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Deep learning optoacoustic tomography with sparse data


The rapidly evolving field of optoacoustic (photoacoustic) imaging and tomography is driven by a constant need for better imaging performance in terms of resolution, speed, sensitivity, depth and contrast. In practice, data acquisition strategies commonly involve sub-optimal sampling of the tomographic data, resulting in inevitable performance trade-offs and diminished image quality. We propose a new framework for efficient recovery of image quality from sparse optoacoustic data based on a deep convolutional neural network and demonstrate its performance with whole body mouse imaging in vivo. To generate accurate high-resolution reference images for optimal training, a full-view tomographic scanner capable of attaining superior cross-sectional image quality from living mice was devised. When provided with images reconstructed from substantially undersampled data or limited-view scans, the trained network was capable of enhancing the visibility of arbitrarily oriented structures and restoring the expected image quality. Notably, the network also eliminated some reconstruction artefacts present in reference images rendered from densely sampled data. No comparable gains were achieved when the training was performed with synthetic or phantom data, underlining the importance of training with high-quality in vivo images acquired by full-view scanners. The new method can benefit numerous optoacoustic imaging applications by mitigating common image artefacts, enhancing anatomical contrast and image quantification capacities, accelerating data acquisition and image reconstruction approaches, while also facilitating the development of practical and affordable imaging systems. The suggested approach operates solely on image-domain data and thus can be seamlessly applied to artefactual images reconstructed with other modalities.

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Fig. 1: The deep convolutional neural network approach based on the U-Net architecture for artefact removal in OA imaging with undersampled data.
Fig. 2: Restoring optoacoustic image quality in whole-body mouse tomography in vivo using a full-view ring-array transducer.
Fig. 3: Network-based correction of limited-view effects induced by insufficient angular coverage of the optoacoustic imaging system.

Data availability

The datasets used for the current study were generated and analysed in our laboratory and are downloadable at The code to reproduce the results of the paper is available at


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This work was partially supported by the European Research Council under grant agreement ERC-2015-CoG-682379.

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Authors and Affiliations



N.D., X.L.D.-B. and D.R. conceived the study. N.D. and X.L.D.-B. carried out the experiments. N.D. implemented the image reconstruction and processing algorithms and analysed the data. D.R. and X.L.D.-B. supervised the study and data analysis. All authors discussed the results and contributed to writing the manuscript.

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Correspondence to Daniel Razansky.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and description of the Supplementary Videos.

Supplementary Video 1

Breathing mouse reconstruction with 32 channels.

Supplementary Video 2

Breathing mouse reconstruction with 128 channels.

Supplementary Video 3

Fly-through reconstruction with 32 channels.

Supplementary Video 4

Fly-through reconstruction with 128 channels.

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Davoudi, N., Deán-Ben, X.L. & Razansky, D. Deep learning optoacoustic tomography with sparse data. Nat Mach Intell 1, 453–460 (2019).

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