13 articles
Articles prepublished February 07, 2012
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Analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) Data
Based on a Fully Automated Laguerre Deconvolution Method
Special Topic: Biosignal Interpretation
P. Pande (1), C. A. Trivedi (1), J. A. Jo (1)
(1) Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA
Summary
Objectives: A novel Fluorescence Lifetime Imaging Microscopy (FLIM) deconvolution method based on the linear expansion of fluorescence decays on a set of orthonormal Laguerre functions was recently proposed. The Laguerre deconvolution method applies linear least-square estimation to estimate the expansion coefficients of all pixel decays simultaneously, performing at least two orders of magnitude faster than the other algorithms. In the original Laguerre FLIM deconvolution implementation, however, the Laguerre parameter α is selected using a heuristic approach, making it unsuitable for online applications. Methods: In this study, we present a fully automated implementation of the Laguerre FLIM deconvolution, whereby the Laguerre parameter α is treated as a free parameter within a nonlinear least-squares optimization scheme. Results: The performance of this method has been successfully validated on simulated data, and experimental FLIM images of standard fluorescent dyes and endogenous tissue fluorescence. Conclusions: The main advantage of the proposed method is that it does not require any user intervention for tuning up the deconvolution process. Thus, we believe this method will facilitate the translation of FLIM to online applications, including real-time clinical diagnosis.
Keywords
Laguerre deconvolution, optimization, Fluorescence Lifetime Imaging Microscopy, nonlinear least squares
DOI
http://dx.doi.org/10.3414/ME09-02-0046You may also be interested in...
| 1. | ||
J. M. Nguyen1, P. Six2, T. Chaussalet3, D. Antonioli1, P. Lombrail1, P. Le Beux4 Methods of Information in Medicine 2007 46 4: 399-405 http://dx.doi.org/10.1160/ME0385 | ||
| 2. | ||
J. A. Jo1, L. Marcu2, Q. Fang3, T. Papaioannou4, J. H. Qiao5, M. C. Fishbein5, B. Beseth6, A. H. Dorafshar6, T. Reil6, D. Baker6, J. Freischlag7 Methods of Information in Medicine 2007 46 2: 206-211 | ||
| 3. | ||
Original Article C. U. Kunz (1), M. Kieser (1) Methods of Information in Medicine 2011 50 4: 372-377 http://dx.doi.org/10.3414/ME10-01-0037 | ||
Preprint Online November 21, 2011
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Analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) Data
Based on a Fully Automated Laguerre Deconvolution Method
Special Topic: Biosignal Interpretation
P. Pande (1), C. A. Trivedi (1), J. A. Jo (1)
(1) Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA
Summary
Objectives: A novel Fluorescence Lifetime Imaging Microscopy (FLIM) deconvolution method based on the linear expansion of fluorescence decays on a set of orthonormal Laguerre functions was recently proposed. The Laguerre deconvolution method applies linear least-square estimation to estimate the expansion coefficients of all pixel decays simultaneously, performing at least two orders of magnitude faster than the other algorithms. In the original Laguerre FLIM deconvolution implementation, however, the Laguerre parameter α is selected using a heuristic approach, making it unsuitable for online applications. Methods: In this study, we present a fully automated implementation of the Laguerre FLIM deconvolution, whereby the Laguerre parameter α is treated as a free parameter within a nonlinear least-squares optimization scheme. Results: The performance of this method has been successfully validated on simulated data, and experimental FLIM images of standard fluorescent dyes and endogenous tissue fluorescence. Conclusions: The main advantage of the proposed method is that it does not require any user intervention for tuning up the deconvolution process. Thus, we believe this method will facilitate the translation of FLIM to online applications, including real-time clinical diagnosis.
Keywords
Laguerre deconvolution, optimization, Fluorescence Lifetime Imaging Microscopy, nonlinear least squares
DOI
http://dx.doi.org/10.3414/ME09-02-0046You may also be interested in...
| 1. | ||
J. M. Nguyen1, P. Six2, T. Chaussalet3, D. Antonioli1, P. Lombrail1, P. Le Beux4 Methods of Information in Medicine 2007 46 4: 399-405 http://dx.doi.org/10.1160/ME0385 | ||
| 2. | ||
J. A. Jo1, L. Marcu2, Q. Fang3, T. Papaioannou4, J. H. Qiao5, M. C. Fishbein5, B. Beseth6, A. H. Dorafshar6, T. Reil6, D. Baker6, J. Freischlag7 Methods of Information in Medicine 2007 46 2: 206-211 | ||
| 3. | ||
Original Article C. U. Kunz (1), M. Kieser (1) Methods of Information in Medicine 2011 50 4: 372-377 http://dx.doi.org/10.3414/ME10-01-0037 | ||
Articles prepublished September 14, 2010
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Analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) Data
Based on a Fully Automated Laguerre Deconvolution Method
Special Topic: Biosignal Interpretation
P. Pande (1), C. A. Trivedi (1), J. A. Jo (1)
(1) Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA
Summary
Objectives: A novel Fluorescence Lifetime Imaging Microscopy (FLIM) deconvolution method based on the linear expansion of fluorescence decays on a set of orthonormal Laguerre functions was recently proposed. The Laguerre deconvolution method applies linear least-square estimation to estimate the expansion coefficients of all pixel decays simultaneously, performing at least two orders of magnitude faster than the other algorithms. In the original Laguerre FLIM deconvolution implementation, however, the Laguerre parameter α is selected using a heuristic approach, making it unsuitable for online applications. Methods: In this study, we present a fully automated implementation of the Laguerre FLIM deconvolution, whereby the Laguerre parameter α is treated as a free parameter within a nonlinear least-squares optimization scheme. Results: The performance of this method has been successfully validated on simulated data, and experimental FLIM images of standard fluorescent dyes and endogenous tissue fluorescence. Conclusions: The main advantage of the proposed method is that it does not require any user intervention for tuning up the deconvolution process. Thus, we believe this method will facilitate the translation of FLIM to online applications, including real-time clinical diagnosis.
Keywords
Laguerre deconvolution, optimization, Fluorescence Lifetime Imaging Microscopy, nonlinear least squares
DOI
http://dx.doi.org/10.3414/ME09-02-0046You may also be interested in...
| 1. | ||
J. M. Nguyen1, P. Six2, T. Chaussalet3, D. Antonioli1, P. Lombrail1, P. Le Beux4 Methods of Information in Medicine 2007 46 4: 399-405 http://dx.doi.org/10.1160/ME0385 | ||
| 2. | ||
J. A. Jo1, L. Marcu2, Q. Fang3, T. Papaioannou4, J. H. Qiao5, M. C. Fishbein5, B. Beseth6, A. H. Dorafshar6, T. Reil6, D. Baker6, J. Freischlag7 Methods of Information in Medicine 2007 46 2: 206-211 | ||
| 3. | ||
Original Article C. U. Kunz (1), M. Kieser (1) Methods of Information in Medicine 2011 50 4: 372-377 http://dx.doi.org/10.3414/ME10-01-0037 | ||
Preprint Online August 05, 2011
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Analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) Data
Based on a Fully Automated Laguerre Deconvolution Method
Special Topic: Biosignal Interpretation
P. Pande (1), C. A. Trivedi (1), J. A. Jo (1)
(1) Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA
Summary
Objectives: A novel Fluorescence Lifetime Imaging Microscopy (FLIM) deconvolution method based on the linear expansion of fluorescence decays on a set of orthonormal Laguerre functions was recently proposed. The Laguerre deconvolution method applies linear least-square estimation to estimate the expansion coefficients of all pixel decays simultaneously, performing at least two orders of magnitude faster than the other algorithms. In the original Laguerre FLIM deconvolution implementation, however, the Laguerre parameter α is selected using a heuristic approach, making it unsuitable for online applications. Methods: In this study, we present a fully automated implementation of the Laguerre FLIM deconvolution, whereby the Laguerre parameter α is treated as a free parameter within a nonlinear least-squares optimization scheme. Results: The performance of this method has been successfully validated on simulated data, and experimental FLIM images of standard fluorescent dyes and endogenous tissue fluorescence. Conclusions: The main advantage of the proposed method is that it does not require any user intervention for tuning up the deconvolution process. Thus, we believe this method will facilitate the translation of FLIM to online applications, including real-time clinical diagnosis.
Keywords
Laguerre deconvolution, optimization, Fluorescence Lifetime Imaging Microscopy, nonlinear least squares
DOI
http://dx.doi.org/10.3414/ME09-02-0046You may also be interested in...
| 1. | ||
J. M. Nguyen1, P. Six2, T. Chaussalet3, D. Antonioli1, P. Lombrail1, P. Le Beux4 Methods of Information in Medicine 2007 46 4: 399-405 http://dx.doi.org/10.1160/ME0385 | ||
| 2. | ||
J. A. Jo1, L. Marcu2, Q. Fang3, T. Papaioannou4, J. H. Qiao5, M. C. Fishbein5, B. Beseth6, A. H. Dorafshar6, T. Reil6, D. Baker6, J. Freischlag7 Methods of Information in Medicine 2007 46 2: 206-211 | ||
| 3. | ||
Original Article C. U. Kunz (1), M. Kieser (1) Methods of Information in Medicine 2011 50 4: 372-377 http://dx.doi.org/10.3414/ME10-01-0037 | ||
Preprint Online July 26, 2011
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Analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) Data
Based on a Fully Automated Laguerre Deconvolution Method
Special Topic: Biosignal Interpretation
P. Pande (1), C. A. Trivedi (1), J. A. Jo (1)
(1) Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA
Summary
Objectives: A novel Fluorescence Lifetime Imaging Microscopy (FLIM) deconvolution method based on the linear expansion of fluorescence decays on a set of orthonormal Laguerre functions was recently proposed. The Laguerre deconvolution method applies linear least-square estimation to estimate the expansion coefficients of all pixel decays simultaneously, performing at least two orders of magnitude faster than the other algorithms. In the original Laguerre FLIM deconvolution implementation, however, the Laguerre parameter α is selected using a heuristic approach, making it unsuitable for online applications. Methods: In this study, we present a fully automated implementation of the Laguerre FLIM deconvolution, whereby the Laguerre parameter α is treated as a free parameter within a nonlinear least-squares optimization scheme. Results: The performance of this method has been successfully validated on simulated data, and experimental FLIM images of standard fluorescent dyes and endogenous tissue fluorescence. Conclusions: The main advantage of the proposed method is that it does not require any user intervention for tuning up the deconvolution process. Thus, we believe this method will facilitate the translation of FLIM to online applications, including real-time clinical diagnosis.
Keywords
Laguerre deconvolution, optimization, Fluorescence Lifetime Imaging Microscopy, nonlinear least squares
DOI
http://dx.doi.org/10.3414/ME09-02-0046You may also be interested in...
| 1. | ||
J. M. Nguyen1, P. Six2, T. Chaussalet3, D. Antonioli1, P. Lombrail1, P. Le Beux4 Methods of Information in Medicine 2007 46 4: 399-405 http://dx.doi.org/10.1160/ME0385 | ||
| 2. | ||
J. A. Jo1, L. Marcu2, Q. Fang3, T. Papaioannou4, J. H. Qiao5, M. C. Fishbein5, B. Beseth6, A. H. Dorafshar6, T. Reil6, D. Baker6, J. Freischlag7 Methods of Information in Medicine 2007 46 2: 206-211 | ||
| 3. | ||
Original Article C. U. Kunz (1), M. Kieser (1) Methods of Information in Medicine 2011 50 4: 372-377 http://dx.doi.org/10.3414/ME10-01-0037 | ||
Preprint Online March 21, 2011
|
|
Analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) Data
Based on a Fully Automated Laguerre Deconvolution Method
Special Topic: Biosignal Interpretation
P. Pande (1), C. A. Trivedi (1), J. A. Jo (1)
(1) Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA
Summary
Objectives: A novel Fluorescence Lifetime Imaging Microscopy (FLIM) deconvolution method based on the linear expansion of fluorescence decays on a set of orthonormal Laguerre functions was recently proposed. The Laguerre deconvolution method applies linear least-square estimation to estimate the expansion coefficients of all pixel decays simultaneously, performing at least two orders of magnitude faster than the other algorithms. In the original Laguerre FLIM deconvolution implementation, however, the Laguerre parameter α is selected using a heuristic approach, making it unsuitable for online applications. Methods: In this study, we present a fully automated implementation of the Laguerre FLIM deconvolution, whereby the Laguerre parameter α is treated as a free parameter within a nonlinear least-squares optimization scheme. Results: The performance of this method has been successfully validated on simulated data, and experimental FLIM images of standard fluorescent dyes and endogenous tissue fluorescence. Conclusions: The main advantage of the proposed method is that it does not require any user intervention for tuning up the deconvolution process. Thus, we believe this method will facilitate the translation of FLIM to online applications, including real-time clinical diagnosis.
Keywords
Laguerre deconvolution, optimization, Fluorescence Lifetime Imaging Microscopy, nonlinear least squares
DOI
http://dx.doi.org/10.3414/ME09-02-0046You may also be interested in...
| 1. | ||
J. M. Nguyen1, P. Six2, T. Chaussalet3, D. Antonioli1, P. Lombrail1, P. Le Beux4 Methods of Information in Medicine 2007 46 4: 399-405 http://dx.doi.org/10.1160/ME0385 | ||
| 2. | ||
J. A. Jo1, L. Marcu2, Q. Fang3, T. Papaioannou4, J. H. Qiao5, M. C. Fishbein5, B. Beseth6, A. H. Dorafshar6, T. Reil6, D. Baker6, J. Freischlag7 Methods of Information in Medicine 2007 46 2: 206-211 | ||
| 3. | ||
Original Article C. U. Kunz (1), M. Kieser (1) Methods of Information in Medicine 2011 50 4: 372-377 http://dx.doi.org/10.3414/ME10-01-0037 | ||
Preprint Online March 04, 2011
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Analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) Data
Based on a Fully Automated Laguerre Deconvolution Method
Special Topic: Biosignal Interpretation
P. Pande (1), C. A. Trivedi (1), J. A. Jo (1)
(1) Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA
Summary
Objectives: A novel Fluorescence Lifetime Imaging Microscopy (FLIM) deconvolution method based on the linear expansion of fluorescence decays on a set of orthonormal Laguerre functions was recently proposed. The Laguerre deconvolution method applies linear least-square estimation to estimate the expansion coefficients of all pixel decays simultaneously, performing at least two orders of magnitude faster than the other algorithms. In the original Laguerre FLIM deconvolution implementation, however, the Laguerre parameter α is selected using a heuristic approach, making it unsuitable for online applications. Methods: In this study, we present a fully automated implementation of the Laguerre FLIM deconvolution, whereby the Laguerre parameter α is treated as a free parameter within a nonlinear least-squares optimization scheme. Results: The performance of this method has been successfully validated on simulated data, and experimental FLIM images of standard fluorescent dyes and endogenous tissue fluorescence. Conclusions: The main advantage of the proposed method is that it does not require any user intervention for tuning up the deconvolution process. Thus, we believe this method will facilitate the translation of FLIM to online applications, including real-time clinical diagnosis.
Keywords
Laguerre deconvolution, optimization, Fluorescence Lifetime Imaging Microscopy, nonlinear least squares
DOI
http://dx.doi.org/10.3414/ME09-02-0046You may also be interested in...
| 1. | ||
J. M. Nguyen1, P. Six2, T. Chaussalet3, D. Antonioli1, P. Lombrail1, P. Le Beux4 Methods of Information in Medicine 2007 46 4: 399-405 http://dx.doi.org/10.1160/ME0385 | ||
| 2. | ||
J. A. Jo1, L. Marcu2, Q. Fang3, T. Papaioannou4, J. H. Qiao5, M. C. Fishbein5, B. Beseth6, A. H. Dorafshar6, T. Reil6, D. Baker6, J. Freischlag7 Methods of Information in Medicine 2007 46 2: 206-211 | ||
| 3. | ||
Original Article C. U. Kunz (1), M. Kieser (1) Methods of Information in Medicine 2011 50 4: 372-377 http://dx.doi.org/10.3414/ME10-01-0037 | ||
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