This paper describes the design and evaluation of the second generation of a robotic system called the light puncture robot for thoracic and abdominopelvic interventional radiology procedures under CT and MRI guidance. It is mounted on the patient's body and positions and inserts the needle according to the trajectory and target chosen by the radiologist in the image. The mechanical design is described in detail along with its forward and inverse kinematics. The robot can be segmented and registered fully automatically in both imaging modalities. Phantom experiments in the CT scanner and preliminary feasibility experiments in the MRI are described, showing a targeting accuracy of 3.3 ± 1.7 mm in gelatin for depths ranging from 30 to 90 mm and needle orientations of -13° to +15° about normal from the patient's skin. A detailed error analysis is discussed along with potential improvements in the system. The potential clinical advantages of the system include higher targeting accuracy for complex dual obliquity trajectories, the need for fewer images, and improved accessibility to MRI-guided interventions.

The study of cerebral microvascular networks requires high-resolution images. However, to obtain statistically relevant results, a large area of the brain (several square millimeters) must be analyzed. This leads us to consider huge images, too large to be loaded and processed at once in the memory of a standard computer. To consider a large area, a compact representation of the vessels is required. The medial axis is the preferred tool for this application. To extract it, a dedicated skeletonization algorithm is proposed. Numerous approaches already exist which focus on computational efficiency. However, they all implicitly assume that the image can be completely processed in the computer memory, which is not realistic with the large images considered here. We present in this paper a skeletonization algorithm that processes data locally (in subimages) while preserving global properties (i.e., homotopy). We then show some results obtained on a mosaic of three-dimensional images acquired by confocal microscopy.