Registrierung und Visualisierung von 3D U/S und CT Datensätzen der Prostata

Brachytherapie ist eine Strahlentherapie, welche u. a. mit Hochenergie-Strahlenquellen in Hohlnadeln, die in den Körper des Patienten eingestochen werden, durchgeführt wird. Die präzise Konturierung des zu bestrahlenden Gewebes, sowie die genaue Plazierung der Hohlnadeln an den Positionen, welche durch das "Pre-Planing" vorgegeben werden, sind hierbei wichtige Arbeitsschritte. Bisher basiert die Behandlung des Prostatakarzionoms mittels Brachytherapie vornehmlich auf CT Aufnahmen, welche aber keine Echtzeit-Visualisierung während der Implantation der Katheter zulassen. Sind sowohl CT als auch 3D U/S Aufnahmen vorhanden, können diese registriert und fusioniert werden, um somit die Vorteile beider Modalitäten zu nutzen. Im folgenden werden die Untersuchungen zur Registrierung sowie Möglichkeiten zur Evaluierung dargestellt

Registration of 3D U/S and CT images of the Prostate

Radiotherapy is a rapidly growing cancer treatment technique. In brachytherapy -- one radiotherapy treatment technique -- pre- and post-planning is usually carried out using CT imaging. As CT scanners cannot easily be moved from one operation room to an other and as CT does not have real-time imaging capability, alternative imaging modalities are needed to realize the vision of image guided surgery. Ultrasound (U/S) is such an alternative imaging modality. For the comparison of U/S and CT image fusion is very useful

Object segmentation and shape reconstruction using computer-assisted segmentation tools

Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. Once an accurate segmentation is obtained, this information may be used by the radiologist to compare the volume and morphology characteristics of each region against known anatomical norms, other regions in the same image set, and corresponding regions in related image sets. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Many methods have been proposed to detect and segment 2D shapes, the most of which is template matching. However their low speed has prevented its wide spread use. Other techniques called snakes or active contours have been used, but the main drawbacks associated with their initialization and poor convergence to boundary concavities limit their utility. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. In this work we will present an effective semi-automatic method, based on the boundary tracking technique, which improves the time when one or more structures are in use. Different segmentation techniques would be proposed for the particular organs of interests (lungs, skin and spine canal) and a 3D shape reconstruction of these regions would be illustrate the efficiency of the segmentation techniques. Finally, the proposed technique would be compared with the manual segmentation obtained from the doctor experts using quantitative (shape matching measures) and qualitative (visual comparison) measures

Accuracy of needle implantation in brachytherapy using a medical AR system - a phantom study

Brachytherapy is the treatment method of choice for patients with a tumor relapse after a radiation therapy with external beams or tumors in regions with sensitive surrounding organs-at-risk, e. g. prostate tumors. The standard needle implantation procedure in brachytherapy uses pre-operatively acquired image data displayed as slices on a monitor beneath the operation table. Since this information allows only a rough orientation for the surgeon, the position of the needles has to be verified repeatedly during the intervention. Within the project Medarpa a transparent display being the core component of a medical Augmented Reality (AR) system has been developed. There, pre-operatively acquired image data is displayed together with the position of the tracked instrument allowing a navigated implantation of the brachytherapy needles. The surgeon is enabled to see the anatomical information as well as the virtual instrument in front of the operation area. Thus, the Medarpa system serves as 'window into the patient'. This paper deals with the results of first clinical trials of the system. Phantoms have been used for evaluating the achieved accuracy of the needle implantation. This has been done by comparing the output of the system (instrument positions relative to the phantom) with the real positions of the needles measured by means of a verification CT scan

Several marker segmentation techniques for use with a medical AR system - A comparison: Presentation held at the International Congress and Exhibition "Computer Assisted Radiology and Surgery (CARS) 2003. June 25 - 28, 2003, London

The Medarpa system is designed to support minimal invasive interventions. A semi-transparent display has been developed to provide the surgeon with information about the patient's anatomy as well as to display the instruments at their positions relative to the patient. Therefor a hybrid tracking system is used. One of the steps for setting up the system is the patient registration where externally attached markers are used, which are visible in the imaging modality used. Their segmentation is performed in a pre-processing step, and it is desireable to do this task using algorithms working (semi-)automatically. In this work three such segmentation methods are compared focusing on accuracy, speed and reliability