BME MS Thesis Defense: "Towards the Automatic Control of Laser Ablation for Surgical Applications" by Karim Tarabein

Monday, July 22, 2019
4:00 pm to 5:00 pm
Floor/Room #: 


The goal of this thesis is to propose and investigate a method of predicting depth of a laser dissection pulse in soft tissue without acquiring material properties of the tissue target or measuring the laser output.  The method proposed is similar to what is used by laser surgical operators today, but uses regression learning to perform on­ the fly predictions in place of a skilled laser surgeon. Power of the laser and the ablation depth were recorded for 57 samples and fed into the regression algorithm. Data  exclusion  was performed  using  Temperature  before  laser  action  as criteria. A linear and logarithmic model was explored using random points from the data post-exclusion, validation RMSE ranged from 135-200 µm. A linear and logarithmic model was explored using data points below a moving power threshold and validated with data points above said threshold, validation RMSE ranged from 108-170 µm. The T.test performed showed there was not a significant difference  between  the linear and the logarithmic models' goodness of fit  metrics,  but  it did show there was a significant difference between the model building methods (randomly selected data points, moving power threshold). The method of building a model using lower power levels to predict larger power levels had better goodness of fit metrics than the method  of selecting data points  at random.

Defense Committee:
Loris Fichera, PhD, Assistant Professor, Computer Science, WPI (Thesis Advisor)
Glenn Gaudette, PhDProfessor, Biomedical Engineering, WPI (Chair)
Cosme Furlong-Vazquez, PhD, Professor, Mechanical Engineering, WPI
Matthew Flegal, Adjunct Instructor, Biomedical Engineering, WPI


Department of Biomedical Engineering
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