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In JoVE (1)
- Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research
Other Publications (2)
Articles by Abbas Cheddad in JoVE
Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research
Anna U. Eriksson*1, Christoffer Svensson*1, Andreas Hörnblad1, Abbas Cheddad1, Elena Kostromina1, Maria Eriksson1, Nils Norlin1, Antonello Pileggi2, James Sharpe3, Fredrik Georgsson4, Tomas Alanentalo1, Ulf Ahlgren1
1Umeå Centre for Molecular Medicine, Umeå University, 2Cell Transplant Center, Diabetes Research Institute, University of Miami,, 3EMBL-CRG Systems Biology Program, Centre for Genomic Regulation, Catalan Institute of Research and Advanced Studies, 4Dept. of Computing Science, Umeå University
We describe the adaptation of optical projection tomography (OPT)1 to imaging in the near infrared spectrum, and the implementation of a number of computational tools. These protocols enable assessments of pancreatic β-cell mass (BCM) in larger specimens, increase the multichannel capacity of the technique and increase the quality of OPT data.
Published January 12, 2013. Keywords: Medicine, Biomedical Engineering, Cellular Biology, Molecular Biology, Biophysics, Pancreas, Islets of Langerhans, Diabetes Mellitus, Imaging, Three-Dimensional, Optical Projection Tomography, Beta-cell Mass, Near Infrared, Computational Processing
Other articles by Abbas Cheddad on PubMed
An Improved Protocol for Optical Projection Tomography Imaging Reveals Lobular Heterogeneities in Pancreatic Islet and Î²-cell Mass Distribution
Islets. Jul-Aug, 2011 | Pubmed ID: 21633198
Optical projection tomography (OPT) imaging is a powerful tool for three-dimensional imaging of gene and protein distribution patterns in biomedical specimens. We have previously demonstrated the possibility, by this technique, to extract information of the spatial and quantitative distribution of the islets of Langerhans in the intact mouse pancreas. In order to further increase the sensitivity of OPT imaging for this type of assessment, we have developed a protocol implementing a computational statistical approach: contrast limited adaptive histogram equalization (CLAHE). We demonstrate that this protocol significantly increases the sensitivity of OPT imaging for islet detection, helps preserve islet morphology and diminish subjectivity in thresholding for tomographic reconstruction. When applied to studies of the pancreas from healthy C57BL/6 mice, our data reveal that, at least in this strain, the pancreas harbors substantially more islets than has previously been reported. Further, we provide evidence that the gastric, duodenal and splenic lobes of the pancreas display dramatic differences in total and relative islet and Î²-cell mass distribution. This includes a 75% higher islet density in the gastric lobe as compared to the splenic lobe and a higher relative volume of insulin producing cells in the duodenal lobe as compared to the other lobes. Altogether, our data show that CLAHE substantially improves OPT based assessments of the islets of Langerhans and that lobular origin must be taken into careful consideration in quantitative and spatial assessments of the pancreas.
IEEE Transactions on Medical Imaging. Jan, 2012 | Pubmed ID: 21768046
Since it was first presented in 2002, optical projection tomography (OPT) has emerged as a powerful tool for the study of biomedical specimen on the mm to cm scale. In this paper, we present computational tools to further improve OPT image acquisition and tomographic reconstruction. More specifically, these methods provide: semi-automatic and precise positioning of a sample at the axis of rotation and a fast and robust algorithm for determination of postalignment values throughout the specimen as compared to existing methods. These tools are easily integrated for use with current commercial OPT scanners and should also be possible to implement in "home made" or experimental setups for OPT imaging. They generally contribute to increase acquisition speed and quality of OPT data and thereby significantly simplify and improve a number of three-dimensional and quantitative OPT based assessments.