We report on femtosecond nanosurgery of fluorescently labeled structures in cells with a spatially superresolved laser beam. The focal spot width is reduced using phase filtering applied with a programmable phase modulator. A comprehensive statistical analysis of the resulting cuts demonstrates an achievable average resolution enhancement of 30 %.
We determined the bubble radius R_(max) for femtosecond optical breakdown in water at 347, 520, and 1040 nm with an unprecedented accuracy (+/-10 nm). At threshold, R_(max) was smaller than the diffraction-limited focus radius and ranged from 190 nm to 320 nm. The increase of R_(max) with laser energy E_(L) is slowest at 347 nm, providing optimum control of cell surgery. Experimental results agree with a model of bubble formation in heated and thermoelastically stretched liquids. Theory predicts a threshold temperature T_(th) approximately equal to 168 degrees C. For T>300 degrees C, a phase explosion sets in, and R_(max) increases rapidly with E_(L).
Two universal reconstruction methods for photoacoustic (also called optoacoustic or thermoacoustic) computed tomography are derived, applicable to an arbitrarily shaped detection surface. In photoacoustic tomography acoustic pressure waves are induced by illuminating a semitransparent sample with pulsed electromagnetic radiation and are measured on a detection surface outside the sample. The imaging problem consists in reconstructing the initial pressure sources from those measurements. The first solution to this problem is based on the time reversal of the acoustic pressure field with a second order embedded boundary method. The pressure on the arbitrarily shaped detection surface is set to coincide with the measured data in reversed temporal order. In the second approach the reconstruction problem is solved by calculating the far-field approximation, a concept well known in physics, where the generated acoustic wave is approximated by an outgoing spherical wave with the reconstruction point as center. Numerical simulations are used to compare the proposed universal reconstruction methods with existing algorithms.
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