Facile Conversion and Optimization of Structured Illumination Image Reconstruction Code into the GPU Environment.

Superresolution, structured illumination microscopy (SIM) is an ideal modality for imaging live cells due to its relatively high speed and low photon-induced damage to the cells. The rate-limiting step in observing a superresolution image in SIM is often the reconstruction speed of the algorithm used to form a single image from as many as nine raw images. Reconstruction algorithms impose a significant computing burden due to an intricate workflow and a large number of often complex calculations to produce the final image.

Rapid, artifact-reduced, image reconstruction for super-resolution structured illumination microscopy.

Super-resolution structured illumination microscopy (SR-SIM) is finding increasing application in biomedical research due to its superior ability to visualize subcellular dynamics in living cells. However, during image reconstruction artifacts can be introduced and when coupled with time-consuming postprocessing procedures, limits this technique from becoming a routine imaging tool for biologists. To address these issues, an accelerated, artifact-reduced reconstruction algorithm termed joint space frequency reconstruction-based artifact reduction algorithm (JSFR-AR-SIM) was developed by integrating a high-speed reconstruction framework with a high-fidelity optimization approach designed to suppress the sidelobe artifact.