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Single pixel hyperspectral bioluminescence tomography based on compressive sensing
Photonics based imaging is a widely utilised technique for the study of biological functions within pre-clinical studies. Specifically, bioluminescence imaging is a sensitive non-invasive and non-contact optical imaging technique that is able to detect distributed (biologically informative) visible...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Optical Society of America
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865106/ https://www.ncbi.nlm.nih.gov/pubmed/31799030 http://dx.doi.org/10.1364/BOE.10.005549 |
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author | Bentley, Alexander Rowe, Jonathan E. Dehghani, Hamid |
author_facet | Bentley, Alexander Rowe, Jonathan E. Dehghani, Hamid |
author_sort | Bentley, Alexander |
collection | PubMed |
description | Photonics based imaging is a widely utilised technique for the study of biological functions within pre-clinical studies. Specifically, bioluminescence imaging is a sensitive non-invasive and non-contact optical imaging technique that is able to detect distributed (biologically informative) visible and near-infrared activated light sources within tissue, providing information about tissue function. Compressive sensing (CS) is a method of signal processing that works on the basis that a signal or image can be compressed without important information being lost. This work describes the development of a CS based hyperspectral Bioluminescence imaging system that is used to collect compressed fluence data from the external surface of an animal model, due to an internal source, providing lower acquisition times, higher spectral content and potentially better tomographic source localisation. The work demonstrates that hyperspectral surface fluence images of both block and mouse shaped phantom due to internal light sources could be obtained at 30% of the time and measurements it would take to collect the data using conventional raster scanning methods. Using hyperspectral data, tomographic reconstruction of internal light sources can be carried out using any desired number of wavelengths and spectral bandwidth. Reconstructed images of internal light sources using four wavelengths as obtained through CS are presented showing a localisation error of ∼3 mm. Additionally, tomographic images of dual-colored sources demonstrating multi-wavelength light sources being recovered are presented further highlighting the benefits of the hyperspectral system for utilising multi-colored biomarker applications. |
format | Online Article Text |
id | pubmed-6865106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Optical Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-68651062019-12-03 Single pixel hyperspectral bioluminescence tomography based on compressive sensing Bentley, Alexander Rowe, Jonathan E. Dehghani, Hamid Biomed Opt Express Article Photonics based imaging is a widely utilised technique for the study of biological functions within pre-clinical studies. Specifically, bioluminescence imaging is a sensitive non-invasive and non-contact optical imaging technique that is able to detect distributed (biologically informative) visible and near-infrared activated light sources within tissue, providing information about tissue function. Compressive sensing (CS) is a method of signal processing that works on the basis that a signal or image can be compressed without important information being lost. This work describes the development of a CS based hyperspectral Bioluminescence imaging system that is used to collect compressed fluence data from the external surface of an animal model, due to an internal source, providing lower acquisition times, higher spectral content and potentially better tomographic source localisation. The work demonstrates that hyperspectral surface fluence images of both block and mouse shaped phantom due to internal light sources could be obtained at 30% of the time and measurements it would take to collect the data using conventional raster scanning methods. Using hyperspectral data, tomographic reconstruction of internal light sources can be carried out using any desired number of wavelengths and spectral bandwidth. Reconstructed images of internal light sources using four wavelengths as obtained through CS are presented showing a localisation error of ∼3 mm. Additionally, tomographic images of dual-colored sources demonstrating multi-wavelength light sources being recovered are presented further highlighting the benefits of the hyperspectral system for utilising multi-colored biomarker applications. Optical Society of America 2019-10-07 /pmc/articles/PMC6865106/ /pubmed/31799030 http://dx.doi.org/10.1364/BOE.10.005549 Text en Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/) . Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. |
spellingShingle | Article Bentley, Alexander Rowe, Jonathan E. Dehghani, Hamid Single pixel hyperspectral bioluminescence tomography based on compressive sensing |
title | Single pixel hyperspectral bioluminescence tomography based on compressive sensing |
title_full | Single pixel hyperspectral bioluminescence tomography based on compressive sensing |
title_fullStr | Single pixel hyperspectral bioluminescence tomography based on compressive sensing |
title_full_unstemmed | Single pixel hyperspectral bioluminescence tomography based on compressive sensing |
title_short | Single pixel hyperspectral bioluminescence tomography based on compressive sensing |
title_sort | single pixel hyperspectral bioluminescence tomography based on compressive sensing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865106/ https://www.ncbi.nlm.nih.gov/pubmed/31799030 http://dx.doi.org/10.1364/BOE.10.005549 |
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