Abstract
Accurately and quantitatively imaging blood oxygen saturation (sO2) is a very meaningful application of photoacoustic tomography (PAT), which is an important indicator for measuring physiological diseases and assisting cancer diagnostic and treatment. Yet, due to the complex optical properties of heterogeneous biological tissues, the diffusely scattered light in the tissue faces the unknown wavelength-dependent optical attenuation and causes the uncertain distribution of the fluence, which fundamentally limits the quantification accuracy of PAT for imaging sO2. To tackle this problem, we propose an architecture, named EDA-Net, with Encoder, Decoder and Aggregator, which can aggregate features for a richer representation. We argue that the dense aggregated information helps to extract the comprehensive context information from the multi-wavelength PA images, then accurately infer the quantitative distribution of sO2. The numerical experiment is performed by using PA images, which are obtained by Monte Carlo optical preprocessing and k-Wave acoustic preprocessing based on clinically-obtained female breast phantom. We also explore the effect of the combination of different wavelengths on the accuracy of estimating sO2 to guide the design of PA imaging systems for meeting clinical needs. The experimental results demonstrate the efficacy and robustness of our proposed method, and also compare it with other methods to further prove the reliability of our quantitative sO2 results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cox, B.T., Laufer, J.G., Beard, P.C., Arridge, S.R.: Quantitative spectroscopic photoacoustic imaging: a review. J. Biomed. Opt. SPIE 17, 061202 (2012)
Liu, Y., et al.: Photoacoustic molecular imaging: from multiscale biomedical applications towards early-stage theranostics. Trends Biotechnol. 34, 420–433 (2016)
Brochu, F.M., Brunker, J., Joseph, J., Tomaszewski, M.R., Morscher, S., Bohndiek, S.E.: Towards quantitative evaluation of tissue absorption coefficients using light fluence correction in optoacoustic tomography. IEEE Trans. Med. Imaging 36, 322–331 (2017)
Tzoumas, S., et al.: Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues. Nat. Commun. 7, 12121 (2016)
Kirchner, T., Gröhl, J., Maier-Hein, L.: Context encoding enables machine learning-based quantitative photoacoustics. J. Biomed. Opt. SPIE 23, 056008 (2018)
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)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested u-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403–2412 (2018)
Lou, Y., Zhou, W., Matthews, T.P., Appleton, C.M., Anastasio, M.A.: Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging. J. Biomed. Opt. SPIE 24, 041015 (2017)
Fang, Q., Boas, D.A.: Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. Opt. Express 17, 20178–20190 (2009)
Treeby, B.E., Cox, B.T.: k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields. J. Biomed. Opt. 15, 1–12 (2010)
Hauptmann, A., et al.: Model-based learning for accelerated, limited-view 3-D photoacoustic tomography. IEEE Trans. Med. Imaging 37, 1382–1393 (2018)
Hochuli, R., Beard, P.C., Cox, B.: Effect of wavelength selection on the accuracy of blood oxygen saturation estimates obtained from photoacoustic images. In: Photons Plus Ultrasound: Imaging and Sensing, vol. 9323. International Society for Optics and Photonics (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, C., Gao, F. (2019). EDA-Net: Dense Aggregation of Deep and Shallow Information Achieves Quantitative Photoacoustic Blood Oxygenation Imaging Deep in Human Breast. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-32239-7_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32238-0
Online ISBN: 978-3-030-32239-7
eBook Packages: Computer ScienceComputer Science (R0)