Predicating ablation volume for laser interstitial thermal therapy (LiTT) for computerassisted planning of minimally invasive neurosurgery

Project reference: DTP_SIE_06 
First supervisor: Rachel Sparks 
Second supervisor: Sebastien Ourselin 

Start Date: October 2020 

Project summary:  Laser interstitial thermal therapy (LiTT) LiTT is a novel therapy which may provide a minimally invasive means of ablating structures within the brain. Thermal ablation is a lesioning technique that has been used in neurosurgery for many years with variable success. The main limitation to earlier methods were the unpredictable nature of thermal lesioning and the lack of real-time monitoring. LiTT is a laser catheter techniques that allows for real-time monitoring of lesioning under MR thermography. However, a critical part to the process, both in terms of safety and efficacy, involves pre-operative planning of the laser trajectory as this ultimate dictates ablation location and volume. 

Project description:  Current computer-assisted planning techniques assume crude geometric approximation of the ablation volume, typically as a 15mm diameter around the laser catheter. However, brain vascularity, proximity to bone and the ventricles result in asymmetric tissue heating. Developing computational models to more accurately estimate the heat transfer, and ultimately predict ablation volume would enable more accurate placement of laser catheter, could provide guidance on the heating parameters to use to obtain optimal ablation results. We propose to develop computation modelling techniques to predict ablation volume and use these models to identify optimal laser catheter parameters. 
The student will develop tools to predict ablation volume for selective lesioning of the amygdalohippocampal complex using a multi-center retrospective dataset of over 100 LiTT procedures. These procedures have a clearly defined trajectory approach, limiting the variability of the ablative volume between patients. For a limited set of patients (20) we have corresponding MR thermography data over the course of the procedure with which to learn and validate the course of heating. 
Second, computational modelling and prediction of heating will be integrated into a computerassisted planning software for the trajectory, and expected heating can be used to aid surgeons in planning trajectories. A prospective trial taking place at Dartmouth will be used to validate the accuracy of the predicted ablation volume. 
Finally, if AHC ablation prediction is accurate, we will investigate expanding this method to other ablative procedures including corpus callosotomy and hypothalamic hamartoma where the approach and anatomy are more variable and may require more complex models of heat dissipation and its relationship to anatomy. 

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