Choosing a deconvolution algorithm can be beneficial when imaging nanoprobes in skin by means of two-photon microscopy. By design, deconvolution algorithms can increase the signal to noise ratio of the raw images and thus make it easier to identify discrete, subresolution nanoprobes from blurry two-photon image data. This poses the favorable benefit of knowing more precise locations of nanoprobes inside skin.
This thesis demonstrates how the Expectation-Maximization deconvolution algorithm (EM algorithm) can be applied to three-dimensional, two-photon images depicting quantum dot nanoprobes inside human skin. This was accomplished in part by devising a way to deliver nanoprobes inside skin by means of low frequency ultrasound. Many nanoprobes become sparsely scattered inside skin when using this nanoprobe delivery methodology.
The scattered nanoprobes resulting from the nanoprobe delivery pose a unique benefit in acquiring an experimental point spread function of the imaging system. This in turn gives an accurate representation of the point spread function that can be used as an input to the EM algorithm. The methodology of utilizing the EM algorithm in this manner is presented.
|Commitee:||Brooks, Dana, DiMarzio, Charles, Niedre, Mark|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- Massachusetts|
|Source:||MAI 50/02M, Masters Abstracts International|
|Subjects:||Biomedical engineering, Electrical engineering, Nanoscience|
|Keywords:||Deconvolution, Image processing, Microscopy, Quantum dots, Skin, Sonophoresis|
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