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WO2025052266A1 - Système et procédé de grossissement de déplacement dans une vidéo laparoscopique en lumière blanche pour la vérification d'un clampage chirurgical - Google Patents

Système et procédé de grossissement de déplacement dans une vidéo laparoscopique en lumière blanche pour la vérification d'un clampage chirurgical Download PDF

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Publication number
WO2025052266A1
WO2025052266A1 PCT/IB2024/058594 IB2024058594W WO2025052266A1 WO 2025052266 A1 WO2025052266 A1 WO 2025052266A1 IB 2024058594 W IB2024058594 W IB 2024058594W WO 2025052266 A1 WO2025052266 A1 WO 2025052266A1
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WO
WIPO (PCT)
Prior art keywords
motion
video feed
blood vessel
surgical
imaging system
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Pending
Application number
PCT/IB2024/058594
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English (en)
Inventor
Alfonso SANTIAGO FERRANDEZ
David J. Hirvonen
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Covidien LP
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Covidien LP
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Publication of WO2025052266A1 publication Critical patent/WO2025052266A1/fr
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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
    • A61B1/00002—Operational features of endoscopes
    • A61B1/00004—Operational features of endoscopes characterised by electronic signal processing
    • A61B1/00009—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope
    • A61B1/000094—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope extracting biological structures
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
    • A61B1/00002—Operational features of endoscopes
    • A61B1/00004—Operational features of endoscopes characterised by electronic signal processing
    • A61B1/00009—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope
    • A61B1/000095—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope for image enhancement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
    • A61B1/00002—Operational features of endoscopes
    • A61B1/00004—Operational features of endoscopes characterised by electronic signal processing
    • A61B1/00009—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope
    • A61B1/000096—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope using artificial intelligence
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
    • A61B1/313—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor for introducing through surgical openings, e.g. laparoscopes
    • A61B1/3132—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor for introducing through surgical openings, e.g. laparoscopes for laparoscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
    • A61B1/00147—Holding or positioning arrangements
    • A61B1/00149—Holding or positioning arrangements using articulated arms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
    • A61B1/00147—Holding or positioning arrangements
    • A61B1/0016—Holding or positioning arrangements using motor drive units
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10068—Endoscopic image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection

Definitions

  • Laparoscopic and robotic surgery rely on real-time visualization of the surgical site as well as the instruments, which are shown on a display.
  • a laparoscopic camera which may be held by a robotic arm, is inserted into the patient to image the surgical site.
  • the laparoscopic camera may operate in a variety of imaging modes including conventional color, or white light, mode and fluorescence mode.
  • white light mode light in the visible spectral range is used to illuminate the tissue surface under observation.
  • Light reflected by the tissue passes through a suitable lens system and is incident on an image sensor built into or attached to the endoscope.
  • the electrical signals from the image sensor are processed into a full color video image which can be displayed on a video monitor or stored in a memory.
  • Surgical clamping is a commonly used technique in many surgical procedures, such as renal vessel clamping in a partial nephrectomy procedures.
  • clamping is not always successful, and some surgeons elect to verify successful clamping was achieved with doppler ultrasound for blood-flow analysis, which adds an additional step during the time critical ischemia duration.
  • doppler ultrasound for blood-flow analysis, which adds an additional step during the time critical ischemia duration.
  • the present disclosure provides an imaging system including a laparoscopic camera, which may be a stereoscope, an image processing device, and a display.
  • the image processing device receives raw video feed from the laparoscopic camera and processes the same to detect subtle motion and color changes that are not visible to the naked eye.
  • the image processing device isolates, amplifies, and displays the enhanced visualizations directly in the white light video or any other type of imaging without the need for additional imaging modalities, e.g., near infrared (NIR) imaging.
  • NIR near infrared
  • Motion magnification utilizes domain specific knowledge to isolate and display motion for the desired structures.
  • a segmentation module executed by the image processing device segments the relevant structures (e.g., arteries and veins).
  • the dominant motion for target structures e.g., low frequency motion due to insufflator or other forces
  • the 2D image or 3D (white light and depth mapping) point cloud representation using appropriate motion compensation.
  • the motion and any color change in the stabilized and warped coordinate system is isolated using heart-rate or breathing movement tuned spatial-temporal bandpass filters or custom machine learning approaches and is also amplified.
  • the amplified motion and color change is inverse warped to the original coordinate system and is then overlayed over the original white light video or the original video feed is otherwise modified.
  • the modified video is then displayed on a screen in real time and the surgeon/technician can visualize the hidden arteries.
  • the visual enhancements provided by the image processing device may also be used to diagnose cardiovascular diseases.
  • Arterial stiffness is a strong indicator of cardiovascular health because of its correlation with high prevalence diseases such as atherosclerosis, type II diabetes, and heart failure with preserved ejection fraction (HFpEF) among other pathologies.
  • the disclosed motion magnification technique may be used to estimate arterial stiffness via a mathematical correlation.
  • the video amplification technique is used to measure the microscopic time-variable vessel diameter during the surgical procedure via white light endoscope video or outside of the surgery room via abdominal ultrasound.
  • the variation of the vessel diameter is used to estimate the vessel area change.
  • the vessel area change may be used as an input to calculate the vessel stiffness and/or distensibility.
  • the present disclosure provides a motion magnification technique for enhanced intraoperative visualization of residual blood flow directly in the white light endoscope video, while accounting for confounding factors such as respiratory motion. Isolation and quantification of subtle motion that is not visible to the naked eye supports additional measurements that are used to calculate arterial stiffness, which may be used to predict a number of diseases states. The arterial stiffness calculation may also be performed non-surgically with modalities such as abdominal ultrasound.
  • the disclosed system and method may be used to reduce ischemia time by elimination of an additional ultrasound step that is normally used in clamping.
  • the system may also improve patient outcomes by providing feedback on clamping efficacy in all cases where surgeons currently elect not to use doppler ultrasound verification.
  • the system can be used for early detection of diseases such as atherosclerosis, type I diabetes, and HFpEF.
  • an imaging system includes a laparoscopic camera capturing a video feed of a surgical site having a blood vessel.
  • the system also includes an image processing device having a nontransient computer readable medium containing program instructions executable by a processor for causing the image processing device to perform a method of receiving the video feed from the laparoscopic camera, detecting the blood vessel in the video feed, extracting dominant motion of the blood vessel, amplifying subtle motion of the blood vessel, and modifying the video feed with the amplified subtle motion to enhance visibility of the blood vessel.
  • the system further includes a screen displaying the modified video feed for verifying clamping of the blood vessel.
  • a surgical robotic system includes a robotic arm holding a laparoscopic camera capturing a video feed of a surgical site that includes a blood vessel.
  • the system also includes an image processing device having a non-transient computer readable medium containing program instructions executable by a processor for causing the image processing device to perform a method of receiving the video feed from the laparoscopic camera, detecting the blood vessel in the video feed, extracting dominant motion of the blood vessel, amplifying subtle motion of the blood vessel, and modifying the video feed with the amplified subtle motion to enhance visibility of the blood vessel.
  • the system further includes a surgeon console including a screen displaying the modified video feed for verifying clamping of the blood vessel.
  • Implementations of the above embodiments may include one or more of the following features.
  • the dominant motion is caused by external forces moving the blood vessel. Extracting the dominant motion may further include stabilizing the dominant motion to remove the dominant motion. Extracting the dominant motion may further include isolating the subtle motion, which may be performed using a heart rate tuned spatial temporal bandpass filter.
  • the laparoscopic camera may include a white light image sensor for capturing visible light. Modifying the video feed may also include applying an inverse warp of the amplified subtle motion to a coordinate system of the video feed captured by the laparoscopic camera.
  • a method for amplifying visualization of blood vessels using visible light imaging includes receiving a video feed of a surgical site that includes a blood vessel from a laparoscopic camera. The method also includes detecting the blood vessel in the video feed, extracting dominant motion of the blood vessel, amplifying subtle motion of the blood vessel, and modifying the video feed with the amplified subtle motion to enhance visibility of the blood vessel. The method further includes displaying the modified video feed on a screen to verify clamping of the blood vessel.
  • Implementations of the above embodiment may include one or more of the following features.
  • the dominant motion is caused by external forces moving the blood vessel. Extracting the dominant motion may further include stabilizing the dominant motion to remove the dominant motion. Extracting the dominant motion may further include isolating the subtle motion, which may be performed using a heart rate tuned spatial temporal bandpass filter. Modifying the video feed may include applying an inverse warp of the amplified subtle motion to a coordinate system of the video feed captured by the laparoscopic camera.
  • FIG. 1 is a schematic diagram of an imaging system according to an embodiment of the present disclosure
  • FIG. 2 shows a flow chart of a method for motion magnification in white light laparoscopic video for verification of surgical clamping according to an embodiment of the present disclosure
  • FIG. 3 shows screenshots of a video feed processed according to the method of FIG. 2;
  • FIG. 4 is a perspective view of a surgical robotic system including a control tower, a console, and one or more surgical robotic arms each disposed on a mobile cart according to an embodiment of the present disclosure;
  • FIG. 5 is a perspective view of a surgical robotic arm of the surgical robotic system of FIG. 4 according to an embodiment of the present disclosure
  • FIG. 6 is a perspective view of a mobile cart having a setup arm with the surgical robotic arm of the surgical robotic system of FIG. 4 according to an embodiment of the present disclosure
  • FIG. 7 is a schematic diagram of a computer architecture of the surgical robotic system of FIG. 4 according to an embodiment of the present disclosure
  • FIG. 8 is a plan schematic view of the surgical robotic system of FIG. 4 positioned about a surgical table according to an embodiment of the present disclosure
  • FIG. 9 is a schematic diagram of a system for determining phases of a surgical procedure according to an embodiment of the present disclosure.
  • FIG. 10 is a flow chart of a method for motion magnification in ultrasound images for estimating blood vessel stiffness according to an embodiment of the present disclosure.
  • an imaging system 100 includes an image processing device 120 configured to couple to one or more cameras, such as a laparoscopic camera 112 that is configured to couple to a laparoscope 111.
  • the system 100 also includes a light source 116 coupled to the camera 112.
  • the light source 116 may include any suitable light sources, e.g., white light, near infrared, etc., having light emitting diodes, lamps, lasers, etc.
  • the image processing device 120 is configured to receive image data signals from the imaging system 100, process the raw image data from the camera 112, and may generate white light images for recording and/or real-time display.
  • the image processing device 120 is also configured to generate images using various Al image augmentations.
  • the laparoscope 111 may be a monoscope or a stereoscope and includes a longitudinal shaft having a plurality of optical components (not shown), such as lenses, mirrors, prisms, and the like disposed in the longitudinal shaft.
  • the laparoscope 111 is coupled to the light source 116 via an optical cable 113.
  • the light source 116 may include a white light source (not shown), which may be light emitting diodes or any other suitable light sources.
  • the optical cable 113 may include one or more optical fibers for transmitting the white light, which illuminates the tissue under observation by the laparoscope 111.
  • the laparoscope 111 collects the reflected white light and transmits the same to a camera 112, which is coupled to a proximal end portion of the laparoscope 111.
  • the laparoscope 111 may be any conventional laparoscopic configured to transmit and collect white light.
  • the camera 112 includes one or more white (e.g., visible) light (VIS) sensors, which may be a complementary metal oxide semiconductor (CMOS) image sensors having any desired resolution, which in embodiments may be 4K, UHD, etc.
  • VIS visible
  • CMOS complementary metal oxide semiconductor
  • the camera 112 is coupled to the image processing device 120 via a transmission cable 115.
  • the image processing device 120 is configured to receive the image data signals, process the raw image data from the camera 112, and generate processed images for recording and/or real-time display.
  • the image processing device 120 includes a frame grabber, which is configured to capture individual, digital still frames from a digital video stream.
  • the frame grabber is coupled via peripheral component interconnect express (PCI-E) bus to one or more processing units, e.g., a first processing unit and a second processing unit.
  • the first processing unit may be configured to perform operations, calculations, and/or sets of instructions described in the disclosure and may be a hardware processor, a field programmable gate array (FPGA), a digital signal processor (DSP), a central processing unit (CPU), a microprocessor, and combinations thereof.
  • the processor may be any logic processor (e.g., control circuit) adapted to execute algorithms, calculations, and/or sets of instructions as described herein.
  • the second processing unit may be a graphics processing unit (GPU) or an FPGA, which is capable of more parallel executions than a CPU (e.g., first processing unit) due to a larger number of cores, e.g., thousands of compute unified device architecture (CUD A) cores, making it more suitable for processing images.
  • GPU graphics processing unit
  • FPGA field-programmable gate array
  • the image processing device 120 also includes various other computer components, such as RAM 30, a storage drive, peripheral ports, input device (e.g., touch screen). Additionally, the image processing device 120 is also coupled to one or more display screens 122 via output ports. The image processing device 120 is configured to output the processed images through any suitable video output port, such as a DISPEAYPORTTM, HDMI®, SDI, etc., that is capable of transmitting processed images at any desired resolution, display rate, and/or bandwidth.
  • any suitable video output port such as a DISPEAYPORTTM, HDMI®, SDI, etc.
  • FIG. 2 shows a method for motion magnification in white light laparoscopic video for verification of surgical clamping, which may be implemented as software instructions executable by the image processing device 120. While described in connection with blood vessels, it should be understood that the present methods may be used in connection with any other body lumen to be clamped.
  • the image processing device 120 detects and segments relevant tissue structures, such as, arteries and veins.
  • the image processing device 120 may include a segmentation module, which may be a computer vision software module that performs detection and segmentation of critical structures using machine leaming/artificial intelligence (ML/AI) algorithms to identify a region of interest (ROI) encompassing, e.g., the blood vessels.
  • FIG. 3 shows sequences of a video stream 130 and visualizes the video processing steps of the method of FIG. 2, including detection of ROI 132.
  • the ROI 132 includes one or more blood vessels 134 as well as one or more clamps 136.
  • the image processing device 120 extracts video frames of the video feed, which is also shown in FIG. 3.
  • the extracted frames include those frames with dominant (i.e., undesired) scene motion, which may be caused by clamps 136 grasping and moving the vessels 134, low frequency motion due to an insufflator, or other forces.
  • the image processing device 120 stabilizes the extracted frames using motion compensation or other image stabilization techniques to remove the dominant motion as shown in FIG. 3.
  • Stabilization may be performed in either 2D or 3D images depending on the type of laparoscope being used.
  • a 3D depth map may be generated from plurality of camera poses and may include a point cloud, which may then be used to represent appropriate motion compensation. The motion compensation shifts the point cloud producing a warped image.
  • the image processing device 120 isolates relevant (i.e., desired) motion of tissue structures, e.g., blood vessels, in the stabilized images as shown in FIG. 3.
  • relevant motion of tissue structures e.g., blood vessels
  • the desired motion and any color change in the stabilized and warped coordinate system is isolated. Isolation of the motion may be based on the measured heart rate of the patient using any suitable heart rate monitor. The heart rate is directly related to blood flow pulsing through the vessels resulting in motion. Thus, a heart rate tuned spatial temporal bandpass filter may be used to isolate the motion of the structures of interest and is then amplified.
  • the isolated desired motion is amplified in the stabilized and warped video coordinate system.
  • isolated and amplified motion is then used to modify the original white light video feed as shown in FIG. 3.
  • the amplified motion and the color change is inverse warped to the original coordinate system and is then used to modify the video in real time.
  • real time refers to almost instantaneous display of video captured by the laparoscopic camera accounting for processing latency. This process enhances visualization of the blood vessels by magnifying the motion of the blood vessels. Thus, once a vessel is properly clamped, the subtle motion caused by the blood flow is visible on one side of the clamps 136. This enhanced visualization allows the user to confirm that clamping was successfully performed.
  • the enhanced motion may be analyzed and detected using a computer vision module.
  • the imaging system 100 may be used with a surgical robotic system 10 of FIGS. 4-9.
  • the system 10 includes a control tower 20, which is connected to all the components of the surgical robotic system 10 including a surgeon console 30 and one or more mobile carts 60.
  • Each of the mobile carts 60 includes a robotic arm 40 having a surgical instrument 50 removably coupled thereto.
  • the robotic arms 40 also couple to the mobile carts 60.
  • the robotic system 10 may include any number of mobile carts 60 and/or robotic arms 40.
  • the surgical instrument 50 is configured for use during minimally invasive surgical procedures.
  • the surgical instrument 50 may be configured for open surgical procedures.
  • the surgical instrument 50 may be an electrosurgical or ultrasonic instrument, such as a forceps configured to seal tissue by compressing tissue between jaw members and applying electrosurgical current or ultrasonic vibrations via an ultrasonic transducer to the tissue.
  • the surgical instrument 50 may be a surgical stapler including a pair of jaws configured to grasp and clamp tissue while deploying a plurality of tissue fasteners, e.g., staples, and cutting stapled tissue.
  • the surgical instrument 50 may be a surgical clip applier including a pair of jaws configured apply a surgical clip onto tissue.
  • the system also includes an electrosurgical generator 57 configured to output electrosurgical (e.g., monopolar or bipolar) or ultrasonic energy in a variety of operating modes, such as coagulation, cutting, sealing, etc.
  • electrosurgical generator 57 configured to output electrosurgical (e.g., monopolar or bipolar) or ultrasonic energy in a variety of operating modes, such as coagulation, cutting, sealing, etc.
  • Suitable generators include a ValleylabTM FT 10 Energy Platform available from Medtronic of Minneapolis, MN.
  • One of the robotic arms 40 may include a laparoscopic camera 51 configured to capture video of the surgical site.
  • the laparoscopic camera 51 may be a stereoscopic camera configured to capture two side-by-side (i.e., left and right) images of the surgical site to produce a video stream of the surgical scene.
  • the laparoscopic camera 51 is coupled to an image processing device 56, which may be disposed within the control tower 20.
  • the image processing device 56 may be any computing device configured to receive the video feed from the laparoscopic camera 51 and output the processed video stream.
  • the surgeon console 30 includes a first, i.e., surgeon, screen 32, which displays a video feed of the surgical site provided by camera 51 of the surgical instrument 50 disposed on the robotic arm 40, and a second screen 34, which displays a user interface for controlling the surgical robotic system 10.
  • the first screen 32 and second screen 34 may be touchscreens allowing for displaying various graphical user inputs.
  • the surgeon console 30 also includes a plurality of user interface devices, such as foot pedals 36 and a pair of hand controllers 38a and 38b which are used by a user to remotely control robotic arms 40.
  • the surgeon console further includes an armrest 33 used to support clinician’s arms while operating the hand controllers 38a and 38b.
  • the control tower 20 includes a screen 23, which may be a touchscreen, and outputs on the graphical user interfaces (GUIs).
  • GUIs graphical user interfaces
  • control tower 20 is configured to control the robotic arms 40, such as to move the robotic arms 40 and the corresponding surgical instrument 50, based on a set of programmable instructions and/or input commands from the surgeon console 30, in such a way that robotic arms 40 and the surgical instrument 50 execute a desired movement sequence in response to input from the foot pedals 36 and the hand controllers 38a and 38b.
  • the foot pedals 36 may be used to enable and lock the hand controllers 38a and 38b, repositioning camera movement and electrosurgical activation/deactivation.
  • the foot pedals 36 may be used to perform a clutching action on the hand controllers 38a and 38b.
  • Clutching is initiated by pressing one of the foot pedals 36, which disconnects (i.e., prevents movement inputs) the hand controllers 38a and/or 38b from the robotic arm 40 and corresponding instrument 50 or camera 51 attached thereto. This allows the user to reposition the hand controllers 38a and 38b without moving the robotic arm(s) 40 and the instrument 50 and/or camera 51. This is useful when reaching control boundaries of the surgical space.
  • Each of the control tower 20, the surgeon console 30, and the robotic arm 40 includes a respective computer 21, 31, 41.
  • the computers 21, 31, 41 are interconnected to each other using any suitable communication network based on wired or wireless communication protocols.
  • Suitable protocols include, but are not limited to, transmission control protocol/intemet protocol (TCP/IP), datagram protocol/intemet protocol (UDP/IP), and/or datagram congestion control protocol (DC).
  • Wireless communication may be achieved via one or more wireless configurations, e.g., radio frequency, optical, Wi-Fi, Bluetooth (an open wireless protocol for exchanging data over short distances, using short length radio waves, from fixed and mobile devices, creating personal area networks (PANs), ZigBee® (a specification for a suite of high level communication protocols using small, low-power digital radios based on the IEEE 122.15.4-1203 standard for wireless personal area networks (WPANs)).
  • wireless configurations e.g., radio frequency, optical, Wi-Fi, Bluetooth (an open wireless protocol for exchanging data over short distances, using short length radio waves, from fixed and mobile devices, creating personal area networks (PANs), ZigBee® (a specification for a suite of high level communication protocols using small, low-power digital radios based on the IEEE 122.15.4-1203 standard for wireless personal area networks (WPANs)).
  • PANs personal area networks
  • ZigBee® a specification for a suite of high level communication protocols using small, low-power digital radios
  • the computers 21, 31, 41 may include any suitable processor (not shown) operably connected to a memory (not shown), which may include one or more of volatile, non-volatile, magnetic, optical, or electrical media, such as read-only memory (ROM), random access memory (RAM), electrically-erasable programmable ROM (EEPROM), non-volatile RAM (NVRAM), or flash memory.
  • the processor may be any suitable processor (e.g., control circuit) adapted to perform the operations, calculations, and/or set of instructions described in the present disclosure including, but not limited to, a hardware processor, a field programmable gate array (FPGA), a digital signal processor (DSP), a central processing unit (CPU), a microprocessor, and combinations thereof.
  • FPGA field programmable gate array
  • DSP digital signal processor
  • CPU central processing unit
  • microprocessor e.g., microprocessor
  • each of the robotic arms 40 may include a plurality of links 42a, 42b, 42c, which are interconnected at joints 44a, 44b, 44c, respectively.
  • the joint 44a is configured to secure the robotic arm 40 to the mobile cart 60 and defines a first longitudinal axis.
  • the mobile cart 60 includes a lift 67 and a setup arm 61, which provides a base for mounting the robotic arm 40.
  • the lift 67 allows for vertical movement of the setup arm 61.
  • the mobile cart 60 also includes a screen 69 for displaying information pertaining to the robotic arm 40.
  • the robotic arm 40 may include any type and/or number of joints.
  • the setup arm 61 includes a first link 62a, a second link 62b, and a third link 62c, which provide for lateral maneuverability of the robotic arm 40.
  • the links 62a, 62b, 62c are interconnected at joints 63a and 63b, each of which may include an actuator (not shown) for rotating the links 62b and 62b relative to each other and the link 62c.
  • the links 62a, 62b, 62c are movable in their corresponding lateral planes that are parallel to each other, thereby allowing for extension of the robotic arm 40 relative to the patient (e.g., surgical table).
  • the robotic arm 40 may be coupled to the surgical table (not shown).
  • the setup arm 61 includes controls 65 for adjusting movement of the links 62a, 62b, 62c as well as the lift 67.
  • the setup arm 61 may include any type and/or number of joints.
  • the third link 62c may include a rotatable base 64 having two degrees of freedom.
  • the rotatable base 64 includes a first actuator 64a and a second actuator 64b.
  • the first actuator 64a is rotatable about a first stationary arm axis which is perpendicular to a plane defined by the third link 62c and the second actuator 64b is rotatable about a second stationary arm axis which is transverse to the first stationary arm axis.
  • the first and second actuators 64a and 64b allow for full three-dimensional orientation of the robotic arm 40.
  • the actuator 48b of the joint 44b is coupled to the joint 44c via the belt 45a, and the joint 44c is in turn coupled to the joint 46b via the belt 45b.
  • Joint 44c may include a transfer case coupling the belts 45a and 45b, such that the actuator 48b is configured to rotate each of the links 42b, 42c and a holder 46 relative to each other. More specifically, links 42b, 42c, and the holder 46 are passively coupled to the actuator 48b which enforces rotation about a pivot point “P” which lies at an intersection of the first axis defined by the link 42a and the second axis defined by the holder 46. In other words, the pivot point “P” is a remote center of motion (RCM) for the robotic arm 40.
  • RCM remote center of motion
  • the actuator 48b controls the angle 0 between the first and second axes allowing for orientation of the surgical instrument 50. Due to the interlinking of the links 42a, 42b, 42c, and the holder 46 via the belts 45a and 45b, the angles between the links 42a, 42b, 42c, and the holder 46 are also adjusted to achieve the desired angle 0. In embodiments, some or all of the joints 44a, 44b, 44c may include an actuator to obviate the need for mechanical linkages.
  • the joints 44a and 44b include an actuator 48a and 48b configured to drive the joints 44a, 44b, 44c relative to each other through a series of belts 45a and 45b or other mechanical linkages such as a drive rod, a cable, or a lever and the like.
  • the actuator 48a is configured to rotate the robotic arm 40 about a longitudinal axis defined by the link 42a.
  • the holder 46 defines a second longitudinal axis and configured to receive an instrument drive unit (IDU) 52 (FIG. 4).
  • the IDU 52 is configured to couple to an actuation mechanism of the surgical instrument 50 and the camera 51 and is configured to move (e.g., rotate) and actuate the instrument 50 and/or the camera 51.
  • IDU 52 transfers actuation forces from its actuators to the surgical instrument 50 to actuate components an end effector 49 of the surgical instrument 50.
  • the holder 46 includes a sliding mechanism 46a, which is configured to move the IDU 52 along the second longitudinal axis defined by the holder 46.
  • the holder 46 also includes a joint 46b, which rotates the holder 46 relative to the link 42c.
  • the instrument 50 may be inserted through a laparoscopic access port 55 (FIG. 6) held by the holder 46.
  • the holder 46 also includes a port latch 46c for securing the access port 55 to the holder 46 (FIG. 5).
  • the robotic arm 40 also includes a plurality of manual override buttons 53 (FIG. 4) disposed on the IDU 52 and the setup arm 61, which may be used in a manual mode. The user may press one or more of the buttons 53 to move the component associated with the button 53.
  • each of the computers 21, 31, 41 of the surgical robotic system 10 may include a plurality of controllers, which may be embodied in hardware and/or software.
  • the computer 21 of the control tower 20 includes a controller 21a and safety observer 21b.
  • the controller 21a receives data from the computer 31 of the surgeon console 30 about the current position and/or orientation of the hand controllers 38a and 38b and the state of the foot pedals 36 and other buttons.
  • the controller 21a processes these input positions to determine desired drive commands for each joint of the robotic arm 40 and/or the IDU 52 and communicates these to the computer 41 of the robotic arm 40.
  • the controller 21a also receives the actual joint angles measured by encoders of the actuators 48a and 48b and uses this information to determine force feedback commands that are transmitted back to the computer 31 of the surgeon console 30 to provide haptic feedback through the hand controllers 38a and 38b.
  • the safety observer 21b performs validity checks on the data going into and out of the controller 21a and notifies a system fault handler if errors in the data transmission are detected to place the computer 21 and/or the surgical robotic system 10 into a safe state.
  • the controller 21a is coupled to a storage 22a, which may be non-transitory computer- readable medium configured to store any suitable computer data, such as software instructions executable by the controller 21a.
  • the controller 21a also includes transitory memory 22b for loading instructions and other computer readable data during execution of the instructions.
  • other controllers of the system 10 include similar configurations.
  • the computer 41 includes a plurality of controllers, namely, a main cart controller 41a, a setup arm controller 41b, a robotic arm controller 41c, and an instrument drive unit (IDU) controller 41d.
  • the main cart controller 41a receives and processes joint commands from the controller 21a of the computer 21 and communicates them to the setup arm controller 41b, the robotic arm controller 41c, and the IDU controller 4 Id.
  • the main cart controller 41a also manages instrument exchanges and the overall state of the mobile cart 60, the robotic arm 40, and the IDU 52.
  • the main cart controller 41a also communicates actual joint angles back to the controller 21a.
  • Each of joints 63a and 63b and the rotatable base 64 of the setup arm 61 are passive joints (i.e., no actuators are present therein) allowing for manual adjustment thereof by a user.
  • the joints 63a and 63b and the rotatable base 64 include brakes that are disengaged by the user to configure the setup arm 61.
  • the setup arm controller 41b monitors slippage of each of joints 63a and 63b and the rotatable base 64 of the setup arm 61, when brakes are engaged or can be freely moved by the operator when brakes are disengaged, but do not impact controls of other joints.
  • the robotic arm controller 41c controls each joint 44a and 44b of the robotic arm 40 and calculates desired motor torques required for gravity compensation, friction compensation, and closed loop position control of the robotic arm 40.
  • the robotic arm controller 41c calculates a movement command based on the calculated torque.
  • the calculated motor commands are then communicated to one or more of the actuators 48a and 48b in the robotic arm 40.
  • the actual joint positions are then transmitted by the actuators 48a and 48b back to the robotic arm controller 41c.
  • the IDU controller 4 Id receives desired joint angles for the surgical instrument 50, such as wrist and jaw angles, and computes desired currents for the motors in the IDU 52.
  • the IDU controller 4 Id calculates actual angles based on the motor positions and transmits the actual angles back to the main cart controller 41a.
  • the surgical robotic system 10 is set up around a surgical table 90.
  • the system 10 includes mobile carts 60a-d, which may be numbered “1” through “4.”
  • each of the carts 60a-d are positioned around the surgical table 90.
  • Position and orientation of the carts 60a-d depends on a plurality of factors, such as placement of a plurality of access ports 55a-d, which in turn, depends on the surgery being performed.
  • the access ports 55a-d are inserted into the patient, and carts 60a-d are positioned to insert instruments 50 and the laparoscopic camera 51 into corresponding ports 55a-d.
  • each of the robotic arms 40a-d is attached to one of the access ports 55a-d that is inserted into the patient by attaching the latch 46c (FIG. 5) to the access port 55 (FIG. 6).
  • the IDU 52 is attached to the holder 46, followed by the SIM 43 being attached to a distal portion of the IDU 52.
  • the instrument 50 is attached to the SIM 43.
  • the instrument 50 is then inserted through the access port 55 by moving the IDU 52 along the holder 46.
  • the SIM 43 includes a plurality of drive shafts configured to transmit rotation of individual motors of the IDU 52 to the instrument 50 thereby actuating the instrument 50.
  • the SIM 43 provides a sterile barrier between the instrument 50 and the other components of robotic arm 40, including the IDU 52.
  • the SIM 43 is also configured to secure a sterile drape (not shown) to the IDU 52.
  • a surgical procedure may include multiple phases, and each phase may include one or more surgical actions.
  • phase represents a surgical event that is composed of a series of steps (e.g., closure).
  • a “surgical action” may include an incision, a compression, a stapling, a clipping, a suturing, a cauterization, a sealing, or any other such actions performed to complete a phase in the surgical procedure.
  • a “step” refers to the completion of a named surgical objective (e.g., hemostasis).
  • certain surgical instruments 50 e.g., forceps
  • the surgical robotic system 10 may include a machine learning (MU) processing system 310 that processes the surgical data using one or more MU models to identify one or more features, such as surgical phase, instrument, anatomical structure, etc., in the surgical data.
  • the ML processing system 310 includes a ML training system 325, which may be a separate device (e.g., server) that stores its output as one or more trained ML models 330.
  • the ML models 330 are accessible by a ML execution system 340.
  • the ML execution system 340 may be separate from the ML training system 325, namely, devices that “train” the models are separate from devices that “infer,” i.e., perform real-time processing of surgical data using the trained ML models 330.
  • System 10 includes a data reception system 305 that collects surgical data, including the video data and surgical instrumentation data.
  • the data reception system 305 can include one or more devices (e.g., one or more user devices and/or servers) located within and/or associated with a surgical operating room and/or control center.
  • the data reception system 305 can receive surgical data in real-time, i.e., as the surgical procedure is being performed.
  • the ML processing system 310 may further include a data generator 315 to generate simulated surgical data, such as a set of virtual images, or record the video data from the image processing device 56, to train the ML models 330 as well as other sources of data, e.g., user input, arm movement, etc.
  • Data generator 315 can access (read/write) a data store 320 to record data, including multiple images and/or multiple videos.
  • the ML processing system 310 also includes a phase detector 350 that uses the ML models to identify a phase within the surgical procedure.
  • Phase detector 350 uses a particular procedural tracking data structure 355 from a list of procedural tracking data structures.
  • Phase detector 350 selects the procedural tracking data structure 355 based on the type of surgical procedure that is being performed. In one or more examples, the type of surgical procedure is predetermined or input by user.
  • the procedural tracking data structure 355 identifies a set of potential phases that may correspond to a part of the specific type of surgical procedure.
  • the procedural tracking data structure 355 may be a graph that includes a set of nodes and a set of edges, with each node corresponding to a potential phase.
  • the edges may provide directional connections between nodes that indicate (via the direction) an expected order during which the phases will be encountered throughout an iteration of the surgical procedure.
  • the procedural tracking data structure 355 may include one or more branching nodes that feed to multiple next nodes and/or may include one or more points of divergence and/or convergence between the nodes.
  • a phase indicates a procedural action (e.g., surgical action) that is being performed or has been performed and/or indicates a combination of actions that have been performed.
  • a phase relates to a biological state of a patient undergoing a surgical procedure.
  • the biological state may indicate a complication (e.g., blood clots, clogged arteries/veins, etc.), pre-condition (e.g., lesions, polyps, etc.).
  • pre-condition e.g., lesions, polyps, etc.
  • the ML models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, etc.
  • the phase detector 350 outputs the phase prediction associated with a portion of the video data that is analyzed by the ML processing system 310.
  • the phase prediction is associated with the portion of the video data by identifying a start time and an end time of the portion of the video that is analyzed by the ML execution system 340.
  • the phase prediction that is output may include an identity of a surgical phase as detected by the phase detector 350 based on the output of the ML execution system 340.
  • the phase prediction in one or more examples, may include identities of the structures (e.g., instrument, anatomy, etc.) that are identified by the ML execution system 340 in the portion of the video that is analyzed.
  • the phase prediction may also include a confidence score of the prediction. Other examples may include various other types of information in the phase prediction that is output.
  • the predicted phase may be used by the controller 21a to determine when to automatically enable motion magnification process.
  • FIG. 10 shows a flow chart of a method for determining arterial stiffness using ultrasound.
  • ultrasound are obtained using any suitable ultrasound probe.
  • the ultrasound images are preprocessed by the image processing device 120 at step 302, which includes detecting and segmenting relevant tissue structures, such as, arteries and veins.
  • the image processing device 120 includes a segmentation module, which may be a computer vision software module that performs detection and segmentation of critical structures using machine leaming/artificial intelligence (ML/AI) algorithms to identify a region of interest (ROI) encompassing, e.g., the blood vessels.
  • ML/AI machine leaming/artificial intelligence
  • identified vessels are augmented in the manner described above with respect to the method of FIG. 2, i.e., by extracting dominant motion of the blood vessel and amplifying subtle motion of the blood vessel.
  • the diameter change is correlated with vessel stiffness.
  • the image processing device 120 may include a ML/AI correlation algorithm which may be trained on a dataset including vessel data (e.g., diameter, stiffness, etc.) and ultrasound images.
  • the correlation algorithm outputs a stiffness parameter at step 308, which may be displayed on the screen along with the ultrasound image.
  • Example 1 An imaging system comprising: a laparoscopic camera capturing a video feed of a surgical site including a blood vessel; an image processing device including a nontransient computer readable medium containing program instructions executable by a processor for causing the image processing device to perform a method of: receiving the video feed from the laparoscopic camera; detecting the blood vessel in the video feed; extracting dominant motion of the blood vessel; amplifying subtle motion of the blood vessel; and modifying the video feed with the amplified subtle motion to enhance visibility of the blood vessel; and a screen displaying the modified video feed to verify clamping of the blood vessel.
  • Example 2 The imaging system according to Example 1, wherein the dominant motion is caused by external forces moving the blood vessel.
  • Example 3 The imaging system according to Example 1, wherein extracting the dominant motion further includes stabilizing the dominant motion to remove the dominant motion.
  • Example 4 The imaging system according to Example 3, wherein extracting the dominant motion further includes isolating the subtle motion.
  • Example 5 The imaging system according to Example 3, wherein isolating the subtle motion is performed using a heart rate tuned spatial temporal bandpass filter.
  • Example 6 The imaging system according to Example 1, wherein the laparoscopic camera includes a white light image sensor for capturing visible light.
  • Example 7 The imaging system according to Example 1, wherein modifying the video feed includes applying an inverse warp of the amplified subtle motion to a coordinate system of the video feed captured by the laparoscopic camera.
  • a surgical robotic system comprising: a robotic arm holding a laparoscopic camera capturing a video feed of a surgical site including a blood vessel; an image processing device including a non-transient computer readable medium containing program instructions executable by a processor for causing the image processing device to perform a method of: receiving the video feed from the laparoscopic camera; detecting the blood vessel in the video feed; extracting dominant motion of the blood vessel; amplifying subtle motion of the blood vessel; and modifying the video feed with the amplified subtle motion to enhance visibility of the blood vessel; and a surgeon console including a screen displaying the modified video feed to verify clamping of the blood vessel.
  • Example 9 The surgical robotic system according to Example 8, wherein the dominant motion is caused by external forces moving the blood vessel.
  • Example 10 The surgical robotic system according to Example 8, wherein extracting the dominant motion further includes stabilizing the dominant motion to remove the dominant motion.
  • Example 11 The surgical robotic system according to Example 10, wherein extracting the dominant motion further includes isolating the subtle motion.
  • Example 12 The surgical robotic system according to Example 10, wherein isolating the subtle motion is performed using a heart rate tuned spatial temporal bandpass fdter.
  • Example 13 The surgical robotic system according to Example 8, wherein the laparoscopic camera includes a white light image sensor for capturing visible light.
  • Example 14 The surgical robotic system according to Example 8, wherein modifying the video feed includes applying an inverse warp of the amplified subtle motion to a coordinate system of the video feed captured by the laparoscopic camera.
  • Example 15 A method for amplifying visualization of blood vessels using visible light imaging, the method comprising: receiving a video feed of a surgical site including a blood vessel from a laparoscopic camera; detecting the blood vessel in the video feed; extracting dominant motion of the blood vessel; amplifying subtle motion of the blood vessel; modifying the video feed with the amplified subtle motion to enhance visibility of the blood vessel; and displaying the modified video feed on a screen to verify clamping of the blood vessel.
  • Example 16 The method according to Example 15, wherein the dominant motion is caused by external forces moving the blood vessel.
  • Example 17 The method according to Example 15, wherein extracting the dominant motion further includes stabilizing the dominant motion to remove the dominant motion.
  • Example 18 The method according to Example 17, wherein extracting the dominant motion further includes isolating the subtle motion.
  • Example 19 The method according to Example 17, wherein isolating the subtle motion is performed using a heart rate tuned spatial temporal bandpass filter.
  • Example 20 The method according to Example 15, wherein modifying the video feed includes applying an inverse warp of the amplified subtle motion to a coordinate system of the video feed captured by the laparoscopic camera.

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Abstract

Un système d'imagerie (100) comprend une caméra laparoscopique (112) capturant un contenu vidéo d'un site chirurgical comportant une lumière corporelle à clamper. Le système comprend également un dispositif de traitement d'image (120) comportant un support lisible par ordinateur non transitoire contenant des instructions de programme exécutables par un processeur pour amener le dispositif de traitement d'image à effectuer un procédé de réception du contenu vidéo provenant de la caméra laparoscopique, à détecter la lumière corporelle dans le contenu vidéo, à extraire un déplacement dominant de la lumière corporelle, à amplifier un déplacement subtil de la lumière corporelle, et à modifier le contenu vidéo à l'aide du déplacement subtil amplifié afin d'améliorer la visibilité de la lumière corporelle. Le système comprend en outre un écran (122) affichant le contenu vidéo modifié.
PCT/IB2024/058594 2023-09-08 2024-09-04 Système et procédé de grossissement de déplacement dans une vidéo laparoscopique en lumière blanche pour la vérification d'un clampage chirurgical Pending WO2025052266A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150087902A1 (en) * 2012-03-30 2015-03-26 Trustees Of Boston University Phase Contrast Microscopy With Oblique Back-Illumination
US20180276823A1 (en) * 2017-03-22 2018-09-27 Verily Life Sciences Llc Detection of Blood Vessels
CN107205624B (zh) * 2014-10-29 2019-08-06 光谱Md公司 用于组织分类的反射式多光谱时间分辨光学成像方法和装备

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150087902A1 (en) * 2012-03-30 2015-03-26 Trustees Of Boston University Phase Contrast Microscopy With Oblique Back-Illumination
CN107205624B (zh) * 2014-10-29 2019-08-06 光谱Md公司 用于组织分类的反射式多光谱时间分辨光学成像方法和装备
US20180276823A1 (en) * 2017-03-22 2018-09-27 Verily Life Sciences Llc Detection of Blood Vessels

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