Accuracy Analysis of an Image Guided Robotic Urology Surgery System

We present an evaluation of the accuracy of a system for image guided radical prostatectomy using the daVinci telemanipulator. The system is split into components and ten sources of error identified. The magnitude of three of these error sources; segmentation of bone from MRI, registration to patient using intraoperative ultrasound, and endoscope tracking error is determined experimentally. The remaining errors are estimated from the literature. We demonstrate that the distribution of ultrasound slices used for registration can reduce the system error by up to 0.7mm. Our results show that our system can localise the prostate to within 3.7mm RMS, and that the largest component of the this error is the segmentation of the pelvic bone from MRI

Design and Evaluation of Image Guidance Systems for RARP

INTRODUCTION: There is a strong appetite amongst laparoscopic surgeons for image guidance during the procedure. It seems intuitively obvious that providing the surgeon with additional information on the location of unseen anatomy can only improve patient outcomes. This is not necessarily the case however. If the system gives information that is not relevant to the procedure it becomes a distraction. Similarly, if the system has large alignment errors the information may be dangerously wrong. One danger is that image guidance systems can be developed on an ad-hoc basis based not on targeted clinical goals but on the technical expertise and research goals of the scientists and engineers involved. Such a system may or may not benefit the patient. However, there is a real danger, as discussed by [1], that such systems will be introduced into surgical practice without proper assessment. We present our minimalist image guidance system for robot assisted radical prostatectomy together with a design and evaluation framework built upwards from the desired clinical outcomes

Image Guided Robotic Radical Prostatectomy

We present results of early trials of a system to overlay preoperative MRI onto endoscope video taken from a daVinci endoscope system during robotic radical prostatectomy. The endoscope is calibrated and tracked so that the MRI can be overlaid in the same coordinate system as the video data and projected onto the screen with the correct camera parameters. The system has two potential applications. The first is that it enables the surgeon to easily refer to the preoperative MRI during surgery. Secondly, it may serve as an initialisation for a model to video registration method to enable the preoperative data to be updated during the procedure. The system has been trialled in five patients, with overlay images provided to the surgeon during surgery in two cases. Further trials are ongoing

Stochastic learning in a neural network with adapting synapses

We consider a neural network with adapting synapses whose dynamics can be analitically computed. The model is made of $N$ neurons and each of them is connected to $K$ input neurons chosen at random in the network. The synapses are $n$-states variables which evolve in time according to Stochastic Learning rules; a parallel stochastic dynamics is assumed for neurons. Since the network maintains the same dynamics whether it is engaged in computation or in learning new memories, a very low probability of synaptic transitions is assumed. In the limit $N\to\infty$ with $K$ large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analitically calculated by flow equations for the macroscopic parameters of the system.Comment: 25 pages, LaTeX fil

Metal-insulator transition in EuO

It is shown that the spectacular metal-insulator transition in Eu-rich EuO can be simulated within an extended Kondo lattice model. The different orders of magnitude of the jump in resistivity in dependence on the concentration of oxygen vacancies as well as the low-temperature resistance minimum in high-resistivity samples are reproduced quantitatively. The huge colossal magnetoresistance (CMR) is calculated and discussed

Use of a CT statistical deformation model for multi-modal pelvic bone segmentation

We present a segmentation algorithm using a statistical deformation model constructed from CT data of adult male pelves coupled to MRI appearance data. The algorithm allows the semi-automatic segmentation of bone for a limited population of MRI data sets. Our application is pelvic bone delineation from pre-operative MRI for image guided pelvic surgery. Specifically, we are developing image guidance for prostatectomies using the daVinci telemanipulator. Hence the use of male pelves only. The algorithm takes advantage of the high contrast of bone in CT data, allowing a robust shape model to be constructed relatively easily. This shape model can then be applied to a population of MRI data sets using a single data set that contains both CT and MRI data. The model is constructed automatically using fluid based non-rigid registration between a set of CT training images, followed by principal component analysis. MRI appearance data is imported using CT and MRI data from the same patient. Registration optimisation is performed using differential evolution. Based on our limited validation to date, the algorithm may outperform segmentation using non-rigid registration between MRI images without the use of shape data. The mean surface registration error achieved was 1.74 mm. The algorithm shows promise for use in segmentation of pelvic bone from MRI, though further refinement and validation is required. We envisage that the algorithm presented could be extended to allow the rapid creation of application specific models in various imaging modalities using a shape model based on CT data

Accurate Single Image Multi-Modal Camera Pose Estimation

Abstract. A well known problem in photogrammetry and computer vision is the precise and robust determination of camera poses with respect to a given 3D model. In this work we propose a novel multi-modal method for single image camera pose estimation with respect to 3D models with intensity information (e.g., LiDAR data with reflectance information). We utilize a direct point based rendering approach to generate synthetic 2D views from 3D datasets in order to bridge the dimensionality gap. The proposed method then establishes 2D/2D point and local region correspondences based on a novel self-similarity distance measure. Correct correspondences are robustly identified by searching for small regions with a similar geometric relationship of local self-similarities using a Generalized Hough Transform. After backprojection of the generated features into 3D a standard Perspective-n-Points problem is solved to yield an initial camera pose. The pose is then accurately refined using an intensity based 2D/3D registration approach. An evaluation on Vis/IR 2D and airborne and terrestrial 3D datasets shows that the proposed method is applicable to a wide range of different sensor types. In addition, the approach outperforms standard global multi-modal 2D/3D registration approaches based on Mutual Information with respect to robustness and speed. Potential applications are widespread and include for instance multispectral texturing of 3D models, SLAM applications, sensor data fusion and multi-spectral camera calibration and super-resolution applications

No Scalar Hair Theorem for a Charged Spherical Black Hole

This paper consolidates noscalar hair theorem for a charged spherically symmetric black hole in four dimension in general relativity as well as in all scalar tensor theories, both minimally and nonminimally coupled, when the effective Newtonian constant of gravity is positive. However, there is an exception when the matter field itself is coupled to the scalar field, such as in dilaton gravity.Comment: 13 pages, Latex format, some minor corrections are made, accepted for publication in Physical Review