17 September 2013 Spatiotemporal analysis for indocyanine green-aided imaging of rheumatoid arthritis in hand joints
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J. of Biomedical Optics, 18(9), 097004 (2013). doi:10.1117/1.JBO.18.9.097004
Abstract
Rheumatoid arthritis (RA) is the most common chronic inflammatory joint disease, with a prevalence of 0.5 to 1% in the general population. Imaging can possibly aid in early diagnosis, crucial to effective personalized therapeutic strategies and treatment follow-up. The intravenous administration of indocyanine green (ICG) has been considered for identifying synovial hyperperfusion as an RA physiological biomarker. However, while the distribution of ICG in the human hand is a time-dependent process, the particular biodistribution dynamic patterns established following intravenous administration have not yet been studied. For this reason, the dynamic relationships of ICG distribution in the human hand in RA patients using a method based on principal component analysis are analyzed. In vivo analyses were corroborated by simulations of clinical scenarios using a finite element method. Observations of spatiotemporal characteristics are contrasted to fluorescence intensity images and magnetic resonance images of the hand joints, employed as the anatomical and diagnostic reference. Processing results for 450 joints from 5 healthy volunteers and 10 patients show that image features obtained from the spatiotemporal analysis offer good congruence with synovitis and reveal better detection performance compared to observations of raw fluorescence intensity images.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Pouyan Mohajerani, Reinhard Meier, Peter B. Noël, Ernst J. Rummeny, Vasilis Ntziachristos, "Spatiotemporal analysis for indocyanine green-aided imaging of rheumatoid arthritis in hand joints," Journal of Biomedical Optics 18(9), 097004 (17 September 2013). http://dx.doi.org/10.1117/1.JBO.18.9.097004
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KEYWORDS
Luminescence

Image processing

Microchannel plates

Magnetic resonance imaging

Principal component analysis

Image segmentation

Tissues

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