Dataset Information

Soft ultrasound priors in optoacoustic reconstruction: Improving clinical vascular imaging.

ABSTRACT: Using the same ultrasound detector, hybrid optoacoustic-ultrasound (OPUS) imaging provides concurrent scans of tissue slices or volumes and visualizes complementary sound- and light-based contrast at similar resolutions. In addition to the benefit of hybrid contrast, spatial co-registration enables images from one modality to be employed as prior information for improving an aspect of the performance of the other modality. We consider herein a handheld OPUS system and utilize structural information from ultrasound images to guide regional Laplacian regularization-based reconstruction of optoacoustic images. Using phantoms and data from OPUS scans of human radial and carotid arteries, we show that ultrasound-driven optoacoustic inversion reduces limited-view artefacts and improves image contrast. In phantoms, prior-integrated reconstruction leads to a 50 % higher contrast-to-noise ratio (CNR) of the image than standard reconstruction, and a 17 % higher structural similarity (SSIM) index. In clinical data, prior-integrated reconstruction detects deep-seated radial arteries with higher CNR than the standard method at three different depths. In this way, the prior-integrated method offers unique insights into atherosclerotic carotid plaques in humans (with p<0.01 between patients and healthy volunteers), potentially paving the way for new abilities in vascular imaging and more generally in optoacoustic imaging.

SUBMITTER: Yang H

PROVIDER: S-EPMC7139114 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Soft ultrasound priors in optoacoustic reconstruction: Improving clinical vascular imaging.

Yang Hong H Jüstel Dominik D Prakash Jaya J Karlas Angelos A Helfen Anne A Masthoff Max M Wildgruber Moritz M Ntziachristos Vasilis V

Photoacoustics 20200310

Using the same ultrasound detector, hybrid optoacoustic-ultrasound (OPUS) imaging provides concurrent scans of tissue slices or volumes and visualizes complementary sound- and light-based contrast at similar resolutions. In addition to the benefit of hybrid contrast, spatial co-registration enables images from one modality to be employed as prior information for improving an aspect of the performance of the other modality. We consider herein a handheld OPUS system and utilize structural informat ...[more]

PMID: 32280585

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Project description:Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.

Dataset Information

Soft ultrasound priors in optoacoustic reconstruction: Improving clinical vascular imaging.

Publications

Soft ultrasound priors in optoacoustic reconstruction: Improving clinical vascular imaging.

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