Surgical removal, or resection, is the most important treatment for brain tumors. When tumors are located near critical brain areas such as motor, sensory, or language functions, the goal of complete resection must be balanced with the goal of preservation of function. Injury to critical white matter connections, or tracts, will leave the patient with serious neurological deficits. However, during surgery, the tracts are not visible to the surgeon's eye, and their consistency may be the same as the tumor. Thus, the surgical treatment of brain tumors can benefit tremendously from more complete, accurate structural-functional brain maps. Diffusion tensor MRI (DTI) is a relatively new MRI modality that is sensitive to the structure of the white matter. This project aims to translate current DTI research into the operating room to address challenges of white matter tract identification during surgery. The long-term objectives of this project are to improve white matter mapping for neurosurgery and to increase our understanding of the effect of neurosurgery on the white matter. The trajectories of the white matter tracts can be mapped using diffusion tensor DTI tractography. But because the tracts of interest must be selected for viewing in a process called virtual dissection that can take 10 minutes per tract, the clinical use of intraoperative DTI tractography has been limited in scope. Most often only one crucial tract has been mapped per patient. We hypothesize that by using the patient's own brain as a reference, i.e. by taking advantage of patient-specific models of crucial tracts contained in their personalized surgical plan, we can quickly and accurately produce an updated brain map when the patient is scanned during surgery. We will develop a software system to produce the white matter brain map on the fly during neurosurgery, and we will perform two validation studies to test our hypothesis. We propose the following objectives that will take advantage of our new state-of-the-art, high field strength intraoperative 3T MRI system and our research neuronavigation software platform, 3D Slicer. (1) Develop and optimize computational methods for rapid identification of crucial fiber tracts in intraoperative DTI. This part of the project will focus on mathematical development of tract similarity measures, and on software design and implementation. (2) Validate speed and accuracy of the system via a multi-rater study and electrical stimulation in the OR. This part of the project will focus on clinical validation of the system by a neurosurgeon and neuroradiologist, and it will allow us to test our original hypothesis that we can quickly create an accurate brain map for the white matter during surgery.
In neurosurgery for brain tumors, the surgeon's aim is to remove as much as possible of the brain tumor while preserving crucial areas of the brain, including gray matter cortex and white matter connections. Thus, the surgical treatment of brain tumors can benefit tremendously from more complete, accurate structural-functional brain maps. The goal of this project is to quickly provide an updated accurate map of the brain's white matter connections to the neurosurgeon during brain surgery.
|O'Donnell, Lauren J; Golby, Alexandra J; Westin, Carl-Fredrik (2013) Fiber clustering versus the parcellation-based connectome. Neuroimage 80:283-9|
|O'Donnell, Lauren J; Rigolo, Laura; Norton, Isaiah et al. (2012) fMRI-DTI modeling via landmark distance atlases for prediction and detection of fiber tracts. Neuroimage 60:456-70|