Refractive-index measurement and inverse correction using optical coherence tomography.

We describe a novel technique for determination of the refractive index of hard biological tissue as well as nonopaque technical samples based on optical coherence tomography (OCT). Our method relies on an inverse refractive-index correction (I-RIC), which matches a measured feature geometry distorted due to refractive-index boundaries to its real geometry. For known feature geometry, the refractive index can be determined with high precision from the best match between the distorted and corrected images. We provide experimental data for refractive-index measurements on a polymethylmethacrylate (PMMA) and on an ex vivo porcine cranial-bone, which are compared to reference measurements and previously published data. Our method is potentially capable of in vivo measurements on rigid biological tissue such as bone as, for example, is required to improve guidance in robot-aided surgical interventions and also for retrieving complex refractive-index profiles of compound materials.

Landmark-based registration of a cochlear model to a human cochlea using conventional CT scans.

Cochlear implants can provide an advanced treatment option to restore hearing. In standard pre-implant procedures, many factors are already considered, but it seems that not all underlying factors have been identified yet. One reason is the low quality of the conventional computed tomography images taken before implantation, making it difficult to assess these parameters. A novel method is presented that uses the Pietsch Model, a well-established model of the human cochlea, as well as landmark-based registration to address these challenges. Different landmark numbers and placements are investigated by visually comparing the mean error per landmark and the registrations' results. The landmarks on the first cochlear turn and the apex are difficult to discern on a low-resolution CT scan. It was possible to achieve a mean error markedly smaller than the image resolution while achieving a good visual fit on a cochlear segment and directly in the conventional computed tomography image. The employed cochlear model adjusts image resolution problems, while the effort of setting landmarks is markedly less than the segmentation of the whole cochlea. As a next step, the specific parameters of the patient could be extracted from the adapted model, which enables a more personalized implantation with a presumably better outcome.

Image Processing of Conventional Computer Tomography Images for Segmentation of the Human Cochlea.

Against the background of increasing numbers of indications for Cochlea implants (CIs), there is an increasing need for a CI outcome prediction tool to assist the process of deciding on the best possible treatment solution for each individual patient prior to intervention. The hearing outcome depends on several features in cochlear structure, the influence of which is not entirely known as yet. In preparation for surgical planning a preoperative CT scan is recorded. The overall goal is the feature extraction and prediction of the hearing outcome only based on this conventional CT data. Therefore, the aim of our research work for this paper is the preprocessing of the conventional CT data and a following segmentation of the human cochlea. The great challenge is the very small size of the cochlea in combination with a fairly bad resolution. For a better distinction between cochlea and surrounding tissue, the data has to be rotated in a way the typical cochlea shape is observable. Afterwards, a segmentation can be performed which enables a feature detection. We can show the effectiveness of our method compared to results in literature which were based on CT data with a much higher resolution. A further study with a much larger amount of data is planned.