Lab
Laboratory of Imaging Technologies
About the lab
R&D in Medical Imaging and Computer Vision
(University of Ljubljana, Faculty of Electrical Engineering, Slovenia)
(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)
Boštjan Likar
Žiga Bizjak
Yevhen Zelinskyi