Purpose: Develop a treatment planning framework for neurosurgeons treating high-grade gliomas with LITT to minimize the learning curve and improve tumor thermal dose coverage. Methods: Deidentified patient images were segmented using the image segmentation software Materialize MIMICS©. Segmented images were imported into the commercial finite element analysis (FEA) software COMSOL Multiphysics© to perform bioheat transfer simulations. The laser probe was modeled as a cylindrical object with radius 0.7 mm and length 100 mm, with a constant beam diameter. A modeled laser probe was placed in the tumor in accordance with patient specific patient magnetic resonance temperature imaging (MRTi) data. The laser energy was modeled as a deposited beam heat source in the FEA software. Penne’s bioheat equation was used to model heat transfer in brain tissue. The cerebrospinal fluid (CSF) was modeled as a solid with convectively enhanced conductivity to capture heat sink effects. In this study, thermal damage-dependent blood perfusion was assessed. Pulsed laser heating was modeled based on patient treatment logs. The stationary heat source and pullback heat source techniques were modeled to compare the calculated tissue damage. The developed bioheat transfer model was compared to MRTi data obtained from a laser log during LITT procedures. The application builder module in COMSOL Multiphysics© was utilized to create a Graphical User Interface (GUI) for the treatment planning framework. Results: Simulations predicted increased thermal damage (10–15%) in the tumor for the pullback heat source approach compared with the stationary heat source. The model-predicted temperature profiles followed trends similar to those of the MRTi data. Simulations predicted partial tissue ablation in tumors proximal to the CSF ventricle. Conclusion: A mobile platform-based GUI for bioheat transfer simulation was developed to aid neurosurgeons in conveniently varying the simulation parameters according to a patient-specific treatment plan. The convective effects of the CSF should be modeled with heat sink effects for accurate LITT treatment planning.

Autodesk Moldflow Insight (AMI) is a Computer-Aided Engineering (CAE) tool used to predict many molding phenomena such as pres-sure, filling pattern, cooling pattern, and deflection of injection molded parts. The purpose of this study is to validate AMI for use with plastic connector housings and determine the key factors in improving simulation accuracy. To validate AMI, a 3D Computer-Aided Design (CAD) model of a hypothetical, simplified connector housing with round circuit holes was created in Creo software. Then, a test mold was built using the simplified connector housing geometry with round core pins for the part circuit holes. The mold was outfitted with pressure sensors to monitor the exact pressures achieved in both the runner system and cavity. Using this mold, the validation part was manufactured more than fifteen times using an identical combination of material and processing conditions. Pressure sensors placed in the mold capture the exact pressures achieved in the part and runner system during molding. The averaged pressure profiles for each sensor are later compared against results from the simulation software to determine accuracy of the simulation program. Computer simulation models of the validation part were created with a mesh density of eight layers through the thickness of the model, which is appropriate for connector housing. The average values of the fifteen identically molded parts are then compared to the simulation results. This study results in an improved method for simulating pressure profiles during plastic injection molding using refined process parameter definitions.