Light-sheet fluorescence microscopy (LSFM) is a powerful technique that can provide high-resolution images of biological samples. Therefore, this technique offers significant improvement for three-dimensional (3D) imaging of living cells. However, producing high-resolution 3D images of a single cell or biological tissues, normally requires high acquisition rate of focal planes, which means a large amount of sample sections. Consequently, it consumes a vast amount of processing time and memory, especially when studying real-time processes inside living cells. We describe an approach to minimize data acquisition by interpolation between planes using a phase retrieval algorithm. We demonstrate this approach on LSFM data sets and show reconstruction of intermediate sections of the sparse samples. Since this method diminishes the required amount of acquisition focal planes, it also reduces acquisition time of samples as well. Our suggested method has proven to reconstruct unacquired intermediate planes from diluted data sets up to 10× fold. The reconstructed planes were found correlated to the original preacquired samples (control group) with correlation coefficient of up to 90%. Given the findings, this procedure appears to be a powerful method for inquiring and analyzing biological samples.
Keywords: Gerchberg-Saxton algorithm; light-sheet microscopy; super resolution.
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