No abstract

We adapt a graphics processing unit (GPU) to dynamic quantitative second-harmonic generation imaging. We demonstrate the temporal advantage of the GPU-based approach by computing the number of frames analyzed per second from SHG image videos showing varying fiber orientations. In comparison to our previously reported CPU-based approach, our GPU-based image analysis results in ∼10× improvement in computational time. This work can be adapted to other quantitative, nonlinear imaging techniques and provides a significant step toward obtaining quantitative information from fast in vivo biological processes.

No abstract