Yuanzheng Gong

Semi-autonomous simulated brain tumor ablation with RAVENII Surgical Robot using behavior tree

Medical robots have been widely used to assist surgeons to carry out dexterous surgical tasks via various ways. Most of the tasks require surgeons operation directly or indirectly. Certain level of autonomy in robotic surgery could not only free the surgeon from some tedious repetitive tasks, but also utilize the advantages of robot: high dexterity and accuracy. This paper presents a semi-autonomous neurosurgical procedure of brain tumor ablation using RAVEN Surgical Robot and stereo visual feedback. By integrating with the behavior tree framework, the whole surgical task is modeled flexibly and intelligently as nodes and leaves of a behavior tree. This paper provides three contributions mainly: (1) describing the brain tumor ablation as an ideal candidate for autonomous robotic surgery, (2) modeling and implementing the semi-autonomous surgical task using behavior tree framework, and (3) designing an experimental simulated ablation task for feasibility study and robot performance analysis.

Bound constrained bundle adjustment for reliable 3D reconstruction

Bundle adjustment (BA) is a common estimation algorithm that is widely used in machine vision as the last step in a feature-based three-dimensional (3D) reconstruction algorithm. BA is essentially a non-convex non-linear least-square problem that can simultaneously solve the 3D coordinates of all the feature points describing the scene geometry, as well as the parameters of the camera. The conventional BA takes a parameter either as a fixed value or as an unconstrained variable based on whether the parameter is known or not. In cases where the known parameters are inaccurate but constrained in a range, conventional BA results in an incorrect 3D reconstruction by using these parameters as fixed values. On the other hand, these inaccurate parameters can be treated as unknown variables, but this does not exploit the knowledge of the constraints, and the resulting reconstruction can be erroneous since the BA optimization halts at a dramatically incorrect local minimum due to its non-convexity. In many practical 3D reconstruction applications, unknown variables with range constraints are usually available, such as a measurement with a range of uncertainty or a bounded estimate. Thus to better utilize these pre-known, constrained, but inaccurate parameters, a bound constrained bundle adjustment (BCBA) algorithm is proposed, developed and tested in this study. A scanning fiber endoscope (the camera) is used to capture a sequence of images above a surgery phantom (the object) of known geometry. 3D virtual models are reconstructed based on these images and then compared with the ground truth. The experimental results demonstrate BCBA can achieve a more reliable, rapid, and accurate 3D reconstruction than conventional bundle adjustment.

Accurate three-dimensional virtual reconstruction of surgical field using calibrated trajectories of an image-guided medical robot.

Abstract. Brain tumor margin removal is challenging because diseased tissue is often visually indistinguishable from healthy tissue. Leaving residual tumor leads to decreased survival, and removing normal tissue causes life-long neurological deficits. Thus, a surgical robotics system with a high degree of dexterity, accurate navigation, and highly precise resection is an ideal candidate for image-guided removal of fluorescently labeled brain tumor cells. To image, we developed a scanning fiber endoscope (SFE) which acquires concurrent reflectance and fluorescence wide-field images at a high resolution. This miniature flexible endoscope was affixed to the arm of a RAVEN II surgical robot providing programmable motion with feedback control using stereo-pair surveillance cameras. To verify the accuracy of the three-dimensional (3-D) reconstructed surgical field, a multimodal physical-sized model of debulked brain tumor was used to obtain the 3-D locations of residual tumor for robotic path planning to remove fluorescent cells. Such reconstruction is repeated intraoperatively during margin clean-up so the algorithm efficiency and accuracy are important to the robotically assisted surgery. Experimental results indicate that the time for creating this 3-D surface can be reduced to one-third by using known trajectories of a robot arm, and the error from the reconstructed phantom is within 0.67 mm in average compared to the model design.

Mapping surgical fields by moving a laser-scanning multimodal scope attached to a robot arm

Endoscopic visualization in brain tumor removal is challenging because tumor tissue is often visually indistinguishable from healthy tissue. Fluorescence imaging can improve tumor delineation, though this impairs reflectance-based visualization of gross anatomical features. To accurately navigate and resect tumors, we created an ultrathin/flexible, scanning fiber endoscope (SFE) that acquires reflectance and fluorescence wide-field images at high-resolution. Furthermore, our miniature imaging system is affixed to a robotic arm providing programmable motion of SFE, from which we generate multimodal surface maps of the surgical field. To test this system, synthetic phantoms of debulked tumor from brain are fabricated having spots of fluorescence representing residual tumor. Three-dimension (3D) surface maps of this surgical field are produced by moving the SFE over the phantom during concurrent reflectance and fluorescence imaging (30Hz video). SIFT-based feature matching between reflectance images is implemented to select a subset of key frames, which are reconstructed in 3D by bundle adjustment. The resultant reconstruction yields a multimodal 3D map of the tumor region that can improve visualization and robotic path planning. Efficiency of creating these maps is important as they are generated multiple times during tumor margin clean-up. By using pre-programmed vector motions of the robot arm holding the SFE, the computer vision algorithms are optimized for efficiency by reducing search times. Preliminary results indicate that the time for creating these 3D multimodal maps of the surgical field can be reduced to one third by using known trajectories of the surgical robot moving the image-guided tool.

Toward real-time quantification of fluorescence molecular probes using target/background ratio for guiding biopsy and endoscopic therapy of esophageal neoplasia

Abstract. Multimodal endoscopy using fluorescence molecular probes is a promising method of surveying the entire esophagus to detect cancer progression. Using the fluorescence ratio of a target compared to a surrounding background, a quantitative value is diagnostic for progression from Barrett’s esophagus to high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC). However, current quantification of fluorescent images is done only after the endoscopic procedure. We developed a Chan–Vese-based algorithm to segment fluorescence targets, and subsequent morphological operations to generate background, thus calculating target/background (T/B) ratios, potentially to provide real-time guidance for biopsy and endoscopic therapy. With an initial processing speed of 2 fps and by calculating the T/B ratio for each frame, our method provides quasireal-time quantification of the molecular probe labeling to the endoscopist. Furthermore, an automatic computer-aided diagnosis algorithm can be applied to the recorded endoscopic video, and the overall T/B ratio is calculated for each patient. The receiver operating characteristic curve was employed to determine the threshold for classification of HGD/EAC using leave-one-out cross-validation. With 92% sensitivity and 75% specificity to classify HGD/EAC, our automatic algorithm shows promising results for a surveillance procedure to help manage esophageal cancer and other cancers inspected by endoscopy.

Toward real-time endoscopically-guided robotic navigation based on a 3D virtual surgical field model

The challenge is to accurately guide the surgical tool within the three-dimensional (3D) surgical field for roboticallyassisted operations such as tumor margin removal from a debulked brain tumor cavity. The proposed technique is 3D image-guided surgical navigation based on matching intraoperative video frames to a 3D virtual model of the surgical field. A small laser-scanning endoscopic camera was attached to a mock minimally-invasive surgical tool that was manipulated toward a region of interest (residual tumor) within a phantom of a debulked brain tumor. Video frames from the endoscope provided features that were matched to the 3D virtual model, which were reconstructed earlier by raster scanning over the surgical field. Camera pose (position and orientation) is recovered by implementing a constrained bundle adjustment algorithm. Navigational error during the approach to fluorescence target (residual tumor) is determined by comparing the calculated camera pose to the measured camera pose using a micro-positioning stage. From these preliminary results, computation efficiency of the algorithm in MATLAB code is near real-time (2.5 sec for each estimation of pose), which can be improved by implementation in C++. Error analysis produced 3-mm distance error and 2.5 degree of orientation error on average. The sources of these errors come from 1) inaccuracy of the 3D virtual model, generated on a calibrated RAVEN robotic platform with stereo tracking; 2) inaccuracy of endoscope intrinsic parameters, such as focal length; and 3) any endoscopic image distortion from scanning irregularities. This work demonstrates feasibility of micro-camera 3D guidance of a robotic surgical tool.

In vivo laser-based imaging of the human fallopian tube for future cancer detection

Inherited mutations in BRCA1 and BRCA2 lead to 20-50% lifetime risk of ovarian, tubal, or peritoneal carcinoma. Clinical recommendations for women with these genetic mutations include the prophylactic removal of ovaries and fallopian tubes by age 40 after child-bearing. Recent findings suggest that many presumed ovarian or peritoneal carcinomas arise in fallopian tube epithelium. Although survival rate is <90% when ovarian cancer is detected early (Stage_I), 70% of women have advanced disease (Stage_III/IV) at presentation when survival is less than 30%. Over the years, effective early detection of ovarian cancer has remained elusive, possibly because screening techniques have mistakenly focused on the ovary as origin of ovarian carcinoma. Unlike ovaries, the fallopian tubes are amenable to direct visual imaging without invasive surgery, using access through the cervix. To develop future screening protocols, we investigated using our 1.2- mm diameter, forward-viewing, scanning fiber endoscope (SFE) to image luminal surfaces of the fallopian tube before laparoscopic surgical removal. Three anesthetized human subjects participated in our protocol development which eventually led to 70-80% of the length of fallopian tubes being imaged in scanning reflectance, using red (632nm), green (532nm), and blue (442nm) laser light. A hysteroscope with saline uterine distention was used to locate the tubal ostia. To facilitate passage of the SFE through the interstitial portion of the fallopian tube, an introducer catheter was inserted 1- cm through each ostia. During insertion, saline was flushed to reduce friction and provide clearer viewing. This is likely the first high-resolution intraluminal visualization of fallopian tubes.

Axial-Stereo 3-D Optical Metrology for Inner Profile of Pipes Using a Scanning Laser Endoscope

As the rapid progress in the development of optoelectronic components and computational power, 3-D optical metrology becomes more and more popular in manufacturing and quality control due to its flexibility and high speed. However, most of the optical metrology methods are limited to external surfaces. This article proposed a new approach to measure tiny internal 3-D surfaces with a scanning fiber endoscope and axial-stereo vision algorithm. A dense, accurate point cloud of internally machined threads was generated to compare with its corresponding X-ray 3-D data as ground truth, and the quantification was analyzed by Iterative Closest Points algorithm.

Feature-Based Three-Dimensional Registration for Repetitive Geometry inMachine Vision

As an important step in three-dimensional (3D) machine vision, 3D registration is a process of aligning two or multiple 3D point clouds that are collected from different perspectives together into a complete one. The most popular approach to register point clouds is to minimize the difference between these point clouds iteratively by Iterative Closest Point (ICP) algorithm. However, ICP does not work well for repetitive geometries. To solve this problem, a feature-based 3D registration algorithm is proposed to align the point clouds that are generated by vision-based 3D reconstruction. By utilizing texture information of the object and the robustness of image features, 3D correspondences can be retrieved so that the 3D registration of two point clouds is to solve a rigid transformation. The comparison of our method and different ICP algorithms demonstrates that our proposed algorithm is more accurate, efficient and robust for repetitive geometry registration. Moreover, this method can also be used to solve high depth uncertainty problem caused by little camera baseline in vision-based 3D reconstruction.

Path planning for semi-automated simulated robotic neurosurgery

This paper considers the semi-automated robotic surgical procedure for removing the brain tumor margins, where the manual operation is a tedious and time-consuming task for surgeons. We present robust path planning methods for robotic ablation of tumor residues in various shapes, which are represented in point-clouds instead of analytical geometry. Along with the path plans, corresponding metrics are also delivered to the surgeon for selecting the optimal candidate in the automated robotic ablation. The selected path plan is then executed and tested on RAVEN™ II surgical robot platform as part of the semi-automated robotic brain tumor ablation surgery in a simulated tissue phantom.