Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation. Image guided robot-assisted surgery is a promising solution that brings significant improvements in outcomes and reduces the physical limitations of human surgeons. However, this technology must be further developed before it can be routinely used in clinics. One of the problems is the lack of proper calibration between the robotic manipulator and appropriate imaging device. In this letter, we developed a flexible framework for hand-eye calibration of an ophthalmic robot with a microscope-integrated optical coherence tomography (OCT) without any markers. The proposed method consists of three main steps are following: First, we estimate the OCT calibration parameters; second, with micro-scale displacements controlled by the robot, we detect and segment the needle tip in three-dimensional OCT volume and; finally, we find the transformation between the coordinate system of the OCT camera and the coordinate system of the robot. We verified the capability of our framework in ex-vivo pig eye experiments and compared the results with a reference method (marker-based). In all experiments, our method showed a small difference from the marker based method, with a mean calibration error of 9.2 μm and 7.0 μm, respectively. Additionally, the noise test shows the robustness of the proposed method.
In the surgical environment, hand-eye calibration is normally transferred into a Procrustean problem by the @cite_11 and has been modified to meet with different surgical requirements in various 3D medical imaging scenarios, e.g. two-camera stereoscopic vision and ultrasound. To determine the unknown fixed transformation between the robot and imaging coordinates, a ball-tip stylus or chessboard is used as a calibrator for hand-eye calibration @cite_27 . However, these markers may influence the conventional surgical workflow as well as cause complication with sterility. @cite_7 designed a tracking collar for rigid laparoscopes hand-eye calibration. The method can achieve 0.85 mm error, and the calibration process can be finished within 3 minutes. In another work, 3D markers were used by @cite_21 to realize ultrasound probe calibration with a robotic arm. In terms of none marker research work, @cite_15 used ultrasound volume registration data to achieve calibration that does not require 3D localizers. @cite_2 proposed a calibration method where an instrument with known geometry is used instead of an additional calibrator. The calibration was achieved without compromising sterility. @cite_3 @cite_20 used constraints based on the needle orientation in 2D 3D ultrasound image to calibrate the ultrasound probe.
@cite_7 @cite_21 @cite_3 @cite_27 @cite_2 @cite_15 @cite_20 @cite_11
2307939460 1977606080 2507022162 2605547838 2561316912 2296482359 2331874877 2093220968
Purpose Laparoscopic liver resection has significant advantages over open surgery due to less patient trauma and faster recovery times, yet it can be difficult due to the restricted field of view and lack of haptic feedback. Image guidance provides a potential solution but one current challenge is in accurate “hand–eye” calibration, which determines the position and orientation of the laparoscope camera relative to the tracking markers. Objective By adding a tracking sensor to a 3D ultrasound (US) probe and thus locating the probe in space, new applications within the fields of image guided surgery and radiation therapy are possible. To locate the US volume in space, a calibration is necessary to determine the mathematical transformation for mapping points from the tracking coordinate system to the US image coordinate system. We present a comprehensive comparison of two different approaches to perform this calibration for 3D US. We propose a minimal solution for the similarity registration (rigid pose and scale) between two sets of 3D lines, and also between a set of co-planar points and a set of 3D lines. The first problem is solved up to 8 discrete solutions with a minimum of 2 line-line correspondences, while the second is solved up to 4 discrete solutions using 4 point-line correspondences. We use these algorithms to perform the extrinsic calibration between a pose tracking sensor and a 2D 3D ultrasound (US) curvilinear probe using a tracked needle as calibration target. The needle is tracked as a 3D line, and is scanned by the ultrasound as either a 3D line (3D US) or as a 2D point (2D US). Since the scale factor that converts US scan units to metric coordinates is unknown, the calibration is formulated as a similarity registration problem. We present results with both synthetic and real data and show that the minimum solutions outperform the correspondent non-minimal linear formulations. Purpose Surgical cameras are prevalent in modern operating theatres often used as surrogates for direct vision. A surgical navigational system is a useful adjunct, but requires an accurate “hand-eye” calibration to determine the geometrical relationship between the surgical camera and tracking markers. In a robot mounted camera arrangement, hand-eye calibration estimates the rigid relationship between the robot and camera coordinate frames. Most hand-eye calibration techniques use a calibration object to estimate the relative transformation of the camera in several views of the calibration object and link these to the forward kinematics of the robot to compute the hand-eye transformation. Such approaches achieve good accuracy for general use but for applications such as robotic assisted minimally invasive surgery, acquiring a calibration sequence multiple times during a procedure is not practical. In this paper, we present a new approach to tackle the problem by using the robotic surgical instruments as the calibration object with well known geometry from CAD models used for manufacturing. Our approach removes the requirement of a custom sterile calibration object to be used in the operating room and it simplifies the process of acquiring calibration data when the laparoscope is constrained to move around a remote centre of motion. This is the first demonstration of the feasibility to perform hand-eye calibration using components of the robotic system itself and we show promising validation results on synthetic data as well as data acquired with the da Vinci Research Kit. 3D UltraSound (US) probes are used in clinical applications for their ease of use and ability to obtain intra-operative volumes. In surgical navigation applications a calibration step is needed to localize the probe in a general coordinate system. This paper presents a new hand-eye calibration method using directly the kinematic model of a robot and US volume registration data that does not require any 3D localizers. First results show a targeting error of 2.34 mm on an experimental setup using manual segmentation of five beads in ten US volumes. Purpose Spatial calibration between a 2D 3D ultrasound and a pose tracking system requires a complex and time-consuming procedure. Simplifying this procedure without compromising the calibration accuracy is still a challenging problem. To use a wrist-mounted sensor (such as a camera) for a robot task, the position and orientation of the sensor with respect to the robot wrist frame must be known. The sensor mounting position can be found by moving the robot and observing the resulting motion of the sensor. This yields a homogeneous transform equation of the form AX=XB, where A is the change in the robot wrist position, B is the resulting sensor displacement, and X is the sensor position relative to the robot wrist. The solution to an equation of this form has one degree of rotational freedom and one degree of translation freedom if the angle of rotation of A is neither 0 nor pi radians. To solve for X uniquely, it is necessary to make two arm movements and form a system of two equations of the form: A sub 1 X=XB sub 1 and A sub 2 X=XB sub 2 . A closed-form solution to this system of equations is developed and the necessary conditions for uniqueness are stated. >