Theoretical model for laser ablation outcome predictions in brain: calibration and validation on clinical MR thermometry images.

ABSTRACT:

Purpose

Neurosurgical laser ablation is experiencing a renaissance. Computational tools for ablation planning aim to further improve the intervention. Here, global optimisation and inverse problems are demonstrated to train a model that predicts maximum laser ablation extent.

Methods

A closed-form steady state model is trained on and then subsequently compared to N = 20 retrospective clinical MR thermometry datasets. Dice similarity coefficient (DSC) is calculated to provide a measure of region overlap between the 57 °C isotherms of the thermometry data and the model-predicted ablation regions; 57 °C is a tissue death surrogate at thermal steady state. A global optimisation scheme samples the dominant model parameter sensitivities, blood perfusion (ω) and optical parameter (

SUBMITTER: Fahrenholtz SJ 

PROVIDER: S-EPMC6295207 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature