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Özbek, A. ; Dean-Ben, X.L. ; Razansky, D.

Compressed optoacoustic sensing of volumetric cardiac motion.

IEEE Trans. Med. Imaging 39, 3250-3255 (2020)
Verlagsversion DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
The recently developed optoacoustic tomography systems have attained volumetric frame rates exceeding 100 Hz, thus opening up new venues for studying previously invisible biological dynamics. Further gains in temporal resolution can potentially be achieved via partial data acquisition, though a priori knowledge on the acquired data is essential for rendering accurate reconstructions using compressed sensing approaches. In this work, we suggest a machine learning method based on principal component analysis for high-frame-rate volumetric cardiac imaging using only a few tomographic optoacoustic projections. The method is particularly effective for discerning periodic motion, as demonstrated herein by non-invasive imaging of a beating mouse heart. A training phase enables efficiently compressing the heart motion information, which is subsequently used as prior information for image reconstruction from sparse sampling at a higher frame rate. It is shown that image quality is preserved with a 64-fold reduction in the data flow. We demonstrate that, under certain conditions, the volumetric motion could effectively be captured by relying on time-resolved data from a single optoacoustic detector. Feasibility of capturing transient (non-periodic) events not registered in the training phase is further demonstrated by visualizing perfusion of a contrast agent in vivo. The suggested approach can be used to significantly boost the temporal resolution of optoacoustic imaging and facilitate development of more affordable and data efficient systems.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Image Reconstruction ; Training ; Principal Component Analysis ; Three-dimensional Displays ; Data Acquisition ; Tomography ; Image Acquisition ; Image Reconstruction ; Image Restoration ; Machine Learning ; Optoacoustic Imaging; Photoacoustic Tomography
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Zeitschrift IEEE Transactions on Medical Imaging
Quellenangaben Band: 39, Heft: 10, Seiten: 3250-3255 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
Förderungen Werner and Hedy Berger-Janser Foundation
European Research Council