Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Deep learning optoacoustic tomography with sparse data

Abstract

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

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 https://doi.org/10.6084/m9.figshare.9250784. The code to reproduce the results of the paper is available at https://github.com/ndavoudi/sparse_artefact_unet.

References

  1. Jathoul, A. P. et al. Deep in vivo photoacoustic imaging of mammalian tissues using a tyrosinase-based genetic reporter. Nat. Photon. 9, 239–246 (2015).

    Article  Google Scholar 

  2. Yao, J. et al. High-speed label-free functional photoacoustic microscopy of mouse brain in action. Nat. Methods 12, 407–410 (2015).

    Article  Google Scholar 

  3. Lin, H.-C. A. et al. Characterization of cardiac dynamics in an acute myocardial infarction model by four-dimensional optoacoustic and magnetic resonance imaging. Theranostics 7, 4470–4479 (2017).

    Article  Google Scholar 

  4. Stoffels, I. et al. Metastatic status of sentinel lymph nodes in melanoma determined noninvasively with multispectral optoacoustic imaging. Sci. Transl. Med. 7, 317ra199 (2015).

    Article  Google Scholar 

  5. Diot, G. et al. Multi-spectral optoacoustic tomography (MSOT) of human breast cancer. Clin. Cancer Res. 23, 6912–6922 (2017).

    Article  Google Scholar 

  6. Knieling, F. et al. Multispectral optoacoustic tomography for assessment of Crohn’s disease activity. N. Engl. J. Med. 376, 1292–1294 (2017).

    Article  Google Scholar 

  7. Lin, L. et al. Single-breath-hold photoacoustic computed tomography of the breast. Nat. Commun. 9, 2352 (2018).

    Article  Google Scholar 

  8. Wang, L. V. & Yao, J. A practical guide to photoacoustic tomography in the life sciences. Nat. Methods 13, 627–638 (2016).

    Article  Google Scholar 

  9. Weber, J., Beard, P. C. & Bohndiek, S. E. Contrast agents for molecular photoacoustic imaging. Nat. Methods 13, 639–650 (2016).

    Article  Google Scholar 

  10. Deán-Ben, X., Gottschalk, S., Mc Larney, B., Shoham, S. & Razansky, D. Advanced optoacoustic methods for multiscale imaging of in vivo dynamics. Chem. Soc. Rev. 46, 2158–2198 (2017).

    Article  Google Scholar 

  11. Taruttis, A. & Ntziachristos, V. Advances in real-time multispectral optoacoustic imaging and its applications. Nat. Photon. 9, 219–227 (2015).

    Article  Google Scholar 

  12. Deán-Ben, X. L. et al. Functional optoacoustic neuro-tomography for scalable whole-brain monitoring of calcium indicators. Light Sci. Appl. 5, e16201 (2016).

    Article  Google Scholar 

  13. Özbek, A., Deán-Ben, X. L. & Razansky, D. Optoacoustic imaging at kilohertz volumetric frame rates. Optica 5, 857–863 (2018).

    Article  Google Scholar 

  14. Guo, Z., Li, C., Song, L. & Wang, L. V. Compressed sensing in photoacoustic tomography in vivo. J. Biomed. Opt. 15, 021311 (2010).

    Article  Google Scholar 

  15. Sandbichler, M., Krahmer, F., Berer, T., Burgholzer, P. & Haltmeier, M. A novel compressed sensing scheme for photoacoustic tomography. SIAM J. Appl. Math. 75, 2475–2494 (2015).

    Article  MathSciNet  Google Scholar 

  16. Arridge, S. et al. Accelerated high-resolution photoacoustic tomography via compressed sensing. Phys. Med. Biol. 61, 8908–8940 (2016).

    Article  Google Scholar 

  17. Meng, J. et al. High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis. J. Biomed. Opt. 21, 076007 (2016).

    Article  Google Scholar 

  18. Gratt, S., Nuster, R., Wurzinger, G., Bugl, M. & Paltauf, G. 64-line-sensor array: fast imaging system for photoactive tomography. Proc. SPIE 8943, 894365 (2014).

    Article  Google Scholar 

  19. Rosenthal, A., Ntziachristos, V. & Razansky, D. Acoustic inversion in optoacoustic tomography: a review. Curr. Med. Imaging Rev. 9, 318–336 (2013).

    Article  Google Scholar 

  20. Hyun, C. M., Kim, H. P., Lee, S. M., Lee, S. & Seo, J. K. Deep learning for undersampled MRI reconstruction. Phys. Med. Biol. 63, 135007 (2018).

    Article  Google Scholar 

  21. Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R. & Rosen, M. S. Image reconstruction by domain-transform manifold learning. Nature 555, 487–492 (2018).

    Article  Google Scholar 

  22. Hammernik, K., Würfl, T., Pock, T. & Maier, A. in Bildverarbeitung für die Medizin 2017 92–97 (Springer, 2017).

  23. Kelly, B., Matthews, T. P. & Anastasio, M. A. Deep learning-guided image reconstruction from incomplete data. Preprint at https://arxiv.org/abs/1709.00584 (2017).

  24. Bungart, B. L. et al. Photoacoustic tomography of intact human prostates and vascular texture analysis identify prostate cancer biopsy targets. Photoacoustics 11, 46–55 (2018).

    Article  Google Scholar 

  25. Zhang, J., Chen, B., Zhou, M., Lan, H. & Gao, F. Photoacoustic image classification and segmentation of breast cancer: a feasibility study. IEEE Access 7, 5457–5466 (2019).

    Article  Google Scholar 

  26. Antholzer, S., Haltmeier, M. & Schwab, J. Deep learning for photoacoustic tomography from sparse data. Inverse Probl. Sci. Eng. 27, 987–1005 (2018).

    Article  MathSciNet  Google Scholar 

  27. Guan, S., Khan, A., Sikdar, S. & Chitnis, P. V. Fully dense UNet for 2D sparse photoacoustic tomography artifact removal. IEEE J. Biomed. Health Inform. https://doi.org/10.1109/JBHI.2019.2912935 (2019).

  28. Hauptmann, A. et al. Model-based learning for accelerated, limited-view 3-D photoacoustic tomography. IEEE Trans. Med. Imaging 37, 1382–1393 (2018).

    Article  Google Scholar 

  29. Waibel, D. et al. Reconstruction of initial pressure from limited view photoacoustic images using deep learning. Proc. SPIE 10494, 104942S (2018).

    Google Scholar 

  30. Cai, C., Deng, K., Ma, C. & Luo, J. End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging. Opt. Lett. 43, 2752–2755 (2018).

    Article  Google Scholar 

  31. Kirchner, T., Gröhl, J. & Maier-Hein, L. Context encoding enables machine learning-based quantitative photoacoustics. J. Biomed. Opt. 23, 056008 (2018).

    Article  Google Scholar 

  32. Reiter, A. & Bell, M. A. L. A machine learning approach to identifying point source locations in photoacoustic data. Proc. SPIE 10064, 100643J (2017).

    Google Scholar 

  33. Allman, D., Reiter, A. & Bell, M. A. L. Photoacoustic source detection and reflection artifact removal enabled by deep learning. IEEE Trans. Med. Imaging 37, 1464–1477 (2018).

    Article  Google Scholar 

  34. Govinahallisathyanarayana, S., Ning, B., Cao, R., Hu, S. & Hossack, J. A. Dictionary learning-based reverberation removal enables depth-resolved photoacoustic microscopy of cortical microvasculature in the mouse brain. Sci. Rep. 8, 985 (2018).

    Article  Google Scholar 

  35. Gutta, S. et al. Deep neural network-based bandwidth enhancement of photoacoustic data. J. Biomed. Opt. 22, 116001 (2017).

    Article  Google Scholar 

  36. Lutzweiler, C. & Razansky, D. Optoacoustic imaging and tomography: reconstruction approaches and outstanding challenges in image performance and quantification. Sensors 13, 7345–7384 (2013).

    Article  Google Scholar 

  37. Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-assisted Intervention (eds Navab, N., Hornegger, J., Wells, W. M. & Frangi, A. F.) 234–241 (Springer, 2015).

  38. Dean-Ben, X. L., Buehler, A., Ntziachristos, V. & Razansky, D. Accurate model-based reconstruction algorithm for three-dimensional optoacoustic tomography. IEEE Trans. Med. Imaging 31, 1922–1928 (2012).

    Article  Google Scholar 

  39. Schwab, J., Antholzer, S., Nuster, R. & Haltmeier, M. Real-time photoacoustic projection imaging using deep learning. Preprint at https://arxiv.org/abs/1801.06693 (2018).

  40. Omar, M. et al. Optical imaging of post-embryonic zebrafish using multi orientation raster scan optoacoustic mesoscopy. Light Sci. Appl. 6, e16186 (2017).

    Article  Google Scholar 

  41. Xu, Y., Wang, L. V., Ambartsoumian, G. & Kuchment, P. Reconstructions in limited‐view thermoacoustic tomography. Med. Phys. 31, 724–733 (2004).

    Article  Google Scholar 

  42. Deán-Ben, X. L. & Razansky, D. On the link between the speckle free nature of optoacoustics and visibility of structures in limited-view tomography. Photoacoustics 4, 133–140 (2016).

    Article  Google Scholar 

  43. Li, L. et al. Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution. Nat. Biomed. Eng. 1, 0071 (2017).

    Article  Google Scholar 

  44. Queiros, D. et al. Modeling the shape of cylindrically focused transducers in three-dimensional optoacoustic tomography. J. Biomed. Opt. 18, 076014 (2013).

    Article  Google Scholar 

  45. Dean-Ben, X. L., Ntziachristos, V. & Razansky, D. Effects of small variations of speed of sound in optoacoustic tomographic imaging. Med. Phys. 41, 073301 (2014).

    Article  Google Scholar 

  46. Fehm, T. F., Deán-Ben, X. L., Ford, S. J. & Razansky, D. In vivo whole-body optoacoustic scanner with real-time volumetric imaging capacity. Optica 3, 1153–1159 (2016).

    Article  Google Scholar 

  47. Tang, J., Dai, X. & Jiang, H. Wearable scanning photoacoustic brain imaging in behaving rats. J. Biophoton. 9, 570–575 (2016).

    Article  Google Scholar 

  48. Nair, A. A., Gubbi, M. R., Tran, T. D., Reiter, A. & Bell, M. A. L. A fully convolutional neural network for beamforming ultrasound images. In 2018 IEEE International Ultrasonics Symposium (IUS) 1–4 (IEEE, 2018).

  49. Neuschmelting, V. et al. Performance of a multispectral optoacoustic tomography (MSOT) system equipped with 2D vs. 3D handheld probes for potential clinical translation. Photoacoustics 4, 1–10 (2016).

    Article  Google Scholar 

  50. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).

  51. Bastien, F. et al. Theano: new features and speed improvements. Preprint at https://arxiv.org/abs/1211.5590 (2012).

  52. Dieleman, S. et al. Lasagne: first release 3 (Zenodo, 2015).

  53. Merčep, E., Herraiz, J. L., Deán-Ben, X. L. & Razansky, D. Transmission–reflection optoacoustic ultrasound (TROPUS) computed tomography of small animals. Light Sci. Appl. 8, 18 (2019).

    Article  Google Scholar 

  54. Xu, M. & Wang, L. V. Universal back-projection algorithm for photoacoustic computed tomography. Phys. Rev. E 71, 016706 (2005).

    Article  Google Scholar 

  55. Estrada, H. C. et al. Virtual craniotomy for high-resolution optoacoustic brain microscopy. Sci. Rep. 8, 1459 (2018).

    Article  Google Scholar 

  56. Rosenthal, A., Razansky, D. & Ntziachristos, V. Fast semi-analytical model-based acoustic inversion for quantitative optoacoustic tomography. IEEE Trans. Med. Imaging 29, 1275–1285 (2010).

    Article  Google Scholar 

  57. Dean-Ben, X. L., Ntziachristos, V. & Razansky, D. Acceleration of optoacoustic model-based reconstruction using angular image discretization. IEEE Trans. Med. Imaging 31, 1154–1162 (2012).

    Article  Google Scholar 

  58. Wang, L. V. & Wu, H.-i. Biomedical Optics: Principles and Imaging (Wiley, 2012).

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Contributions

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.

Corresponding author

Correspondence to Daniel Razansky.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Davoudi, N., Deán-Ben, X.L. & Razansky, D. Deep learning optoacoustic tomography with sparse data. Nat Mach Intell 1, 453–460 (2019). https://doi.org/10.1038/s42256-019-0095-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-019-0095-3

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing