Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.

Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.

Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.

Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.

Abstract: There is provided a method for determining the concentration of a fluorophore in a medium using moments of order k of the fluorescence signal. The method allows higher fidelity 3-dimensional reconstructions of the fluorophore in the medium. The method can be applied in imaging of fluorophores in biological tissues.

Abstract: There is provided a method for detecting and characterizing abnormalities within biological tissues. The method involves the characterization of the optical properties of the tissue to derive relative values of physiological properties between normal and suspicious regions of the tissue. In some aspects of the invention optical imaging and other imaging modalities are combined to detect and identify a disease state of the tissue.

Abstract: There is provided a method for detecting and characterizing abnormalities within biological tissues. The method involves the characterization of the optical properties of the tissue to derive relative values of physiological properties between normal and suspicious regions of the tissue. In some aspects of the invention optical imaging and other imaging modalities are combined to detect and identify a disease state of the tissue.

Abstract: There is provided a method for determining the concentration of a fluorophore in a medium using moments of order k of the fluorescence signal. The method allows higher fidelity 3-dimensional reconstructions of the fluorophore in the medium. The method can be applied in imaging of fluorophores in biological tissues.

Abstract: The present invention relates to a method and a system for optical imaging of an object in transmission configuration. The method and system obtain contour coordinates of the object using source/detector configurations references and acquire optical data from a region of interest (ROI) of the object. Then, the method and system apply a weighting factor to said optical data as a function of the contour coordinates, and reconstruct an image of the ROI using the weighted optical data and photon diffusion equation.

Abstract: There is provided a method for optimizing wavelength selection for multiwavelength optical data acquisition of chromophores in a turbid medium. The optimization is based on the minimization of a criterion based on the variance matrix of chromophores estimate features. The method can advantageously be used to obtain physiological information from biological tissue.