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An inversion scheme for hybrid fluorescence ‎molecular tomography using a fuzzy inference system.

IEEE Trans. Med. Imaging 35, 381-390 (2016)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
The imaging performance of fluorescence molecular tomography (FMT) improves when information from the underlying anatomy is incorporated into the inversion scheme, in the form of priors. The requirement for incorporation of priors has recently driven the development of hybrid FMT systems coupled to other modalities, such as X-ray CT and MRI. A critical methodological aspect in this case relates to the particular method selected to incorporate prior information obtained from the anatomical imaging modality into the FMT inversion. We propose herein a new approach for utilizing prior information, which preferentially minimizes residual errors associated with measurements that better describe the anatomical segments considered. This preferential minimization was realized using a weighted least square (WLS) approach, where the weights were optimized using a Mamdani-type fuzzy inference system. The method of priors introduced herein was deployed as a two-step structured regularization approach and was verified with experimental measurements from phantoms as well as ex vivo and in vivo animal studies. The results demonstrate accurate performance and minimization of reconstruction bias, without requiring user input for setting the regularization parameters. As such, the proposed method offers significant progress in incorporation of anatomical priors in FMT and, as a result, in realization of the full potential of hybrid FMT.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Anatomical Priors ; Fluorescence ; Fmt ; Fuzzy Systems ; Hybrid Imaging ; Molecular Imaging ; Optical Imaging ; Tomography; Ray Computed-tomography; X-ray; In-vivo; Imaging-system; Mediated Tomography; Small Animals; Born Ratio; Mri; Regularization; Information
Sprache englisch
Veröffentlichungsjahr 2016
Prepublished im Jahr 2015
HGF-Berichtsjahr 2015
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Quellenangaben Band: 35, Heft: 2, Seiten: 381-390 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Begutachtungsstatus Peer reviewed
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505500-001
PubMed ID 26340771
Erfassungsdatum 2015-09-10