CLICK-TO-CORRECT FOR AUTOMATIC VESSEL LUMEN BORDER TRACING

The present disclosure provides a system and technique to correct a lumen border based on identified region(s) on a IVUS image. The identified region(s) are received via an input device and indicate locations inside or outside the actual lumen border and further can indicate areas where an automatically identified lumen border is inaccurate. An updated lumen border is determined based on the region(s).

TECHNICAL FIELD

The present disclosure generally relates to intravascular ultrasound (IVUS) imaging systems. Particularly, but not exclusively, the present disclosure relates to identifying a vascular border.

BACKGROUND

Ultrasound devices insertable into patients have proven diagnostic capabilities for a variety of diseases and disorders. For example, intravascular ultrasound (IVUS) imaging systems have been used as an imaging modality for diagnosing blocked blood vessels and providing information to aid medical practitioners in selecting and placing stents and other devices to restore or increase blood flow.

IVUS imaging systems includes a control module (with a pulse generator, an image acquisition and processing components, and a monitor), a catheter, and a transducer disposed in the catheter. The transducer-containing catheter is positioned in a lumen or cavity within, or in proximity to, a region to be imaged, such as a blood vessel wall or patient tissue in proximity to a blood vessel wall. The pulse generator in the control module generates electrical pulses that are delivered to the transducer and transformed to acoustic pulses that are transmitted through patient tissue. The patient tissue (or other structure) reflects the acoustic pulses and reflected pulses are absorbed by the transducer and transformed to electric pulses. The transformed electric pulses are delivered to the image acquisition and processing components and converted into images displayable on the monitor.

However, it can be difficult for physicians to identify anatomical segments, lumen (vessel) borders, disease presence, plaque burden, ultrasound artifacts, etc. from the raw IVUS images. Further, it is difficult to correlate raw IVUS images to angiogram and venogram images. Thus, there here is a need for user interfaces and software tools to communicate information from the raw IVUS images to a user.

BRIEF SUMMARY

In general, the present disclosure provides an improved lumen detection system in which areas of poor lumen detection are identified and the lumen is corrected in these areas automatically.

The disclosure can be implemented as a method, comprising: receiving, via an input device, an indication of a region of an intravascular ultrasound (IVUS) image of a vessel of a patient corresponding to a location either inside or outside a lumen border of the vessel; generating, at a processing component of an IVUS system, an updated lumen border based on an initially detected lumen border and the region; generating a graphical user interface (GUI) comprising visualizations of the IVUS image and the updated lumen border; and causing the GUI to be displayed on a display.

In further embodiments, the method can comprise receiving a series of IVUS images of the vessel of a patient from an IVUS catheter, the series of IVUS images comprising a plurality of frames, the IVUS image a one of the plurality of frames.

In further embodiments, the method can comprise automatically detecting the initially detected lumen border based on the IVUS image.

In further embodiments, the method can comprise generating an initial GUI comprising visualizations of the IVUS image and the initially detected lumen border; and causing the initial GUI to be displayed on a display.

In further embodiments of the method, receiving the indication of the region of the IVUS image of the vessel comprises receiving via a mouse or a touch screen an indication of a location on the GUI corresponding to the region.

In further embodiments of the method, the region comprises an indication of whether the region is within or without the lumen border.

In further embodiments of the method, generating the updated lumen border comprises deriving a graph cut segmentation of the IVUS image; and identifying the updated lumen border based on the graph cut segmentation and an iterative energy minimization where the region is the minimization parameter.

In further embodiments of the method, generating the updated lumen border comprises deriving a ranked list of a plurality of segmentation hypotheses; and identifying the updated lumen border based on the ranked list of the plurality of segmentation hypotheses and the region.

In further embodiments of the method, generating the updated lumen border comprises concatenating a Euclidian distance map of the IVUS image and the region to generate a pair of images; generating the updated lumen border based on an inference from a machine learning model using the pair of images as input to the machine learning model.

In further embodiments of the method, the machine learning model is a neural network.

In further embodiments of the method, the Euclidian distance map is based on a red, green, blue (RBG) channel segregation of the IVUS images and the region resulting in a plurality of pairs of images.

In further embodiments of the method, the region is a first region, and the method comprises receiving, via the input device, an indication of a second region of the IVUS image of the vessel of the patient corresponding to a location either within or without the lumen border of the vessel, wherein the Euclidian distance map is based on the first region and the second region resulting in a plurality of pairs of images.

In further embodiments of the method, the first region is inside the lumen border and the second region is outside the lumen border.

With some embodiments, the disclosure can be implemented as an apparatus, comprising a processor coupled to a memory, the memory comprising instructions executable by the processor, the processor configured to couple to an intravascular ultrasound (IVUS) imaging system and configured to execute the instructions, which instructions when executed cause the processor to implement the method of any embodiments described herein.

With some embodiments, the disclosure can be implemented as at least one machine readable storage device, comprising a plurality of instructions that in response to being executed by a processor of an intravascular ultrasound (IVUS) imaging system cause the processor to implement the method of any embodiments described herein.

In some embodiments, the disclosure can be implemented as an apparatus for an intravascular ultrasound (IVUS) imaging system, the apparatus can comprise a display; a computer input device; a processor coupled to the computer input device and the display; and a memory coupled to the processor, the memory comprising instructions executable by the processor, which when executed cause the processor to: receive, via the computer input device, an indication of a region of an IVUS image of a vessel of a patient corresponding to a location either inside or outside a lumen border of the vessel; generate an updated lumen border based on an initially detected lumen border and the region; generate a graphical user interface (GUI) comprising visualizations of the IVUS image and the updated lumen border; and cause the GUI to be displayed on the display.

In further embodiments of the apparatus, the region comprises an indication of whether the region is within or without the lumen border.

In further embodiments of the apparatus, the instructions, which when executed by the processor further cause the processor to derive a graph cut segmentation of the IVUS image; and identify the updated lumen border based on the graph cut segmentation and an iterative energy minimization where the region is the minimization parameter.

In further embodiments of the apparatus, the instructions, which when executed by the processor further cause the processor to derive a ranked list of a plurality of segmentation hypotheses; and identify the updated lumen border based on the ranked list of the plurality of segmentation hypotheses and the region.

In further embodiments of the apparatus, the instructions, which when executed by the processor further cause the processor to concatenate a Euclidian distance map of the IVUS image and the region to generate a pair of images; and generate the updated lumen border based on an inference from a machine learning model using the pair of images as input to the machine learning model.

With some embodiments, the disclosure can be implemented as at least one machine readable storage device, comprising a plurality of instructions that in response to being executed by a processor of an intravascular ultrasound (IVUS) imaging system cause the processor to receive, via the computer input device coupled to the processor, an indication of a region of an IVUS image of a vessel of a patient corresponding to a location either inside or outside a lumen border of the vessel; generate an updated lumen border based on an initially detected lumen border and the region; generate a graphical user interface (GUI) comprising visualizations of the IVUS image and the updated lumen border; and cause the GUI to be displayed on a display coupled to the processor.

In further embodiments of the storage device, the Euclidian distance map is based on a red, green, blue (RBG) channel segregation of the IVUS images and the region resulting in a plurality of pairs of images, and wherein the region is a first region, the instructions in response to be executed by the processor, further cause the processor to receive, via the input device, an indication of a second region of the IVUS image of the vessel of the patient corresponding to a location either within or without the lumen border of the vessel, wherein the Euclidian distance map is based on the first region and the second region resulting in a plurality of pairs of images.

In further embodiments of the storage device, the first region is inside the lumen border and the second region is outside the lumen border.

DETAILED DESCRIPTION

The foregoing has broadly outlined the features and technical advantages of the present disclosure such that the following detailed description of the disclosure may be better understood. It is to be appreciated by those skilled in the art that the embodiments disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. The novel features of the disclosure, both as to its organization and operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description and is not intended as a definition of the limits of the present disclosure.

As noted, the present disclosure relates to IVUS systems and automatic assessment of the IVUS images. In particular, the disclosure provides a graphical user interface (GUI) arranged to convey information related to the IVUS images and lesion assessment and provide for the user to manipulate the information. As such, an example IVUS imaging system, patient vessel, and series of IVUS images are described.

Suitable IVUS imaging systems include, but are not limited to, one or more transducers disposed on a distal end of a catheter configured and arranged for percutaneous insertion into a patient.FIG.1illustrates one embodiment of an IVUS imaging system100. The IVUS imaging system100includes a catheter102that is couplable to a control system104. The control system104may include, for example, a processor106, a pulse generator108, and a drive unit110. The pulse generator108forms electric pulses that may be input to one or more transducers (not shown) disposed in the catheter102.

With some embodiments, mechanical energy from the drive unit110can be used to drive an imaging core (also not shown) disposed in the catheter102. In at least some embodiments, electric signals transmitted from the one or more transducers may be input to the processor106for processing. In at least some embodiments, the processed electric signals from the one or more transducers can be used to form a series of images, described in more detail below. For example, a scan converter can be used to map scan line samples (e.g., radial scan line samples, or the like) to a two-dimensional Cartesian grid, which can be used as the basis for a series of IVUS images that can be displayed for a user.

In at least some embodiments, the processor106may also be used to control the functioning of one or more of the other components of the control system104. For example, the processor106may be used to control at least one of the frequency or duration of the electrical pulses transmitted from the pulse generator108, the rotation rate of the imaging core by the drive unit110. Additionally, where IVUS imaging system100is configured for automatic pullback, the drive unit110can control the velocity and/or length of the pullback.

FIG.2illustrates an image200of a vessel202of a patient. As described, IVUS imaging systems (e.g., IVUS imaging system100, or the like) are used to capture a series of images or a “recording” or a vessel, such as, vessel202. For example, an IVUS catheter (e.g., catheter102) is inserted into vessel202and a recording, or a series of IVUS images, is captured as the catheter102is pulled back from a distal end204to a proximal end206. The catheter102can be pulled back manually or automatically (e.g., under control of drive unit110, or the like).

FIG.3AandFIG.3Billustrates two-dimensional (2D) representations of IVUS images of vessel202. For example,FIG.3Aillustrates IVUS images300adepicting a longitudinal view of the IVUS recording of vessel202between proximal end206and distal end204.

FIG.3Billustrates an image frame300bdepicting an on-axis (or short axis, or cross-section) view of vessel202at point302. Said differently, image frame300bis a single frame or single image from a series of IVUS images that can be captured between distal end204and proximal end206as described herein. As introduced above, the present disclosure provides systems and techniques to process raw IVUS images to detect lumen (or vessel) borders. In particular, the present disclosure provides a technique to correct or improve automatic detection of lumen borders.

For example, IVUS images300adepicts an entire series of IVUS images taken of vessel202between distal end204and proximal end206. At specific locations along the vessel202, a physician may desire to look at the on-axis view of the vessel202to assess the health of the vessel. With some IVUS imaging systems (e.g., IVUS imaging system100, or the like) a vessel border can be automatically drawn or indicated on an image of the on-axis view of the vessel (described in greater detail below). However, often the vessel border is incorrect or inaccurate. Conventional IVUS imaging systems require the physician to manually correct the vessel border, which is a time consuming and tedious process. The present disclosure provides to an automated technique to correct inaccurate vessel borders.

FIG.4illustrates an IVUS image visualization system400, according to some embodiments of the present disclosure. In general, IVUS image visualization system400is a system for processing, annotating, and presenting IVUS images. IVUS image visualization system400can be implemented in a commercial IVUS guidance or navigation system, such as, for example, the AVVIGO ® Guidance System available from Boston Scientific ®. The present disclosure provides advantages over prior or conventional IVUS navigation systems in that the improved GUI will reduce the time needed for patients to be in treatment. For example, the present disclosure can be implemented in an IVUS navigation system to efficiently communicate IVUS information to a user and allow the user to manipulate the information.

With some embodiments, IVUS image visualization system400could be implemented as part of control system104of IVUS imaging system100. Alternatively, control system104could be implemented as part of IVUS image visualization system400. As depicted, IVUS image visualization system400includes a computing device402. Optionally, IVUS image visualization system400includes IVUS imaging system100and display404.

Computing device402can be any of a variety of computing devices. In some embodiments, computing device402can be incorporated into and/or implemented by a console of display404. With some embodiments, computing device402can be a workstation or server communicatively coupled to IVUS imaging system100and/or display404. With still other embodiments, computing device402can be provided by a cloud based computing device, such as, by a computing as a service system accessibly over a network (e.g., the Internet, an intranet, a wide area network, or the like). Computing device402can include processor406, memory408, input and/or output (I/O) devices410, network interface412, and IVUS imaging system acquisition circuitry414.

The processor406may include circuity or processor logic, such as, for example, any of a variety of commercial processors. In some examples, processor406may include multiple processors, a multi-threaded processor, a multi-core processor (whether the multiple cores coexist on the same or separate dies), and/or a multi-processor architecture of some other variety by which multiple physically separate processors are in some way linked. Additionally, in some examples, the processor406may include graphics processing portions and may include dedicated memory, multiple-threaded processing and/or some other parallel processing capability. In some examples, the processor406may be an application specific integrated circuit (ASIC) or a field programmable integrated circuit (FPGA).

The memory408may include logic, a portion of which includes arrays of integrated circuits, forming non-volatile memory to persistently store data or a combination of non-volatile memory and volatile memory. It is to be appreciated, that the memory408may be based on any of a variety of technologies. In particular, the arrays of integrated circuits included in memory120may be arranged to form one or more types of memory, such as, for example, dynamic random access memory (DRAM), NAND memory, NOR memory, or the like.

I/O devices410can be any of a variety of devices to receive input and/or provide output. For example, I/O devices410can include, a keyboard, a mouse, a joystick, a foot pedal, a display, a touch enabled display, a haptic feedback device, an LED, or the like.

Network interface412can include logic and/or features to support a communication interface. For example, network interface412may include one or more interfaces that operate according to various communication protocols or standards to communicate over direct or network communication links. Direct communications may occur via use of communication protocols or standards described in one or more industry standards (including progenies and variants). For example, network interface412may facilitate communication over a bus, such as, for example, peripheral component interconnect express (PCIe), non-volatile memory express (NVMe), universal serial bus (USB), system management bus (SMBus), SAS (e.g., serial attached small computer system interface (SCSI)) interfaces, serial AT attachment (SATA) interfaces, or the like. Additionally, network interface412can include logic and/or features to enable communication over a variety of wired or wireless network standards (e.g., 802.11 communication standards). For example, network interface412may be arranged to support wired communication protocols or standards, such as, Ethernet, or the like. As another example, network interface412may be arranged to support wireless communication protocols or standards, such as, for example, Wi-Fi, Bluetooth, ZigBee, LTE, 5G, or the like.

The IVUS imaging system acquisition circuitry414may include circuity including custom manufactured or specially programmed circuitry configured to receive or receive and send signals between IVUS imaging system100including indications of an IVUS run, a series of IVUS images, or a frame or frames of IVUS images.

Memory408can include instructions416. During operation processor406can execute instructions416to cause computing device402to receive (e.g., from IVUS imaging system100, or the like) a recording of an “IVUS run” and store the recording as IVUS images418in memory408. For example, processor406can execute instructions416to receive information elements from IVUS imaging system100comprising indications of IVUS images captured by catheter102while being pulled back from distal end204to proximal end206, which images can include indications of the anatomy and/or structure of vessel202, including vessel walls and plaque. It is to be appreciated that IVUS images418can be stored in a variety of image formats or even non-image formats or data structures that comprise indications of vessel202. Further, IVUS images418includes several “frames” or individual images that, when represented co-linearly can be used to form an image of the vessel202, such as, for example, as represented by IVUS images300aand/or300b.

Processor406can execute instructions416to identify an initial lumen border420based on IVUS images418and generate a GUI422comprising a visual indication of the IVUS images418and initial lumen border420. It is to be appreciated that a variety of different techniques exist for detecting lumen (e.g., vessel) borders. The present disclosure does not attempt to completely describe these systems and is instead, intended to provide systems and methods to correct inaccurate borders identified by such automated techniques. However, a general description of an automated lumen border detection process is provided for clarity of presentation. In such an illustrative process, processor406can execute instructions416to modulate IVUS images418(e.g., to identify different tissue types, different regions of tissue, border between tissue and air, etc.) and identify segments of the lumen border based on the modulated image data. Accordingly, initial lumen border420includes several border segments (or line segments). Said differently, initial lumen border420comprises several points on IVUS images418, which when connected identify an outline of the predicted vessel border. Processor406can execute instructions416to generate GUI422comprising indications of IVUS images418and initial lumen border420and cause GUI422to be displayed on display404.

As noted above, automated vessel border prediction methods are not always accurate. As such, a physician viewing GUI422may determine that initial lumen border420is inaccurate. To better assist the physician in treating the patient, a corrected outline or depiction of the vessel border may be desired. As such, processor406can execute instructions416to receive regions of misidentified border424and to generate an updated lumen border426based on IVUS images418, initial lumen border420, regions of misidentified border424, and models428. This is described in greater detail below with reference to example images of a vessel lumen.

FIG.5Aillustrates an image500showing an on-axis (short axis, cross-section, etc.) view of a vessel. Image500can correspond to a frame of IVUS images418. As noted above, processor406can execute instructions416to automatically detect (or identify) a border of the vessel depicted in IVUS images418.

To that end,FIG.5Billustrates image500showing the on-axis view of the vessel as well as an automatically detected lumen border502. As can be seen, the automatically detected lumen border502comprises several points504, which when connected for automatically detected lumen border502. However, as further noted above, in some instances, automatically detected lumen border502is incorrect. As can be seen, automatically detected lumen border502is not correct in the image displayed inFIG.5B. Conventionally, to correct inaccurate automatically detected lumen border502, a physician would need to manually move each points504. The present disclosure, however, provides that a physician can designate points on image500in which the border is inaccurate.

For example,FIG.5Cillustrates image500showing automatically detected lumen border502as well as points506aand506b.In general, points506aand/or506bcan be designated as identifying a region within the lumen border (e.g., point506b) or a region outside the lumen border (e.g., point506a). With some embodiments, processor406can execute instructions416to receive indications (e.g., from a mouse, from a touch screen, from another input device, or the like) of points506aand/or506bas well as an indication of whether the points designate regions inside out outside the lumen border. It is to be appreciated that any number of points506aand506bcan be received (e.g., one point, two points as shown, three points, four points, etc.). Examples are not limited in this context.

Responsive to receiving an indication of points506aand/or506b,an updated lumen border can be determined from the original lumen border, the image500, and a model. For example, processor406can execute instructions416to determine updated lumen border426from IVUS images418(e.g., image500), initial lumen border420(e.g., automatically detected lumen border502), and regions of misidentified border424(e.g., points506aand/or506b). For example,FIG.5Dillustrates image500showing an updated lumen border508determined based on automatically detected lumen border502, point506a,point506b,and image500. As can be seen, the updated lumen border508includes points point506bwithin the border and excludes points point506afrom within the border.

FIG.5Eillustrates image500showing just the updated lumen border508. In some embodiments, responsive to determining updated lumen border508, a GUI can be generated to depict the updated lumen border508. For example, processor406can execute instructions416to generate GUI422comprising an indication of the frame of IVUS images418(e.g., image500) and the updated lumen borders426(e.g., updated lumen border508).

FIG.6illustrates a logic flow600to generate an updated lumen border, according to some embodiments of the present disclosure. The logic flow600can be implemented by IVUS image visualization system400and will be described with reference to IVUS image visualization system400for clarity of presentation. However, it is noted that logic flow600could also be implemented by an IVUS system different than IVUS image visualization system400.

Logic flow600can begin at block602. At block602“receive a series of intravascular ultrasound (IVUS) images of a vessel of a patient, the series of IVUS images comprising a plurality of frames” a series of IVUS images captured via an IVUS catheter percutaneously inserted in a vessel of a patent can be received. For example, information elements comprising indications of IVUS images418can be received from IVUS imaging system100where catheter102is (or was) percutaneously inserted into vessel202. The IVUS images418can comprise frames of images representative of images captured while the catheter102is pulled back from distal end204to proximal end206. Processor406can execute instructions416to receive information elements comprising indications of IVUS images418from IVUS imaging system100, or directly from catheter102as may be the case.

Continuing to block604“automatically detect, at a processing component of an IVUS system, an initial lumen border based on a one of the IVUS images” an initial lumen border is automatically detected by processing circuitry of the IVUS system. For example, processor406can execute instructions416to automatically generate initial lumen border420from a one of the IVUS images received at block602.

Continuing to block606“generate an initial GUI comprising visualizations of the one of the IVUS images and the initial lumen border and cause the initial GUI to be displayed on a display” an initial GUI comprising visualizations of the one (e.g., frame) of the IVUS image and the initially detected lumen border is generated. For example, processor406can execute instructions416to generate GUI422comprising an indication of IVUS images418(e.g., an on-axis view of the vessel) and initial lumen border420.

Continuing to block608“receive, via an input device, an indication of a region of the one of the IVUS images corresponding to a location either inside or outside a lumen border of the vessel” an indication of a region of the IVUS image corresponding to a location either inside or outside the actual lumen border is received via an input device of the IVU system. For example, processor406can execute instructions416to receive (e.g., via input and/or output (I/O) devices410such as a mouse, track pad, pen style input device, touch screen, or the like) a region. (or regions) of the IVUS image and an indication of the whether the region is inside or outside the actual lumen border of the vessel.

Continuing to block610“generate, at the processing component of the IVUS system, an updated lumen border based on the initially detected lumen border and the region” an updated lumen border is generated from the initially detected lumen border and the region (or regions). For example, processor406can execute instructions416to generate updated lumen border426from regions of misidentified border424and initial lumen border420. With some examples, processor406can execute instructions416to derive a graph cut segmentation of the IVUS image and determine the update lumen border based on applying an iterative energy minimization algorithm (e.g., models428) to the graph cut segmentation using the region (or regions) as the minimization parameter.

In some embodiments, processor406can execute instructions416to derive a ranked list of a plurality of segmentation hypotheses of the one of the IVUS images418and use the regions of misidentified border424as a supervisory signal to identify new segments for the updated lumen border426based on a search algorithm (e.g., models428).

With some embodiments, processor406can execute instructions416to concatenate a Euclidian distance map (e.g., RGB channel based map, or the like) of the one of the IVUS images418to generate a pair of images and can use the pairs of images as input to a machine leaning model (e.g., models428) to generate the updated lumen border426from the model inference. In such embodiments, the model can be a neural network (NN), a fully convolutional network (FCN), a convoluted neural network (CNN), or the like.

FIG.7illustrates a logic flow700to generate an updated border based on Euclidean distance and a machine learning model. With some embodiments, logic flow700can be implemented by an IVUS system (e.g., IVUS image visualization system400) as part of logic flow600. Logic flow700can begin at block702where regions of misidentified border424can be identified. As described above, the regions can indicate areas either inside or outside the actual lumen border. Continuing to block704the image and regions can be concatenated using Euclidean distance and channel maps (e.g., RBG channels, or the like) resulting in pairs of images. Logic flow700can continue to block706where the pairs of images are used as input to a machine learning models428(e.g., NN, FCN, CNN, or the like) and the inference from the models428comprises an indication of the updated lumen border426.

FIG.8illustrates computer-readable storage medium800. Computer-readable storage medium800may comprise any non-transitory computer-readable storage medium or machine-readable storage medium, such as an optical, magnetic or semiconductor storage medium. In various embodiments, computer-readable storage medium800may comprise an article of manufacture. In some embodiments, computer-readable storage medium800may store computer executable instructions802with which circuitry (e.g., processor106, processor406, IVUS imaging system acquisition circuitry414, and the like) can execute. For example, computer executable instructions802can include instructions to implement operations described with respect to instructions416, logic flow600, logic flow700, and/or GUI422. Examples of computer-readable storage medium800or machine-readable storage medium may include any tangible media capable of storing electronic data, including volatile memory or non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writeable or re-writeable memory, and so forth. Examples of computer executable instructions802may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, object-oriented code, visual code, and the like.

FIG.9illustrates a diagrammatic representation of a machine900in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein. More specifically,FIG.9shows a diagrammatic representation of the machine900in the example form of a computer system, within which instructions908(e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine900to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions908may cause the machine900to instructions416ofFIG.4, logic flow600ofFIG.6, logic flow700ofFIG.7, or the like. More generally, the instructions908may cause the machine900to identify an updated lumen border as described herein during a pre-PCI, peri-PCI, or post-PCI using IVUS. It is noted that the present disclosure provides specific and discrete implementations of GUI representations and behavior that is a significant improvement over the prior art. In particular, the present disclosure provides an improvement to computing technology in that lumen borders can be corrected without the time consuming requirement that a physician manually adjust each segment of the border.

The instructions908transform the general, non-programmed machine900into a particular machine900programmed to carry out the described and illustrated functions in a specific manner. In alternative embodiments, the machine900operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine900may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine900may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions908, sequentially or otherwise, that specify actions to be taken by the machine900. Further, while only a single machine900is illustrated, the term “machine” shall also be taken to include a collection of machines900that individually or jointly execute the instructions908to perform any one or more of the methodologies discussed herein.

The machine900may include processors902, memory904, and I/O components942, which may be configured to communicate with each other such as via a bus944. In an example embodiment, the processors902(e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor906and a processor910that may execute the instructions908. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. AlthoughFIG.9shows multiple processors902, the machine900may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory904may include a main memory912, a static memory914, and a storage unit916, both accessible to the processors902such as via the bus944. The main memory904, the static memory914, and storage unit916store the instructions908embodying any one or more of the methodologies or functions described herein. The instructions908may also reside, completely or partially, within the main memory912, within the static memory914, within machine-readable medium918within the storage unit916, within at least one of the processors902(e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine900.

Communication may be implemented using a wide variety of technologies. The I/O components942may include communication components940operable to couple the machine900to a network920or devices922via a coupling924and a coupling926, respectively. For example, the communication components940may include a network interface component or another suitable device to interface with the network920. In further examples, the communication components940may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices922may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

The various memories (i.e., memory904, main memory912, static memory914, and/or memory of the processors902) and/or storage unit916may store one or more sets of instructions and data structures (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions908), when executed by processors902, cause various operations to implement the disclosed embodiments.

Terms used herein should be accorded their ordinary meaning in the relevant arts, or the meaning indicated by their use in context, but if an express definition is provided, that meaning controls.

By using genuine models of anatomy more accurate surgical plans may be developed than through statistical modeling.

Terms used herein should be accorded their ordinary meaning in the relevant arts, or the meaning indicated by their use in context, but if an express definition is provided, that meaning controls.