Fluorescence Imaging Flow Cytometry With Enhanced Image Resolution

Abstract

In one aspect, a system for performing flow cytometry is disclosed, which comprises a laser for generating laser radiation for illuminating a sample, at least one detector for detecting at least a portion of a radiation emanating from the sample in response to said illumination so as to generate a temporal signal corresponding to said detected radiation, and an analysis module for receiving said temporal signal and performing a statistical analysis of said signal based on a forward model to reconstruct an image of said sample.

1 .- 27 . (canceled) 
     
     
         28 . A method for performing flow cytometry, comprising:
 illuminating a sample with a laser radiation,   deploying a detector to detect at least a portion of a radiation emanating from the sample in response to said illumination and to generate a temporal signal corresponding to said detected radiation, and   utilizing a computer processor to perform a statistical analysis of said signal based on a forward model so as to reconstruct an image of said sample.   
     
     
         29 . The method of  claim 28 , wherein said image is any of a fluorescence, a darkfield, and a brightfield image. 
     
     
         30 . The method of  claim 29 , wherein said laser radiation includes at least two optical frequencies shifted from one another by a radiofrequency to elicit fluorescence radiation from the sample, wherein said temporal signal comprises one or more beat frequencies associated with the radiofrequency-separated optical frequencies. 
     
     
         31 . The method of  claim 30 , further comprising processing said temporal signal to generate a fluorescence image and using said fluorescence image as a seed image for performing said statistical analysis. 
     
     
         32 . The method of  claim 29 , wherein said statistical analysis employs a least squares method to obtain said reconstructed image by minimizing a sum of squared residuals corresponding to a difference between said detected temporal signal and a respective temporal signal inferred from said forward model. 
     
     
         33 . The method of  claim 32 , wherein said temporal signal is a fluorescence signal. 
     
     
         34 . The method of  claim 33 , wherein said temporal signal is a scattering signal. 
     
     
         35 . The method of  claim 31 , further comprising modeling said detected temporal signal as a plurality of temporal segments each having one sinusoidal and one cosinusoidal term. 
     
     
         36 . The method of  claim 35 , wherein said statistical analysis employs a least squares regression analysis so as to obtain values for parameters of said model of the temporal signal by minimizing a sum of squared residuals corresponding to differences between said modeled and the respective measured temporal segments. 
     
     
         37 . The method of  claim 29 , wherein said forward model comprises a non-linear model. 
     
     
         38 . The method of  claim 37 , wherein said statistical analysis comprises a gradient descent optimization method. 
     
     
         39 . The method of  claim 38 , wherein said gradient descent optimization method calculates an error gradient indicative of a distance between an expected temporal signal based on said forward model and the measured temporal signal and iteratively computes an updated image by stepping a previous image down the error gradient. 
     
     
         40 . The method of  claim 39 , wherein said gradient descent optimization method starts said iterative computation with an initial estimated image computed based on the measured temporal signal. 
     
     
         41 . The method of  claim 40 , further comprising using said processor to compute said initial estimated image via application of a Fast Fourier Transform (FFT) to said measured temporal signal. 
     
     
         42 . The method of  claim 29 , wherein said statistical analysis employs a priori information about said measured temporal signal in combination with Bayesian spectral estimation to reconstruct said image of the sample. 
     
     
         43 . The method of  claim 42 , wherein said a priori information indicates that said temporal signal is composed of a number of sinusoids of unknown frequencies and amplitudes. 
     
     
         44 . The method of  claim 43 , wherein said Bayesian spectral estimation provides estimates of said unknown frequencies and amplitudes of the sinusoids. 
     
     
         45 . The method of  claim 29 , wherein said statistical analysis employs a particle swarm optimization method. 
     
     
         46 . The method of  claim 29 , wherein said statistical analysis employs a genetic algorithm. 
     
     
         47 . The method of  claim 31 , wherein said radiofrequency is in a range of about 10 MHz to about 250 MHz. 
     
     
         48 . The method of  claim 29 , wherein said sample comprises any of a cell, a micro-vesicle, a cellular fragment, a liposome, a bead, and a small organism. 
     
     
         49 . The method of  claim 29 , wherein said laser radiation has a frequency in a range of about 300 THz to about 1000 THz.