Abstract: The present invention relates to systems and methods for reducing noise in image data. Preferred embodiments relate to methods for analyzing two-photon in vivo imaging of biological systems. With neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, for example, harmonic regression plus colored noise model and an efficient cyclic descent algorithm for parameter estimation. This approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio.

Abstract: The present invention relates to systems and methods for reducing noise in image data. Preferred embodiments relate to methods for analyzing two-photon in vivo imaging of biological systems. With neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, for example, harmonic regression plus colored noise model and an efficient cyclic descent algorithm for parameter estimation. This approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio.