When imaging with classical waves, multiple scattering (MS) is often seen as an unavoidable obstacle. The diffraction-limited resolution obtainable with methods such as microscopy requires that single-scattering (SS) dominates; for depths where MS processes become important, such methods result in an image without any connection to the reflectivity of the medium. Conversely, techniques such as diffuse optical tomography take advantage of the diffuse nature of light, but their resolution power is limited. To do better, methods such as wavefront shaping and adaptive optics have been developed. Focussing through a thick diffusive layer was demonstrated using a transmission matrix approach consisting of the measurement of Green’s functions between each pixel of a spatial light modulator (SLM) and of a charge-coupled device (CCD) camera across the medium. To image inside a multiple-scattering medium, we present a matrix approach based on the experimental measurement of a reflection matrix from the medium. An analysis based on the geometric and statistical properties of this reflection matrix can enhance the SS contribution which would otherwise be swamped by MS at large depths, and correct the resulting image for aberration effects induced by the turbid medium itself. The correction does not require the presence of bright scatterers, does not rely on any feedback loop and works even at depths where the field-of-view contains several isoplanatic patches. Here we present the application of our reflection matrix approach to optical imaging in biological tissues. Compared to OCT and related methods, we demonstrate an extension of the current imaging-depth limit.
Laura Cobus, Amaury Badon, Dayan Li, Geoffroy Lerosey, Claude Boccara, Mathias Fink, and Alexandre Aubry, "Scattering reflection matrix approach to ultra-deep imaging through biological media (Conference Presentation)," Proc. SPIE 10502, Adaptive Optics and Wavefront Control for Biological Systems IV, 105020Y (Presented at SPIE BiOS: January 29, 2018; Published: 15 March 2018); https://doi.org/10.1117/12.2290704.5752161027001.
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