Laser-tissue interactions: modeling and robotic control

Lasers are used in a growing number of surgical procedures as ideal tools for cutting, ablating or coagulating tissue. Their use is gaining popularity with the progress of precision medicine, which aims to treat diseases with minimal damage to healthy tissue. This is because laser power can be modulated to produce precise and high-quality effects on tissue, such as clean incisions on soft tissue. This is crucial for the success of delicate operations, such as microsurgeries. 

In traditional laser surgery, the incision quality depends entirely on the surgeon’s experience and ability to manipulate the laser and its parameters. Extensive training is required to develop an effective laser cutting technique, which includes both (i) the acquisition of basic knowledge of the physical principles behind laser ablation of tissue; and (ii) the ability to manipulate the laser parameters and its exposure time in order to provide accurate cutting.

It is not trivial to regulate all related parameters to achieve high-quality laser ablation outcomes. It is even more difficult to control such system to achieve a desired cutting depth. Therefore, this research aims at investigating and modeling the interaction between laser energy and soft tissues as the enabling knowledge for the creation of novel technologies for precision laser surgery. This includes assistive technologies and surgical automation methods.

Cognitive modeling of laser-tissue interactions

In this research, we are using regression and optimization techniques to model the laser ablation process on soft tissues. Data from real tissue ablations are acquired based on controlled experiments and confocal microscopy analysis. This data is then used to identify the parameters of theoretical laser-tissue interaction models, producing equations that can accurately describe the laser ablation process.


Sample results from soft tissue laser ablation experiments

Depth map

Depth map of a laser ablation crater obtained from confocal microscopy


Automatic control of soft tissue laser ablation

Once the laser-tissue interactions are modeled, we can devise automatic controllers capable of executing laser incisions with high accuracy in all three dimensions (desired trajectory on tissue surface and depth).



Assistive systems for ablation depth control

The identified laser ablation models allow us to estimate ablation depth in real-time. This is a rich piece of information that can be exploited, for example, to enhance the surgeon awareness during laser procedures or to implement supervisory algorithms for enhanced surgical safety.

Real-time incision depth information can be fed-back to the surgeon using different modalities and sensory channels (such as visual, auditory or haptic). Understanding which modality is best suited for this application is not trivial. Therefore, our research has also investigated the pros and cons of different potential technologies.

The first option to be tested was visual feedback through augmented reality, as presented in the video below. Subsequently, a more comprehensive study involved kinesthetic and vibrotactile haptic feedback.

Real-time visual feedback on estimated laser incision depth



Results from experiments comparing different feedback technologies for laser incision depth control: Kinesthetic and vibrotactile haptic feedback can effectively enhance such control


Related publications

  1. Olivieri, E., Barresi, G., Caldwell, D., Mattos, L., “Haptic Feedback for Control and Active Constraints in Contactless Laser Surgery: Concept, Implementation and EvaluationIEEE Transactions on Haptics, vol. 11, issue 2, pp. 241-254, ISSN 1939-1412, April-June 2018
  2. Acemoglu, A., Fichera, L., Kepiro, I., Caldwell, D., Mattos, L., “Laser Incision Depth Control in Robot-Assisted Soft Tissue MicrosurgeryJournal of Medical Robotics Research, Vol. 02, No. 03, September 2017
  3. Fichera, L., Pardo, D., Illiano, P., Ortiz, J., Caldwell, D., Mattos, L., “Online Estimation of Laser Incision Depth for Transoral Microsurgery: Approach and Preliminary EvaluationThe International Journal of Medical Robotics and Computer Assisted Surgery, March 2015
  4. Pardo, D., Fichera, L., Caldwell, D., Mattos, L., “Learning temperature dynamics on agar-based phantom tissue surface during single point CO2 laser exposureNeural Processing Letters Journal, Springer, October 2014
  5. Fichera, L., Pacchierotti, C., Olivieri, E., Prattichizzo, D., Mattos, L., “Kinesthetic and Vibrotactile Haptic Feedback Improves the Performance of Laser Microsurgery2016 IEEE Haptics Symposium (HAPTICS 2016), pp. 59 – 64, Philadelphia, USA, April 2016
  6. Fichera, L., Pardo, D., Illiano, P., Caldwell, D., Mattos, L., “Feed Forward Incision Control for Laser Microsurgery of Soft Tissue” (Best Paper Award Finalist) Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA 2015), pp. 1235–1240, Seattle, USA, May 2015
  7. Fichera, L., Kepiro, I., Illiano, P., Caldwell, D., Mattos, L., “Towards Automatic Laser Incision of Soft Tissues for Transoral Microsurgery” 5th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery (CRAS 2015), Brussels, Belgium, September, 2015
  8. Fichera, L., Pardo, D., Caldwell, D., Mattos, L., “New Assistive Technologies for Laser Microsurgery” 4th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery (CRAS 2014), Genoa, Italy, October, 2014
  9. Pardo, D., Fichera, L., Caldwell, D., Mattos, L., “Thermal supervision during robotic laser microsurgeryProceedings of the 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2014), pp. 363 – 368, São Paulo, Brazil, August 2014
  10. Fichera, L., Pardo, D., Mattos, L., “Artificial cognitive supervision during robot-assisted laser surgery” 3rd Joint Workshop on New Technologies for Computer/Robot Assisted Surgery, Verona, Italy, September, 2013
  11. Fichera, L., Pardo, D., Mattos, L., “Supervisory System for Robot Assisted Laser Phonomicrosurgery2013 International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, Osaka, Japan, July 2013
  12. Fichera, L., Pardo, D., Mattos, L., “Modeling Tissue Temperature Dynamics during Laser ExposureLecture Notes in Computer Science, 12th International Work-Conference on Artificial Neural Networks (IWANN 2013), pp. 96 – 106, Tenerife, Spain, June 12–14, 2013