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In JoVE (1)
- Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research
Other Publications (1)
Articles by Christoffer Svensson 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.
Other articles by Christoffer Svensson on PubMed
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.