Computed microscopy: quantitative, deep-tissue imaging

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Optical microscopy is the most widely used imaging tool in laboratories all around the world. Indeed, According to BCC market research, the global optical microscopy market will be worth US$6.3 billion in 2020. Several Nobel prizes have been awarded for contributions made to the development of optical microscopy, including most recently in 2014. There is, however, a major limitation facing optical microscopy: it is difficult, if not impossible, to image tissue hidden beneath layers of overlying tissue. This occurs for the same reason that it is difficult to see clearly through a window covered in rain drops - tissue is highly scattering, like rain drops, and critically degrades image quality. This is important as it prevents in-tact tissue from being imaged in its natural environment, requiring tissue to instead be sliced into thin sections. A variety of approaches have been used in an attempt to overcome this problem. All such approaches are generally similar in that they insert hardware into the microscope in an attempt to compensate for the degradation due to the sample. This is similar to humans using spectacles to overcome imperfections of their eye. The main difference is that opticians are able to precisely determine the imperfections that each eye has, and thus design spectacles which perfectly compensate for them. No such method has been developed for measuring sample induced imperfections, or aberrations, present in microscope images.This project proposes to do just that: measure the imperfections caused by the sample itself. This will be achieved by computing the optical structure of the sample (i.e., how light travels in the sample) via a two stage process. Firstly, the sample will be imaged by a microscope capable of performing rapid three-dimensional imaging called an optical coherence microscope (OCM). OCM works very much like ultrasound imaging, except light is used instead of sound waves. The second step involves developing a sophisticated computational procedure for calculating the sample's optical structure from the OCM image. This will be performed using a recently mathematical model, developed recently by the project team, which allows OCM images to be predicted from a given sample structure. Clearly, our task is to solve the opposite problem: calculate the sample's structure given a measured OCM image. Formal techniques have been established for solving the problem in the opposite fashion which will be adapted specifically for this project.Once the sample's optical structure has been solved, in a follow-on project, existing methods will be employed for restoring optical fluorescence microscope images which have been degraded by the sample itself. This will enable fluorescence microscopy to be performed at depths within tissue which are currently inaccessible. This will be highly advantageous to many biological researchers in the UK and the world.