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Abstract #1839

Fast Preoperative Planning Method for MR-Guided Laser Ablation in Brain

Erol Yeniaras1, David T. A. Fuentes2, Samuel J. Fahrenholtz1, Jeffrey S. Weinberg3, Florian Maier1, Anil Shetty4, John D. Hazle1, R. Jason Stafford1

1Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States; 2Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States; 3Neurosurgery, MD Anderson Cancer Center, Houston, TX, United States; 4Visualase, Inc, Houston, TX, United States

In this work we introduce a new computational methodology and for minimally-invasive MR-guided laser-induced thermal therapy for brain tumors. We also evaluate the viability of the steady-state method on real human data retrospectively.

Keywords

ablation ablations accuracy achieve adapted akin applicator applicators approaches architecture around assisted authors axes biomedical biopsy blue boundaries brain brings built cancer complex complications components computational conditions conductive confidence convective cost critical critically custom damage delivery distribution edema enables environment established evaluate evaluated expected expressed facilitate fast feasibility findings friendly graphical guidance guided hardware health heating highest human immense inaccuracies incorporating induced interface international introduce isotherm isotherms john keyhole laser literature machine macro major measured minor model monitoring mouse navigation necessarily neurosurgery open operator opinions optical outcomes overall overlays parallel planning platform plotted potential power preoperative previously procedural procedure procedures quantitative rapidly reached reflect registered registration respectively retrospective scanner scattering scenarios segmentation segmentations selection simulated simulation simulations slicer software solver source standardized steady step structures subjects surgery system systems target tasks temperature temperatures therapy thermal tissue tools treatment tumor type uncertainty user vessels views virtual visible visualization volume watts whereas white