Dynamic 3D lung map view for tool navigation inside the lung

Information

  • Patent Grant
  • 11172989
  • Patent Number
    11,172,989
  • Date Filed
    Thursday, March 11, 2021
    3 years ago
  • Date Issued
    Tuesday, November 16, 2021
    2 years ago
Abstract
A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.
Description
BACKGROUND
Technical Field

The present disclosure relates to the treatment of patients with lung diseases and, more particularly, to devices, systems, and methods for implementing a dynamic 3D lung map view for tool navigation inside a patient's lungs.


Description of Related Art

Lung cancer has an extremely high mortality rate, especially if it is not diagnosed in its early stages. The National Lung Screening Trial has demonstrated that a reduction in mortality occurs if diagnostic scans such as computed tomography (CT) scans are used for early detection for those at risk of contracting the disease. While CT scans increase the possibility that small lesions and nodules in the lung can be detected, these lesions and nodules still require biopsy and cytological examination before a diagnosis can be rendered and treatment can be undertaken.


To perform a biopsy, as well as many treatments, navigation of tools within the lungs to the point of biopsy or treatment is necessary. Accordingly, improvements to systems and methods of navigating are continually being sought.


SUMMARY

Provided in accordance with the present disclosure is a method for implementing a dynamic three-dimensional (3D) lung map view for navigating a probe inside a patient's lungs.


In an aspect of the present disclosure, the method includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, the probe including a location sensor in operative communication with the navigation system, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, resenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.


In another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes zooming in when the probe approaches the target.


In yet another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes zooming in when the diameter of an airway within which the probe is sensed to be located is less than a predetermined threshold.


In another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes changing the presented view to a view wherein the airway tree bifurcation is maximally spread.


In yet another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes aligning the view with the sensed location of the probe to show where the probe is and what lies ahead of the probe.


In another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes changing the presented view to be orthogonal to a vector from the probe to the pathway.


In yet another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes changing the presented view to be perpendicular to the sensed location of the probe in relation to the 3D model to show the area around the probe.


In another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes changing the presented view to be behind the sensed location of the probe in relation to the 3D model to show the area ahead of the probe.


In yet another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes changing the presented view to be at the tip of the probe and orthogonal to the directing in which the probe is moving.


In another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes changing the presented view to be perpendicular to a vector from the probe to the target to show the alignment of the probe to the target.


In yet another aspect of the present disclosure, iteratively adjusting the presented view of the 3D model includes rotating the presented view around a focal point to improve a 3D perception of the sensed location of the probe in relation to the 3D model.


In a further aspect of the present disclosure, updating the presented view by removing at least part of an object includes removing at least part of an object which is outside of a region of interest.


In yet a further aspect of the present disclosure, updating the presented view by removing at least part of an object includes removing at least part of an object which is obstructing the probe.


In a further aspect of the present disclosure, updating the presented view by removing at least part of an object includes removing at least part of an object which is obstructing the target.


In yet a further aspect of the present disclosure, updating the presented view by removing at least part of an object includes removing at least part of an object which is not relevant to the sensed location of the probe.


In a further aspect of the present disclosure, updating the presented view by removing at least part of an object includes removing at least part of an object which is not relevant to a current selected state of the navigation system.


In another aspect of the present disclosure, the method further includes presenting an alert.


In a further aspect of the present disclosure, presenting an alert includes presenting an alert when the probe is approaching the pleura.


In yet a further aspect of the present disclosure, presenting an alert includes presenting an alert when the tool is approaching major blood vessels.


In a further aspect of the present disclosure, presenting an alert includes presenting an alert when the sensed location of the probe is off of the planned pathway.


Any of the above aspects and embodiments of the present disclosure may be combined without departing from the scope of the present disclosure.





BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects and features of the present disclosure are described hereinbelow with references to the drawings, wherein:



FIG. 1 depicts a system diagram of an example electromagnetic navigation (EMN) system which may be used to create and display a dynamic 3D lung map view, according to an embodiment of the present disclosure;



FIG. 2 depicts a schematic diagram of an example workstation forming part of the EMN system of FIG. 1 which may be used to create and display a dynamic 3D lung map view, according to an embodiment of the present disclosure;



FIG. 3 is a flowchart illustrating an example method for creating a dynamic 3D lung map view, according to an embodiment of the present disclosure;



FIG. 4 illustrates an example view of a user interface that may be presented on the workstation of FIG. 2 showing an example of a dynamic 3D lung map view, according to an embodiment of the present disclosure;



FIG. 5 illustrates an example of an unadjusted 3D lung map view, according to an embodiment of the present disclosure;



FIG. 6 illustrates an example of a dynamic 3D lung map view, according to an embodiment of the present disclosure; and



FIG. 7 illustrates another example of a dynamic 3D lung map view, according to an embodiment of the present disclosure.





DETAILED DESCRIPTION

Devices, systems, and methods for implementing a dynamic 3D lung map view for tool navigation inside a patient's lungs are provided in accordance with the present disclosure. A location sensor may be incorporated into different types of tools and catheters to track the location and assist in navigation of the tools. The tracked location of the location sensor may be used to visually show the location of a tool on the dynamic 3D lung map. The location of the location sensor within the body of a patient, with reference to a 3D map or 2D images as well as a planned pathway assists the clinician in navigating lungs of the patient. However, because of the amounts of data being presented and the ability to show details of the airways, there is a desire to assist the clinician and eliminate unessential data or data regarding portions of the anatomy that are unrelated to a specific navigation or a specific procedure. In addition, there is a desire to harness this detailed anatomical data and alert the clinician regarding proximity to certain anatomical features. These and other aspects of the present disclosure are detailed herein below.


The dynamic 3D lung map view, as disclosed herein, is one of a variety of views that may be presented by an electromagnetic navigation (EMN) system which may be used by a clinician to perform an ELECTROMAGNETIC NAVIGATION BRONCHOSCOPY® (ENB) procedure. Among other tasks that may be performed using the EMN system are planning a pathway to target tissue, navigating a positioning assembly to the target tissue, and navigating a variety of tools, such as a locatable guide (LG) and/or a biopsy tool to the target tissue.


An ENB procedure generally involves at least two phases: (1) planning a pathway to a target located within, or adjacent to, the patient's lungs; and (2) navigating a probe to the target along the planned pathway. These phases are generally referred to as (1) “planning” and (2) “navigation.” An example of the planning software described herein can be found in U.S. patent application Ser. Nos. 13/838,805, 13/838,997, and 13/839,224, all of which are filed by Covidien LP on Mar. 15, 2013 and entitled “Pathway Planning System and Method,” all of which are incorporated herein by reference. An example of the planning software can be found in commonly assigned U.S. Provision Patent Application No. 62/020,240 entitled “SYSTEM AND METHOD FOR NAVIGATING WITHIN THE LUNG” the entire contents of which are incorporated herein by reference.


Prior to the planning phase, the patient's lungs are imaged by, for example, a computed tomography (CT) scan, although additional applicable methods of imaging will be known to those skilled in the art. The image data assembled during the CT scan may then be stored in, for example, the Digital Imaging and Communications in Medicine (DICOM) format, although additional applicable formats will be known to those skilled in the art. The CT scan image data may then be loaded into a planning software application (“application”) to be used during the planning phase of the ENB procedure.


The application may use the CT scan image data to generate a three-dimensional (3D) model of the patient's lungs. The 3D model may include, among other things, a model airway tree corresponding to the actual airways of the patient's lungs, and showing the various passages, branches, and bifurcations of the patient's actual airway tree. Additionally, the 3D model may include lesions, markers, blood vessels, and/or a 3D rendering of the pleura. While the CT scan image data may have gaps, omissions, and/or other imperfections included in the image data, the 3D model is a smooth representation of the patient's airways, with any such gaps, omissions, and/or imperfections in the CT scan image data filled in or corrected. As described in more detail below, the 3D model may be viewed in various orientations. For example, if a clinician desires to view a particular section of the patient's airways, the clinician may view the 3D model represented in a 3D rendering and rotate and/or zoom in on the particular section of the patient's airways. Additionally, during the navigation phase of an ENB procedure, while a tool is being navigated through the patient's airways, the clinician may want to have the presented view of the 3D model dynamically updated as the tool is navigated. Such a dynamic 3D lung map view is disclosed below.


Prior to the start of the navigation phase of an ENB procedure, the 3D model is registered with the actual lungs of the patient. One potential method of registration involves navigating a locatable guide into each lobe of the patient's lungs to at least the second bifurcation of the airways of that lobe. The position of the locatable guide is tracked during this registration phase, and the 3D model is iteratively updated based on the tracked position of the locatable guide within the actual airways of the patient's lungs. This registration process is described in commonly-owned U.S. Provisional Patent Application Ser. No. 62/020,220 entitled “Real-Time Automatic Registration Feedback”, filed on Jul. 2, 2014, by Brown et al. With reference to FIG. 1, an EMN system 10 is provided in accordance with the present disclosure. One such EMN system is the ELECTROMAGNETIC NAVIGATION BRONCHOSCOPY® system currently sold by Covidien LP. As shown in FIG. 1, EMN system 10 generally includes an operating table 40 configured to support a patient; a bronchoscope 50 configured for insertion through the patient's mouth and/or nose into the patient's airways; monitoring equipment 60 coupled to bronchoscope 50 for displaying video images received from bronchoscope 50; a tracking system 70 including a tracking module 72, a plurality of reference sensors 74, and an electromagnetic field generator 76; a workstation 80 including software and/or hardware, such as an EMN application 81, used to facilitate pathway planning, identification of target tissue, and navigation to the target tissue.



FIG. 1 also depicts two types of catheter guide assemblies 90, 100. Both catheter guide assemblies 90, 100 are usable with the EMN system 10 and share a number of common components. Each catheter guide assembly 90, 100 includes a handle 91, which is connected to an extended working channel (EWC) 96. EWC 96 is sized for placement into the working channel of bronchoscope 50. In operation, a locatable guide (LG) 92, including an electromagnetic (EM) sensor 94, is inserted into EWC 96 and locked into position such that the sensor 94 extends a desired distance beyond the distal tip 93 of EWC 96. The location of EM sensor 94, and thus the distal end of EWC 96, within an electromagnetic field generated by the electromagnetic field generator 76 can be derived by the tracking module 72, and the workstation 80. Catheter guide assemblies 90, 100 have different operating mechanisms, but each contain a handle 91 that can be manipulated by rotation and compression to steer the distal tip 93 of LG 92 and EWC 96. Catheter guide assemblies 90 are currently marketed and sold by Covidien LP under the name SUPERDIMENSION® Procedure Kits, similarly catheter guide assemblies 100 are currently sold by Covidien LP under the name EDGE™ Procedure Kits, both kits include a handle 91, EWC 96, and LG 92. For a more detailed description of the catheter guide assemblies 90, 100, reference is made to commonly-owned U.S. patent application Ser. No. 13/836,203 entitled “MICROWAVE ABLATION CATHETER AND METHOD OF UTILIZING THE SAME”, filed on Mar. 15, 2013 by Ladtkow et al., the entire contents of which are hereby incorporated by reference.


As illustrated in FIG. 1, the patient is shown lying on operating table 40 with bronchoscope 50 inserted through the patient's mouth and into the patient's airways. Bronchoscope 50 includes a source of illumination and a video imaging system (not explicitly shown) and is coupled to monitoring equipment 60, e.g., a video display, for displaying the video images received from the video imaging system of bronchoscope 50.


Catheter guide assemblies 90, 100 including LG 92 and EWC 96 are configured for insertion through a working channel of bronchoscope 50 into the patient's airways (although the catheter guide assemblies 90, 100 may alternatively be used without bronchoscope 50). LG 92 and EWC 96 are selectively lockable relative to one another via a locking mechanism 99. A six degrees-of-freedom electromagnetic tracking system 70, e.g., similar to those disclosed in U.S. Pat. No. 6,188,355 and published PCT Application Nos. WO 00/10456 and WO 01/67035, entitled “Wireless six-degree-of-freedom locator”, filed on Dec. 14, 1998 by Gilboa, the entire contents of each of which is incorporated herein by reference, or any other suitable positioning measuring system, is utilized for performing navigation, although other configurations are also contemplated. Tracking system 70 is configured for use with catheter guide assemblies 90, 100 to track the position of EM sensor 94 as it moves in conjunction with EWC 96 through the airways of the patient, as detailed below.


As shown in FIG. 1, electromagnetic field generator 76 is positioned beneath the patient. Electromagnetic field generator 76 and the plurality of reference sensors 74 are interconnected with tracking module 72, which derives the location of each reference sensor 74 in six degrees of freedom. One or more of reference sensors 74 are attached to the chest of the patient. The six degrees of freedom coordinates of reference sensors 74 are sent to workstation 80, which includes EMN application 81 where sensors 74 are used to calculate a patient coordinate frame of reference.


Also shown in FIG. 1 is a biopsy tool 102 that is insertable into catheter guide assemblies 90, 100 following navigation to a target and removal of LG 92. The biopsy tool 102 is used to collect one or more tissue sample from the target tissue. As detailed below, biopsy tool 102 is further configured for use in conjunction with tracking system 70 to facilitate navigation of biopsy tool 102 to the target tissue, and tracking of a location of biopsy tool 102 as it is manipulated relative to the target tissue to obtain the tissue sample. Though shown as a biopsy tool in FIG. 1, those of skill in the art will recognize that other tools including for example microwave ablation tools and others may be similarly deployed and tracked as the biopsy tool 102 without departing from the scope of the present disclosure.


Although the EM sensor 94 is described above as being included in LG 92 it is also envisioned that EM sensor 94 may be embedded or incorporated within biopsy tool 102 where biopsy tool 102 may alternatively be utilized for navigation without need of LG 92 or the necessary tool exchanges that use of LG 92 requires. A variety of useable biopsy tools are described in U.S. Provisional Patent Application Nos. 61/906,732 and 61/906,762 both entitled “DEVICES, SYSTEMS, AND METHODS FOR NAVIGATING A BIOPSY TOOL TO A TARGET LOCATION AND OBTAINING A TISSUE SAMPLE USING THE SAME”, filed Nov. 20, 2013 and U.S. Provisional Patent Application No. 61/955,407 having the same title and filed Mar. 14, 2014, the entire contents of each of which are incorporated herein by reference and useable with the EMN system 10 as described herein.


During procedure planning, workstation 80 utilizes computed tomographic (CT) scan image data for generating and viewing a three-dimensional (3D) model of the patient's airways, enables the identification of target tissue on the 3D model (automatically, semi-automatically or manually), and allows for the selection of a pathway through the patient's airways to the target tissue. The 3D model may be presented on a display monitor associated with workstation 80, or in any other suitable fashion.


Using workstation 80, various views of the 3D model may be presented and may be manipulated by a clinician to facilitate identification of a target and selection of a suitable pathway through the patient's airways to access the target. For example, EMN application 81 may be configured in various states to display the 3D model in a variety of view modes. Some of these view modes may include a dynamic 3D lung map view, as further described below. For each view of the 3D model, the angle from which the 3D model is displayed may correspond to a view point. The view point may be fixed at a predefined location and/or orientation, or may be adjusted by the clinician operating workstation 80.


The 3D model may also show marks of the locations where previous biopsies were performed, including the dates, times, and other identifying information regarding the tissue samples obtained. These marks may also be selected as the target to which a pathway can be planned. Once selected, the pathway is saved for use during the navigation procedure.


Following procedure planning, a procedure may be undertaken in which the EM sensor 94, in conjunction with tracking system 70, enables tracking of EM sensor 94 (and thus the distal end of the EWC or the tool 102) as EM sensor 94 is advanced through the patient's airways following the pathway planned during the procedure planning phase.


Turning now to FIG. 2, there is shown a system diagram of workstation 80. Workstation 80 may include memory 202, processor 204, display 206, network interface 208, input device 210, and/or output module 212. Memory 202 may store EMN application 81 and/or CT data 214. EMN application 81 may, when executed by processor 204, cause display 206 to present user interface 216. The EMN application 81 provides the interface between the sensed position of the EM sensor 94 and the image and planning data developed in the planning phase.


Referring now to FIG. 3, there is shown an aspect which may be incorporated into an EMN application 81. Specifically, FIG. 3 depicts a flowchart of an exemplary method of creating a dynamic 3D lung map view. During an ENB procedure, this example method may be started when a clinician selects a dynamic 3D lung map view button 402 in an EMN application 81 user interface 400. Alternatively, button 402 may be a drop down menu from which the clinician may select the dynamic 3D lung map view from among a plurality of available views. Starting at step S302, the view point from which the 3D model is displayed may be automatically adjusted in relation to the tracked location of a tool, which is depicted as a probe 406 in FIGS. 4-7, inside the patient's lungs. Adjusting the view point may include moving the view point in relation to the 3D model and/or zooming in on the 3D model to display a closer image of the 3D model. As shown in FIG. 6 below, an unadjusted 3D lung map view (FIG. 5) is adjusted such that the position of a probe 406 is more clearly shown in relation to the position of a target 510 and surrounding airways 512. The view point may further be adjusted according to the direction in which probe 406 is being navigated and/or to be orthogonal to a vector between the tip of probe 406 and a target 510 or in relation to the pathway 408, as is shown in FIGS. 6 and 7, which depict the 3D model from a view point orthogonal to vector 614 which runs from the tip of digital probe 406 to target 510. The view point may further be adjusted by zooming in when probe 406 approaches target 510 or airways having a diameter less than a predetermined threshold. In an embodiment, a preferred view point may be such that the displayed view of the 3D model shows the bifurcations of the airway tree around digital probe 406 as maximally spread, that is, a view point from a direction showing the airway tree with as few overlapping branches as possible. In an embodiment the view point may be moved to be above probe 406 in relation to the 3D model, or behind probe 406 in relation to the 3D model, in order to provide the clinician with a clearer understanding of the position of probe 406 in relation to surrounding objects. In such an embodiment, the dynamic 3D lung map view may show the area of the 3D model in front of and around the tool, as shown in FIG. 7. In another embodiment, the view point may be moved such that the view presented by EMN application 81 is looking ahead out of the tip of digital probe 406.


Next, at step S304, EMN application 81 determines whether any objects are visible from the current view point but are outside of a region of interest for the current navigation procedure. An example might be other targets, or portions of the patient's physiology, such as blood vessels and the heart, which lie outside of the region in which the pathway is located, such as in other lobes of the patient's lungs, or along other branches of airway tree 404 which are not used for the current procedure. If EMN application 81 determines that such objects are visible, those objects may be removed from the view at step S306, as shown below by FIG. 7.


Thereafter, or if EMN application 81 determines that there are no such objects in the view, processing proceeds to step S308, where EMN application 81 determines whether there are objects obstructing the view of digital probe 406 and/or target 510. For example, depending on the angle of the view point, the surrounding airways which do not form part of the planned pathway may lie in the line of sight and between the view point and probe 406 or target 510. If EMN application 81 determines that such objects are obstructing the view of probe 406 or target 510, those objects may be removed from the view at step S310, as shown below by FIG. 7.


Thereafter, or if EMN application 81 determines that there are no such objects in the view, processing proceeds to step S312, where EMN application 81 determines if there are any objects visible in the view which are unrelated to the position of probe 406, the type of tool being used in the current navigation procedure, or the selected state of EMN application 81. For example, markers indicating the location of previous biopsies at different target locations may be within the view angle from the view point, but are not relevant to the current procedure, as shown below by FIG. 7. Another example may be targets 722 which are part of the current navigation plan but have at least one other target 510 which must be visited first. Such targets 722 may become visible or “unhidden” once target 510 has been visited and the necessary procedures performed. If EMN application 81 determines that such objects are within the view, those objects may be removed from the view at step S314.


Thereafter, or if EMN application 81 determines that there are no such objects in the view, processing proceeds to step S316, where EMN application 81 determines whether digital probe 406, and thus sensor 94, is approaching the pleural boundaries of the patient's lungs. EMN application 81 may determine that sensor 94 is approaching the pleural boundaries of the patient's lungs based on, for example, the distance between sensor 94 and the pleura, the angle between sensor 94 and the pleura, the speed at which sensor 94 is moving, and/or any combination thereof. The determination may further be based on a known or estimated rate of navigational errors. When sensor 94 is close to the pleura, there is an increased risk of injury, such as pneumothorax, to the patient, and the clinician may want to be aware of that to proceed with added caution. Thus, if EMN application 81 determines that sensor 94 is close to the pleura, EMN application 81 may present an alert to the clinician at step S318. EMN application 81 may also take known or estimated navigational errors into account when determining whether sensor 94 is approaching the pleura.


Thereafter, or if EMN application 81 determines that sensor 94 is not approaching the pleura, processing proceeds to step S320, where EMN application 81 determines whether sensor 94 is approaching one or more major blood vessels. As with the pleura, when sensor 94 is close to major blood vessels, particularly where a tool 102, such as a biopsy or microwave ablation tool, is being deployed, there is added risk of injury to the patient, and the clinician may want to be aware that sensor 94 is close to major blood vessels to proceed with added caution. Thus, if EMN application 81 determines that sensor 94 is close major blood vessels, EMN application 81 may present an alert to the clinician at step S322. Additionally, as with the pleura, EMN application 81 may take known or estimated navigational errors into account when determining whether sensor 94 is approaching major blood vessels.


Thereafter, or if EMN application 81 determines that sensor 94 is not approaching any major blood vessels, processing proceeds to step S324, where EMN application 81 determines whether probe 406 has arrived at the target. If EMN application 81 determines that probe 406 has not arrived at the target, processing returns to step S302. In this way, the dynamic 3D lung map view is continuously updated and/or adjusted during the navigation procedure. If EMN application 81 determines that digital probe 406 has arrived at the target, processing proceeds to step S326, where EMN application 81 determines whether there are more targets to be visited. If EMN application 81 determines that there are no more targets to be visited, processing ends. Otherwise, processing returns to step S302.



FIG. 4 illustrates an example user interface that may be presented by workstation 80 showing an example view of the 3D model. User interface (UI) 400 includes a button 402 which may be used to select and/or enable the dynamic 3D lung map view. UI 400 further shows an airway tree 404, a digital probe 406, and a pathway 408.



FIG. 5 illustrates an example of an unadjusted 3D lung map view which may be presented by EMN application 81 via UI 400. The unadjusted 3D lung map view shows the probe 406 within the 3D model, corresponding to the location of sensor 94 within the patient's airways. Also shown by the unadjusted 3D lung map view are the airway tree 404, the pathway 408, the target 510, the surrounding airways 512, and the pleura 514 of the lungs. The unadjusted 3D lung map view may be adjusted manually.



FIG. 6 illustrates an example view of a dynamic 3D lung map view which may be presented by EMN application 81. The example dynamic 3D lung map view shows the same probe 406 and target 510 as the unadjusted 3D lung map view of FIG. 5. However, the dynamic 3D lung map view has been adjusted by zooming in on the 3D model to show the position of probe 406 in relation to target 510. The dynamic 3D lung map view has further been aligned with the tip of probe 406, or a vector 614 from digital probe 406 to target 510, and positioned such that pathway 408 and surrounding airways 512 may clearly be seen. A line 614 indicates the line of sight from the tip of digital probe 406 intersecting with target 510.



FIG. 7 illustrates an example dynamic 3D lung map view wherein objects have been hidden or “ghosted out” to more clearly show the objects and components of the 3D model which are relevant to the current procedure, according to an embodiment of this disclosure. Hiding or “ghosting out” objects may involve completely removing such objects from the displayed 3D lung map, or such objects may be shown in a different way from objects which are not hidden, for example with a higher level of transparency. The example dynamic 3D lung map view includes airway tree 404, probe 406, pathway 408, target 510, surrounding airways 512, and vector 614, as described above with reference to FIGS. 4-6.


The example dynamic 3D lung map view further shows additional targets 718 which have been hidden because they are located outside of the region of interest, as described above with regard to step S304 of FIG. 3. The example dynamic 3D lung map view also shows that a branch 720 of airway tree 404 which overlaps with pathway 408 and target 510, and thus obstructs the view of pathway 408 and target 510, has been hidden, as described above with regard to step S308 of FIG. 3. Additionally, the example dynamic 3D lung map view shows that a target 722 which does lie within the region of interest but is not relevant to the current procedure has been hidden, as described above with regard to step S312 of FIG. 3. Target 722 may, for example, be a subsequent target on the current pathway to which the tool will be navigated during the current procedure, but it is not yet relevant to the procedure because at least one other target 510 must first be visited. The current pathway may be divided into two or more portions: a first portion 408 representing the portion of the pathway to be navigated to the current target 510, and additional portions 708 representing the portion of the pathway leading to the next target 722 to be visited. The dynamic 3D lung map view also shows that other objects, such as markers 724, are hidden because they are not relevant to the current procedure. Markers 724 may be, for example, markers indicating the locations where previous biopsies were performed.


By using the dynamic 3D lung map view described above during an ENB procedure, the clinician may be presented with a continuously updated view of the 3D model which is adjusted as the tool, and thus sensor 94, is moved through the patient's airways. The dynamic 3D lung map view presents the clinician with a view of the 3D model from a viewpoint which clearly shows digital probe 406, and removes objects which may obscure digital probe 406, airway tree 404, target 510, and/or other objects which are relevant to the ENB procedure being performed.


Detailed embodiments of devices, systems incorporating such devices, and methods using the same as described herein. However, these detailed embodiments are merely examples of the disclosure, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for allowing one skilled in the art to variously employ the present disclosure in appropriately detailed structure. While the preceding embodiments are described in terms of bronchoscopy of a patient's airways, those skilled in the art will realize that the same or similar devices, systems, and methods may be used in other lumen networks, such as, for example, the vascular, lymphatic, and/or gastrointestinal networks as well.


With respect to memory 202 described above in connection with FIG. 2, the memory 202 may include any non-transitory computer-readable storage media for storing data and/or software that is executable by processor 204 and which controls the operation of workstation 80. In an embodiment, memory 202 may include one or more solid-state storage devices such as flash memory chips. Alternatively or in addition to the one or more solid-state storage devices, memory 202 may include one or more mass storage devices connected to the processor 204 through a mass storage controller (not shown) and a communications bus (not shown). Although the description of computer-readable media contained herein refers to a solid-state storage, it should be appreciated by those skilled in the art that computer-readable storage media can be any available media that can be accessed by the processor 204. That is, computer readable storage media includes non-transitory, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, Blu-Ray or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by workstation 80.


Network interface 208 may be configured to connect to a network such as a local area network (LAN) consisting of a wired network and/or a wireless network, a wide area network (WAN), a wireless mobile network, a Bluetooth network, and/or the internet. Input device 210 may be any device by means of which a user may interact with workstation 80, such as, for example, a mouse, keyboard, foot pedal, touch screen, and/or voice interface. Output module 212 may include any connectivity port or bus, such as, for example, parallel ports, serial ports, universal serial busses (USB), or any other similar connectivity port known to those skilled in the art.


Further aspects of image and data generation, management, and manipulation useable in either the planning or navigation phases of an ENB procedure are more fully described in commonly-owned U.S. Provisional Patent Application Ser. No. 62/020,177 entitled “Methods for Marking Biopsy Location”, filed on Jul. 2, 2014, by Brown; U.S. Provisional Patent Application Ser. No. 62/020,238 entitled “Intelligent Display”, filed on Jul. 2, 2014, by Kehat et al.; U.S. Provisional Patent Application Ser. No. 62/020,242 entitled “Unified Coordinate System for Multiple CT Scans of Patient Lungs”, filed on Jul. 2, 2014, by Greenburg; U.S. Provisional Patent Application Ser. No. 62/020,245 entitled “Alignment CT”, filed on Jul. 2, 2014, by Klein et al.; U.S. Provisional Patent Application Ser. No. 62/020,250 entitled “Algorithm for Fluoroscopic Pose Estimation”, filed on Jul. 2, 2014, by Merlet; U.S. Provisional Patent Application Ser. No. 62/020,261 entitled “System and Method for Segmentation of Lung”, filed on Jul. 2, 2014, by Markov et al.; and U.S. Provisional Patent Application Ser. No. 62/020,258 entitled “Cone View—A Method of Providing Distance and Orientation Feedback While Navigating in 3D”, filed on Jul. 2, 2014, by Lachmanovich et al., the entire contents of all of which are hereby incorporated by reference.


While several embodiments of the disclosure have been shown in the drawings, it is not intended that the disclosure be limited thereto, as it is intended that the disclosure be as broad in scope as the art will allow and that the specification be read likewise. Therefore, the above description should not be construed as limiting, but merely as exemplifications of particular embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the claims appended hereto.

Claims
  • 1. A system for guiding navigation of a tool in a lung of a patient, comprising: an electromagnetic (EM) field generator configured to generate an electromagnetic field;a tool including an EM sensor configured to sense the electromagnetic field, wherein the tool is configured to be navigated inside a patient's lung;a processor in operative communication with the EM sensor;a display in operative communication with the processor; anda memory having stored thereon instructions, wherein when the instructions are executed by the processor, the processor: determines a location of the tool based on the electromagnetic field sensed by the EM sensor;receives a navigation plan including at least one planned pathway to at least one target in the patient's lung;presents, on the display, a 3D lung map view of a three-dimensional (3D) model of the patient's lung showing the at least one planned pathway, the at least one target, and the determined location of the tool;adjusts the 3D lung map view of the 3D model on the display as the tool is navigated through an airway of the lungs of the patient; andremoves at least a part of an object forming part of the 3D model from the 3D lung map view.
  • 2. The system of claim 1, wherein adjusting the 3D lung map view includes updating the location of the tool.
  • 3. The system of claim 1, further comprising a workstation, wherein the workstation includes the processor, the display, and the memory.
  • 4. The system of claim 1, further comprising reference sensors, wherein, when the instructions are executed by the processor, the processor further calculates a patient coordinate frame of reference.
  • 5. The system of claim 1, further comprising an input device, wherein, when the instructions are executed by the processor, the processor further receives input from a user through the input device, andwherein removing the at least a part of the object is performed in response to the input from the user.
  • 6. The system of claim 1, wherein, when the instructions are executed by the processor, the processor further: receives computer tomography (CT) scan image data of the lungs of a patient; andgenerates the 3D model of the lungs of the patient using the CT scan image data.
  • 7. The system of claim 6, wherein the object is at least one of another target or a branch of an airway tree.
  • 8. The system of claim 1, wherein, when the instructions are executed by the processor, the processor further determines that the object is outside a region of interest, the object obstructs the tool, the object obstructs the target, or the object is not relevant to a position or state of the tool, and wherein the removing the at least a part of the object is performed in response to determining the object is outside a region of interest, the object obstructs the tool, the object obstructs the target, or that the object is not relevant to a position or state of the tool.
  • 9. A system comprising: an electromagnetic (EM) field generator configured to generate an electromagnetic field; anda tool including an EM sensor;a display;a processor; anda memory having stored thereon instructions, wherein when the instructions are executed by the processor, the processor: determines a location of the tool based on the electromagnetic field sensed by the EM sensor;receives a navigation plan including at least one planned pathway to at least one target in a patient's lung;presents, on the display, a 3D lung map view of a 3D model of the patient's lung showing the at least one planned pathway, the at least one target, the tool, and a line of sight indicator from the tool;updates the location of the tool on the display based on the determined location of the tool; andadjusts the 3D lung map view of the 3D model on the display based on the updated location of the tool.
  • 10. The system of claim 9, wherein, when the instructions are executed by the processor, the processor further aligns the 3D lung map view with the location of the tool to show the location of the tool and a view looking ahead out of a tip of the tool.
  • 11. The system of claim 9, wherein, when the instructions are executed by the processor, the processor further changes the 3D lung map view according to a direction in which the tool is being navigated or to be orthogonal to a vector from the tool to the at least one planned pathway or the at least one target.
  • 12. The system of claim 9, wherein the line of sight indicator intersects with the at least one target.
  • 13. A system for guiding navigation of a tool in lungs of a patient, the system comprising: an electromagnetic (EM) field generator in operative communication with an EM sensor associated with a tool;a display;a processor; anda memory having stored thereon a three-dimensional (3D) model of the lungs of the patient based on computer tomography (CT) scan image data, and instructions, wherein when the instructions are executed by the processor, the processor: receives EM location information from the EM sensor;determines a location of the tool based on the EM location information;presents, on the display, a 3D lung map view of the 3D model showing at least one planned pathway, at least one target, and the determined location of the tool; andadjusts the 3D lung map view of the 3D model on the display as the tool is navigated through an airway of the lungs of the patient.
  • 14. The system according to claim 13, further comprising an input device, wherein, when the instructions are executed by the processor, the processor further: receives input from a user through the input device; andzooms in on the 3D model in response to receiving the input from the user.
  • 15. The system according to claim 13, wherein zooming in on the 3D model includes zooming in on a section of the patient's airways.
  • 16. The system according to claim 13, wherein, when the instructions are executed by the processor, the processor further changes the 3D lung map view to be perpendicular to the location of the tool in relation to the 3D model to show an area around the tool.
  • 17. The system according to claim 13, wherein, when the instructions are executed by the processor, the processor further rotates the 3D lung map view to improve a 3D perception of the location of the tool in relation to the 3D model.
  • 18. The system according to claim 17, wherein, when the instructions are executed by the processor, the processor further changes the 3D lung map view to be behind the location of the tool in relation to the 3D model to show an area ahead of the tool.
  • 19. The system according to claim 13, wherein, when the instructions are executed by the processor, the processor further changes the 3D lung map view to be perpendicular to a vector from the tool to the at least one target to show alignment of the tool to the at least one target.
  • 20. The system according to claim 19, wherein the vector is a line indicating a line of sight from a tip of the tool intersecting with the at least one target.
CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 17/068,820, filed Oct. 12, 2020, which is a continuation of U.S. patent application Ser. No. 16/828,947, filed Mar. 24, 2020, now U.S. Pat. No. 10,799,297, which is a continuation of U.S. patent application Ser. No. 16/418,495, filed May 21, 2019, now U.S. Pat. No. 10,646,277, which is a continuation of U.S. patent application Ser. No. 16/148,174, filed Oct. 1, 2018, now U.S. Pat. No. 10,660,708, which is a continuation of U.S. patent application Ser. No. 15/828,551, filed Dec. 1, 2017, now U.S. Pat. No. 10,105,185, which is a continuation of U.S. patent application Ser. No. 15/447,472, filed Mar. 2, 2017, now U.S. Pat. No. 9,848,953, which is a continuation of U.S. patent application Ser. No. 14/751,257, filed Jun. 26, 2015, now U.S. Pat. No. 9,603,668, which claims the benefit of the filing date of provisional U.S. Patent Application No. 62/020,262, filed Jul. 2, 2014.

US Referenced Citations (282)
Number Name Date Kind
5592939 Martinelli Jan 1997 A
5611025 Lorensen et al. Mar 1997 A
5676673 Ferre et al. Oct 1997 A
5697377 Wittkampf Dec 1997 A
5699799 Xu et al. Dec 1997 A
5715836 Kliegis et al. Feb 1998 A
5729129 Acker Mar 1998 A
5752513 Acker et al. May 1998 A
5782762 Vining Jul 1998 A
5881124 Giger et al. Mar 1999 A
5891030 Johnson et al. Apr 1999 A
5913820 Bladen et al. Jun 1999 A
5920319 Vining et al. Jul 1999 A
5967980 Ferre et al. Oct 1999 A
5971767 Kaufman et al. Oct 1999 A
5987960 Messner et al. Nov 1999 A
6019725 Vesely et al. Feb 2000 A
6047080 Chen et al. Apr 2000 A
6083162 Vining Jul 2000 A
6138045 Kupinski et al. Oct 2000 A
6151404 Pieper Nov 2000 A
6167296 Shahidi Dec 2000 A
6181348 Geiger Jan 2001 B1
6201387 Govari Mar 2001 B1
6233476 Strommer et al. May 2001 B1
6246784 Summers et al. Jun 2001 B1
6266551 Osadchy et al. Jul 2001 B1
6332089 Acker et al. Dec 2001 B1
6346940 Fukunaga Feb 2002 B1
6366800 Vining et al. Apr 2002 B1
6381485 Hunter et al. Apr 2002 B1
6387092 Burnside et al. May 2002 B1
6466815 Saito et al. Oct 2002 B1
6496188 Deschamps et al. Dec 2002 B1
6501848 Carroll et al. Dec 2002 B1
6501981 Schweikard et al. Dec 2002 B1
6505065 Yanof et al. Jan 2003 B1
6522907 Bladen et al. Feb 2003 B1
6526162 Asano et al. Feb 2003 B2
6535756 Simon et al. Mar 2003 B1
6578579 Burnside et al. Jun 2003 B2
6584174 Schubert et al. Jun 2003 B2
6603868 Ludwig et al. Aug 2003 B1
6611793 Burnside et al. Aug 2003 B1
6650927 Keidar Nov 2003 B1
6651669 Burnside Nov 2003 B1
6694163 Vining Feb 2004 B1
6757557 Bladen et al. Jun 2004 B1
6783523 Qin et al. Aug 2004 B2
6792390 Burnside et al. Sep 2004 B1
6829379 Knoplioch et al. Dec 2004 B1
6850794 Shahidi Feb 2005 B2
6892090 Verard et al. May 2005 B2
6898263 Avinash et al. May 2005 B2
6909913 Vining Jun 2005 B2
6920347 Simon et al. Jul 2005 B2
6925200 Wood et al. Aug 2005 B2
7006677 Manjeshwar et al. Feb 2006 B2
7072501 Wood et al. Jul 2006 B2
7085400 Holsing et al. Aug 2006 B1
7096148 Anderson et al. Aug 2006 B2
7149564 Vining et al. Dec 2006 B2
7167180 Shibolet Jan 2007 B1
7174202 Bladen et al. Feb 2007 B2
7179220 Kukuk Feb 2007 B2
7236558 Saito et al. Jun 2007 B2
7301332 Govari et al. Nov 2007 B2
7315639 Kuhnigk Jan 2008 B2
7324104 Bitter et al. Jan 2008 B1
7336809 Zeng et al. Feb 2008 B2
7397937 Schneider et al. Jul 2008 B2
7428334 Schoisswohl et al. Sep 2008 B2
7452357 Vlegele et al. Nov 2008 B2
7505809 Strommer et al. Mar 2009 B2
7517320 Wibowo et al. Apr 2009 B2
7518619 Stoval et al. Apr 2009 B2
7630752 Viswanathan Dec 2009 B2
7630753 Simon et al. Dec 2009 B2
7659912 Akimoto et al. Feb 2010 B2
7702153 Hong et al. Apr 2010 B2
7751865 Jascob et al. Jul 2010 B2
7756316 Odry et al. Jul 2010 B2
7788060 Schneider Aug 2010 B2
7792565 Vining Sep 2010 B2
7805269 Glossop Sep 2010 B2
7809176 Gündel Oct 2010 B2
7811294 Strommer et al. Oct 2010 B2
7822461 Geiger et al. Oct 2010 B2
7901348 Soper et al. Mar 2011 B2
7907772 Wang et al. Mar 2011 B2
7929014 Akimoto et al. Apr 2011 B2
7951070 Ozaki et al. May 2011 B2
7969142 Krueger et al. Jun 2011 B2
7985187 Wibowo et al. Jul 2011 B2
8009891 Vaan Aug 2011 B2
8049777 Akimoto et al. Nov 2011 B2
8055323 Sawyer Nov 2011 B2
8102416 Ito et al. Jan 2012 B2
8126241 Zarkh et al. Feb 2012 B2
8131344 Strommer et al. Mar 2012 B2
8170328 Masumoto et al. May 2012 B2
8199981 Koptenko et al. Jun 2012 B2
8200314 Bladen et al. Jun 2012 B2
8202213 Ito et al. Jun 2012 B2
8208708 Homan et al. Jun 2012 B2
8219179 Ganatra et al. Jul 2012 B2
8257346 Qin et al. Sep 2012 B2
8267927 Dalal et al. Sep 2012 B2
8290228 Cohen et al. Oct 2012 B2
8298135 Ito et al. Oct 2012 B2
8335359 Fidrich et al. Dec 2012 B2
8391952 Anderson Mar 2013 B2
8417009 Mizuno Apr 2013 B2
8494612 Vetter et al. Jul 2013 B2
8509877 Mori et al. Aug 2013 B2
8672836 Higgins et al. Mar 2014 B2
8682045 Vining et al. Mar 2014 B2
8696549 Holsing et al. Apr 2014 B2
8698806 Kunert et al. Apr 2014 B2
8700132 Ganatra et al. Apr 2014 B2
8706184 Mohr et al. Apr 2014 B2
8706193 Govari et al. Apr 2014 B2
8709034 Keast et al. Apr 2014 B2
8730237 Ruijters et al. May 2014 B2
8768029 Helm et al. Jul 2014 B2
8784400 Roschak Jul 2014 B2
8798227 Tsukagoshi et al. Aug 2014 B2
8798339 Mielekamp et al. Aug 2014 B2
8801601 Prisco et al. Aug 2014 B2
8819591 Wang et al. Aug 2014 B2
8827934 Chopra et al. Sep 2014 B2
8862204 Sobe et al. Oct 2014 B2
9008754 Steinberg et al. Apr 2015 B2
9129048 Stonefield et al. Sep 2015 B2
9603668 Weingarten et al. Mar 2017 B2
9770216 Brown et al. Sep 2017 B2
9848953 Weingarten et al. Dec 2017 B2
9918659 Chopra et al. Mar 2018 B2
9974525 Weingarten et al. May 2018 B2
10373719 Soper et al. Aug 2019 B2
10376178 Chopra Aug 2019 B2
10405753 Sorger Sep 2019 B2
10478162 Barbagli et al. Nov 2019 B2
10480926 Froggatt et al. Nov 2019 B2
10524866 Srinivasan et al. Jan 2020 B2
10555788 Panescu et al. Feb 2020 B2
10610306 Chopra Apr 2020 B2
10638953 Duindam et al. May 2020 B2
10674970 Averbuch et al. Jun 2020 B2
10682070 Duindam Jun 2020 B2
10706543 Donhowe et al. Jul 2020 B2
10709506 Coste-Maniere et al. Jul 2020 B2
10743748 Gilboa Aug 2020 B2
10772485 Schlesinger et al. Sep 2020 B2
10796432 Mintz et al. Oct 2020 B2
10823627 Sanborn et al. Nov 2020 B2
10827913 Ummalaneni et al. Nov 2020 B2
10835153 Rafii-Tari et al. Nov 2020 B2
10885630 Li et al. Jan 2021 B2
20030013972 Makin Jan 2003 A1
20050182295 Soper et al. Aug 2005 A1
20050207630 Chan et al. Sep 2005 A1
20080118135 Averbuch May 2008 A1
20080123921 Gielen et al. May 2008 A1
20080183073 Higgins et al. Jul 2008 A1
20090012390 Pescatore et al. Jan 2009 A1
20090030306 Miyoshi et al. Jan 2009 A1
20090096807 Silverstein et al. Apr 2009 A1
20100290693 Cohen et al. Nov 2010 A1
20100310146 Higgins et al. Dec 2010 A1
20100312094 Guttman et al. Dec 2010 A1
20110085720 Averbuch Apr 2011 A1
20110237897 Gilboa Sep 2011 A1
20110251607 Kruecker et al. Oct 2011 A1
20120203065 Higgins et al. Aug 2012 A1
20120249546 Tschirren et al. Oct 2012 A1
20120280135 Bal Nov 2012 A1
20120287238 Onishi et al. Nov 2012 A1
20130165854 Sandhu et al. Jun 2013 A1
20130231556 Holsing et al. Sep 2013 A1
20130303945 Blumenkranz et al. Nov 2013 A1
20130317352 Case et al. Nov 2013 A1
20140035798 Kawada et al. Feb 2014 A1
20140066766 Stonefield et al. Mar 2014 A1
20140298270 Wiemker Oct 2014 A1
20140343408 Tolkowsky Nov 2014 A1
20150148690 Chopra et al. May 2015 A1
20150265368 Chopra Sep 2015 A1
20150305612 Hunter et al. Oct 2015 A1
20150313503 Seibel Nov 2015 A1
20160000302 Brown et al. Jan 2016 A1
20160000414 Brown et al. Jan 2016 A1
20160005220 Weingarten et al. Jan 2016 A1
20160157939 Larkin et al. Jun 2016 A1
20160183841 Duindam et al. Jun 2016 A1
20160192860 Allenby et al. Jul 2016 A1
20160287344 Donhowe et al. Oct 2016 A1
20170112576 Coste-Maniere et al. Apr 2017 A1
20170172664 Weingarten et al. Jun 2017 A1
20170209071 Zhao et al. Jul 2017 A1
20170265952 Donhowe et al. Sep 2017 A1
20170311844 Zhao et al. Nov 2017 A1
20170319165 Averbuch Nov 2017 A1
20180078318 Barbagli et al. Mar 2018 A1
20180153621 Duindam et al. Jun 2018 A1
20180235709 Donhowe et al. Aug 2018 A1
20180240237 Donhowe et al. Aug 2018 A1
20180256262 Duindam et al. Sep 2018 A1
20180263706 Averbuch Sep 2018 A1
20180279852 Rafii-Tari et al. Oct 2018 A1
20180325419 Zhao et al. Nov 2018 A1
20190000559 Berman et al. Jan 2019 A1
20190000560 Berman et al. Jan 2019 A1
20190008413 Duindam et al. Jan 2019 A1
20190038359 Weingarten et al. Feb 2019 A1
20190038365 Soper et al. Feb 2019 A1
20190065209 Mishra et al. Feb 2019 A1
20190110839 Rafii-Tari et al. Apr 2019 A1
20190175062 Rafii-Tari et al. Jun 2019 A1
20190183318 Froggatt et al. Jun 2019 A1
20190183585 Rafii-Tari et al. Jun 2019 A1
20190183587 Rafii-Tari et al. Jun 2019 A1
20190192234 Gadda et al. Jun 2019 A1
20190209016 Herzlinger et al. Jul 2019 A1
20190209043 Zhao et al. Jul 2019 A1
20190216548 Ummalaneni Jul 2019 A1
20190239723 Duindam et al. Aug 2019 A1
20190239831 Chopra Aug 2019 A1
20190250050 Sanborn et al. Aug 2019 A1
20190254649 Walters et al. Aug 2019 A1
20190269462 Weingarten et al. Sep 2019 A1
20190269470 Barbagli et al. Sep 2019 A1
20190272634 Li et al. Sep 2019 A1
20190298160 Ummalaneni et al. Oct 2019 A1
20190298451 Wong et al. Oct 2019 A1
20190320878 Duindam et al. Oct 2019 A1
20190320937 Duindam et al. Oct 2019 A1
20190336238 Yu et al. Nov 2019 A1
20190343424 Blumenkranz et al. Nov 2019 A1
20190350659 Wang et al. Nov 2019 A1
20190365199 Zhao et al. Dec 2019 A1
20190365479 Rafii-Tari Dec 2019 A1
20190365486 Srinivasan et al. Dec 2019 A1
20190380787 Ye et al. Dec 2019 A1
20200000319 Saadat et al. Jan 2020 A1
20200000526 Zhao Jan 2020 A1
20200008655 Schlesinger et al. Jan 2020 A1
20200030044 Wang et al. Jan 2020 A1
20200030461 Sorger Jan 2020 A1
20200038750 Kojima Feb 2020 A1
20200043207 Lo et al. Feb 2020 A1
20200046431 Soper et al. Feb 2020 A1
20200046436 Tzeisler et al. Feb 2020 A1
20200054399 Duindam et al. Feb 2020 A1
20200060512 Holsing Feb 2020 A1
20200060771 Lo et al. Feb 2020 A1
20200069192 Sanborn et al. Mar 2020 A1
20200077870 Dicarlo et al. Mar 2020 A1
20200078095 Chopra et al. Mar 2020 A1
20200078103 Duindam et al. Mar 2020 A1
20200085514 Blumenkranz Mar 2020 A1
20200109124 Pomper et al. Apr 2020 A1
20200129045 Prisco Apr 2020 A1
20200129239 Bianchi et al. Apr 2020 A1
20200138515 Wong May 2020 A1
20200146588 Hunter May 2020 A1
20200155116 Donhowe et al. May 2020 A1
20200170623 Averbuch Jun 2020 A1
20200170720 Ummalaneni Jun 2020 A1
20200179058 Barbagli et al. Jun 2020 A1
20200188038 Donhowe et al. Jun 2020 A1
20200205903 Srinivasan et al. Jul 2020 A1
20200205904 Chopra Jul 2020 A1
20200214664 Zhao et al. Jul 2020 A1
20200229679 Zhao et al. Jul 2020 A1
20200242767 Zhao et al. Jul 2020 A1
20200275860 Duindam Sep 2020 A1
20200297442 Adebar et al. Sep 2020 A1
20200315554 Averbuch et al. Oct 2020 A1
20200330795 Sawant et al. Oct 2020 A1
20200352427 Deyanov Nov 2020 A1
20200364865 Donhowe et al. Nov 2020 A1
Foreign Referenced Citations (24)
Number Date Country
0013237 Jul 2003 BR
0116004 Jun 2004 BR
101877996 Nov 2010 CN
103068294 Apr 2013 CN
486540 Sep 2016 CZ
2709512 Aug 2017 CZ
2884879 Jan 2020 CZ
3413830 Sep 2019 EP
3478161 Feb 2020 EP
3641686 Apr 2020 EP
3644885 May 2020 EP
3644886 May 2020 EP
2002306403 Oct 2002 JP
2009018184 Jan 2009 JP
2011193885 Oct 2011 JP
PA03005028 Jan 2004 MX
225663 Jan 2005 MX
226292 Feb 2005 MX
246862 Jun 2007 MX
265247 Mar 2009 MX
284569 Mar 2011 MX
2009138871 Nov 2009 WO
2011102012 Aug 2011 WO
2013192598 Dec 2013 WO
Non-Patent Literature Citations (15)
Entry
Notice of Allowance issued in U.S. Appl. No. 17/199,433 dated Jul. 27, 2021.
Canadian Office Action issued in Canadian Application No. 2953390 dated May 20, 2021, 6 pages.
Chinese Office Action dated Dec. 3, 2018 issued in corresponding CN Appln. No. 201580035779.3.
Chinese Office Action for application No. 201580035779.3 dated Nov. 28, 2017 with English Translation (8 pages).
Chinese Rejection Decision issued in Chinese Patent Application No. 201580035779.3 dated Aug. 19, 2019, 4 pages. No English translation available.
Chinese Office Action dated December 3, 2018 issued in corresponding CN Appln. No. 201580035779.3.
Examination Report No. 1 for standard patent application issued in Australian Patent Application No. 2019204469 dated Oct. 10, 2019, 3 pages.
Examination Report No. 1 for standard patent application issued in Australian Patent Application No. 2020205248 dated Nov. 6, 2020, 4 pages.
Extended European Search Report for application No. 15814621.7 dated Mar. 12, 2018 (9 pages).
Japanese Office Action for application No. 2016-575425 dated Mar. 19, 2019 with English translation.
Notification of the Fourth Office Action issued in Chinese Patent Application No. 201580035779.3 dated Jun. 21, 2019, 17 pages.
Srikantha et al. “Ghost Detection and Removal for High Dynamic Range Images: Recent Advances”. Published in “Signal Processing: Image Communication (2012) 10.1016/j.image.2012.02.001”. DOI: 10.1016/j.image.2012.02.001 (Year: 2012).
The Fifth Office Action issued in Chinese Patent Application No. 201580035779.3 dated Apr. 1, 2021 with English Translation.
U.S. Office Action issued in U.S. Appl. No. 16/148,174 dated Aug. 8, 2019, 43 pages.
US Office Action issued in U.S. Appl. No. 17/068,820 dated Sep. 24, 2021.
Related Publications (1)
Number Date Country
20210196392 A1 Jul 2021 US
Provisional Applications (1)
Number Date Country
62020262 Jul 2014 US
Continuations (7)
Number Date Country
Parent 17068820 Oct 2020 US
Child 17199429 US
Parent 16828947 Mar 2020 US
Child 17068820 US
Parent 16418495 May 2019 US
Child 16828947 US
Parent 16148174 Oct 2018 US
Child 16418495 US
Parent 15828551 Dec 2017 US
Child 16148174 US
Parent 15447472 Mar 2017 US
Child 15828551 US
Parent 14750257 Jun 2015 US
Child 15447472 US