, etc.). The frame reticles 224A-224B move with the set of arcs 220, such that the first frame reticle 224A and the second frame reticle 224B are in a known location relative to the other components of the set of arcs 220. The frame reticles 224A-224B may be positioned based on the type of surgery or surgical procedure being performed on the patient 208.

[0101] Fig. 3A illustrates a CT image 300a taken with a 40 cm O-Arm and has a 20 cm FOV. The image 300a has a fully sampled region 305. The As illustrated in Fig. 3 A, the bottom portion 301 (e.g., a region beyond the fully sampled region 305) of the image 300a is not available.Using an Al model (e.g., FOV module 129, image data for the bottom portion 301 (e.g., a region beyond a fully sampled region) may be reconstructed. Using the reconstructed image data for the bottom portion 301 of the image 300a, a second image (e.g., image 300b) may be generated that expands the FOV to 25 cm. As illustrated in Fig. 3B, the bottom portion 303 is reconstructed using an Al model (e.g., FOV module 129).

[0102] Figs. 3C-E illustrate an example of generating training data for the Al module (e.g., FOV module 129). CT image data (Fig. 3C) is simulated using a longer detector (e.g., 50 cm), and the image data for Fig. 3D is used to generate simulated CT scan data (Fig. 3E) used to train the Al module (e.g., FOV module 129). In embodiments, the Al model (e.g., FOV module 129) may be trained using CT to CBCT simulations (e.g., using CT values to simulate CBCT), in this manner we can create simulated CBCT projection data using a 40cm length detector to produce O-arm-like image data that can mimic the images when reconstructed. The same data is then simulated with a 50 cm length detector, to achieve a 25cm FOV reconstruction without artifacts.A0012955The Al model (e.g., the FOV module 129) will then be trained using the 25 cm reconstruction from a 40 cm length detector as input, and using the 25 cm reconstruction from 50 cm length detector as the ground-truth output that the Al model learns to mimic.

[0103] Fig. 4 illustrates an example of a process flow 400 in accordance with aspects of the present disclosure.

[0104] In some examples, process flow 400 may be implemented by aspects of the system 100 and 200 described herein.

[0105] In the following description of the process flow 400, the operations may be performed in a different order than the order shown, or the operations may be performed in different orders or at different times. Certain operations may also be left out of the process flow 400, or other operations may be added to the process flow 400.

[0106] It is to be understood that any device (e.g., computing device 102, imaging device 112, etc.) of the system 100 may perform the operations shown.

[0107] At step 410, the process flow 400 may include receiving image data associated with an image captured by the imaging device. In embodiments, the image data may be a CT scan taken by an imaging device 112. The image data includes data for a fully sampled region (e.g., 20 cm FOV) and partial data for a region beyond the fully sampled region.

[0108] At step 420, the process flow 400 may include the partial data for the region beyond the fully sampled region is reconstructed to generate reconstruct data. For example, the same dataset (e.g., image data for a CT scan) is reconstructed with a larger FOV. Said another way, image data for the region beyond the fully sampled region (e.g., the region 305) may be reconstructed.

[0109] At step 425, the process flow 400 may include the reconstructed image data for the region beyond the fully sampled region generated in the previous step is enhanced using an Al model to generate an enhanced image (e.g. Fig. 3B). At step 430, the process flow 400 may include the enhanced image is displayed. The enhanced image may also be used for preoperative registration (e.g., image merge, stereotactic frame registration, etc.), artifact correction, etc.

[0110] The process flow 400 (and / or one or more operations 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 an imaging system (including the imaging device 112), 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 process flow 400. The at least one processor may perform operations of the process flow 400 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 operations of aA0012955 function as shown in the process flow 400. One or more portions of the process flow 400 may be performed by the processor executing any of the contents of memory, such as image processing 120, a segmentation 122, a transformation 124, a registration 128, and / or FOV module 129. [oni] As noted above, the present disclosure encompasses methods with fewer than all of the features identified in Fig. 4 (and the corresponding description of the process flow 400), as well as methods that include additional features beyond those identified in Fig. 4 (and the corresponding description of the process flow 400). The present disclosure also encompasses methods that include 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 include a registration or any other correlation.

[0112] 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, implementations, and / or configurations for the purpose of streamlining the disclosure. The features of the aspects, implementations, and / or configurations of the disclosure may be combined in alternate aspects, implementations, 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, implementation, 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 implementation of the disclosure.

[0113] Moreover, though the foregoing has included description of one or more aspects, implementations, 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, implementations, 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.

[0114] The phrases “at least one,” “one or more,” “or,” 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,” “A, B, and / or C,” and “A, B, 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.A0012955

[0115] 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.

[0116] The term “automatic” and variations thereof, as used herein, refers to any process or operation, which is typically continuous or semi-continuous, done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material.”

[0117] Aspects of the present disclosure may take the form of an implementation that is entirely hardware, an implementation that is entirely software (including firmware, resident software, micro-code, etc.) or an implementation combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.” Any combination of one or more computer-readable medium(s) may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.

[0118] A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD- ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0119] A computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium that is not a computer-readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including, but notA0012955 limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0120] The terms “determine,” “calculate,” “compute,” and variations thereof, as used herein, are used interchangeably and include any type of methodology, process, mathematical operation or technique.

[0121] The techniques of this disclosure may also be described in the following examples.

[0122] Example 1. A system, comprising: a processor; and a memory storing data thereon that, when processed by the processor, enables the processor to: receive image data associated with an image captured by an imaging device, wherein the image data includes data for a fully sampled region and partial data for a region beyond the fully sampled region; generate a second image by reconstructing the partial data for the region beyond the fully sampled region, wherein the second image includes reconstruct data for a reconstructed portion beyond the fully sampled region; and display the second image.

[0123] Example 2. The system of example 1, wherein the data further enables the processor to: receive second image data associated with the second image, wherein the second image data includes the reconstruct data for the reconstructed portion beyond the fully sampled region;enhance, using an Artificial Intelligence (Al) model, the reconstruct data for the reconstructed portion of the second image to generate an enhanced image; and output the enhanced image.

[0124] Example 3. The system of example 2, wherein the Al model is trained using simulated computed tomography (CT) data.

[0125] Example 4. The system of example 3, wherein the simulated CT data is simulated using a simulated 0-arm with a 50 cm length detector.

[0126] Example 5. The system of any preceding example, wherein the simulated CT data is used to generate a cone beam computed tomography (CBCT) dataset.

[0127] Example 6. The system of example 2, wherein the Al model is trained using reconstructions from cropped CBCT detector data.

[0128] Example 7. The system of example 2, wherein the enhanced image is used for registration to a preoperative image.

[0129] Example 8. The system of example 7, wherein the registration comprises image merge or stereotactic frame registration.

[0130] Example 9. The system of 2, wherein the fully sampled region of the image comprises a field of view (FOV) of 20 cm, and wherein the enhanced image comprises a FOV of 25 cm.A0012955

[0131] Example 10. The system of example 2, wherein the enhanced image is used for artifact correction.

[0132] Example 11. The system of example 10, wherein the artifact correction includes beamhardening correction or truncation artifact correction.

[0133] Example 12. The system of any preceding example, wherein the imaging device comprises an 0-arm or a C-arm.

[0134] Example 13. A method, comprising receiving image data associated with an image captured by an imaging device, wherein the image data includes data for a fully sampled region and partial data for a region beyond the fully sampled region; reconstructing the partial data for the region beyond the fully sampled region to generate reconstruct data; enhancing, using an Al model, the reconstruct data for the region beyond the fully sampled region to generate an enhanced image; and outputting the enhanced image.

[0135] Example 14. The method of example 13, wherein the Al model is trained using simulated CT data, wherein the CT data is simulated using a simulated 0-arm with a 50cm length detector, and wherein the enhanced image comprises a field of view (FOV) of 25 cm.

[0136] Example 15. The method of example 14, wherein the Al model is trained using reconstructions from cropped CBCT detector data.

[0137] Example 16. The method of any preceding example, wherein the image data comprises a CBCT dataset, and wherein CT data is used to generate the CBCT dataset.

[0138] Example 17. The method of any preceding example, wherein the enhanced image is used for artifact correction.

[0139] Example 18. A system, comprising:an imaging device; a processor; and a memory storing data thereon that, when processed by the processor, enable the processor to: receive image data associated with an image captured by the imaging device, wherein the image data includes data for a fully sampled region and partial data for a region beyond the fully sampled region; reconstruct the partial data for the region beyond the fully sampled region to generate reconstruct data; enhance, using an Al model, the reconstruct data for the region beyond the fully sampled region to generate an enhanced image; and output the enhanced image.

[0140] Example 19. The system of example 18, wherein the Al model is trained using simulated CT data, wherein the simulated CT data is generated using a simulated 0-arm with a 50 cm length detector, and wherein the enhanced image comprises a field of view (FOV) of 25 cm.

[0141] Example 20. The system of any preceding example, wherein the enhanced image is used for artifact correction, and wherein the artifact correction includes bone beam-hardening correction or truncation artifact correction.

Claims

A0012955CLAIMSWhat is claimed is:

1. A system (100), comprising: a processor (104); and a memory (106) storing data thereon that, when processed by the processor (104), enables the processor (104) to: receive image data associated with an image captured by an imaging device (112), wherein the image data includes data for a fully sampled region (305) and partial data for a region beyond the fully sampled region (301); generate a second image (300b) by reconstructing the partial data for the region beyond the fully sampled region (305), wherein the second image (300b) includes reconstruct data for a reconstructed portion (303) beyond the fully sampled region (305); and display the second image (300b).

2. The system of claim 1, wherein the data further enables the processor to: receive second image data associated with the second image, wherein the second image data includes the reconstruct data for the reconstructed portion beyond the fully sampled region (305); enhance, using an Artificial Intelligence (Al) model (129), the reconstruct data for the reconstructed portion of the second image (300b) to generate an enhanced image; and output the enhanced image.

3. The system of claim 2, wherein the Al model (129) is trained using simulated computed tomography (CT) data.

4. The system of claim 3, wherein the simulated CT data is simulated using a simulated O- arm with a 50 cm length detector.

5. The system of claim 3, wherein the simulated CT data is used to generate a cone beam computed tomography (CBCT) dataset.

6. The system of claim 2, wherein the Al model (129) is trained using reconstructions from cropped CBCT detector data.A00129557. The system of claim 2, wherein the enhanced image is used for registration to a preoperative image.

8. The system of claim 7, wherein the registration comprises image merge or stereotactic frame registration.

9. The system of 2, wherein the fully sampled region (305) of the image (300a) comprises a field of view (FOV) of 20 cm, and wherein the enhanced image comprises a FOV of 25 cm.

10. The system of claim 2, wherein the enhanced image is used for beam -hardening correction or truncation artifact correction.

11. A method, comprising: receiving image data associated with an image (300a) captured by an imaging device (112), wherein the image data includes data for a fully sampled region (305) and partial data for a region beyond the fully sampled region (301); reconstructing the partial data for the region beyond the fully sampled region (301) to generate reconstruct data; enhancing, using an Al model (129), the reconstruct data for the region beyond the fully sampled region (301) to generate an enhanced image (300b); and outputting the enhanced image (300b).

12. The method of claim 11, wherein the image data comprises a CBCT dataset, and wherein CT data is used to generate the CBCT dataset.

13. A system (100), comprising: an imaging device (112); a processor (104); and a memory (106) storing data thereon that, when processed by the processor (104), enable the processor (104) to: receive image data associated with an image (300a)captured by the imaging device (112), wherein the image data includes data for a fully sampled region (305) and partial data for a region beyond the fully sampled region (301); reconstruct the partial data for the region beyond the fully sampled region (301) to generate reconstruct data;A0012955 enhance, using an Al model (129), the reconstruct data for the region beyond the fully sampled region (301) to generate an enhanced image (300b); and output the enhanced image (300b).

14. The system of claim 13, wherein the Al model (129) is trained using simulated CT data, wherein the simulated CT data is generated using a simulated O-arm with a 50 cm length detector, and wherein the enhanced image comprises a field of view (FOV) of 25 cm.

15. The system of claim 13, wherein the enhanced image (300b) is used for artifact correction, and wherein the artifact correction includes bone beam-hardening correction or truncation artifact correction.

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