A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
This addresses the challenge of real-time brain shift correction during image-guided neurological surgery, though it appears incremental as it builds on existing registration methods with active user input.
The authors tackled the problem of brain shift compensation in neurosurgery by developing a feature-driven active framework for Ultrasound-to-Ultrasound registration, achieving robust and accurate results on clinical data.
A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data acquired during neurosurgery.