Brain tumor enhancement revealed by 3D intraoperative ultrasound imaging in a navigation system

Introduction. Brain tumour resections are commonly guided using a navigation system based on preoperative MR data. In order to update the brain status which deforms during the surgery, 3D intraoperative ultrasound (3D-iUS) data are acquired. Tumour interpretation in the 3D-iUS data is however complex because of the image noise, artefacts and missing tumour parts. We propose therefore to enhance the visualization of the brain tumour in the 3D-iUS data using the navigation system. Methods. Our method is based on a tumour model generated by semi-automatic segmentation of the tumour in the preoperative MR data. The tumour model is pre-registered into the patient coordinate system using the transform matrix computed by the navigation system. The brain shift is then estimated using a rigid block matching algorithm between the pre-registered tumour model and the 3D-iUS data. The obtained fine-registered tumour model surface is finally described as a mesh and visualized in the navigation system overlapped on the 3D-iUS data. Results. This method was off-line tested on five patient data including 3D-iUS data and enhanced 3D-iUS data acquired with a contrast agent. In order to evaluate our method the Dice Similarity Index (DSI) was respectively computed between manual delineations of the tumours in the 3D-iUS data considered as references and the pre-registered and fineregistered tumour model. Results demonstrated that the DSI values increased after fine registration and in the enhanced 3D-iUS data. Conclusion. The advantage of using a tumour model combined to a rigid registration technique is to be robust to image artefacts and to be able to reconstitute the tumour information when it is missing in the 3D-iUS data. In order to better match the tumour model with the 3D-iUS data, the fine registered model will be used as initialization to a deformable segmentation method.