Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.576347
Title: Sensorless adaptive optics in advanced microscopy techniques
Author: Müllenbroich, Marie Caroline
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2012
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Abstract:
Adaptive optics is a technique that is able to restore diffraction limited performance in optical systems that are adversely affected by optical aberrations. In this doctoral thesis adaptive optics technology has been implemented in systems for imaging and micromanipulation whose performance degrade with increasing penetration depth due to refractive index mismatch. With a focus on optical trapping and nonlinear microscopy, the combination of a deformable membrane mirror and a random search optimisation algorithm is used to improve system performance at depth by pre-shaping the incoming wavefront with aberrations that are equal but opposite to those that degrade quality. The approach used in this work does not require the use of a wavefront sensor but instead the optimisation algorithm randomly alters the shape of the deformable membrane mirror until a specifically chosen merit factor is satisfactorily improved. This work demonstrates the improvement of the specific quality that is chosen as the merit factor by factors ranging from 1.2 to 8 fold. Specifically, the lateral trapping strength of an optical trap was improved by an average factor of 3.8 at a depth of 131 m by optimising on a merit factor that is directly proportional to lateral trapping strength. In the nonlinear microscope, signal intensity is increased more than 7 fold when imaging fibroblast cells at a depth of 92 m by optimising on the fluorescence intensity. Limitations and pitfalls in the implementation of sensorless adaptive optics are examined and possible solutions are investigated. Optical systems that have been optimised in this way reduce the risk of photo-induced damage by reducing the necessary incident laser power and therefore improve sample viability.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.576347  DOI: Not available
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