International Journal of Humanoid Robotics

The concept of the Acrobot^® system is described. The technical details of the complete system are then outlined, including the pre-operative planner which incorporates 3D CT models together with CAD models of prostheses that can be used to plan the leg alignment, position the prostheses, plan the shape of the cuts required and generate the regions within which cuts must be constrained. The robotic system is also described, together with the methods for locating and clamping the patient. An outline is given of the means by which the preoperative model is registered or aligned to the intra-operative position of the patient and of the robot, without the need for fiducial markers. Post-operative results are given, for both total knee replacement and also for the more recent clinical trials using a minimally invasive robotic procedure for uni-condylar arthroplasty.

This paper presents a novel four degree of freedom haptic system for virtual reality knee arthroscopy training. The compact device, named OrthoForce, has a large workspace and force feedback in all axes (yaw, pitch, roll and translation). The robust device uses a novel haptic rendering algorithm to provide smooth haptic response at multiple simultaneous contact points anywhere along the tool (including twist feedback with the hook). It is capable of displaying high impedances that realistically simulate the impact with hard objects. In particular, the novel use of a ball screw spline provides a maximum impedance of 20 N/mm for translation as well as roll torque feedback in a small structure. Not only does this device meet the requirements for haptic use in knee arthroscopy training, but due to its small size and large range of motion, it has applications in other virtual reality training systems for a range of minimally invasive procedures.

Prostate surface detection from ultrasound images plays a key role in our recently developed ultrasound guided robotic biopsy system. However, due to the low contrast, speckle noise and shadowing in ultrasound images, this still remains a difficult task. In the current system, a 3D prostate surface is reconstructed from a sequence of 2D outlines, which are performed manually. This is arduous and the results depend heavily on the user's expertise. This paper presents a new practical method, called Evolving Bubbles, based on the level set method to semi-automatically detect the prostate surface from transrectal ultrasound (TRUS) images. To produce good results, a few initial bubbles are simply specified by the user from five particular slices based on the prostate shape. When the initial bubbles evolve along their normal directions, they expand, shrink, merge and split, and finally are attracted to the desired prostate surface. Meanwhile, to remedy the boundary leaking problem caused by gaps or weak boundaries, domain specific knowledge of the prostate and statistical information are incorporated into the Evolving Bubbles. We apply the bubbles model to eight 3D and four stacks of 2D TRUS images and the results show its effectiveness.

In this paper, a prototype of a robotic system for accurate and consistent insertion of a percutaneous biopsy needle into the prostate is presented. A transrectal ultrasound (TRUS) probe is used to collect a series of two-dimensional (2D) images of the prostate. These images are used to create a 3D computer model of the organ which is used by the urologist to define the biopsy points within the prostate. The robotic system then calculates the required trajectory of the needle, which can be simulated on the computer. Following this, the robot is configured accordingly and the actual biopsy is performed. Cadaveric and human trials have validated the robot's needle placement error to be less than 2.5 mm. Our future work includes the integration of a cancer predictive modality into the system to increase the cancer detection rate. The robotic system could also be modified to accurately place foreign bodies into the prostate, which could improve therapeutic procedures such as Brachytherapy.

We report the current development of a new computer assisted system for local and remote training of transurethral resection of the prostate (TURP). The system is based on a 3D computer model of the prostate gland, which simulates deformations and resections of prostatic tissue. Simulated resections of the prostate can be repeatedly performed by the residents of urology to enhance their training. Automatic prostate recognition software was developed for the construction of a large library of prostate models. We report the software development of the system and show promising preliminary results of a new prostate recognition algorithm.

The development of prostate biopsy robotics can make biopsies both automatic and accurate. However, intervention from urologists is still needed to define the location of biopsy cores. With the aid of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging (MRSI) diagnosis information obtained pre-operationally, it is possible to guide the biopsy needle towards those sites where cancer is suspected, thereby achieving higher detection rates. In this paper, a deformable image registration method is presented for the purpose of merging MRI/MRSI and transrectal ultrasound (TRUS) images. Given the poor quality of ultrasound (US) images and the deformation occurring across modalites, a thin-plate spline transformation is used to match the prostate surfaces and thereafter their volumes. A deformable prostate phantom that simulates the condition in humans was also set up for validation purposes. Fifteen fiducial markers were implanted inside the phantom prostate to act as the reference of "ground truth." The phantom study shows that our method can achieve an accuracy around 1.28 ± 0.50 mm, with voxel dimensions of 0.5 × 0.5 × 0.5 mm³. This result is promising since none of the knowledge about the interior prostate is utilized in the algorithm. Experimental results on patient data are also presented.

This paper presents novel techniques and their architectural translations for real-time image segmentation of endoscopic images, which are required for micro-robotic auto navigation systems. The proposed technique is based on a two-step process to segment the lumen regions from endoscopic images. In the first step, an adaptive progressive thresholding technique based on Otsu's method is employed to obtain a preliminary region of interest of the lumen region. A novel architecture for the between-class variance computation of Otsu's method is presented to meet the real-time requirements of the system. The proposed implementation employs binary logarithmic computations to eliminate the complex divisions and multiplications in Otsu's procedure. In the second step, an Iris filter is employed to enhance the boundary of the region of interest to facilitate an accurate detection of the lumen region. An architecture based on the coordinate rotation digital computer is proposed to simplify the complex computations of trigonometric functions required by the Iris filter operation. Software simulations demonstrate that the proposed technique requires a significantly smaller number of iterations to obtain an accurate segmentation result as compared to a previously reported method. In addition, synthesis results on the field-programmable gate array show that the proposed architectures can achieve high performance with low hardware resource utilization.

For decades, humans have used robots to overcome their natural limitations in their interaction with their environment. In recent years, robots have been developed to assist surgeons in performing surgeries. Since these robots are meant for surgeons' use, the design and considerations for safety, reliability and the human–robot interface are of primary importance. One of the most common modes of controlling surgical robots is using a master and slave layout. This paper explores the latest developments in the area of master–slave robotic systems for surgery.