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Three-dimensional (3D), in vivo and in live imaging of living samples with a sub-micrometer resolution is a current necessity for biomedical researches which corresponds to a hot topic for microscopy engineers. It is likely that the role of computational approaches is yet underestimated and underused in 3D-microscopy. This paper is an illustration of the fundamental role that could be played by such strategies. Usual imaging depths in biomedical microscopy reach few hundreds of micrometers in optimal conditions when multiphoton approaches are favored. However, light scattering and absorption still damage image quality, especially as imaging depth increases. Our approach rests on the multi-parametric 3D-PSF estimation of the true 3DGaussian model of the PSF along multi-millimeter depth. Image acquisition in MPM generates stacks of 2D images constituting an overall 3D-image. 3D-volumes of images containing a single object are selected and isolated all along the depth using automatic morphological tools. Finally, our computational 3DGaussian shape-fitting algorithm named FIGARO is applied on each individual PSF and quantify PSF full-width at half maximum (FWHM) in the 3 dimensions simultaneously with PSF tilt angles. FWHM evolution in the 3 dimensions along the 2 mm depth highlights a highly significant effect of spherical aberrations. Starting from standard values of PSF measured at sample top compared to what expected in MPM, we show an increase with a factor three of the PSF FWHM in axial plane when the sample bottom is reached, and no modification of PSF dimensions in lateral plane all along the depth. The main direction of the PSF shows a convergence toward a focal point that will be discussed

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Multi-parametric 3D-point-spread function estimation in deep multiphoton microscopy with an original computational strategy dedicated to the reconstruction of muscle images