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8 March 2019 Heuristic-based optimal path planning for neurosurgical tumor ablation
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Abstract
In brain tumor ablation procedures, imaging for path planning and tumor ablation are performed in two different sessions. Using pre-operative MR images, the neurosurgeon determines an optimal ablation path to maximize tumor ablation in a single path ablation while avoiding critical structures in the brain. After pre-operative path planning the patient undergoes brain surgery. Manual planning for brain tumor ablation is time-intensive. In addition, the preoperative images may not precisely match the intra-operative images due to brain shift after opening the skull. Surgeons sometimes therefore adjust the path planned during the surgery, which leads to increased anaesthesia and operation time. In this paper, a new heuristic-based search algorithm is introduced to find an optimal ablation path for brain tumors, that can be used both pre- and intra-operatively. The algorithm is intended to maximize the safe ablation region with a single path ablation. Given the tumor location, healthy tissue locations, and a random start point on the skull from medical images, our proposed algorithm computes all plausible entry points on the skull and then searches for different ablation paths that intersect with the tumor, avoids the critical structures, and finds the optimal path. We implemented Breadth First Search (BFS), Dijkstra, and our proposed heuristic based algorithms. In this paper we report the results of a comparative study for these methods in terms of the search space explored and required computation time to find an optimal ablation path.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ajeet Wankhede, Likhita Madiraju, Dipam Patel, Kevin Cleary, Chima Oluigbo, and Reza Monfaredi "Heuristic-based optimal path planning for neurosurgical tumor ablation", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512H (8 March 2019); https://doi.org/10.1117/12.2512352
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