Autofluorescence in the Oral Cavity - Revision history

imported>Student2018: /* Verifying Fluorescence Estimate */

2018-12-14T19:56:11Z

Verifying Fluorescence Estimate

← Older revision		Revision as of 19:56, 14 December 2018
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	===Verifying Fluorescence Estimate===		===Verifying Fluorescence Estimate===
	Because the fluorescence data was estimated, we sought to verify the estimate. First, we checked whether the radiance measured under both blue and tungsten light agreed well with the predicted radiance when we sum spectra of the individual illuminants scaled by the tongue’s reflectance (Figure 6). We observed good agreement between this prediction and measurement, with disagreements attributed to fluorescence. In order to check that the found fluorescence values were reasonable, we compared the average fluorescence data to fluorescence measurements previously taken by Veld et. al. (Figure 7). The shape of the fluorescence estimate matched remarkably well to this data, suggesting that our estimate is reasonable.		Because the fluorescence data was estimated, we sought to verify the estimate. First, we checked whether the radiance measured under both blue and tungsten light agreed well with the predicted radiance when we sum spectra of the individual illuminants scaled by the tongue’s reflectance (Figure 6). We observed good agreement between this prediction and measurement, with disagreements attributed to fluorescence. In order to check that the found fluorescence values were reasonable, we compared the average fluorescence data to fluorescence measurements previously taken by Veld et. al. (Figure 7) [2]. The shape of the fluorescence estimate matched remarkably well to this data, suggesting that our estimate is reasonable.

	[[File:Predict_radiance_1.png\|thumb\|center\|500px\|Figure 6. Measured radiance is plotted against estimates based on reflectance. When the tungsten light only is scaled by reflectance, there is a clear mismatch. Addition of the scaled blue light spectrum results in a much closer fit.]]		[[File:Predict_radiance_1.png\|thumb\|center\|500px\|Figure 6. Measured radiance is plotted against estimates based on reflectance. When the tungsten light only is scaled by reflectance, there is a clear mismatch. Addition of the scaled blue light spectrum results in a much closer fit.]]

imported>Student2018: /* Singular Value Decomposition */

2018-12-14T19:55:54Z

Singular Value Decomposition

← Older revision		Revision as of 19:55, 14 December 2018
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	===Singular Value Decomposition===		===Singular Value Decomposition===
	Wu and Qu have previously suggested that the broad shorter wavelength peak between 500 and 550 nm may be due to overlapping NADH FAD, and keratin fluorescence, while the sharp peak above 600 nm may be due to porphyrin derivatives. Given this suggestion, it seemed natural to first limit our SVD to two basis functions. The two basis functions with the largest singular values were kept and used in reconstruction of the measured fluorescence approximation (Figures 8 and 9). The success of this reconstruction is likely due to the fact that the first basis function largely follows the shape of the mean.		Wu and Qu have previously suggested that the broad shorter wavelength peak between 500 and 550 nm may be due to overlapping NADH FAD, and keratin fluorescence, while the sharp peak above 600 nm may be due to porphyrin derivatives [3]. Given this suggestion, it seemed natural to first limit our SVD to two basis functions. The two basis functions with the largest singular values were kept and used in reconstruction of the measured fluorescence approximation (Figures 8 and 9). The success of this reconstruction is likely due to the fact that the first basis function largely follows the shape of the mean.

	[[File:two_bases.png\|thumb\|center\|500px\|Figure 8. The two basis functions with the largest singular values are plotted as functions of wavelength.]]		[[File:two_bases.png\|thumb\|center\|500px\|Figure 8. The two basis functions with the largest singular values are plotted as functions of wavelength.]]

imported>Student2018: /* Least Squares Estimation */

2018-12-14T19:55:43Z

Least Squares Estimation

← Older revision		Revision as of 19:55, 14 December 2018
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	As we hope to use this information for diagnostic purposes, it would presumably be advantageous for our basis functions to reflect the biology. Therefore, we chose basis functions based on previously measured fluorescence data and performed least-squares estimation to find how well these functions could explain our collected data. This approach has the additional advantage over SVD that it eliminates the possibility that a basis function can simply follow the mean.		As we hope to use this information for diagnostic purposes, it would presumably be advantageous for our basis functions to reflect the biology. Therefore, we chose basis functions based on previously measured fluorescence data and performed least-squares estimation to find how well these functions could explain our collected data. This approach has the additional advantage over SVD that it eliminates the possibility that a basis function can simply follow the mean.

	For this estimation, we chose to use the basis set of Wu and Qu’s reported fluorescence curve, representing the overlapping fluorescence of NADH, FAD, and keratin, and Bjurshammar’s porphyrin emission curve (Figure 12). Coefficients were calculated using least squares estimation in Matlab, and the fluorescence data was reconstructed using these coefficients. The original fluorescence estimate data was plotted against reconstructions making use of SVD and least-squares basis sets (Figure 13). Qualitatively, there is reasonable agreement for both reconstructions, suggesting that choosing a basis set based on the biology is worth further consideration. To quantify the performance of each reconstruction, we calculated the Euclidean norm of the difference between our original data and our reconstruction. The Euclidean norm from the least squares reconstruction was found to be ~1.09 times the Euclidean norm from the SVD reconstruction with two basis functions. Since SVD finds two basis functions to minimize this norm, this close match in performance again suggests that our choice of basis set based on biology is very promising.		For this estimation, we chose to use the basis set of Wu and Qu’s reported fluorescence curve, representing the overlapping fluorescence of NADH, FAD, and keratin, and Bjurshammar’s porphyrin emission curve (Figure 12) [3,4]. Coefficients were calculated using least squares estimation in Matlab, and the fluorescence data was reconstructed using these coefficients. The original fluorescence estimate data was plotted against reconstructions making use of SVD and least-squares basis sets (Figure 13). Qualitatively, there is reasonable agreement for both reconstructions, suggesting that choosing a basis set based on the biology is worth further consideration. To quantify the performance of each reconstruction, we calculated the Euclidean norm of the difference between our original data and our reconstruction. The Euclidean norm from the least squares reconstruction was found to be ~1.09 times the Euclidean norm from the SVD reconstruction with two basis functions. Since SVD finds two basis functions to minimize this norm, this close match in performance again suggests that our choice of basis set based on biology is very promising.

	[[File:ls_bases.png\|thumb\|center\|500px\|Figure 12. Fluorescent data from Wu and Qu and Bjurshammar are used as the basis functions in least squares estimation.]]		[[File:ls_bases.png\|thumb\|center\|500px\|Figure 12. Fluorescent data from Wu and Qu and Bjurshammar are used as the basis functions in least squares estimation.]]

imported>Student2018: /* Singular Value Decomposition */

2018-12-14T19:54:23Z

Singular Value Decomposition

← Older revision		Revision as of 19:54, 14 December 2018
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	[[File:svd_5_four_bases.png\|thumb\|center\|500px\|Figure 10.2. Fluorescence estimate and SVD reconstruction using four basis functions are plotted for Subject 5.]]		[[File:svd_5_four_bases.png\|thumb\|center\|500px\|Figure 10.2. Fluorescence estimate and SVD reconstruction using four basis functions are plotted for Subject 5.]]

	This illustrated to us two fundamental approaches to choosing the number of basis functions. The first approach is to let the biology inform our choice of basis size - given that we expect n fluorescent species to be present, we expect n basis functions will explain our data. The second approach is to choose how well we want to explain the data and let the singular values dictate our choice of basis size. We can plot the cumulative variability described against the number of basis functions used, set a threshold for the variability we want to explain, and choose the lowest number of basis functions that will reach this threshold (Figure 11).		This illustrated to us two fundamental approaches to choosing the number of basis functions. The first approach is to let the biology inform our choice of basis size - given that we expect n fluorescent species to be present, we expect n basis functions will explain our data. The second approach is to choose how well we want to explain the data and let the singular values dictate our choice of basis size. We can plot the cumulative variability described against the number of basis functions used, set a threshold for the variability we want to explain, and choose the lowest number of basis functions that will reach this threshold (Figure 11). However, with only 14 data sets, there is no clear correct choice.

	[[File:cumulative_variance.png\|thumb\|center\|500px\|Figure 11. Cumulative variance explained is plotted as a function of the number of bases used. As expected, this function monotonically increases, reaching 1 when 14 bases (the total number of data sets) are used.]]		[[File:cumulative_variance.png\|thumb\|center\|500px\|Figure 11. Cumulative variance explained is plotted as a function of the number of bases used. As expected, this function monotonically increases, reaching 1 when 14 bases (the total number of data sets) are used.]]

imported>Student2018: /* Least Squares Estimation */

2018-12-14T19:53:28Z

Least Squares Estimation

← Older revision		Revision as of 19:53, 14 December 2018
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	===Least Squares Estimation===		===Least Squares Estimation===

	As we hope to use this information for diagnostic purposes, it would presumably be advantageous for our basis functions to reflect the biology. Therefore, we ~~choose~~ basis functions based on previously measured fluorescence data and performed least-squares estimation to find how well these functions could explain our collected data. This approach has the additional advantage over SVD that it eliminates the possibility that a basis function can simply follow the mean.		As we hope to use this information for diagnostic purposes, it would presumably be advantageous for our basis functions to reflect the biology. Therefore, we chose basis functions based on previously measured fluorescence data and performed least-squares estimation to find how well these functions could explain our collected data. This approach has the additional advantage over SVD that it eliminates the possibility that a basis function can simply follow the mean.

	For this estimation, we chose to use the basis set of Wu and Qu’s reported fluorescence curve, representing the overlapping fluorescence of NADH, FAD, and keratin, and Bjurshammar’s porphyrin emission curve (Figure 12). Coefficients were calculated using least squares estimation in Matlab, and the fluorescence data was reconstructed using these coefficients. The original fluorescence estimate data was plotted against reconstructions making use of SVD and least-squares basis sets (Figure 13). Qualitatively, there is reasonable agreement for both reconstructions, suggesting that choosing a basis set based on the biology is worth further consideration. To quantify the performance of each reconstruction, we calculated the Euclidean norm of the difference between our original data and our reconstruction. The Euclidean norm from the least squares reconstruction was found to be ~1.09 times the Euclidean norm from the SVD reconstruction with two basis functions. Since SVD finds two basis functions to minimize this norm, this close match in performance again suggests that our choice of basis set based on biology is very promising.		For this estimation, we chose to use the basis set of Wu and Qu’s reported fluorescence curve, representing the overlapping fluorescence of NADH, FAD, and keratin, and Bjurshammar’s porphyrin emission curve (Figure 12). Coefficients were calculated using least squares estimation in Matlab, and the fluorescence data was reconstructed using these coefficients. The original fluorescence estimate data was plotted against reconstructions making use of SVD and least-squares basis sets (Figure 13). Qualitatively, there is reasonable agreement for both reconstructions, suggesting that choosing a basis set based on the biology is worth further consideration. To quantify the performance of each reconstruction, we calculated the Euclidean norm of the difference between our original data and our reconstruction. The Euclidean norm from the least squares reconstruction was found to be ~1.09 times the Euclidean norm from the SVD reconstruction with two basis functions. Since SVD finds two basis functions to minimize this norm, this close match in performance again suggests that our choice of basis set based on biology is very promising.

imported>Student2018: /* Verifying Fluorescence Estimate */

2018-12-14T19:52:18Z

Verifying Fluorescence Estimate

← Older revision		Revision as of 19:52, 14 December 2018
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	===Verifying Fluorescence Estimate===		===Verifying Fluorescence Estimate===
	Because the fluorescence data was estimated, we sought to verify the estimate. First, we checked whether the radiance measured under both blue and tungsten light agreed well with the predicted radiance when we sum spectra of the individual illuminants scaled by the tongue’s reflectance (Figure XX). We observed good agreement between this prediction and measurement, with disagreements attributed to fluorescence. In order to check that the found fluorescence values were reasonable, we compared the average fluorescence data to fluorescence measurements previously taken by Veld et. al. (Figure XX). The shape of the fluorescence estimate matched remarkably well to this data, suggesting that our estimate is reasonable.		Because the fluorescence data was estimated, we sought to verify the estimate. First, we checked whether the radiance measured under both blue and tungsten light agreed well with the predicted radiance when we sum spectra of the individual illuminants scaled by the tongue’s reflectance (Figure 6). We observed good agreement between this prediction and measurement, with disagreements attributed to fluorescence. In order to check that the found fluorescence values were reasonable, we compared the average fluorescence data to fluorescence measurements previously taken by Veld et. al. (Figure 7). The shape of the fluorescence estimate matched remarkably well to this data, suggesting that our estimate is reasonable.

	[[File:Predict_radiance_1.png\|thumb\|center\|500px\|Figure 6. Measured radiance is plotted against estimates based on reflectance. When the tungsten light only is scaled by reflectance, there is a clear mismatch. Addition of the scaled blue light spectrum results in a much closer fit.]]		[[File:Predict_radiance_1.png\|thumb\|center\|500px\|Figure 6. Measured radiance is plotted against estimates based on reflectance. When the tungsten light only is scaled by reflectance, there is a clear mismatch. Addition of the scaled blue light spectrum results in a much closer fit.]]

	[[File:Average_vs_veld.png\|thumb\|center\|500px\|Figure 7. Our fluorescence estimate and Veld et.al.'s reported data are ~~scaled~~ and plotted together, exhibiting excellent agreement in shape.]]		[[File:Average_vs_veld.png\|thumb\|center\|500px\|Figure 7. Our fluorescence estimate and Veld et.al.'s reported data are normalized and plotted together, exhibiting excellent agreement in shape.]]



	===Singular Value Decomposition===		===Singular Value Decomposition===

imported>Student2018: /* Least Squares Estimation */

2018-12-14T19:51:25Z

Least Squares Estimation

← Older revision		Revision as of 19:51, 14 December 2018
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	===Least Squares Estimation===		===Least Squares Estimation===

	SVD imposes few restrictions on the basis functions produced. The formulation of the decomposition is likely to suggest the highest singular value basis as simply the mean of the data. Choosing a set of basis functions and performing least squares estimation to find the associated coefficients will circumvent this. We therefore sought to choose a set of basis functions from previously collected fluorescence data and fit our data to these functions, allowing us to link data directly to the underlying biology. Least squares estimation was also performed in Matlab.		SVD imposes few restrictions on the basis functions produced. The formulation of the decomposition is likely to suggest the highest singular value basis as simply the mean of the data. Choosing a set of basis functions and performing least squares estimation to find the associated coefficients will circumvent this. We therefore sought to choose a set of basis functions from previously collected fluorescence data and fit our data to these functions, allowing us to link data directly to the underlying biology. Least squares estimation was also performed in Matlab, and data was reconstructed for evaluation of this procedure.

	==Results==		==Results==

imported>Student2018: /* Background */

2018-12-14T19:48:22Z

Background

← Older revision		Revision as of 19:48, 14 December 2018
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	==Background==		==Background==
	In 2003, Veld et.al. conducted a comprehensive study aimed at characterizing the autofluorescence of healthy oral mucosa in different parts of the mouth. This group recognized the power of autofluorescence spectroscopy in oral cancer detection and hoped to construct a reference database of fluorescent spectra from healthy oral mucosa. In this study, spectra were recorded from 97 volunteers under a selection of excitation wavelengths between 350 and 450 nm at different anatomical sites. Of particular interest to this project, the fluorescence measured on the dorsal side of the tongue exhibited a broad peak between 500 and 550 nm and a sharp peak above 600 nm.		In 2003, Veld et.al. conducted a comprehensive study aimed at characterizing the autofluorescence of healthy oral mucosa in different parts of the mouth. This group recognized the power of autofluorescence spectroscopy in oral cancer detection and hoped to construct a reference database of fluorescent spectra from healthy oral mucosa. In this study, spectra were recorded from 97 volunteers under a selection of excitation wavelengths between 350 and 450 nm at different anatomical sites. Of particular interest to this project, the fluorescence measured on the dorsal side of the tongue exhibited a broad peak between 500 and 550 nm and a sharp peak above 600 nm [2].

	In 2006, Wu and Qu studied autofluorescence of human epithelial tissue and obtained a similar broad fluorescent spectrum around 500 nm in oral tissue excited at 405 nm. They hypothesized that this peak may be due to overlapping of NADH, FAD, and keratin fluorescent spectra. They also observed a sharp peak in esophageal fluorescence around 680 nm and hypothesized that this peak may be attributed to porphyrin derivatives.		In 2006, Wu and Qu studied autofluorescence of human epithelial tissue and obtained a similar broad fluorescent spectrum around 500 nm in oral tissue excited at 405 nm. They hypothesized that this peak may be due to overlapping of NADH, FAD, and keratin fluorescent spectra. They also observed a sharp peak in esophageal fluorescence around 680 nm and hypothesized that this peak may be attributed to porphyrin derivatives [3].

			In 2017, Bjurshammar published a thesis exploring “Porphyrins and Phototherapy of Oral Bacteria.” This study discussed the spectral properties of porphyrins, a group of macromolecules found in most bacteria typically in the oral cavity. Notably, protoporphyrin IX has a sharp fluorescence emission peak around 635 nm and is efficiently excited at 405 nm [4].

	In 2017, Bjurshammar published a thesis exploring “Porphyrins and Phototherapy of Oral Bacteria.” This study discussed the spectral properties of porphyrins, a group of macromolecules found in most bacteria typically in the oral cavity. Notably, protoporphyrin IX has a sharp fluorescence emission peak around 635 nm and is efficiently excited at 405 nm.
	The data from these studies is shown in Figure 1. The results of all of these studies suggest to us that we can expect to see significant autofluorescence in healthy oral tissue, due either to autofluorescence of oral mucosa or to the presence of porphyrins. This project seeks to collect further data about fluorescence and its ties to oral health. Fluorescence on volunteers’ tongues was measured under 400 nm light, chosen because it will excite relevant fluorescent species and is visible, preventing damage from unseen light. This data was analyzed using singular-value decomposition and least squares estimation in an effort to elucidate connections between fluorescence data and oral health.		The data from these studies is shown in Figure 1. The results of all of these studies suggest to us that we can expect to see significant autofluorescence in healthy oral tissue, due either to autofluorescence of oral mucosa or to the presence of porphyrins. This project seeks to collect further data about fluorescence and its ties to oral health. Fluorescence on volunteers’ tongues was measured under 400 nm light, chosen because it will excite relevant fluorescent species and is visible, preventing damage from unseen light. This data was analyzed using singular-value decomposition and least squares estimation in an effort to elucidate connections between fluorescence data and oral health.

imported>Student2018: /* Introduction */

2018-12-14T19:47:48Z

Introduction

← Older revision		Revision as of 19:47, 14 December 2018
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	==Introduction==		==Introduction==
	In recent years, dentists have begun using the VELscope Vx, a handheld scope that enables fluorescence imaging to visualize oral mucosal abnormalities, in oral exams. This technology enables imaging of abnormalities that may not be visible to the naked eye and has enhanced diagnosis of oral cancer and dysplasia. This device offers clear proof that there is interesting health data contained in oral fluorescence, and this project seeks to pursue that information. Specifically, this project aims to determine what data exists in the fluorescent spectrum, how we can reliably obtain this data, and how this can be used in diagnosis of oral disease.		In recent years, dentists have begun using the VELscope Vx, a handheld scope that enables fluorescence imaging to visualize oral mucosal abnormalities, in oral exams. This technology enables imaging of abnormalities that may not be visible to the naked eye and has enhanced diagnosis of oral cancer and dysplasia [1]. This device offers clear proof that there is interesting health data contained in oral fluorescence, and this project seeks to pursue that information. Specifically, this project aims to determine what data exists in the fluorescent spectrum, how we can reliably obtain this data, and how this can be used in diagnosis of oral disease.

	==Background==		==Background==

imported>Student2018: /* Average Fluorescence */

2018-12-14T07:16:57Z

Average Fluorescence

← Older revision		Revision as of 07:16, 14 December 2018
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	==Results==		==Results==

	===~~Average~~ Fluorescence===		===Fluorescence Estimation===
	Data was collected from 13 different subjects, with one subject volunteering for data collection before and after brushing their teeth, for a total of 14 data sets. The fluorescence estimation was performed on all data sets, and an average fluorescence estimate was calculated (Figures 4 and 5).		Data was collected from 13 different subjects, with one subject volunteering for data collection before and after brushing their teeth, for a total of 14 data sets. The fluorescence estimation was performed on all data sets, and an average fluorescence estimate was calculated (Figures 4 and 5).