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Dehner, C. ; Olefir, I. ; Basak, K. ; Jüstel, D. ; Ntziachristos, V.

Deep-learning-based electrical noise removal enables high spectral optoacoustic contrast in deep tissue.

IEEE Trans. Med. Imaging 41, 3182-3193 (2022)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Previously employed signal processing techniques have proven insufficient to remove the effects of electrical noise because they typically rely on simplified models and fail to capture complex characteristics of signal and noise. Moreover, they often involve time-consuming processing steps that are unsuited for real-time imaging applications. In this work, we develop and demonstrate a discriminative deep learning approach to separate electrical noise from optoacoustic signals prior to image reconstruction. The proposed deep learning algorithm is based on two key features. First, it learns spatiotemporal correlations in both noise and signal by using the entire optoacoustic sinogram as input. Second, it employs training on a large dataset of experimentally acquired pure noise and synthetic optoacoustic signals. We validated the ability of the trained model to accurately remove electrical noise on synthetic data and on optoacoustic images of a phantom and the human breast. We demonstrate significant enhancements of morphological and spectral optoacoustic images reaching 19% higher blood vessel contrast and localized spectral contrast at depths of more than 2 cm for images acquired in vivo. We discuss how the proposed denoising framework is applicable to clinical multispectral optoacoustic tomography and suitable for real-time operation.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Noise Reduction ; Thermal Noise ; Tomography ; Deep Learning ; Neural Networks ; Image Reconstruction ; Absorption ; Breast Cancer ; Denoising ; Dynamic Msot ; Photoacoustic Tomography ; Signal Decomposition ; Sinogram
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Zeitschrift IEEE Transactions on Medical Imaging
Quellenangaben Band: 41, Heft: 11, Seiten: 3182-3193 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)
Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505500-001
G-503800-001
DOI 10.1109/TMI.2022.3180115
WOS ID WOS:000876061700017
PubMed ID 35657832
Erfassungsdatum 2022-11-21
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