A Fast Deconvolution Algorithm for 3D Microscope Images

DESCRIPTION (provided by applicant): The time lapse 3D microscope imaging of living cells is widely used in many biomedical research areas. This technique potentially generates hundreds of image volumes in a single experiment, which are critical to the visualization and quantification of fundamental biological mechanisms such as gene expression and protein dynamics. Deconvolution is often applied to the images to improve contrast and resolution before further image analysis and measurements are performed. Deconvolution may take 3-5 hours to process data produced in an experiment of 15-30 minutes, creating a bottleneck in the data flow. Current deconvolution algorithms are either robust but not efficient enough for the large number of images (e.g. MLEM algorithm), or fast but sensitive to noise (e.g. Gold's algorithm). A novel efficient and robust algorithm is proposed which assembles the current leading algorithms constructively to take advantage of their individual strengths. A preliminary study has shown its potential to increase the deconvolution speed by several factors. The immediate benefits are that the deconvolution time is significantly reduced, and the research throughput is increased. The Phase I research will focus on the development of the core algorithm. Sample data sets, primarily wide-field microscope images, will be obtained from collaborators, and feasibility will be evaluated by examining the feature structures in the deconvolved images based on commonly accepted criteria. An analysis of how the PSF varies throughout a time-lapse experiment will be undertaked to assess if the PSF needs to be continually re-estimated throughout the deconvolution of the sequence. The Phase II research will investigate further acceleration methods for deconvolution, thoroughly assess the performance of the algorithm using wide-field, spinning disk confocal and laser scanning confocal microscope images, and a fast deconvolution software module will be developed. Application of the fast algorithms to processing very large single data volumes will also be investigated in Phase II.