Name
Abstract | ||
|---|---|---|
This paper presents the simulation and learning of soft tissue temperature dynamics when exposed to laser radiation. Monte Carlo simulation is used to represent the photon distribution in the tissue while machine learning techniques are used to obtain the mapping from controllable laser inputs (power, pulse rate and exposure time) to the correspondent changes in temperature. This model is required to predict the effects of laser-tissue interaction during surgery, i.e., tissue incision depth and carbonization. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-38682-4_12 | IWANN (2) |
Keywords | Field | DocType |
monte carlo simulation,soft tissue temperature dynamic,laser radiation,tissue incision depth,pulse rate,laser exposure,laser-tissue interaction,controllable laser input,correspondent change,photon distribution,exposure time | Computer science,Carbonization,Artificial intelligence,Pulse rate,Photon,Laser exposure,Monte Carlo method,Pattern recognition,Simulation,Optics,Nonlinear regression,Laser,Radiation | Conference |
Volume | ISSN | Citations |
7903 | 0302-9743 | 2 |
PageRank | References | Authors |
0.44 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Loris Fichera | 1 | 21 | 3.71 |
| Diego Pardo | 2 | 12 | 1.80 |
| Leonardo S. Mattos | 3 | 123 | 28.31 |