Surgical robotic system for optimal ultrasound probe guidance

Abstract

A surgical robotic system includes a laparoscopic ultrasound probe insertable through a first access port. The ultrasound probe includes an ultrasound transducer for obtaining ultrasound data of tissue. The system also includes a laparoscopic camera insertable through a second access port. The camera is configured to capture video data of the ultrasound probe and the tissue. The system also includes an image processing device for receiving the ultrasound data and the video data and determining whether pressure applied by the ultrasound probe on the tissue is optimal based on the ultrasound data and the video data. The system further includes a surgeon console having a screen for displaying a graphical user interface for outputting a prompt in response to the pressure being not optimal.

Description

SURGICAL ROBOTIC SYSTEM FOR OPTIMAL ULTRASOUND PROBE GUIDANCECROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 552,771, filed February 13, 2024, the entire content of which is incorporated herein by reference.BACKGROUND

[0002] Surgical robotic systems are currently being used in a variety of medical procedures, including minimally invasive surgical procedures. Some surgical robotic systems include a surgeon console controlling a surgical robotic arm and a surgical instrument having an end effector (e.g., forceps or grasping instrument) coupled to and actuated by the robotic arm. In operation, the robotic arm is moved to a position over a patient and then guides the surgical instrument into a small incision via a surgical port or a natural orifice of a patient to position the end effector at a work site within the patient’s body. A variety of different types of instruments are used with surgical robotic systems that are designed to perform specific functions, such as ultrasound probes.

[0003] Intraoperative ultrasound may be used in tumor resection procedures, where the clinician needs to confirm location of the tumor and the surrounding vasculature using an intraoperative ultrasound probe on the organ’s surface. In surgical robotic systems, the surgeon may accomplish this task by grasping a robotic drop-in ultrasound probe with a robotic grasping instrument and manipulating the ultrasound probe on the organ. In surgical robotic systems that lack haptic feedback based on applied pressure by the instrument or probe on the tissue, it is important to maintain constant pressure on the tissue in order to acquire ultrasound images with consistent tumor and critical structures depth estimation from ultrasound images.SUMMARY

[0004] The present disclosure provides a surgical robotic system which utilizes robot kinematics and a stereo or monocular laparoscopic camera to measure a dense depth map and the distance between the ultrasound probe and the tissue to determine pressure and / or contact between the ultrasound probe and the tissue, thereby facilitating optimal manipulation.

[0005] According to one embodiment, the robotic system is configured to maintain constant pressure on an ultrasound probe inserted through a probe access port in the body cavity. Thesystem includes a robotic arm holding a robotic grasper instrument that is inserted through an instrument access port. The robotic grasper holds and controls the ultrasound probe. The system also includes a stereo or monocular laparoscopic camera inserted through a camera access port. The camera captures images of the tissue surface, and an image processing device generates a depth map of the surgical site from the images. A processor receives the depth map and the images and calculates the distance between the ultrasound probe and the tissue surface using the dense depth map and images. Pressure is determined based on image analysis of the images and the depth map. In particular, pressure may be determined based on color changes in the images and indentation captured through the depth map. The processor then generates control signals for the robotic grasper instrument via the robotic arm to maintain constant pressure on the probe. The control signal may be in the form of stiffening or loosening the hand controllers being used by the surgeon. In further embodiments, pressure changes may be also monitored by measuring the force being exerted on tissue using torque sensors in the robotic arm and / or the instrument. The measured torque may then be used to adjust the pressure exerted on the ultrasound probe in order to maintain constant contact with tissue.

[0006] In one aspect of the above embodiment, the processor is further configured to calculate pressure based on measurement of displacement between the actual pose of the probe or a grasper holding the probe and the commanded pose from kinematics of the robotic arm.

[0007] In another aspect of the above embodiment, the processor is further configured to analyze the depth map to calculate the distance between the ultrasound probe and the tissue surface at multiple points around the ultrasound probe. The processor continuously receives feedback from the depth map generated from a stereo or monocular laparoscopic camera and dynamically adjusts the position and orientation of the grasper instrument based on the depth map.

[0008] A method for tissue surface-guided optimal ultrasound probe navigation is also provided and may be implemented as software instructions stored in a memory and executable by the processor. Surface-guided optimal ultrasound probe navigation may be provided by ensuring constant contact between the ultrasound probe and tissue. The mode may be entered manually, e.g., through a graphical user interface (GUI), or automatically by detecting a specific phase of the surgical procedure (e.g., ultrasound probe being used). The GUI may output one or more prompts asking for confirmation that the probe is in full contact with tissue and that the ultrasound image does not show an excessive pressure signature in ultrasound images slice. Ultrasound imageprocessing may be used to measure the image deformation in the first few rows of ultrasound images by comparing the first rows with the remaining rows of the ultrasound image. This process may be used to generate an ultrasound image deformation map. If the top region in the ultrasound image is deformed (e.g., compressed) as compared to the bottom rows, the user is instructed to exert less pressure. Conversely, if the top region in ultrasound image shows an air gap (i.e., due to insufficient contact), the user is instructed to push the probe into contact with the tissue.

[0009] Camera image processing may be used to measure the tissue color changes in response to probe pressure on tissue. If the tissue in contact with ultrasound probe shows excessive color change, the user is instructed to put less pressure on the probe. This process is repeated until the user has established optimal contact with tissue. This initial contact may be used as a calibration setting or a reference point for the initial position of the ultrasound probe.

[0010] The method also includes generating a dense depth map of the tissue surface from the images captured by the stereo or monocular laparoscopic camera and calculating the distance between the ultrasound probe and the tissue surface based on the dense depth map using a processor (e.g., controller of the surgical robotic system or the image processing device). In addition, a topographic map based on dense depth map may also be calculated.

[0011] In certain aspects, the GUI may generate prompts asking the clinician to specify the points to survey the organs, and a path is planned to move the probe on the organ semi-automatically with constant contact with tissue. In embodiments, a digital twin of the robotic system may be used. A digital twin is a digital model of the robotic system and is used to simulate physical world situations and their outcomes. The user may use the digital twin to draw or otherwise map out the 3D profile (e.g., points for survey) on the surgeon display for the ultrasound scan.

[0012] In one embodiment, the method implements virtual springs attached to the path so the probe stays in contact with the tissue surface. Virtual springs denote control of the robotic arm and / or the instrument to maintain the ultrasound probe on the specified points. The tension of these springs may be tuned based on deviation from the depth map and the deformation in ultrasound images as determined by the ultrasound image processing device.

[0013] The method may also include generating control signals to adjust the position and orientation of robotic arm and / or instrument holding the ultrasound probe, ensuring constant probe pressure on the tissue. The method may further include providing continuous feedback from the laparoscopic camera to the control system for real-time adjustment of the robotic manipulator. Thefeedback may be in the form of stiffening or loosening the hand controllers making sure the ultrasound probe stays in constant contact with tissue.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Various embodiments of the present disclosure are described herein with reference to the drawings, wherein:

[0015] FIG. 1 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 movable cart according to an embodiment of the present disclosure;

[0016] FIG. 2 is a perspective view of a surgical robotic arm of the surgical robotic system of FIG.1 according to an embodiment of the present disclosure;

[0017] FIG. 3 is a perspective view of a movable cart having a setup arm with the surgical robotic arm of the surgical robotic system of FIG. 1 according to an embodiment of the present disclosure;

[0018] FIG. 4 is a schematic diagram of a computer architecture of the surgical robotic system of FIG. 1 according to an embodiment of the present disclosure;

[0019] FIG. 5 is a plan schematic view of movable carts of FIG. 1 positioned about a surgical table according to an aspect of the present disclosure;

[0020] FIG. 6 is a perspective view, with parts separated, of an instrument drive unit and a surgical instrument according to an embodiment of the present disclosure;

[0021] FIG. 7 is a perspective view of an end effector, according to an embodiment of the present disclosure, for use in the surgical robotic system of FIG. 1;

[0022] FIG. 8 is a side view of an ultrasound probe for use with the surgical robotic system according to an embodiment of the present disclosure;

[0023] FIG. 9 is a perspective view of the ultrasound probe being gripped by a gripper surgical instrument according to an embodiment of the present disclosure;

[0024] FIG. 10 shows a schematic diagram of the ultrasound probe and a laparoscopic camera according to an embodiment of the present disclosure;

[0025] FIG. 11 is a flow chart of a method for tissue surface-guided ultrasound probe navigation according to an embodiment of the present disclosure;

[0026] FIG. 12 shows an ultrasound image with insufficient probe contact according to an embodiment of the present disclosure;

[0027] FIG. 13 shows a laparoscopic camera image of the ultrasound probe according to an embodiment of the present disclosure;

[0028] FIG. 14 shows a first set of ultrasound images with tissue compression according to an embodiment of the present disclosure;

[0029] FIG. 15 shows a second set of ultrasound images from the ultrasound probe making insufficient contact according to an embodiment of the present disclosure; and

[0030] FIG. 16 is a schematic diagram of a system for determining phases of a surgical procedure according to an embodiment of the present disclosure.DETAILED DESCRIPTION

[0031] Embodiments of the presently disclosed surgical robotic system are described in detail with reference to the drawings, in which like reference numerals designate identical or corresponding elements in each of the several views.

[0032] With reference to FIG. 1, a surgical robotic system 10 includes a control tower 20, which is communicatively coupled to all of the components of the surgical robotic system 10 including a surgeon console 30 and one or more movable carts 60. Each of the movable carts 60 includes a robotic arm 40 having a surgical instrument 50 coupled thereto. The robotic arms 40 also couple to the movable carts 60. The robotic system 10 may include any number of movable carts 60 and / or robotic arms 40.

[0033] The surgical instrument 50 is configured for use during minimally invasive surgical procedures. In embodiments, the surgical instrument 50 may be configured for open surgical procedures. 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 endoscope 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 as described below configured to receive the video feed from the laparoscopic camera 51 and output the processed video stream.

[0034] The surgeon console 30 includes a first display 32, which displays a video feed of the surgical site provided by a camera 51 disposed on the robotic arm 40, and a second display 34, which displays a user interface for controlling the surgical robotic system 10. The first display 32and the second display 34 may be touchscreens allowing for displaying various graphical user inputs selectable or movable by the user.

[0035] 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 the robotic arms 40. The surgeon console further includes an armrest 33 used to support clinician’ s arms while the clinician is operating the hand controllers 38a and 38b.

[0036] The control tower 20 can also include a display 23, which may be a touchscreen, and outputs on the graphical user interfaces (GUIs). The control tower 20 also acts as an interface between the surgeon console 30 and one or more of the robotic arms 40. In particular, the 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 response to the instructions and / or input, the 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 system 10 can be configured so that the foot pedals 36 may be used to affect one or more of a wide variety of system functions, such as to enable and lock the hand controllers 38a and 38b, reposition camera movement, and activate / deactivate an electrosurgical instrument. In particular, 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 from) the hand controllers 38a and / or 38b such that the robotic arm 40 and corresponding instrument 50 or camera 51 are not actuated. 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, for instance.

[0037] 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. The term “network,” whether plural or singular, as used herein, denotes a data network, including, but not limited to, the Internet, Intranet, a wide area network, or a local area network. Suitable protocols include, but are not limited to, transmission control protocol / intemet protocol (TCP / IP), datagram protocol / internet protocol (UDP / IP), and / or datagram congestion control protocol (DCCP). Wireless communication may be achieved via one or more wirelessconfigurations, e.g., radio frequency (RF), 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)).

[0038] The computers 21, 31, 41 may include any suitable processor (not shown) connected operably 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, such as a hardware processor, a field programmable gate array (FPGA), a digital signal processor (DSP), a central processing unit (CPU), a microprocessor, and combinations thereof. Those skilled in the art will appreciate that the processor may be substituted for by using any logic processor (e.g., control circuit) adapted to execute algorithms, calculations, and / or set of instructions described herein.

[0039] With reference to FIG. 2, each of the robotic arms 40 may include a plurality of links 42a, 42b, 42c, which are interconnected at joints 44b and 44c, respectively. Other configurations of links and joints may be utilized as known by those skilled in the art. The joint 44a is configured to secure the robotic arm 40 to the movable cart 60 and defines a first longitudinal axis. With reference to FIG. 3, the movable cart 60 includes a lift 67 and a setup arm 61, which provides a base for mounting of the robotic arm 40. The lift 67 allows for vertical movement of the setup arm 61 and, thereby, of the robotic arms 40 mounted on the setup arm 61. The movable cart 60 also includes a display 69 for displaying information pertaining to the robotic arm 40. In embodiments, the robotic arms 40 may include any type and / or number of joints.

[0040] With further reference to FIG. 3, 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 arms 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. In particular, the links 62a, 62b, 62c are movable in corresponding lateral planes, which are parallel to each other, thereby allowing for extension of the robotic arm 40 relative to the patient (e.g., surgicaltable). In embodiments, 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. In embodiments, the setup arm 61 may include any type and / or number of joints.

[0041] The third link 62c may include a rotatable base 64 having two degrees of freedom. In particular, 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 along with the lift 67 allow for full three-dimensional orientation of the robotic arm 40.

[0042] Returning to FIG. 2, 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. Thus, the actuator 48b controls the angle 9 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 in order to achieve the desired angle 9. In embodiments, some or all of the joints 44a, 44b, 44c may include an actuator to obviate the need for mechanical linkages.

[0043] The joints 44a and 44b include respective actuators 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. In particular, the actuator 48a is configured to rotate the robotic arm 40 about a longitudinal axis defined by the link 42a.

[0044] With reference to FIG. 2, the holder 46 defines a second longitudinal axis and configured to receive an instrument drive unit (IDU) 52 (FIG. 1). 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 thesurgical 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. During endoscopic procedures, the instrument 50 may be inserted through an endoscopic access port 55 (FIG. 3) 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. 2).

[0045] The IDU 52 is attached to the holder 46, followed by a sterile interface module (SIM) 43 being attached to a distal portion of the IDU 52. The SIM 43 is configured to secure a sterile drape (not shown) to the IDU 52. The instrument 50 is then 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. In addition, the SIM 43 provides a sterile barrier between the instrument 50 and the other components of the robotic arm 40, including the IDU 52.

[0046] The robotic arm 40 also includes a plurality of manual override buttons 53 (FIG. 1) 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.

[0047] With reference to FIG. 4, 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.

[0048] 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 4 Id. 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 movable 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.

[0049] 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.

[0050] The IDU controller 41d 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 41d calculates actual angles based on the motor positions and transmits the actual angles back to the main cart controller 41a.

[0051] The robotic arm 40 is controlled in response to a pose of the hand controller controlling the robotic arm 40, e.g., the hand controller 38a, which is transformed into a desired pose of the robotic arm 40 through a hand eye transform function executed by the controller 21a. The hand eye function, as well as other functions described herein, is / are embodied in software executable by the controller 21a or any other suitable controller described herein. The pose of one of the hand controllers 38a may be embodied as a coordinate position and roll-pitch-yaw (RPY) orientation relative to a coordinate reference frame, which is fixed to the surgeon console 30. The desired pose of the instrument 50 is relative to a fixed frame on the robotic arm 40. The pose of the handcontroller 38a is then scaled by a scaling function executed by the controller 21a. In embodiments, the coordinate position may be scaled down and the orientation may be scaled up by the scaling function. In addition, the controller 21a may also execute a clutching function, which disengages the hand controller 38a from the robotic arm 40. In particular, the controller 21a stops transmitting movement commands from the hand controller 38a to the robotic arm 40 if certain movement limits or other thresholds are exceeded and in essence acts like a virtual clutch mechanism, e.g., limits mechanical input from effecting mechanical output.

[0052] The desired pose of the robotic arm 40 is based on the pose of the hand controller 38a and is then passed by an inverse kinematics function executed by the controller 21a. The inverse kinematics function calculates angles for the joints 44a, 44b, 44c of the robotic arm 40 that achieve the scaled and adjusted pose input by the hand controller 38a. The calculated angles are then passed to the robotic arm controller 41c, which includes a joint axis controller having a proportional-derivative (PD) controller, the friction estimator module, the gravity compensator module, and a two-sided saturation block, which is configured to limit the commanded torque of the motors of the joints 44a, 44b, 44c.

[0053] With reference to FIG. 5, the surgical robotic system 10 is set up around a surgical table 90. The system 10 includes movable carts 60a-d, which may be numbered “1” through “4.” During setup, 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. Once the port placements are determined, 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.

[0054] During use, 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. 2) to the access port 55 (FIG. 3). The IDU 52 is attached to the holder 46, followed by the SIM 43 being attached to a distal portion of the IDU 52. Thereafter, 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.

[0055] With reference to FIG. 6, the IDU 52 is shown in more detail and is configured to transfer power and actuation forces from its motors 152a-d to the instrument 50 to drive movement of components of the instrument 50, such as articulation, rotation, pitch, yaw, clamping, cutting, etc.The IDU 52 may also be configured for the activation or firing of an electrosurgical energy-based instrument or the like (e.g., cable drives, pulleys, friction wheels, rack and pinion arrangements, etc.).

[0056] The IDU 52 includes a motor pack 150 and a sterile barrier housing 130. Motor pack 150 includes motors 152a-d for controlling various operations of the instrument 50. The instrument 50 is removably couplable to IDU 52. As the motors 152a-d of the motor pack 150 are actuated, rotation of the drive transfer shafts 154a, 154b, 154c, 154d of the motors 152a-d, respectively, is transferred to drive assemblies of the instrument 50. The instrument 50 is configured to transfer rotational forces / movement supplied by the IDU 52 (e.g., via the motors 152a-d of the motor pack 150) into longitudinal movement or translation of the cables or drive shafts to effect various functions of an end effector 200 (FIG. 7).

[0057] Each of the motors 152a-d includes a current sensor 153, a torque sensor 155, and a position sensor 157, which may be an angular motor position sensor. For conciseness only, operation of the motor 152a is described below. The sensors 153, 155, 157 monitor performance of the motor 152a. The current sensor 153 is configured to measure current draw of the motor 152a and the torque sensor 155 is configured to measure motor torque. The torque sensor 155 may be any force or strain sensor including one or more strain gauges configured to convert mechanical forces and / or strain into a sensor signal indicative of the torque output by motor 152a. Position sensor 157 may be any device that provides a sensor signal indicative of the number of rotations of the motor 152a, such as a mechanical encoder or an optical encoder. Parameters which are measured and / or determined by position sensor 157 may include speed, distance, revolutions per minute, position, and the like. Sensor signals from sensors 153, 155, 157 are transmitted to the IDU controller 41d (FIG. 4), which then controls the motors 152a-d based on the sensor signals, via an actuator controller 159. In particular, the actuator controller 159 controls torque outputted and angular velocity of the motors 152a-d. In embodiments, additional position sensors may also be used, which include, but are not limited to, potentiometers coupled to movable components and configured to detect travel distances, Hall Effect sensors, accelerometers, and gyroscopes. In embodiments, a single controller can perform the functionality of the IDU controller 41d and the actuator controller 159.

[0058] With continued reference to FIGS. 6 and 7, instrument 50 includes an adapter 160 having a housing 162 at a proximal end portion thereof and an elongated shaft 164 that extends distallyfrom housing 162. The instrument 50 also includes the end effector 200 as shown in FIG. 7. The housing 162 is configured to selectively couple to IDU 52, to enable the motors 152a-d of IDU 52 to operate the end effector 200 of the instrument 50 (FIG. 7). As shown in FIG. 6, the housing 162 supports a drive assembly (not shown) that mechanically and / or electrically cooperates with the motors 152a-d of the IDU 52. The drive assembly of instrument 50 may include any suitable electrical and / or mechanical component to effectuate driving force / movement.

[0059] The surgical instrument 50 also includes an end effector 200 coupled to the elongated shaft 164. The end effector 200 may include any number of degrees of freedom allowing the end effector 200 to articulate, pivot, etc., relative to the elongated shaft 164. The end effector 200 may be any suitable surgical end effector configured to treat tissue, such as a dissector, grasper, sealer, stapler, etc.

[0060] As shown in FIG. 7, the end effector 200 may include a pair of opposing jaws 120 and 122 that are movable relative to each other. The jaws 120 and 122 may be grippers as shown or any other suitable type of jaws, e.g., shears, sealers, etc. In embodiments, the end effector 200 may include a proximal portion 112 having a first axle 113 and a distal portion 114 (i.e., wrist portion). The end effector 200 may be actuated using a plurality of cables 201a-d routed through proximal and distal portions 112 and 114 around their respective pulleys 112a, 112b, 114a, 114b, which are integrally formed as arms of the proximal and distal portions 112 and 114. Each of the cables 201a-d is actuated by a respective motor 152a-d via corresponding couplers disposed in adapter 160. In embodiments, the end effector 200, namely, the distal portion 114 and the jaws 120 and 122, may be articulated about the axis “ A-A” to control a yaw angle of the end effector with respect to a longitudinal axis “X-X”. The distal portion 114 includes a second axle 115 with first jaw 120 and a second jaw 122 pivotably coupled to the second axle 115. The jaws 120 and 122 are configured to pivot about an axis “B-B” defined by the second axle 115 allowing for controlling a pitch angle of the jaws 120 and 122 as well as opening and closing the jaws 120 and 122. The yaw, pitch, and jaw angles between the jaws 120 and 122 as they are moved between open and closed positions are controlled by adjusting the tension and / or length and direction (e.g., proximal or distal) of the cables 201a-d as shown in FIG. 8. The end effector 200 may also include a cable displacement sensor 116 configured to measure position of the cables 201a-d. Thus, the end effector 200 may have three degrees of freedom, yaw, pitch, and jaw angle between jaws 120 and 122. Control algorithms for a cable actuated instrument 50 are also described in InternationalPatent Application No. PCT / US2022 / 019703, “Surgical Robotic System for Realignment of Wristed Instruments,” filed on March 10, 2022.

[0061] FIG. 8 shows an ultrasound probe 170, which may be a drop-in, laparoscopic ultrasound probe. The ultrasound probe 170 is configured to generate ultrasound data by emitting acoustic waves into the tissue and to receive the reflected acoustic waves, i.e., ultrasound data. The received reflected acoustic waves are then processed by the image processing device 56 to identify various properties of the tissues through which the acoustic wave traveled, such as a density of the tissue. The laparoscopic camera 51 is coupled to the image processing device 56, which may include any computing device configured to receive the ultrasound data from the ultrasound probe 170 and to generate ultrasound images.

[0062] The ultrasound probe 170 includes a shaft 172 and an end effector 174 including an ultrasound transducer 176 disposed at a distal end portion of the shaft 172. The end effector 174 may be articulated relative to the shaft 172 (e.g., about pivot pin). Any suitable articulating mechanism used in surgical instruments may be used. The ultrasound probe 170 may be a drop- in ultrasound probe that is configured to be grasped at a protrusion 178 by the instrument 50 as shown in FIGS. 8 and 9. In embodiments, the ultrasound probe 170 may be configured as an instrument attachable to the IDU 52 and controllable directly through the surgeon console 30 in the same manner as any of the instruments 50 and / or camera 51. FIG. 10 shows such an embodiment of the ultrasound probe 170, which is inserted through the first access port 55a. The camera 51 is inserted through the second access port 55b such that the ultrasound transducer 176 is within the field of view of the camera 51 and is visible on the display screen 32. In embodiments where the ultrasound probe 170 is a drop-in probe, the instrument 50 is inserted through the third access port 55c and is used to maneuver the ultrasound probe 170 in contact and over tissue to obtain ultrasound images. The robotic control of the ultrasound probe 170 described below via the system 10 applies to both embodiments, where the ultrasound probe 170 is directly controllable by the system 10 (i.e., via the IDU 52) or grasped by the instrument 50.

[0063] The system 10 is initially configured for ultrasound operation by inserting the ultrasound probe 170 into the body cavity and navigating the ultrasound probe 170 to the tissue that is to be scanned by ultrasound. Navigation may be done by grasping the ultrasound probe 170 using the instrument 50 and / or controlling the ultrasound probe 170 directly, depending on the type of the ultrasound probe 170. While movement of the ultrasound probe 170 is described below using onlythe instrument 50, the same control commands may be provided to the IDU 52 and the robotic arm 40 controlling the instrument 50 to control the movement of ultrasound probe 170 directly. In addition, the camera 51 is also positioned within the body cavity and pointed towards the surgical site such that the ultrasound probe 170 and instrument 50 and their respective end effectors are within the field of view of the camera 51.

[0064] FIG. 11 shows a method 400 for tissue surface-guided optimal ultrasound probe navigation. The method may be implemented as software instructions stored in a memory and executable by a processor (e.g., controller 21a, image processing device 56, etc.). The ultrasound guidance software process may be initiated either manually or automatically. A GUI may be displayed on one of the display screens 32 and / or 34, which may be used to manually start the ultrasound probe navigation process. The navigation process may be started automatically in response to occurrence of a triggering event, e.g., insertion of the ultrasound probe 170 through the access port 55a, or a phase, which may be detected using a machine learning (ML) processing system 310 of FIG. 16.

[0065] At step 402, while the ultrasound probe 170 is moved into contact with tissue, the image processing device 56 receives and processes the ultrasound data from the ultrasound probe 170 and the video feed from the camera 51 of the ultrasound probe 170 contacting tissue. The ultrasound data and the video image data are used to determine the amount of pressure the ultrasound probe 170 is exerting on the tissue. This may be done manually by the user or automatically using image analysis software as described in further detail below.

[0066] At step 404, the ultrasound images are analyzed to determine a degree of pressure being applied by the ultrasound probe 170 on the tissue. Ultrasound images are analyzed to measure the image deformation in first few rows of ultrasound images and compare the first rows with the remaining rows of ultrasound images. The comparison is used to generate an ultrasound image deformation map. Thus, if the top region in ultrasound image is deformed (e.g., compressed) more than the bottom rows, the processor outputs a prompt or a command to exert less pressure. Conversely, if the top region in the ultrasound image shows an air gap, a prompt or a command is provided to push the ultrasound probe 170 into contact with tissue. FIG. 12 shows an ultrasound image with insufficient probe contact, resulting in no image.

[0067] At step 406, video images are analyzed to estimate contact pressure on tissue by the ultrasound probe 170. Contact pressure may be determined based on color changes in response topressure. If the tissue in contact with ultrasound probe shows excessive color change, the user is instructed to put less pressure on the probe.

[0068] At step 408, the processor confirms if initial contact is established. If no contact is detected, then the process repeats by returning to step 402. If contact is detected, the initial contact is used as a reference (i.e., calibration) point for determining sufficient pressure. The calibration of sufficient pressure may be used in subsequent methods controlling contact pressure of the ultrasound probe 170 that are described below. In embodiments, the system 10 may automatically transition from the method 400 to subsequent contact pressure control.

[0069] In further embodiments, an automated reinforcement learning (RL) algorithm embodied as software executable by the processor may be used to determine optimal contact pressure. The RL algorithm may be trained on data with different amounts of pressure applied by an ultrasound probe and corresponding ultrasound and endoscope images. The RL algorithm may be tuned to reward better images capture and de-incentivize worse images capture. During execution of the ultrasound guidance software process, vision (e.g., video data from the camera 51) and kinematics data from the system 10 may be fed into the RL algorithm, which produces kinematics force delta, which may then be used to adjust the pressure applied by the ultrasound probe 170. As used herein, kinematics data includes any data related to movement of any robotic arm and / or instrument, such as joint space data, which may include the angles or positions of each individual joint in the robot's arm or instrument, position and orientation of the robot's end effector, etc.

[0070] Method 400 also provides for vision-based ultrasound probe contact pressure (i.e., compression) estimation. At step 410 images from the camera 51 capturing the tissue surface are used to generate a dense depth map of the surgical site. In embodiments where the camera 51 is stereoscopic, two images are captured from slightly different viewpoints and depth is calculated by comparing the disparities between corresponding pixels in both images. In embodiments where the camera 51 is a monoscope, deep learning techniques may be used, which are based on advanced neural networks trained on data to infer depth from a single image, making it possible to generate depth maps without specialized hardware, such as MiDaS (Monocular Depth Inference for Self- Supervised Learning), DPT (Dense Prediction Transformers), AdaBins (Adaptive Bins for Efficient Monocular Depth Estimation), etc. In embodiments, hybrid approaches may be used which combine multiple techniques for improved accuracy and robustness, such as using deep learning to refine depth maps generated by hardware sensors.

[0071] At step 412, the ultrasound probe 170, the instrument 50, as well as tissue of interest are localized in the video feed and are segmented via boundaries as shown in FIG. 13.

[0072] At step 414, the depth map is used for estimating contact pressure on the tissue by the ultrasound probe 170. In particular, the depth map is used to determine the distance between the ultrasound probe 170 and the tissue surface. Thus, if a gap between the ultrasound probe 170 and the tissue surface is detected in the video feed, then there is no contact, and a prompt or a command is provided to push the ultrasound probe 170 into contact with tissue at step 415. Excessive pressure is determined from the video images based on color change in the tissue, i.e., due to decreased blood perfusion at the pressure point. Tissue color change is used to calculate a pressure map for the tissue. The pressure map may be displayed as a color overlay on the video image and may include hue saturation to represent contact pressure estimation. If excessive pressure is detected, the processor outputs a prompt or a command to exert less pressure at step 415.

[0073] Pressure of the ultrasound prove 170 may also be determined by analyzing the ultrasound images, which may be performed in parallel with video feed image processing of steps 410-414. At step 416, the ultrasound image is analyzed by the image processing device 56 which executes an ML image processing algorithm for identifying the tissue and / or structures in multiple frames of ultrasound images. At step 418, the ML image processing algorithm measures deformation (e.g., compression) of the tissue and / or structures due to applied pressure as shown in FIG. 14. The algorithm may be trained on ultrasound images of uncompressed and compressed tissue at varying amounts of applied pressure such that the algorithm is configured to recognize when the tissue and / or the structures are compressed due to pressure. If excessive pressure is detected, the processor outputs a prompt or a command to exert less pressure at step 415.

[0074] At step 420, the image processing device 56 also detects if insufficient pressure is applied by measuring the air gap (i.e., rows of black pixels) in the ultrasound images as shown in FIG. 15 or if no image is present, as shown in FIG. 13. Thus, if the top region in the ultrasound image shows an air gap, a prompt or a command is provided to push the ultrasound probe 170 into contact with tissue at step 415. The air gap may be segmented using a semantic segmentation network trained on ultrasound images. The semantic segmentation network may also have a new object class to highlight all pixels in ultrasound image that correspond to air gap pixels. The network can have a binary classification head to flag if ultrasound image shows air gap.

[0075] In addition, to video and ultrasound imaging being used to determine pressure applied by the ultrasound probe 170, the system 10 may also use robot kinematics for ultrasound probe contact pressure estimation. The kinematics-based pressure estimation may be performed in parallel with video feed image processing of steps 410-414 and ultrasound image processing of steps 416-420. At step 422, the system 10 calculates force feedback based on contact pressure estimation. The system 10 calculates forward kinematics at each point in the movement of the ultrasound probe 170 to predict how far the ultrasound probe 170 would have moved, i.e., commanded position. The system 10 then measures how far the ultrasound probe 170 has actually moved, i.e., actual position, from the video feed and calculates a difference between the commanded position to the actual position. The calculated difference in positions is then used to determine the force feedback, i.e., pressure applied by the ultrasound probe 170. The force feedback may be used in autonomous (i.e., automatic movement of the ultrasound probe 170 by the system 10) mode or semi- autonomous modes to maintain uniform force feedback.

[0076] In embodiments, displacement may be calculated not only for the ultrasound probe 170, where the transformation between the grasper end effector 200 and the ultrasound probe 170 is unknown, but for the grasper instrument 50 as well, where the kinematics are known. Then, since instrument 50 and ultrasound probe 170 are rigidly attached, the system 10 may estimate pressure by analyzing the kinematics of at the grasper instrument 50. This is useful in cases where the ultrasound probe 170 is completely or partially obscured by the instrument 50 in the image data.

[0077] In one embodiment, a robot kinematics pipeline is implemented as an augmented reality overlay that renders a synthetic scene showing where the ultrasound probe 170 is located based on kinematics. The synthetic scene of the ultrasound slice may be rendered in 3D overlayed on the surgeon display screen 32 for pseudo depth perception.

[0078] At step 424, the force feedback is compared to a threshold corresponding to optimal probe contact. If excessive pressure is detected, the processor outputs a prompt or a command to exert less pressure at step 415. If the force is too low, a prompt or a command is provided to push the ultrasound probe 170 into contact with tissue at step 415.

[0079] The prompts provided at step 415 may include audio, visual, and / or haptic guidance. Visual guidance may include 3D arrows displayed on the GUI in specific orientations to let the clinician know whether to exert less or more pressure and / or how to properly orient the ultrasound probe 170. In further embodiments, a 3D model of the ultrasound probe 170 may be provided(e.g., rendered as a transparency or a wireframe) to guide the user to follow the 3D model to the desired location to obtain desired ultrasound scan.

[0080] The estimated pressure of the ultrasound probe 170 may be used to facilitate ultrasound survey of tissue. An autonomous or semi-autonomous navigation path may be generated for scanning the organ. Initially, a pre-operative 3D model with a segmented organ surface is registered to the organ in the video feed using a manual, interactive or fully automatic registration process. The 3D model may be also analyzed for deformation to determine pressure on the tissue surface. The image processing device 56 then generates a topographical map covering the tissue or organ surface such that the tissue of interest, e.g., tumor, is located centrally and the map covers the tumor from all sides. The processor then generates an optimal path covering the generated topographical map starting from a starting point to another point, until the entire region is scanned by the passage of the ultrasound probe 170. The system 10 may output a prompt asking the user to confirm the navigation outline. The generated path may also include virtual springs attached to the path so the ultrasound probe 170 stays in contact with the tissue surface. Virtual springs denote control of ultrasound probe 170 by the robotic arm 40 to maintain the ultrasound probe 170 on the specified points. The tension of these springs may be tuned based on deviation from the depth map and the deformation in ultrasound images measured from the ultrasound image process.

[0081] In semi-autonomous mode, the system 10 confirms the hand controllers 38a and / or 38b are gripped using proximity sensors or other contact verification sensors and stop the semi-automatic mode if the hand controllers 38a and / or 38b are not gripped. The user then moves the ultrasound probe 170 along the generated path with the aid of the virtual springs. The hand controllers 38a and 38b may provide haptic feedback if the user navigates away from the path. In fully autonomous mode, the ultrasound probe 170 is moved automatically by the robotic arm 40 along the generated path.

[0082] With reference to FIG. 16, the surgical robotic system 10 may include a machine learning (ML) processing system 310 that processes the surgical data using one or more ML 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 areseparate from devices that “infer,” i.e., perform real-time processing of surgical data using the trained ML models 330.

[0083] 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.

[0084] The ML processing system 310, in some examples, may further include a data generator 315 to generate simulated surgical data, such as a set of virtual or masked 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.

[0085] 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.

[0086] In some examples, 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. In some instances, 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. In some instances, a phase relates to a biological state of a patient undergoing a surgical procedure. For example, the biological state may indicate a complication (e.g., blood clots, clogged arteries / veins, etc.), pre-condition (e.g., lesions, polyps,etc.). In some examples, the ML models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, etc.

[0087] 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. Further, 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 enable surface-guided ultrasound probe manipulation aides.

[0088] It will be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the above description should not be construed as limiting, but merely as exemplifications of various embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the claims appended thereto.

[0089] The following examples are illustrative of the techniques described herein.

[0090] Example 1. A surgical robotic system comprising: a laparoscopic ultrasound probe insertable through a first access port, the ultrasound probe including an ultrasound transducer for obtaining ultrasound data of tissue; a laparoscopic camera insertable through a second access port, the camera configured to capture video data of the laparoscopic ultrasound probe and the tissue; an image processing device for receiving the ultrasound data and the video data and determining whether pressure applied by the ultrasound probe on the tissue is constant based on the ultrasound data and the video data; and a surgeon console including a screen for displaying a graphical user interface for outputting a prompt in response to the pressure being not constant.

[0091] Example 2. The surgical robotic system according to Example 1, further comprising a first robotic arm holding the laparoscopic ultrasound probe and a second robotic arm holding the laparoscopic camera.

[0092] Example s. The surgical robotic system according to Example 2, wherein the first robotic arm includes a grasper instrument for grasping and maneuvering the laparoscopic ultrasound probe.

[0093] Example 4. The surgical robotic system according to Example 3, further comprising a processor for calculating force feedback based on a difference between a commanded position and an actual position of the laparoscopic ultrasound probe held by the grasper instrument.

[0094] Example 5. The surgical robotic system according to Example 4, wherein the processor calculates the commanded position based on forward kinematics of the first robotic arm.

[0095] Example 6. The surgical robotic system according to Example 5, wherein the processor calculates the actual position from the video data of the laparoscopic camera.

[0096] Example 7. The surgical robotic system according to Example 6, wherein the processor further calculates actual position by fusing forward kinematics of the first robotic arm with the position from the video data of the laparoscopic camera.

[0097] Example 8. The surgical robotic system according to Example 1, wherein the image processing device determines whether the pressure is constant by analyzing a plurality of ultrasound images of the ultrasound data to detect at least one structure of the tissue and determine whether the at least one structure of the tissue is compressed by segmenting the at least one structure in the plurality of ultrasound images, tracking the at least one structure across video frames, and computing a temporal structural deformation signature across plurality of ultrasound video frames.

[0098] Example 9. The surgical robotic system according to Example 1, wherein the image processing device determines whether the pressure is constant by segmenting an ultrasound image of the ultrasound data and comparing a plurality of pixels marked as airgap to a pre-determined threshold to determine whether an air gap is present in the ultrasound image.

[0099] Example 10. The surgical robotic system according to Example 1, wherein the image processing device determines whether the pressure is constant by generating a depth map from the video data and determining whether a gap is present between the ultrasound probe and the tissue.

[0100] Example 11. The surgical robotic system according to Example 1, wherein the image processing device determines whether the pressure is constant based on a color change in a video image of the video data.

[0101] Example 12. A method for ultrasound probe operation using a surgical robotic system, the method comprising: controlling a robotic arm to move a laparoscopic ultrasound probe including an ultrasound transducer for obtaining ultrasound data of tissue; capturing video data of the laparoscopic ultrasound probe and the tissue through a laparoscopic camera; receiving the ultrasound data and the video data at an image processing device; determining whether pressure applied by the ultrasound probe on the tissue is constant based on at least one of the ultrasound data and the video data or a difference between in commanded and actual motor positions and torques of the robotic arm; and outputting a prompt in response to the pressure being not constant on a graphical user interface of a surgeon console.

[0102] Example 13. The method according to Example 12, wherein the laparoscopic ultrasound probe is moved by a first robotic arm holding and the laparoscopic camera is moved by a second robotic arm.

[0103] Example 14. The method according to Example 13, wherein the first robotic arm includes a grasper instrument for grasping and maneuvering the laparoscopic ultrasound probe.

[0104] Example 15. The method according to Example 14, further comprising calculating, at a processor, force feedback based on a difference between a commanded position and an actual position of the grasper instrument holding the laparoscopic ultrasound probe.

[0105] Example 16. The method according to Example 15, wherein calculating the commanded position is based on forward kinematics of the first robotic arm.

[0106] Example 17. The method according to Example 15, wherein calculating the actual position is based on: computing the position of the grasper instrument holding the ultrasound probe from the laparoscopic camera video and computing the position of the ultrasound probe from the laparoscopic camera video; and optionally fusing the position of the ultrasound probe determined from video processing with a difference between commanded and actual motor positions and commanded and actual motor torques of the robotic arm.

[0107] Example 18. The method according to Example 12, wherein determining whether the pressure is constant further includes analyzing an ultrasound image of the ultrasound data to determine whether at least one structure of the tissue is compressed.

[0108] Example 19. The method according to Example 12, wherein determining whether the pressure is constant further includes analyzing an ultrasound image of the ultrasound data to determine whether an air gap is present in the ultrasound image.

[0109] Example 20. The method according to Example 12, wherein determination of contact pressure and feedback to user further includes: generating a depth map from the video data; determining whether a gap is present between the laparoscopic ultrasound probe and the tissue; and implementing a plurality of virtual guides that apply force feedback to user input devices moving the user input devices away from a trajectory that would apply suboptimal contact pressure between the ultrasound probe and the tissue.

[0110] Example 21. A surgical robotic system comprising: a first robotic arm holding a laparoscopic ultrasound probe insertable through a first access port, the laparoscopic ultrasound probe including an ultrasound transducer for obtaining ultrasound data of tissue; a second robotic arm holding a laparoscopic camera insertable through a second access port, the laparoscopic camera configured to capture video data of the laparoscopic ultrasound probe and the tissue; a processor for receiving kinematic data from the first robotic arm, the ultrasound data from the laparoscopic ultrasound probe, and the video data from the camera, and determining whether pressure applied by the ultrasound probe on the tissue is constant based on at least one of the ultrasound data, the video data, the kinematic data, motor torque data, or motor position data; and a surgeon console including a screen for displaying a graphical user interface for outputting a prompt in response to the pressure being not constant including instructions to achieve constant pressure of the ultrasound probe on the tissue.

[0111] Example 22. A method of ultrasound probe induced tissue deformation comprising: generating a dense depth map of a surgical site including tissue and an ultrasound probe controllable by a robotic arm; detecting a plurality of surface key points and key regions in the dense depth map; tracking of surface key points and key regions in the dense depth map; generating a spatio-temporal deformation signature based on tracking of the surface key points and key regions; generating a tissue deformation map based on the spatio-temporal deformation signature; and providing at least one of feedback to a user based on the tissue deformation map alerting the user to reduce ultrasound probe pressure or to a controller operating the robotic arm in an autonomous mode to adjust movement of the ultrasound probe to reduce pressure on tissue.

Claims

WHAT IS CLAIMED IS:

1. A surgical robotic system (10) comprising: a laparoscopic ultrasound probe (170) insertable through a first access port (55a-d), the ultrasound probe including an ultrasound transducer (176) for obtaining ultrasound data of tissue; a laparoscopic camera (51) insertable through a second access port (55a-d), the camera configured to capture video data of the laparoscopic ultrasound probe and the tissue; an image processing device (56) for receiving the ultrasound data and the video data and determining whether pressure applied by the ultrasound probe on the tissue is constant based on the ultrasound data and the video data; and a surgeon console (30) including a screen for displaying a graphical user interface for outputting a prompt in response to the pressure being not constant.

2. The surgical robotic system according to claim 1, further comprising a first robotic arm (40a-d) holding the laparoscopic ultrasound probe and a second robotic arm (40a-d) holding the laparoscopic camera.

3. The surgical robotic system according to claim 2, wherein the first robotic arm includes a grasper instrument (50) for grasping and maneuvering the laparoscopic ultrasound probe.

4. The surgical robotic system according to claim 3, further comprising a processor (21a) for calculating force feedback based on a difference between a commanded position and an actual position of the laparoscopic ultrasound probe held by the grasper instrument.

5. The surgical robotic system according to claim 4, wherein the processor calculates the commanded position based on forward kinematics of the first robotic arm.

6. The surgical robotic system according to claim 5, wherein the processor calculates the actual position from the video data of the laparoscopic camera.

7. The surgical robotic system according to claim 6, wherein the processor further calculates actual position by fusing forward kinematics of the first robotic arm with the position from the video data of the laparoscopic camera.

8. The surgical robotic system according to any preceding claim, wherein the image processing device determines whether the pressure is constant by analyzing a plurality of ultrasound images of the ultrasound data to detect at least one structure of the tissue and determine whether the at least one structure of the tissue is compressed by segmenting the at least one structure in the plurality of ultrasound images, tracking the at least one structure across video frames, and computing a temporal structural deformation signature across plurality of ultrasound video frames.

9. The surgical robotic system according to any preceding claim, wherein the image processing device determines whether the pressure is constant by segmenting an ultrasound image of the ultrasound data and comparing a plurality of pixels marked as airgap to a predetermined threshold to determine whether an air gap is present in the ultrasound image.

10. The surgical robotic system according to any preceding claim, wherein the image processing device determines whether the pressure is constant by generating a depth map from the video data and determining whether a gap is present between the ultrasound probe and the tissue.

11. The surgical robotic system according to any preceding claim, wherein the image processing device determines whether the pressure is constant based on a color change in a video image of the video data.

Images