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WO2025061532A1 - Détection et alerte pour intrusion de dispositif dans des régions anatomiques vulnérables - Google Patents

Détection et alerte pour intrusion de dispositif dans des régions anatomiques vulnérables Download PDF

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Publication number
WO2025061532A1
WO2025061532A1 PCT/EP2024/075288 EP2024075288W WO2025061532A1 WO 2025061532 A1 WO2025061532 A1 WO 2025061532A1 EP 2024075288 W EP2024075288 W EP 2024075288W WO 2025061532 A1 WO2025061532 A1 WO 2025061532A1
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WIPO (PCT)
Prior art keywords
interventional device
risk
vasculature
processor
aneurysm
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PCT/EP2024/075288
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English (en)
Inventor
V Srikrishnan
Ayushi Sinha
Javad Fotouhi
Leili SALEHI
Amin FEIZPOUR
Brian Curtis LEE
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Koninklijke Philips NV
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Koninklijke Philips NV
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Publication of WO2025061532A1 publication Critical patent/WO2025061532A1/fr
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/12—Arrangements for detecting or locating foreign bodies
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/48—Diagnostic techniques
    • A61B6/486—Diagnostic techniques involving generating temporal series of image data
    • A61B6/487—Diagnostic techniques involving generating temporal series of image data involving fluoroscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/101—Computer-aided simulation of surgical operations
    • A61B2034/105—Modelling of the patient, e.g. for ligaments or bones
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
    • A61B2034/2046—Tracking techniques
    • A61B2034/2055—Optical tracking systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/30—Surgical robots
    • A61B2034/301—Surgical robots for introducing or steering flexible instruments inserted into the body, e.g. catheters or endoscopes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/36—Image-producing devices or illumination devices not otherwise provided for
    • A61B90/37—Surgical systems with images on a monitor during operation
    • A61B2090/376—Surgical systems with images on a monitor during operation using X-rays, e.g. fluoroscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/44—Constructional features of apparatus for radiation diagnosis
    • A61B6/4429—Constructional features of apparatus for radiation diagnosis related to the mounting of source units and detector units
    • A61B6/4435—Constructional features of apparatus for radiation diagnosis related to the mounting of source units and detector units the source unit and the detector unit being coupled by a rigid structure
    • A61B6/4441—Constructional features of apparatus for radiation diagnosis related to the mounting of source units and detector units the source unit and the detector unit being coupled by a rigid structure the rigid structure being a C-arm or U-arm

Definitions

  • the following relates generally to vulnerable anatomical region mapping. More particularly, embodiments herein relate to vulnerable anatomical region mapping for interventional device treatment dynamics.
  • some embodiments herein predict a risk score of the interventional device puncturing an aneurysm by using imaging data in the live scan. More specifically, some embodiments herein describe a method to minimise the risk of aneurysm rupture during the interventional procedure. Since the risk of aneurysm rupture and complexity of procedure are closely intertwined, the method described in some embodiments also simplifies the workflow for the interventionalist. Therefore, this as a workflow improvement for the operator specifically directed toward improving patient safety.
  • FIG. 1 is an illustration of a block diagram of an example interventional device intrusion support system according to an embodiment
  • FIG. 2 is an illustration of a block diagram of another example interventional device intrusion support system according to an embodiment
  • FIG. 4 is an illustration of a flowchart of a method for managing interventional device intrusion support according to an embodiment
  • FIG. 5 is an illustration of a flowchart of a method for managing interventional device intrusion support according to an embodiment
  • FIG. 6 is an illustration of a sequence diagram of a further method for managing interventional device intrusion support according to an embodiment
  • FIG. 7 is an illustration of a block diagram of a computer program product according to an embodiment
  • FIG. 8 is a further illustration of a system according to an embodiment
  • FIG. 9 is an illustration of a hardware apparatus including a semiconductor package according to an embodiment
  • FIG. 10 is a schematic diagram illustrating an example C-arm imaging system for managing interventional device intrusion support, in accordance with some aspects of the present disclosure.
  • FIG. 11 is an illustration of a block diagram of another example interventional device intrusion support system according to an embodiment.
  • FIG. 1 is an illustration of a block diagram of an example interventional device intrusion support system 100 according to an embodiment.
  • the interventional device intrusion support system 100 may include a main platform 102 (e.g., also referred to as a care platform herein).
  • the main platform 102 may be embodied as a server computer or a plurality of server computers (e.g., interconnected to form a server cluster, cloud computing resource, the like, and/or combinations thereof).
  • the main platform 102 is a care platform designed for one or more aspects of patient care.
  • the main platform 102 includes one or more of the following care platforms Performance Management Data Platform, Medical Asset Track & Trace System, Virtualized Imaging Solution, Centralized Care Management System, Interoperability Solution, Electronic Medical Record (EMR) platform, radiology information system (RIS), picture archiving and communication system (PACS), etc.
  • EMR Electronic Medical Record
  • RIS radiology information system
  • PES picture archiving and communication system
  • there may be many care platforms connected to various clinical and operational data sources e g., Health Level Seven (HL7), Fast Healthcare Interoperability Resources (FHIR), machine logs, Real-Time Location System (RTLS), sensors, etc.
  • HL7 Health Level Seven
  • FHIR Fast Healthcare Interoperability Resources
  • RTLS Real-Time Location System
  • sensors etc.
  • a first application 104 though an Nth application 106 which may include an interventional device intrusion support application 108, may be associated with the main platform 102.
  • the operations of the interventional device intrusion support application 108 provide for receiving two-dimensional (2D) fluoroscopy sequences (or the like) from an imaging system (e.g., an interventional X-ray imaging system or the like) during treatment (e.g., endovascular coiling, flow diverter deployment, stent deployment, balloon angioplasty, atherectomy procedures, or the like).
  • an imaging system e.g., an interventional X-ray imaging system or the like
  • treatment e.g., endovascular coiling, flow diverter deployment, stent deployment, balloon angioplasty, atherectomy procedures, or the like.
  • the interventional device intrusion support system 100 may include a patient monitor 110, a sensor 112 (e.g., one or more sensors 112 that may be associated with a care facility, sensors 112 that may be associated with a patient 114, the like, and/or combinations thereof), an interventional device 116, a medical management device 118, medical imager 119, a database 120, a user interface 122 (e.g., one or more user interfaces 122 may be associated with a user 124), an information system 126, the like, and/or combinations thereof.
  • a sensor 112 e.g., one or more sensors 112 that may be associated with a care facility, sensors 112 that may be associated with a patient 114, the like, and/or combinations thereof
  • an interventional device 116 e.g., a medical management device 118, medical imager 119
  • a database 120 e.g., a user interface 122 (e.g., one or more user interfaces 122 may be associated with a
  • the main platform 102, the patient monitor 110, the sensor 112, the interventional device 116, the medical management device 118, medical imager 119, the database 120, the user interface 122, and/or the information system 126 may be in communication with one another via Internet based communicating, cloud based communication, wired communication, wireless communication, the like, and/or combinations thereof
  • the patient monitor 110 may be utilized to access patient data from and/or enter patient data to the database 120.
  • the patient monitor 110 may communicate measured patient data (e.g., via one of more of the sensors 112).
  • the patient monitor 110 may be configured to monitor a patient for vital signs and the like, and the patient monitor 110 may communicate such measured patient data to the database 120 or to a user interface 122.
  • the sensors 112 may determine dynamic patient condition data including patient vitals (e.g., blood pressure, pulse, temperature, respiration, and/or the like) and/or patient test results (e.g., creatine level, urine flow, potassium level, oxygen saturation, blood glucose level, carbon dioxide level, and/or the like).
  • patient vitals e.g., blood pressure, pulse, temperature, respiration, and/or the like
  • patient test results e.g., creatine level, urine flow, potassium level, oxygen saturation, blood glucose level, carbon dioxide level, and/or the like.
  • the sensors 112 may be minimally invasive-style sensors (e.g., by puncturing the skin, sensing through the skin, the like, and/or combinations thereof). Sensors 112 may be wired or wireless.
  • the interventional device 116 may be a guidewire and a catheter used to navigate to the intracranial aneurysm. In another such example, the interventional device 116 may be a flow diverter or a set of coils used to deliver treatment to the intracranial aneurysm. [0034] In some embodiments, the interventional device 116 may enable the administration of one or more patient procedures (e.g., endovascular therapy).
  • patient procedures e.g., endovascular therapy
  • the medical management device 118 may be utilized to access patient data from and/or enter patient data to the database 120.
  • the medical management device 118 may communicate measured patient data.
  • the medical management device 118 may be configured to monitor medication delivery to a patient and may communicate such measured patient data to the database 120.
  • the medical management device 118 may supply and/or monitor the administration of one or more patient medications (e.g., blood pressure medications, diuretic medications, anesthesia medications, anti -coagulant medications, vitamin supplements, and/or the like) and/or other fluids (e.g., contrast, saline, and/or the like).
  • patient medications e.g., blood pressure medications, diuretic medications, anesthesia medications, anti -coagulant medications, vitamin supplements, and/or the like
  • other fluids e.g., contrast, saline, and/or the like.
  • the medical imager 119 may include one or more medical imaging devices, medical imaging systems, the like, and/or combinations thereof.
  • the medical imager 119 may include an interventional imaging device including an X-ray device (such as a fixed C-arm imaging system and/or a mobile C-arm imaging device), an ultrasound device, and/or the like.
  • the medical imager 119 may include a non-interventional imaging device such as a magnetic resonance imaging (MRI) device, a computed tomography (CT) device, and/or the like.
  • MRI magnetic resonance imaging
  • CT computed tomography
  • the medical imager 119 may include a radiology information system (RIS), a picture archiving and communication system (PACS), the like, and/or combinations thereof, where images acquired from interventional or non-interventional imaging systems may be stored.
  • RIS radiology information system
  • PES picture archiving and communication system
  • the medical imager 119 is associated with standardized format medical imaging information to acquire, transmit, store, retrieve, print, process, and display medical imaging information (e.g., Digital Imaging and Communications in Medicine (DICOM) data).
  • DICOM Digital Imaging and Communications in Medicine
  • FIG. 10 is a schematic diagram illustrating an example interventional imaging system 1000, in accordance with some aspects of the present disclosure.
  • the system 1000 includes one or more processors 1010 that are configured to perform one or more aspects of the below-described methods.
  • the system 1000 may consist of an X-ray imaging system 1020, which may be configured to provide a single X-ray image, or a temporal sequence of X-ray images also known as fluoroscopy images.
  • the X-ray imaging system 1020 may also be configured to provide a temporal sequence of contrast-enhanced images, from which digital subtraction angiography (DSA) images are generated by subtracting a mask image (and upon which motion compensation may be performed).
  • DSA digital subtraction angiography
  • X-ray imaging system 1020 is illustrated as a C-arm X-ray system with the top of the X-ray imaging system 1020 being the X-ray detector and the bottom of the X-ray imaging system 1020 being the X-ray source.
  • the system 1000 may also include a display 1030 for displaying the acquired X-ray and/or DSA image.
  • the system 1000 may also include a patient bed 1040.
  • the system 1000 may also include a user interface device such as a keyboard, and/or a pointing device such as a mouse, and/or a joystick to control any of the components of the system 1000. These items may be in communication with each other via wired or wireless communication.
  • FIG. 11 is an illustration of a block diagram of another example interventional device intrusion support system 1100 according to an embodiment.
  • the interventional device intrusion support system 1100 includes an X-ray imaging system 1120 (e.g., such as the example interventional imaging system 1000 of FIG. 10), a workstation 1140, an Artificial Intelligence (Al) controller 1170 and an Al training system 1180.
  • the X-ray imaging system 1120 captures medical images that include imagery of an interventional medical device 1101.
  • the interventional medical device 1101 may enable the administration of one or more patient procedures (e g., endovascular therapy).
  • patient procedures e g., endovascular therapy
  • an interventional medical device 1101 is used to perform endovascular coiling, flow diverter deployment, stent deployment, balloon angioplasty therapy, atherectomy therapy, the like, and/or combinations thereof.
  • Machine learning, artificial intelligence and/or image processing techniques can be used to segment and track the interventional medical device 1101 present in the image during live fluoroscopic imaging.
  • Examples of the X-ray imaging system 1120 include a fixed C-arm X-ray system such as Azurion from Koninklijke Philips N.V. or a mobile X-ray system such as Veradius from Koninklijke Philips N.V.
  • the X-ray imaging system 1120 is a medical apparatus that generates a plurality of images during an interventional medical procedure and may include an X-ray tube adapted to generate X-rays and an X-ray detector configured to acquire X-ray images.
  • the workstation 1140 includes a controller 1150, an interface 1153, a monitor 1155 and a touch panel 1156.
  • the controller 1150 includes a memory 1151 that stores instructions and a processor 1152 that executes the instructions.
  • the Al controller 1170 includes a memory 1171 that stores instructions and a processor 1172 that executes the instructions.
  • the Al controller 1170 may dynamically implement trained machine learning models based on images received by the workstation 1140 from the X-ray imaging system 1120.
  • the Al controller 1170 is integrated with the workstation 1140.
  • functionality of the Al controller 1170 as described herein may be performed by the controller 1150.
  • the Al controller 1170 may include a network computer configured to receive a stream of a plurality of images from a system computer implemented by the workstation 1140 and apply the trained machine learning model to the plurality of images. The significance of the images may be determined according to a standardized metric that may be applied across different clinical sites and users.
  • the metric used to determine the significance may vary based on the context in which the images are being analyzed.
  • the metric may vary based on subject matter being sought as triggers in the images, and the subject matter may include medical instruments, anatomical structures, motion(s), or presence of people.
  • the significance metric may be determined by experts in the field(s) in which the machine learning model described herein is applied. Accordingly, the significance may be appropriately termed a contextual significance that reflects the context of the subject matter in the images deemed significant.
  • the significance may be appropriately termed a predetermined significance that reflects significance previously set for the images that will be deemed significant.
  • the significance may be appropriately termed a standardized significance that reflects significance set as a standard for a particular type of subject matter. The significance may be appropriately described in other terms consistent with the descriptions herein.
  • the Al training system 1180 includes an Al training controller 1181.
  • the Al training controller 1181 may include a memory (not shown) that stores instructions and a processor (not shown) that executes the instructions.
  • the Al training system 1180 may train machine learning models as described herein and provide the trained machine learning model to the Al controller 1170 and/or the workstation 1140 in FIG. 11.
  • the Al training system 1180 may include a controller configured to train the machine learning model.
  • the machine learning model may be a convolutional neural network (CNN) model, a temporal convolutional network (TCN) model, a recurrent neural network (RNN) model, a transformer model, a Hidden Markov model (HMM), and so forth.
  • CNN convolutional neural network
  • TCN temporal convolutional network
  • RNN recurrent neural network
  • HMM Hidden Markov model
  • the controller 1150 may be a data processing controller that is configured to receive consecutive fluoroscopy images from the X-ray imaging system 1120.
  • the fluoroscopy images may be images that are generated and stored by the X-ray imaging system 1120 during a clinical procedure.
  • the fluoroscopy images may be further enhanced by other information from the X-ray imaging system 1120 such as a C-arm position, radiation dose, identification of procedure phase, identification of the interventional medical device 1101 in the image, image generation settings, type of scanning protocol type, as well as patient characteristics including information from the patient's electronic medical record (EMR).
  • EMR electronic medical record
  • the interface 1153 interfaces the workstation 1140 to the X-ray imaging system 1120.
  • the interface 1153 may include a receiver that receives a stream of a plurality of images from the X- ray imaging system 1120.
  • the interface 1153 may be a port or an adapter that accepts a cable line from the X-ray imaging system 1120.
  • the monitor 1155 displays images generated by the X-ray imaging system 1120.
  • the monitor 1155 may also display instructions or alerts for a clinician using the interventional device intrusion support system 1100.
  • the touch panel 1156 accepts touch instructions from a clinician, such as instructions input via a mouse or keyboard.
  • the touch panel 1156 may also accept touch input such as via a keypad, touchpad, touchscreen, or the like.
  • the workstation 1140 may receive time-series interventional X-ray images such as fluoroscopy X-ray images from the X-ray imaging system 1120.
  • the workstation 1140 may also receive digital subtraction angiography (DSA) images from the X-ray imaging system 1120.
  • DSA digital subtraction angiography
  • the workstation 1140 may also receive radiation exposure information, and other system information such as C-arm position, radiation settings and table position.
  • the workstation 1140 applies trained machine learning models to the X-ray images to detect aneurysms in the anatomy, track an interventional medical device 1101 in the anatomy, determine a risk map to define one or more risk regions around the detected aneurysm, and determine a risk score of the interventional device contacting the detected aneurysm based on the tracking of the interventional device and the risk map.
  • a care provider may access patient data and/or enter patient data through an analog device, a non-networked patient monitor, a nonnetworked interventional device, a non-networked medical management device, the like, and/or combinations thereof.
  • the database 120 may include one or more types of patient data.
  • the database 120 may include patient data including medical images, patient health information (PHI) (including patient age, height, weight, and so on), laboratory result data, microbiology data, medication data, vital sign data, care order data, admission discharge and transfer data, and/or the like.
  • PHI patient health information
  • database refers to a collection of data and information organized in such a way as to allow the data and information to be stored, retrieved, updated, and/or manipulated.
  • database as used herein may also refer to databases that may reside locally or that may be accessed from a remote location (e.g., via remote network servers).
  • the information system 126 may have or have access to one or more types of patient data that are the same or in addition to the patient data of the database 120.
  • the information system 126 may be a Hospital Information System (HIS).
  • HIS Hospital Information System
  • Such a Hospital Information System (HIS) has patient data including Health Level Seven (HL7) data, Fast Healthcare Interoperability Resources (FHIR) data, the like, and/or combinations thereof.
  • HL7 Health Level Seven
  • FHIR Fast Healthcare Interoperability Resources
  • the information system 126 has or has access to one or more of the following information sources: an Electronic Medical Record (EMR), a radiology information system (RIS), a picture archiving and communication system (PACS), medical imaging devices, a Real-Time Location System (RTLS), sensors, machine logs, a Performance Management Data Platform, a Medical Asset Track & Trace System, a Virtualized Imaging Solution, a Centralized Care Management System, an Interoperability Solution, etc.
  • EMR Electronic Medical Record
  • RIS radiology information system
  • PES picture archiving and communication system
  • RTLS Real-Time Location System
  • Performance Management Data Platform e.g., a Medical Asset Track & Trace System
  • Virtualized Imaging Solution e.g., a Centralized Care Management System
  • an Interoperability Solution e.g., a Performance Management Data Platform, a Medical Asset Track & Trace System, a Virtualized Imaging Solution, a Centralized Care Management System, an Interoperability Solution, etc.
  • the database 120 and/or the information system 126 may include or be associated with a simulated database.
  • a simulated database may store simulated or estimated patient data.
  • interventional device intrusion support system 100 may utilize some measured patient data from the patient monitor 110, the sensor 112, the medical management device 118, the medical imager 119, the user interface 122, the information system 126, and/or the database 120 to generate some other simulated or estimated patient data and store in the simulated database.
  • the interventional device intrusion support system 100 may utilize digital twin technology to perform the estimation, for example.
  • such simulated or estimated patient data may be marked to indicate its simulated or estimated nature (rather than measured patient data).
  • a weight factor may be applied to the simulated or estimated patient data so that the simulated or estimated patient data may have a lower weight than corresponding measured patient data.
  • the interventional device intrusion support system 100 may be utilized as an element of an Integrated Clinical Environment (ICE).
  • ICE Integrated Clinical Environment
  • the “Integrated Clinical Environment (ICE)” refers to a platform to create a medical Internet of Things (loT) associated with the care of a patient.
  • the interventional device intrusion support system 100 may support many real-time clinical decision support algorithms. Additionally, or alternatively, the interventional device intrusion support system 100 may support closed loop control algorithms of medical devices in the ICE.
  • 2D fluoroscopy sequences are received from an imaging system (e.g., an interventional X-ray imaging system or the like) during endovascular treatment (e.g., endovascular coiling, flow diverter deployment, stent deployment, balloon angioplasty, atherectomy procedures, or the like).
  • an imaging system e.g., an interventional X-ray imaging system or the like
  • endovascular treatment e.g., endovascular coiling, flow diverter deployment, stent deployment, balloon angioplasty, atherectomy procedures, or the like.
  • An interventional X-ray imaging system includes an X-ray tube adapted to generate X- rays and an X-ray detector configured to acquire X-ray images.
  • Examples of such systems are a fixed monoplane and biplane C-arm X-ray system, a mobile C-arm X-ray system, the like, and/or combinations thereof.
  • the X-ray imaging system can acquire both regular fluoroscopy (X-ray) images as well as contrast enhanced fluoroscopy images like digital subtraction angiography (DSA) images.
  • DSA digital subtraction angiography
  • Machine learning, artificial intelligence and/or image processing techniques can be used to segment and track the interventional device 116 present in the image during live fluoroscopic imaging.
  • FIG. 2 is an illustration of a block diagram of another example interventional device intrusion support system 200 according to an embodiment.
  • the interventional device intrusion support system 200 includes a control unit 201 with an interventional device intrusion support logic 208, an interventional device 216, a medical imager 219, a robotic control manipulator 221, the like, and/or combinations thereof.
  • the control unit 201, the interventional device 216, the medical imager 219, and/or the robotic control manipulator 221 may be in communication with one another via Internet based communication, cloud-based communication, wired communication, wireless communication, the like, and/or combinations thereof.
  • the control unit 201 may include a processor 202 and a memory 204 communicatively coupled to the processor 202.
  • the memory 204 may include interventional device intrusion support logic 208 as a set of instructions.
  • the interventional device intrusion support logic 208 may be implemented as software (e g., as illustrated by interventional device intrusion support application 108 in FIG. 1).
  • the interventional device intrusion support logic 208 when executed by the processor 202, implements one or more aspects of the method 500 (FIG. 5), the method 600 (FIG. 6), as will be discussed in greater detail below.
  • the processor 202 may include a general purpose controller, a special purpose controller, a storage controller, a storage manager, a memory controller, a microcontroller, a general purpose processor, a special purpose processor, a central processor unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), the like, and/or combinations thereof. Further, embodiments may include distributed processing, component/object distributed processing, parallel processing, the like, and/or combinations thereof. For example, virtual computer system processing may implement one or more of the methods or functionalities as described herein, and the processor 202 described herein may be used to support such virtual processing.
  • the memory 204 is an example of a computer-readable storage medium.
  • memory 204 may be any memory which is accessible to the processor 202, including, but not limited to RAM memory, registers, and register files, the like, and/or combinations thereof. References to “computer memory” or “memory” should be interpreted as possibly being multiple memories.
  • the memory may for instance be multiple memories within the same computer system.
  • the memory may also be multiple memories distributed amongst multiple computer systems or computing devices.
  • the robotic control manipulator 221 is configured to perform one or more operations in response to a risk score. For example, such operations may be utilized to inhibit movement of the interventional device 216, relieve rotational torque of the interventional device 216, relieve linear tension of the interventional device 216, the like, and/or combinations thereof.
  • the memory 204 stores logic (e.g., interventional device intrusion support logic 208) that includes a set of instructions executable by the processor 202, which when executed by the processor 202, cause the processor 202 to receive a set of medical images of an anatomy from the medical imager 219.
  • An interventional device 216 is tracked in the anatomy based on the set of medical images.
  • An aneurysm is detected in the anatomy.
  • a risk map is determined to define one or more risk regions around the detected aneurysm.
  • a risk score of the interventional device contacting the detected aneurysm is determined based on the tracking of the interventional device and the risk map.
  • FIG. 3 is an illustration of an interventional treatment of an aneurysm according to an embodiment. As illustrated, techniques described herein are applicable to intracranial aneurysms 304. It will be understood, however, that the techniques described herein are applicable to any interventional procedure where the motion of the intervention device can cause a significant harm to the patient.
  • FIG. 4 is an illustration of a flowchart of a method 400 for managing interventional device intrusion support according to an embodiment.
  • method 400 includes detection of the aneurysm at operation 408.
  • the aneurysm may be detected from fluoroscopic image data 402.
  • a risk score may be set to zero at operation 410.
  • a risk map is determined to define one or more risk regions around the detected aneurysm. For instance, a distance-based risk map may be defined around the detected aneurysm to define a high, medium, and/or low risk region around the aneurysm at operation 412.
  • the interventional device may be detected and tracked at operation 404.
  • the interventional device tip and length may be tracked from fluoroscopic image data 402.
  • the device location at a given time is determined at operation 406.
  • a risk score (e.g., a probability of the device entering the high-risk region) may be predicted at operation 414 and assigned at operation 416 based on the motion model derived from the device tip and/or device length detection and tracking (e.g., determined at operation 406) in combination with the distance-based risk map (e.g., determined at operation 412).
  • R t denotes the “risk” or a “score” of the device rupturing the aneurysm at time t
  • P is the set of learnable parameters for the risk prediction model.
  • HMMs Hidden Markov models
  • TCNs temporal convolutional models
  • RNNs recurrent neural networks
  • transformer networks simple statistical models, and/or combinations thereof.
  • the model uses the information from the current observation of the aneurysm location along with temporal device tracking and the past states of the risk. For instance, interventional devices are tracked temporally (e.g., as devices are navigated up the aorta, through the aortic arch, and into the carotid artery) to estimate torque or tension buildup such that when the devices are near the aneurysm, the aggregate information can be used to estimate risk of sudden movement or sudden intrusion of the device into the aneurysm. Similarly, as the device is twisted or pulled back to release tension, temporal states of risk can be updated to compute the new reduced risk. These are the primary inputs to and outputs of the algorithm.
  • the method 400 can include additional information from hardware including a robot manipulating the interventional device.
  • the extra information can be hardware information like translation data, rotation information from the robotic controller, the like, and/or combinations thereof.
  • a combination of two-dimensional and three-dimensional (2D/3D) information can be used to predict the risk score.
  • fluoroscopy images are 2D projection images of the three-dimensional (3D) anatomy. Therefore, the risk map including a distance map or any such measure will necessarily contain errors (e.g., due to foreshortening) in the depth direction, which is not captured by 2D projection images.
  • preoperative 3D image data and/or intraoperative 3D image data is available (e g., computed tomography angiography (CTA) or 3D rotational angiography (3DRA) images)
  • CTA computed tomography angiography
  • 3DRA 3D rotational angiography
  • the system can provide the risk score back to a robotic control manipulator in a closed loop control fashion.
  • this algorithm can act as a soft inhibitor for the robotic controller. For instance, if the risk is too high, the robotic controller may slow down the translation of the interventional device near the aneurysm. Additionally, or alternatively, the robot could also be trained to take specific action when the device tip is close to the aneurysm and the risk of intrusion is high. That is, since the robot has information about how the device was twisted to navigate the device up to the aneurysm, it could apply opposing twist to the device to release the tension before the device is pushed closer to the aneurysm.
  • FIG. 5 shows an example method 500 for interventional device intrusion support according to an embodiment.
  • the method 500 may generally be implemented in the interventional device intrusion support system 100 (FIG. 1) and/or the interventional device intrusion support system 200 (FIG. 2), already discussed.
  • the method 500 (as well as method 600 (FIG. 6) may be implemented in logic instructions (e.g., software), configurable logic (e.g., firmware), fixed- functionality hardware logic (e.g., hardware), etc., or any combination thereof.
  • logic instructions e.g., software
  • configurable logic e.g., firmware
  • fixed- functionality hardware logic e.g., hardware
  • the methods described herein may be performed at least in part by cloud processing.
  • Illustrated processing block 502 provides for receiving or obtaining a set of medical images of an anatomy (e.g., vasculature).
  • the set of medical images include one or more two-dimensional fluoroscopy images. Multiple fluoroscopy images may be acquired over time or may be acquired from various arms of a C-arm imaging system (e.g., a biplane system has two arms, a frontal arm and a lateral arm, that can acquire images from two views at the same time).
  • the set of medical images may include an interventional device in the anatomy (e g., vasculature).
  • the set of medical images include one or more three-dimensional estimate images.
  • the one or more two dimensional and three dimensional images are combined to generate overlays. Overlays are based on two-dimensional fluoroscopy images taken during a procedure used in conjunction with a three dimensional image taken for the procedure. The two-dimensional and three-dimensional images must be aligned or registered so that three-dimensional information can appropriately enhance the two-dimensional image via the overlay. Such overlays are used to track the interventional devices (imaged in two-dimensional X- ray imaging) in the three-dimensional anatomy.
  • Illustrated processing block 504 provides for tracking the interventional device in the anatomy.
  • the position of the interventional device may be tracked in the anatomy based on the set of medical images.
  • machine learning algorithms artificial intelligence (Al) algorithms, image processing algorithms, the like, or some combination thereof can be used to segment and track the interventional device present in the image.
  • the interventional device tip and length may be tracked from fluoroscopic image data.
  • interventional devices are tracked temporally (e.g., as devices are navigated up the aorta, through the aortic arch, and into the carotid artery) to estimate location with respect to the aneurysm.
  • machine learning algorithms artificial intelligence (Al) algorithms, image processing algorithms, the like, or some combination thereof can be used to train models that can segment and track the interventional device present in the image using training data consisting of annotated interventional devices present in fluoroscopic images containing various anatomies including the aorta, aortic arch, carotid artery, aneurysm, and so forth.
  • Annotations may be made manually by an expert.
  • annotations may be made automatically by collecting fluoroscopic image data with the interventional device on known anatomical background which can then be subtracted from the fluoroscopic image data.
  • location may be determined from the data from the interventional device itself, which may include positional location data.
  • interventional devices are also tracked temporally to estimate torque or tension build-up such that when the devices are near the aneurysm, the aggregate information can be used to estimate risk of sudden movement or sudden intrusion of the device into the aneurysm.
  • estimations of torque and/or tension build-up may be based on data from the interventional device itself.
  • the data from the interventional device may itself include device type, positional location data, rotational torque data, linear tension data, the like, and/or combinations thereof. Additionally, or alternatively, this data may be estimated from fluoroscopic image data and/or other devices that may be used to manipulate (e.g., push, pull, rotate) the interventional device such as an endovascular robot.
  • Illustrated processing block 506 provides for detecting an aneurysm in the anatomy (or other abnormality in the vasculature). For example, such aneurysms may be detected in the anatomy based on the set of medical images. For example, the aneurysm may be detected from fluoroscopic image data. Additionally, or alternatively, the aneurysm may be detected in 3D imaging data.
  • detection of the aneurysm may be performed using machine learning algorithms, artificial intelligence (Al) algorithms, image processing algorithms, the like, or some combination thereof.
  • algorithms may be used to train models that can detect aneurysms using training data consisting of annotated aneurysms of varying sizes and in various locations collected from a large set of patients.
  • the aneurysm may be identified manually by a user using a user interface such as by pointing on a touch screen, or clicking using a mouse, or drawing a bounding box using a mouse, and so forth.
  • Illustrated processing block 508 provides for determining a risk map.
  • a risk map may be determined or accesses that defines one or more regions around the detected aneurysm.
  • a risk map may be used to define one or more regions around the detected aneurysm, which each have a level of risk associated with an interventional device inserted into the anatomy contacting the aneurysm.
  • a distance-based risk map may be defined around the detected aneurysm to define a high, medium, and/or low risk region around the aneurysm.
  • the term “around” the aneurysm generally refers to regions near the detected aneurysm, including within the aneurysm and the parent vessel.
  • the risk map is a distance-based risk map.
  • the distance-based risk map may be calculated within the aneurysm and the parent vessel by computing the shortest distance to the aneurysm wall from any point within the aneurysm and the parent vessel. As this distance increases, the risk decreases.
  • the risk map may include other factors such as regions of the aneurysm wall that are more vulnerable to rupture. For instance, the aneurysm wall near the dome of the aneurysm is typically thinner and, therefore, weaker than the aneurysm wall near the base or neck of the aneurysm. The distance-based risk map may, therefore, be weighted by distance from the aneurysm neck.
  • the same distance away from the aneurysm wall near the neck may be associated with lower risk than that near the aneurysm dome.
  • the distance-based risk map may also be weighted by the size of the aneurysm. For example, the same distance away from the aneurysm wall in a smaller aneurysm may be associated with lower risk than that in a larger aneurysm.
  • the risk map may be determined by other techniques, such as a database may include various historical images of different vasculatures having different aneurysms and each image annotated with a risk map specific to the vasculature and aneurysm in the respective historical image.
  • the risk map in each historical image may also be specific to a type of interventional device entering the vasculature.
  • the risk map in each historical image may be annotated with risk scores indicating the risk of the interventional device at different positions in the images.
  • the risk map in each historical image may be defined based on position of the interventional device with the vasculature, conditions related to the anatomy at or near the aneurysm (e.g., narrowness of vessel, tortuosity of vessel, angle of vessel, etc), characteristics the aneurysm (e.g., size, shape, strength, location in vasculature), and/or characteristics of the interventional device (e.g., size, type, flexibility).
  • conditions related to the anatomy at or near the aneurysm e.g., narrowness of vessel, tortuosity of vessel, angle of vessel, etc
  • characteristics the aneurysm e.g., size, shape, strength, location in vasculature
  • characteristics of the interventional device e.g., size, type, flexibility
  • Illustrated processing block 510 provides for determining a risk score (e.g., intrusion risk score) of the interventional device contacting the detected aneurysm.
  • a risk score of the interventional device contacting the detected aneurysm may be determined based on the position of the tracking interventional device in the anatomy and the risk map.
  • determining the risk score may be performed using machine learning algorithms, artificial intelligence (Al) algorithms, the like, or some combination thereof.
  • R t denotes the “risk” or a “score” of the device rupturing the aneurysm at time t, which is dependent on D t , device location at time t, and P, a set of learnable parameters for the risk prediction model.
  • the set of learnable parameters, P may, for instance, encode the risk associated with various distances from the aneurysm wall.
  • R t may be dependent on device locations tracked temporally through a time window from t — (/ + 1) to t, where N is the size of the time window.
  • the risk score can be based on an expert’s hand annotated categorical levels of risks associated with various distances from the aneurysm wall in aneurysms in various locations and in varying sizes collected from a large set of patients, for example.
  • the risk score provides a prediction of the probability of the outcome of the interventional device contacting the aneurysm.
  • FIG. 6 is a sequence diagram of an example of another method 600 for interventional device intrusion support according to an embodiment.
  • the method 600 may generally be implemented in the interventional device intrusion support system 100 (FIG. 1) and/or the interventional device intrusion support system 200 (FIG. 2), already discussed.
  • Illustrated processing block 634 provides for detecting an aneurysm. For example, detection of the aneurysm may be performed using machine learning algorithms, Al algorithms, image processing algorithms, the like, or some combination thereof.
  • Illustrated processing block 636 provides for determining a risk map.
  • the set of medical images include one or more three-dimensional estimate images.
  • the one or more three-dimensional estimate image overlays are generated based on two dimensional fluoroscopy images taken during a procedure used in conjunction with a three dimensional image taken for the procedure.
  • Illustrated processing block 651 provides for providing positional data, rotational torque data, linear tension data, the like, and/or combinations thereof. For example, such data may be provided from an interventional device 616 being utilized.
  • Illustrated processing block 652 provides for tracking the interventional device.
  • an interventional device may be tracked in the anatomy based on the set of medical images.
  • Illustrated processing block 654 provides for detecting an aneurysm.
  • detection of the aneurysm may be performed using machine learning algorithms, Al algorithms, image processing algorithms, the like, or some combination thereof.
  • the risk map is a distance-based risk map.
  • operations to determine the distance-based risk map are further based on a risk model.
  • a risk model is based on one or more machine learnable parameters.
  • Illustrated processing block 662 provides for determining a risk score.
  • the determined risk score may be transferred to the display 620 and/or robotic control manipulator 621.
  • a risk score of the interventional device contacting the detected aneurysm may be determined based on the tracking of the interventional device and the risk map.
  • such a risk score describes a probability of the device entering the high-risk region and may be defined based on a motion model derived from the device tip/length detection and tracking.
  • the risk score is determined further based on a motion model.
  • the motion model considers device type, positional location, rotational torque, linear tension of the interventional device, the like, and/or combinations thereof.
  • the device type is associated with an associated stiffness level of the interventional device (e.g., stiffer devices will have higher risk than softer devices).
  • such a motion model is further based on data from the interventional device itself.
  • the data from the interventional device itself includes device type, positional location data, rotational torque data, linear tension data, the like, and/or combinations thereof.
  • Illustrated processing block 664 provides for generating guidance.
  • the generated guidance may be transferred to the display 620 and/or robotic control manipulator 621 in addition to or in the alternative to the risk score.
  • Illustrated processing block 666 provides for presenting the risk score and/or risk map to a user via the display 620. For example, this presentation may be continually updated as the intervention proceeds.
  • Illustrated processing block 668 provides for providing guidance to a user via the display 620. For example, such guidance may instruct a user regarding risks or techniques to reduce risks for the current intervention.
  • Illustrated processing block 670 provides for controlling the interventional device.
  • the robotic control manipulator may be utilized to take over full control of the interventional device, provide haptic feedback, soft inhibit movement (e.g., provide resistance to slow down the intervention), relieve tension (e.g., rotational torque and/or linear tension) in the interventional device, the like, and/or combinations thereof.
  • the robotic control manipulator is to perform one or more operations in response to the risk score. For example, the robotic control manipulator is to inhibit movement of the interventional device, relieve rotational torque of the interventional device, or relieve linear tension of the interventional device.
  • the procedures described herein may provide a framework for clinical deployment of decision support algorithms. These procedures can work together with many clinical decision support (CDS) algorithms (such as acute kidney injury (AKI), acute respiratory distress syndrome (ARDS), acute decompensated heart failure (ADHF), etc ).
  • CDS clinical decision support
  • Clinical decision support (CDS) refers to computer-based support of clinical staff responsible for making decisions for the care of patients.
  • Computer-based support for clinical decision-making staff may take many forms, from patient-specific visual/numeric health status indicators to patient-specific health status predictions and patient-specific health care recommendations. Further, the procedures described herein may be deployed on analytics platforms (such as Inference Engine, Critical Care Information System, Interoperability Solution, etc.) in conjunction with CDS algorithms.
  • analytics platforms such as Inference Engine, Critical Care Information System, Interoperability Solution, etc.
  • FIG. 7 illustrates a block diagram of an example computer program product 700.
  • computer program product 700 includes a machine-readable storage 702 that may also include logic 704.
  • the machine-readable storage 702 may be implemented as a non-transitory machine-readable storage.
  • the logic 704 may be implemented as machine-readable instructions, such as software, for example.
  • the logic 704 when executed, implements one or more aspects of the method 500 (FIG. 5), the method 600 (FIG. 6), and/or realize the system 100 (FIG. 1 and/or FIG. 2), already discussed.
  • FIG. 8 shows an illustrative example of a system 800.
  • the system 800 may include a processor 802 and a memory 804 communicatively coupled to the processor 802.
  • the memory 804 may include logic 806 as a set of instructions.
  • the logic 806 may be implemented as software.
  • the logic 806, when executed by the processor 802, implements one or more aspects of the method 500 (FIG. 5), the method 600 (FIG. 6), and/or realize the system 100 (FIG. 1 and/or FIG. 2), already discussed.
  • the processor 802 may include a general purpose controller, a special purpose controller, a storage controller, a storage manager, a memory controller, a microcontroller, a general purpose processor, a special purpose processor, a central processor unit (CPU), the like, and/or combinations thereof.
  • embodiments may include distributed processing, component/object distributed processing, parallel processing, the like, and/or combinations thereof.
  • virtual computer system processing may implement one or more of the methods or functionalities as described herein, and the processor 802 described herein may be used to support such virtual processing.
  • the memory 804 is an example of a computer-readable storage medium.
  • memory 804 may be any memory which is accessible to the processor 802, including, but not limited to RAM memory, registers, and register files, the like, and/or combinations thereof. References to “computer memory” or “memory” should be interpreted as possibly being multiple memories.
  • the memory may for instance be multiple memories within the same computer system.
  • the memory may also be multiple memories distributed amongst multiple computer systems or computing devices.
  • FIG. 9 shows an illustrative semiconductor apparatus 900 (e.g., chip and/or package).
  • the illustrated semiconductor apparatus 900 includes one or more substrates 902 (e.g., silicon, sapphire, or gallium arsenide) and logic 904 (e.g., configurable logic and/or fixed-functionality hardware logic) coupled to the substrate(s) 902.
  • the logic 904 implements one or more aspects of the method 500 (FIG. 5), the method 600 (FIG. 6), and/or realize the system 100 (FIG. 1 and/or FIG. 2), already discussed.
  • logic 904 may include transistor array and/or other integrated circuit/IC components.
  • configurable logic and/or fixed-functionality hardware logic embodiments of the logic 904 may include configurable logic such as, for example, programmable logic arrays (PLAs), field programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), or fixed-functionality logic hardware using circuit technology such as, for example, application specific integrated circuit (ASIC), complementary metal oxide semiconductor (CMOS) or transistor-transistor logic (TTL) technology, the like, and/or combinations thereof.
  • PLAs programmable logic arrays
  • FPGAs field programmable gate arrays
  • CPLDs complex programmable logic devices
  • ASIC application specific integrated circuit
  • CMOS complementary metal oxide semiconductor
  • TTL transistor-transistor logic
  • any two components so associated can also be viewed as being “operably connected”, or “operably coupled”, to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable”, to each other to achieve the desired functionality.
  • operably couplable include but are not limited to physically mateable and/or physically interacting components.
  • the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements.
  • This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified.
  • processors other unit, the like, and/or combinations thereof may fulfill the functions of several items recited in the claims.
  • a computer program may be stored/distributed on a suitable computer readable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.
  • a suitable computer readable medium such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

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Abstract

L'invention concerne un système permettant de détecter l'intrusion d'un dispositif d'intervention dans une région vulnérable autour d'un anévrisme. Le système comprend un processeur qui obtient un ensemble d'images médicales du dispositif d'intervention dans un système vasculaire. Le processeur suit une position du dispositif d'intervention dans le système vasculaire sur la base de l'ensemble d'images médicales et détecte l'anévrisme dans le système vasculaire. Le processeur accède en outre à une carte de risque qui définit une ou plusieurs régions autour de l'anévrisme détecté. Le processeur détermine un score de risque d'intrusion sur la base de la position suivie du dispositif d'intervention et de la carte de risque. Le score de risque d'intrusion définit un risque que le dispositif d'intervention vienne en contact avec l'anomalie détectée.
PCT/EP2024/075288 2023-09-18 2024-09-11 Détection et alerte pour intrusion de dispositif dans des régions anatomiques vulnérables Pending WO2025061532A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9724061B2 (en) * 2013-11-18 2017-08-08 Samsung Electronics Co., Ltd. X-ray imaging apparatus and method of controlling the same
EP2931127B1 (fr) * 2012-12-13 2018-02-21 Koninklijke Philips N.V. Système d'intervention
EP4147643A1 (fr) * 2020-05-07 2023-03-15 Imed Technologies, Inc. Dispositif de traitement d'image, procédé de traitement d'image, programme, et système de traitement d'image

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2931127B1 (fr) * 2012-12-13 2018-02-21 Koninklijke Philips N.V. Système d'intervention
US9724061B2 (en) * 2013-11-18 2017-08-08 Samsung Electronics Co., Ltd. X-ray imaging apparatus and method of controlling the same
EP4147643A1 (fr) * 2020-05-07 2023-03-15 Imed Technologies, Inc. Dispositif de traitement d'image, procédé de traitement d'image, programme, et système de traitement d'image

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