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Advances in technique and instrumentation have enabled surgeons to perform a growing array of procedures through laparoscopy. However, these efforts have often been compromised by exerting excessive forces during retraction of the structures necessary for anatomical view. Here, we present a comparative study of human and robotic performance in force controlled organ retraction during laparoscopic Nissen fundoplication (LNF). Six female pigs (20–25 kg) were anesthetized, intubated, placed on mechanical ventilation, and pneumoperitoneum (13mm Hg CO2) was established. A force sensing retractor (FSR) was constructed to record the forces applied in retracting the stomach during dissection of the esophageal hiatus (EH). The FSR was calibrated using known forces and then operated by either human alone or robot under human guidance using the FSR data. The EH was visualized, dissected, and LNF completed. Less force was utilized with robotic (74.3±10.5 grams) versus human (108.9±34.3 grams) retraction (p=0.007) to obtain proper anatomical view of the EH. No significant differences were observed for retraction setup time (robot 14.3±0.8 minutes and human 13.7±9.9 minutes) or hiatal dissection time (robot 14.0±3.0 minutes and human 14.0±6.1 minutes). These preliminary results present our continuing effort to develop and evaluate an automated surgical assistant for laparoscopy. As increasingly advanced, personnel-intensive laparoscopic procedures are performed, robotic retraction may present a superior alternative to human retraction by minimizing the forces exerted on organs yet maintaining excellent anatomical view.

A tracking algorithm is presented that extracts line features for use with a 3D reconstruction of blood vessels of the brain. The result is not only a segmented blood vessel tree but also a line intensity measure, and moreover directions, center lines and diameters are calculated. The algorithm consists of four modules. The first module is a star-shaped prefilter whose result are directions and amplitudes of prospective line structures at each point of the input image. These features are input to a connectivity module that connects neighbouring pixels with similar properties by vectors. The next module tracks the lines along the vectors as long as particular continuity rules are fulfilled and calculates a line intensity measure. Based only on the directions and intensities the fourth module extracts structural information such as the line diameters and the center lines very fast. The only input parameter of the algorithm is the minimum contrast of the lines to be detected.

Today the integration of information from different imaging modalities in medicine such as Computer Tomography or Magnetic Resonance Imaging (MRI) is left to the physician and gets little support from computers. In the case of neuroradiological diagnosis, information about cerebral blood vessels is available from 3D volume data from Magnetic Resonance Angiography (MRA) and from 2D images generated by Digital Subtraction Angiography (DSA). The DSA images have a higher resolution than MRA data, and therefore neuroradiologists are highly interested in a 3D reconstruction of cerebral blood vessels from different DSA projections. On the other hand, MRA contains important functional information, the velocity of blood flow. This paper describes work in progress to make available to the physician the full 3D information from both imaging modalities including an approach to 3D reconstruction from DSA im ages which makes use of the MRA data. The 3D DSA reconstruction also opens the way to an integration of information from DSA with completely different types of information, for example information on anatomical structure or soft tissue from MRI. An integral part of this work is a pilot system for clinical validation.

Wir stellen einen Ansatz zur Extraktion schmaler Adern in verrauschten medizinischen Einzelbildern vor. Es werden im wesentlichen Eigenschaften wie die nahezu konstante Aderdicke, die Parallelitat und gleiche Steilheit der Rander als Vorwissen benutzt. Obwohl an Verzweigungsstellen noch Erkennungsfehler auftreten, reicht die Auflosung auserhalb dieser Bereiche bis zu einer Aderbreite von 2-3 Bildpunkten. Der Algorithmus ist klinisch noch nicht erprobt und nicht als Produkt erhaltlich.