Researchers have devised a new computational de-scattering approach termed de-scattering with excitation patterning in temporal focusing microscopy (DEEP-TFM). They employed two-photon patterned excitation with wide-field temporal focusing, but the signal was detected using a wide-field imaging detector.
They created a modified temporal focusing microscope that uses a digital micromirror device to transmit arbitrary excitation patterns onto the focal plane. A camera detects the emission light from the modulated excitation. Excitation patterns maintain their fidelity despite traversing via scattering surfaces because of NIR wavelengths.
Scattering at or near the surface has a minor impact on TFM images; nevertheless, it decreases the images’ high-frequency information as the imaging depth increases. DEEP-TFM combines excitation pattern information with captured images to reconstruct a de-scattered image computationally. In wide-field imaging, the number of images required to de-scatter a single FOV depends only on the imaging depth.
The effectiveness of DEEP-TFM was first demonstrated in a simulation by comparing it to TFM and PSMPM. A stack of mouse neuron images (256 by 256 by 156 voxels), acquired experimentally with a point-scanning two-photon microscope, was used as the ground truth. PSMPM requires over 10 million measurements for this (one for each voxel). Then, for each depth plane, the ground truth data were convolved with the corresponding depth’s scattering point spread function (sPSF) to generate the simulated TFM images.
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