System and Method for Diagnosing a Biological Sample

Abstract

The present disclosure provides for a system and method for analyzing biological samples to thereby provide a diagnosis. A system may comprise an illumination source, a filter and a detector configured to generate at least one of: a visible data set representative of a biological sample, a SWIR data set representative of a biological sample, and combinations thereof. A method may comprise illuminating a biological sample to generate a plurality of photons, filtering a said plurality of interacted photons, detecting

1 . A method comprising:
 illuminating a biological sample to thereby generate a first plurality of interacted photons;   filtering said first plurality of interacted photons;   detecting said first plurality of interacted photons to thereby generate a test data set representative of said biological sample, wherein said test data set comprises at least one of: a test SWIR data set, a test visible data set, and combinations thereof; and   analyzing said test data set to thereby diagnose at least one of: a disease state of said biological sample, a metabolic state of said biological sample, a clinical outcome of said biological sample, a disease progression of said biological sample, and combinations thereof.   
     
     
         2 . The method of  claim 1  wherein said first plurality of interacted photons are selected from the group consisting of: photons scattered by said biological sample, photons reflected by said biological sample photons absorbed by said biological sample, photons emitted by said biological sample, and combinations thereof. 
     
     
         3 . The method of  claim 1  wherein said biological sample comprises a tissue sample. 
     
     
         4 . The method of  claim 1  wherein said biological sample comprises an organ sample. 
     
     
         5 . The method of  claim 1  wherein said biological sample comprises at least one cell. 
     
     
         6 . The method of  claim 1  wherein said biological sample comprises at least one of: a kidney sample, a prostate sample, a breast sample, a pancreatic sample, a brain sample, a skin sample, an intestinal sample, a colon sample, a liver sample, a cardiac sample, a lung sample, an esophageal sample, a bladder sample, a blood sample, a urethral sample, an ovarian sample, a uterine sample, a testicular sample, a bone sample, a stomach sample, a tracheal sample, a tongue sample, a diaphragm sample, a nerve sample, a rectal sample, and combinations thereof. 
     
     
         7 . The method of  claim 1  wherein said test data set comprises at least one hyperspectral SWIR image representative of said biological sample. 
     
     
         8 . The method of  claim 1  wherein said test data set comprises at least one hyperspectral visible image representative of said biological sample. 
     
     
         9 . The method of  claim 1  wherein said test data set comprises at least one of: a spatially accurate wavelength resolved SWIR image, a SWIR spectrum, and combinations thereof. 
     
     
         10 . The method of  claim 1  wherein said test data set comprises at least one of: a spatially accurate wavelength resolved visible image, a visible spectrum, and combinations thereof. 
     
     
         11 . The method of  claim 1  further comprising providing a reference database comprising a plurality of reference data sets, each reference data set associated with at least one of: a known disease state, a known metabolic state, a known clinical outcome, a known disease progression, and combinations thereof. 
     
     
         12 . The method of  claim 1  wherein said analyzing further comprises comparing said test data set to at least one reference data set. 
     
     
         13 . The method of  claim 12  wherein said comparing is achieved by applying at least one chemometric technique. 
     
     
         14 . The method of  claim 13  wherein said chemometric technique is selected from the group consisting of: principle components analysis, partial least squares discriminate analysis, cosine correlation analysis, Euclidian distance analysis, k-means clustering, multivariate curve resolution, band t. entropy method, mahalanobis distance, adaptive subspace detector, spectral mixture resolution, and combinations thereof. 
     
     
         15 . The method of  claim 1  wherein said illuminating comprises wide-field illumination. 
     
     
         16 . The method of  claim 1  wherein said diagnosing further comprises assigning a Gleason score to said biological sample. 
     
     
         17 . The method of  claim 1  further comprising selecting a pre-determined vector space that mathematically describes said test data set;
 transforming said test data set into said pre-determined vector space; and 
 analyzing a distribution of said transformed test data set in the pre-determined vector space to thereby diagnose said biological sample. 
 
     
     
         18 . A system for analyzing a biological sample comprising:
 an illumination source, configured so as to illuminate a biological sample to thereby generate a first plurality of interacted photons;   a filter for filtering said first plurality of interacted photons into a plurality of predetermined wavelength bands;   a detector for detecting said first plurality of interacted photons and generating a test data set representative of said sample.   
     
     
         19 . The system of  claim 18  wherein said detector comprises a focal plane array detector. 
     
     
         20 . The system of  claim 19  wherein said focal plane array detector comprises at least one of: a CMOS detector, a CCD detector, an ICCD detector, a germanium detector, a InGaAs detector, and combinations thereof. 
     
     
         21 . The system of  claim 18  wherein said filter comprises a tunable filter. 
     
     
         22 . The system of  claim 21  wherein said tunable filter is selected from the group consisting of: a liquid crystal tunable filter, a multi-conjugate tunable filter, an acousto-optical tunable filter, a Lyot liquid crystal tunable filter, an Evans split-element liquid crystal tunable filter, a Solc liquid crystal tunable filter, a ferroelectric liquid crystal tunable filter, a Fabry Perot liquid crystal tunable filter, and combinations thereof. 
     
     
         23 . The system of  claim 18  further comprising a fiber array spectral translator device. 
     
     
         24 . The system of  claim 23  wherein said fiber array spectral translator device comprises a two-dimensional array of optical fibers drawn into a one-dimensional fiber stack so as to effectively convert a two-dimensional field of view into a curvilinear field of view, and wherein said two-dimensional array of optical fibers is configured to receive said photons and transfer said photons out of said fiber array spectral translator device and to at least one of: a filter, a detector, and combinations thereof. 
     
     
         25 . The system of  claim 18  further comprising a reference database comprising a plurality of reference data sets, each reference data set associated with at least one of: a known disease state, a known metabolic state, a known clinical outcome, a known disease progression, and combinations thereof. 
     
     
         26 . The system of  claim 18  further comprising a means for comparing said test data set to at least one reference data set in said reference database. 
     
     
         27 . The system of  claim 18  wherein said illumination source is configured for wide-field illumination. 
     
     
         28 . A storage medium containing machine readable program code, which when executed by a processor, causes the processor to perform the following:
 illuminate a biological sample to thereby generate a first plurality of interacted photons;   filter said first plurality of interacted photons to thereby separate said first plurality of interacted photons into a plurality of predetermined wavelength bands;   detect said first plurality of interacted photons to thereby generate a test data set representative of said biological sample, wherein said test data set comprises at least one of: a test SWIR data set, a test visible data set, and combinations thereof; and   analyze said test data set to thereby determine at least one of: a disease state of said biological sample, a metabolic state of said biological sample, a clinical outcome of said biological sample, a disease progression of said biological sample, and combinations thereof.   
     
     
         29 . The storage medium of  claim 28  wherein said machine readable program code, when executed by a processor to analyze said test data, further causes said processor to: compare said test data set to at least one reference data set in a reference database, wherein each said reference data set is associated with at least one of: a known disease state, a known metabolic state, a known clinical outcome, a known disease progression, and combinations thereof. 
     
     
         30 . The storage medium of  claim 29  wherein said machine readable program code, when executed by a processor to compare said test data set to at least one reference data set further causes said processor to perform said comparison by applying at least one chemometric technique. 
     
     
         31 . The storage medium of  claim 28  wherein said machine readable program code, when executed by a processor further causes said processor to: compare said test data set to at least one reference data set in a reference database to thereby assign a Gleason score to said biological sample.