System and Method for Diagnosing a Biological Sample
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.