Pixels or Coordinates? Computational Methods in Super-resolution Microscopy: Scientific Computing and Matrix Computations Seminar

Seminar: Scientific Computing | April 23 | 11 a.m.-12 p.m. | 380 Soda Hall

 Prof. Bo Huang, UCSF

 Electrical Engineering and Computer Sciences (EECS)

In super-resolution microscopy methods based on single-molecule switching (STORM/PALM), each camera frame samples a random, sparse subset of probe molecules in the samples. The final super-resolution image is assembled with the molecule coordinates extracted from thousands of frames. We have developed a sparse-signal recovery technique using compressed sensing to analyze camera images with highly overlapping fluorescent spots. This method allows one camera image to sample an order of magnitude more fluorophores, thus improving the temporal resolution of live imaging. In order to increase the total duration of observation limited by fluorophore photobleaching, we utilized the redundant information from adjacent snapshots in a time sequence. With a deformable registration algorithm, we have effectively reduced the number of camera frames required for super-resolution image reconstruction. Finally, we developed a framework for correlation analysis of coordinate-based super-resolution images. We have shown a variety of applications of this framework, including image alignment, single-molecule tracking and the quantification of colocalization.

 odedsc@cs.berkeley.edu, 510-516-4321