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Compressed Optoacoustic Sensing of Volumetric Cardiac Motion


Ozbek, Ali; Dean-Ben, Xose Luis; Razansky, Daniel (2020). Compressed Optoacoustic Sensing of Volumetric Cardiac Motion. IEEE Transactions on Medical Imaging, 39(10):3250-3255.

Abstract

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.

Abstract

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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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Pharmacology and Toxicology
07 Faculty of Science > Institute of Pharmacology and Toxicology

04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Scopus Subject Areas:Physical Sciences > Software
Health Sciences > Radiological and Ultrasound Technology
Physical Sciences > Computer Science Applications
Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Electrical and Electronic Engineering, Radiological and Ultrasound Technology, Software, Computer Science Applications
Language:English
Date:1 October 2020
Deposited On:01 Feb 2021 15:51
Last Modified:02 Feb 2021 21:00
Publisher:Institute of Electrical and Electronics Engineers
ISSN:0278-0062
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/tmi.2020.2985134
PubMed ID:32746091

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