Lab

Laboratory of Imaging Technologies


About the lab

R&D in Medical Imaging and Computer Vision
(University of Ljubljana, Faculty of Electrical Engineering, Slovenia)

Featured research (6)

Reflectance acquired with a multimode optical fiber probe can be related to optical properties of an investigated turbid medium by utilizing a light propagation model. During this step, a calibration of the light propagation model is required, as the modeled reflectance is normalized to the light energy of the source fiber, while the experimentally acquired reflectance is normalized to a reflective standard. Since currently established calibration methods based on liquid and solid turbid phantoms suffer from drawbacks such as low stability and dependence on other characterization methods, we propose a new method for reflectance calibration that is based on modeling and acquisition of probe-to-target distance reflectance profiles from first surface mirrors. We show that the spectrally resolved calibration factors can be estimated with a repeatability of 2% and agree within 10% with the reference values obtained by using turbid phantoms based on aqueous suspensions of polystyrene microspheres.
Significance: Current open-source Monte Carlo (MC) method implementations for light propagation modeling are many times tedious to build and require third-party licensed software that can often discourage prospective researchers in the biomedical optics community from fully utilizing the light propagation tools. Furthermore, the same drawback also limits rigorous cross-validation of physical quantities estimated by various MC codes. Aim: Proposal of an open-source tool for light propagation modeling and an easily accessible dataset to encourage fruitful communications amongst researchers and pave the way to a more consistent comparison between the available implementations of the MC method. Approach: The PyXOpto implementation of the MC method for multilayered and voxelated tissues based on the Python programming language and PyOpenCL extension enables massively parallel computation on numerous OpenCL-enabled devices. The proposed implementation is used to compute a large dataset of reflectance, transmittance, energy deposition, and sampling volume for various source, detector, and tissue configurations. Results: The proposed PyXOpto agrees well with the original MC implementation. However, further validation reveals a noticeable bias introduced by the random number generator used in the original MC implementation. Conclusions: Establishing a common dataset is highly important for the validation of existing and development of MC codes for light propagation in turbid media.

Lab head

Members (5)

Tomaž Vrtovec
  • University of Ljubljana
Ziga Spiclin
  • University of Ljubljana
Miran Bürmen
  • University of Ljubljana
Peter Naglič
  • University of Ljubljana
Maksimilijan Bregar
  • University of Ljubljana
Boštjan Likar
Boštjan Likar
  • Not confirmed yet
Žiga Bizjak
Žiga Bizjak
  • Not confirmed yet
Yevhen Zelinskyi
Yevhen Zelinskyi
  • Not confirmed yet