Surgical resection is the initial treatment for nearly all brain tumors and the extent of resection is strongly correlated with prognosis. However, because brain tumors, especially gliomas, are intimately involved in surrounding functioning brain tissue, aggressive resection must be balanced against the risk of causing new neurological deficits. Modern advances in anatomical and functional imaging and the widespread adoption of neuro-navigation now help neurosurgeons to plan and execute an optimal surgical approach. Unfortunately, changes in the shape of the brain during surgery, known as brain shift, invalidate the assumption of all commercial neuro-navigation systems that preoperative data can be mapped to patient coordinates using rigid registration. Because brain shift progresses during surgery, the rigid registration of neuro- navigation systems is least accurate at the critical final stages of resection when the marginal tissue is being removed. There has been more than 20 years of research invested in measuring, modeling and compensating for brain shift with the goal of providing neuro-navigation systems with an accurate nonrigid registration from preoperative image data to the patient’s brain in the presence of brain shift. While results are promising, they are not yet accurate enough to be incorporated into commercial systems. Nonrigid registration is subject to both measurement and modeling uncertainty that varies throughout 3D space. Most nonrigid registration methods do not attempt to quantify this uncertainty and, to our knowledge, there have been no attempts to present this uncertainty to the surgeon. We believe that it is important to make surgeons aware of this uncertainty so that they can make informed decisions, particularly in locations where uncertainty is high. In this project, we plan to investigate nonrigid registration algorithms that model registration uncertainty explicitly, semi-automatic and fully-automatic nonrigid registration methods that utilize registration uncertainty to iteratively guide registration improvements, and visualization paradigms for effective presentation of registration uncertainty to surgeons in the surgical environment. We hypothesize that effective representation and visualization of registration uncertainty for brain shift correction in neuro-navigation will 1) lead to iterative semi-automatic and fully-automatic nonrigid registration methods that improve registration accuracy and 2) allow neurosurgeons to make more informed decisions during tumor resections that will lead to increased clinical impact of image-guided neurosurgery. We will carry out the following Aims: 1. Develop novel feature- based image registration algorithms that represent uncertainty explicitly; 2. Use registration uncertainty maps to guide semi- and fully-automatic nonrigid registration; 3. Evaluate the utility of nonrigid registration with uncertainty visualization in a clinical setting.