학술논문

Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain
Document Type
Original Paper
Source
International Journal of Computer Assisted Radiology and Surgery: A journal for interdisciplinary research, development and applications of image guided diagnosis and therapy. July 2014 9(4):659-667
Subject
Planning software
MRI guidance
Laser-induced thermal therapy
3D Slicer
Treatment simulation
Tissue ablation
Language
English
ISSN
1861-6410
1861-6429
Abstract
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 (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.