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Tzoumas, S. ; Kravtsiv, A. ; Gao, Y.* ; Bühler, A. ; Ntziachristos, V.

Statistical molecular target detection framework for multispectral optoacoustic tomography.

IEEE Trans. Med. Imaging 35, 2534-2545 (2016)
Postprint DOI PMC
Open Access Green
Statistical sub-pixel detection via the adaptive matched filter (AMF) has been shown to improve the molecular imaging sensitivity and specificity of optoacoustic (photoacoustic) imaging. Applied to multispectral optoacoustic tomography (MSOT), AMF assumes that the spatially-varying tissue spectra follow a multivariate Gaussian distribution, that the spectrum of the target molecule is precisely known and that the molecular target lies in "low probability" within the data. However, when these assumptions are violated, AMF may result in considerable performance degradation. The objective of this work is to develop a robust statistical detection framework that is appropriately suited to the characteristics of MSOT molecular imaging. Using experimental imaging data, we perform a statistical characterization of MSOT tissue images and conclude to a detector that is based on the t-distribution. More importantly, we introduce a method for estimating the covariance matrix of the background-tissue statistical distribution, which enables robust detection performance independently of the molecular target size or intensity. The performance of the statistical detection framework is assessed through simulations and experimental in vivo measurements and compared to previously used methods.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Covariance Contamination ; Molecular Imaging ; Multispectral Optoacoustic Tomography ; Photoacoustic Tomography ; Spectral Unmixing ; Statistical Sub-pixel Detection; Hyperspectral Imaging Data; Photoacoustic Images; Tissue; Distributions
Sprache englisch
Veröffentlichungsjahr 2016
HGF-Berichtsjahr 2016
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Zeitschrift IEEE Transactions on Medical Imaging
Quellenangaben Band: 35, Heft: 12, Seiten: 2534-2545 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505500-001
DOI 10.1109/TMI.2016.2583791
WOS ID WOS:000391547700003
PubMed ID 27337713
Erfassungsdatum 2016-06-29
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