Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain

• Published in: International journal for computer assisted radiology and surgery Vol. 9; no. 4; pp. 659 - 667
• Main Authors: Yeniaras, E., Fuentes, D. T., Fahrenholtz, S. J., Weinberg, J. S., Maier, F., Hazle, J. D., Stafford, R. J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2014
• Subjects: Brain - surgery; Computer Imaging; Computer Science; Health Informatics; Humans; Imaging; Laser Therapy - methods; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Radiology; Retrospective Studies; Software; Surgery; Vision; Planning software; 3D Slicer; MRI guidance; Treatment simulation; Laser-induced thermal therapy; Tissue ablation
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-013-0948-x

Purpose    An open-source software system for planning magnetic resonance (MR)-guided laser-induced thermal therapy (MRgLITT) in brain is presented. The system was designed to provide a streamlined and operator-friendly graphical user interface (GUI) for simulating and visualizing potential outcomes of various treatment scenarios to aid in decisions on treatment approach or feasibility. Methods    A portable software module was developed on the 3D Slicer platform, an open-source medical imaging and visualization framework. The module introduces an interactive GUI for investigating different laser positions and power settings as well as the influence of patient-specific tissue properties for quickly creating and evaluating custom treatment options. It also provides a common treatment planning interface for use by both open-source and commercial finite element solvers. In this study, an open-source finite element solver for Pennes’ bioheat equation is interfaced to the module to provide rapid 3D estimates of the steady-state temperature distribution and potential tissue damage in the presence of patient-specific tissue boundary conditions identified on segmented MR images. Results    The total time to initialize and simulate an MRgLITT procedure using the GUI was < 5 min. Each independent simulation took < 30 s, including the time to visualize the results fused with the planning MRI. For demonstration purposes, a simulated steady-state isotherm contour ( 57 ∘ C ) was correlated with MR temperature imaging ( N  = 5). The mean Hausdorff distance between simulated and actual contours was 2.0 mm ( σ = 0.4 mm ) , whereas the mean Dice similarity coefficient was 0.93 ( σ = 0.026 ) . Conclusions    We have designed, implemented, and conducted initial feasibility evaluations of a software tool for intuitive and rapid planning of MRgLITT in brain. The retrospective in vivo dataset presented herein illustrates the feasibility and potential of incorporating fast, image-based bioheat predictions into an interactive virtual planning environment for such procedures.