When one tries to look deeper into cells or tissues, optical aberrations caused by the specimen itself lead to an enlarged Point Spread Function (PSF) and hence a degradation of the optical resolution. Adaptive optics makes it possible to correct these aberrations and preserve the ultimate (small) PSF, using an active element such as a deformable mirror.
However, another important limitation when imaging into biological tissues is light scattering, which is the deflection of rays from their original directions by random refractive index inhomogeneities due to cell components and supramolecular structures. In the extreme case, this leads to a speckle pattern and one may think that any information about the object is lost.
Two main questions then arise :
- How to describe the transition, from the aberration regime to the speckle one ? More precisely, what are the parameters (imaging depth, properties of the refractive index inhomogeneities, etc.) that drive this transition ?
- Can one make profit of the speckle characteristics to still get information about the object ?
We propose to study these questions using fluorescence fluctuation techniques. These methods make it possible to measure protein concentration, mobility and interactions in living specimen, by analysing the signal fluctuations caused by fluorescent molecules as they diffuse across a small observation volume. Any distortion of the observation volume induces a bias on the measurements. At LIPhy, we have shown that adaptive optics can be used to stabilize fluorescence fluctuations measurement in aberrating samples, like homogeneous solutions or single cells . We now want to go further by combining adaptive optics and fluorescence fluctuation techniques to understand the limits and potentialities of this approach in scattering media.
The experiments will be performed on home-built microscopes equipped with deformable mirrors for aberration correction, using scattering phantoms or cellular layers.
Antoine Delon – email@example.com – 04 76 63 58 01
Irène Wang – firstname.lastname@example.org – 04 76 51 47 27