Noise suppressed, multifocus image fusion for enhanced intraoperative navigation

Authors

  • Paolo Fumene Feruglio,

    1. Center for System Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston 02114, USA
    2. Department Neurological, Neuropsychological, Morphological and Movement Sciences, University of Verona, Strada Le Grazie 8, 37134 Verona, Italy
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    • Equal contribution.

  • Claudio Vinegoni,

    Corresponding author
    1. Center for System Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston 02114, USA
    • Phone: 857.891.4272, Fax: 617.643.6133
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    • Equal contribution.

  • Lioubov Fexon,

    1. Center for System Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston 02114, USA
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  • Greg Thurber,

    1. Center for System Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston 02114, USA
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  • Andrea Sbarbati,

    1. Department Neurological, Neuropsychological, Morphological and Movement Sciences, University of Verona, Strada Le Grazie 8, 37134 Verona, Italy
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  • Ralph Weissleder

    1. Center for System Biology, Massachusetts General Hospital and Harvard Medical School, Richard B. Simches Research Center, 185 Cambridge Street, Boston 02114, USA
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Abstract

Current intraoperative imaging systems are typically not able to provide ‘sharp’ images over entire large areas or entire organs. Distinct structures such as tissue margins or groups of malignant cells are therefore often difficult to detect, especially under low signal-to-noise-ratio conditions. In this report, we introduce a noise suppressed multifocus image fusion algorithm, that provides detailed reconstructions even when images are acquired under sub-optimal conditions, such is the case for real time fluorescence intraoperative surgery. The algorithm makes use of the Anscombe transform combined with a multi-level stationary wavelet transform with individual threshold-based shrinkage. While the imaging system is integrated with a respiratory monitor triggering system, it can be easily adapted to any commercial imaging system. The developed algorithm is made available as a plugin for Osirix. (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

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