Research Projects

Die klinische Rechtsmedizin gewann in den letzten Jahren aufgrund einer Sensibilisierung der Öffentlichkeit gegenüber häuslicher und sexueller Gewalt, Gewalt gegenüber Kindern und Verdachtsfällen von medizinischen Behandlungsfehlern stark an Bedeutung. Die forensische Untersuchung von Lebenden ist bis heute jedoch auf eine äussere Besichtigung des Körpers beschränkt.

Das neue Ludwig-Boltzmann-Institut (LBI) für klinisch-forensische Bildgebung hat zum Ziel, Verfahren zur Erfassung von inneren Verletzungsbefunden als Grundlage für forensische Gutachten zu entwickeln. Mittels Computertomographie (CT) und Magnetresonanztomographie (MRT), welche in der Klinik etabliert sind, können zusätzliche, objektiv nachweisbare innere Verletzungsbefunde erhoben werden, die eine verbesserte Einschätzung der ausgeübten Gewalt gegen die untersuchte Person ermöglichen. Die Methoden sind jedoch auf klinische Diagnostik ausgerichtet, während forensisch wichtige Befunde nicht oder nicht optimal dargestellt werden.

Das Institut fuer Maschinelles Sehen und Darstellen kooperiert mit dem LBI zur Entwicklung neuer Methoden der Bildverarbeitung und Computergrafik zum Zwecke der Bildgebung.

After medical treatment of visually handicapped people it is desirable to evaluate the benefit of the treatment for the patient. Especially the capability of the patient to orient himself in a three-dimensional environment, to navigate and recognize obstacles is of interest.

For a clinical evaluation under controlled circumstances a labyrinth has been built through which the patient ha to navigate. Obstacles may be randomly placed in the labyrinth. A multi-camera system keeps track of the patients movements and extracts parameters such as position, speed, head rotation etc.

Breast cancer is the most common cancer among women. In the European Community breast cancer is diagnosed in 157,000 cases and kills 70 000 annually. Early detection is essential to increase the surveillance rate. Therefore a lot of work has been done in recent years to enhance the prediction quality of the three most promising image modalities for breast cancer detection: X-ray imaging is used for screening of woman older than 50 years. Younger woman with dense breasts can be examined with sonography. Functional magnet resonance imaging gives insight in the interaction of different tissue types with the contrast agent.

Radiologists face the difficulty of combining the information obtained by non invasive methods to reduce the need for biopsy and surgery. In this project methods to fuse X-ray and fMR images will be researched.

Partner: Image Diagnost

Introduction

Research interests in single-modality nonlinear registration include four different kinds of subproblems. Deformable registration of two or more CT lung data sets at different states in the breathing cycle going from Functional Residual Capacity (FRC, expiration) to Total Lung Capacity (TLC, inspiration) for modelling breathing motion and deriving lung ventilation. Deformable registration of a contrast-enhanced and a native CT lung data set for deriving lung perfusion. Deformable registration of contrast-enhanced and native CT liver data sets at one or several phases in the contrast-uptake cycle for liver perfusion. And finally, highly accurate partially rigid bone registration for head and neck CT-Angiography applications to extract bone structures from CTA images.

Deformable Lung Registration

The input for this task consists of native CT thorax scans at two or more different breathing states between Total Lung Capacity (TLC, inspiration) and Functional Residual Capacity (FRC, expiration). Deformable registration of distinct breathing states is a prerequisite for deriving ventilation information by simple subtraction of expiration from inspiration data or by fusion with special functional scans and it leads to models of breathing motion in the lung. For this purpose we have available high resolution sheep lung data at up to five distinct static breathing states and human lung data at inspiration/expiration. Another application of deformable lung registration is the fusion of native and contrast-enhanced CT lung data to show perfusion information again either by subtraction or by fusion with a special scan. A notion of vessel consistency should be included in the deformable registration, since it is important that the same amount of vessels is regarded before and after registration.

Deformable Liver Registration

Similar to the lung registration, liver registration for perfusion measurements is a topic of interest. Contrast-enhancing techniques are used to get up to 8 liver images at different phases of the contrast uptake cycle. Each of these images has to be registered to a native scan to correct motions due to breathing. Afterwards subtraction techniques are used to derive the amount of perfusion in the liver. The setup of the registration algorithm is very similar to the lung registration problem.

Partially Rigid Bone Registration

The intended application of rigid bone registration is a very accurate registration of bones from native and contrast-enhanced CT images of the head and the neck. In contrast-enhanced images vessels and bones have very similar intensities, such that simple segmentation algorithms like thresholding do not work which are frequently used for CTA image studies. The intended strategy for the removal of bone structures is to take a simple (threshold-based) bone segmentation taken from the native image and register it to the contrast-enhanced image. Registration is necessarysince small patient movements may occur (especially in the neck and shoulder area) between the acquisition of both kinds of images. Registration has to be very accurate in this area, since there are vessel structures that lie close to or inside the bone structures as well. It can be assumed that the bones themselves are rigid but the relative position of bones to each other may change. Pairs of bones should be registered rigidly but the relative bone movements are taken into account leading to a partially rigid registration scheme.

Soft tissue like tendons, arteries, veins or skins are important biological materials. A greater understanding of the foundations and interactions of structure and function of soft tissue, and, in particular, the associated mechanobiology is of great interest in the field. A thorough understanding of the complex interrelations between mechanical factors and the associated biological responses may help to improve diagnostics which allow disease and injury to be treated earlier. The research proposed here will develop a fully automatic system for analyzing macroscopic structures obtained from histological images of arteries by means of modern computer vision techniques. Besides being interesting from the mechanobiological point of view the structural analysis of images of collagen fibers poses also several challenging questions from a computer vision point of view. In particular, due to the wide variety of different appearances of collagen fibers in images this task is non trivial. The main task of this research is the development of novel segmentation techniques for robustly segmenting individual fibril bundles and estimating their parameters, like location and shape, fibril density, mean fibril orientation, wriggling of fibrils etc. This will be achieved by developing novel perceptual grouping methods operating on the extracted orientation data of fibrils. Another major challenge of this research is to extend the structural analysis from 2D to 3D.

Resection is the treatment of choice for patients suffering from liver tumors. Knowledge about involved liver segments, tumor size and topographic relationship of the tumor to vessels is needed for the decision if sufficient liver function capacity is guaranteed after a resection and for detailed planning of a possible resection. The main information sources are cross sectional imaging modalities like CT which deliver 2D images. The radiologist has thus to put all the information of the cross sectional images together in order to provide the surgeons with the needed information about the 3D topology. This process is difficult, tedious and time consuming. Combining the methods of medical computer vision and graphics a liver surgery planning system can be developed that enables a better overview and thus helps unfolding the full potential of surgical methods. Available approaches show that a number of improvements, specially on the fields of automation of segmentation and user friendly visualization are necessary to attain clinical applicability and gain the full acceptance by radiologists and surgeons. The goal of this research project is to develop an experimental environment for the staging of liver operations. Special efforts will be put on two issues. First, a fully automated segmentation of the liver, its vessels and tumors will be studied. Second, the environment for the interactive, cooperative visualization of the medical sensor data, the extracted anatomical structures, and for the use of tools to assess the best surgical approach will be developed and assessed. After development, the approaches for segmentation/partitioning, visualization and interactive resection will undergo a careful validation procedure.