Multiresolution, model-based segmentation of MR angiograms.

During the last decade, the quality of MR angiograms has risen substantially and their clinical utility has been demonstrated progressively. This acceptance has created a need for tools with which to summarize and display the information available. We have used a model-based segmentation technique to extract vascular morphology and local flow parameters from phase contrast MR angiograms. A multiresolution data structure is used as the basis of recursive decision-making to identify regions of blood flow. The resulting data representation allows more efficient data handling in subsequent processing and visualization and is directly applicable to the creation of a connected graph model of vascular regions. We describe this flow feature extraction algorithm and demonstrate the utility of the results.

Computer-assisted surgical planning for cerebrovascular neurosurgery.

OBJECTIVE: We used three-dimensional reconstructed magnetic resonance images for planning the operations of 16 patients with various cerebrovascular diseases. We studied the cases of these patients to determine the advantages and current limitations of our computer-assisted surgical planning system as it applies to the treatment of vascular lesions. METHODS: Magnetic resonance angiograms or thin slice gradient echo magnetic resonance images were processed for three-dimensional reconstruction. The segmentation, based on the signal intensities and voxel connectivity, separated each anatomic structure of interest, such as the brain, vessels, and skin. A three-dimensional model was then reconstructed by surface rendering. This three-dimensional model could be colored, made translucent, and interactively rotated by a mouse-controlled cursor on a workstation display. In addition, a three-dimensional blood flow analysis was performed, if necessary. The three-dimensional model was used to assist in three stages of surgical planning, as follows: 1) to choose the best method of intervention, 2) to evaluate surgical risk, 3) to select a surgical approach, and 4) to localize lesions. RESULTS: The generation of three-dimensional models allows visualization of pathological anatomy and its relationship to adjacent normal structures, accurate lesion volume determination, and preoperative computer-assisted visualization of alternative surgical approaches. CONCLUSION: Computer-assisted surgical planning is useful for patients with cerebrovascular disease at various stages of treatment. Lesion identification, therapeutic and surgical option planning, and intraoperative localization are all enhanced with these techniques.