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A deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound

Nature Machine Intelligence volume 5, pages 1130–1141 (2023)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Multispectral optoacoustic tomography is a high-resolution functional imaging modality that can non-invasively access a broad range of pathophysiological phenomena. Real-time imaging would enable translation of multispectral optoacoustic tomography into clinical imaging, visualize dynamic pathophysiological changes associated with disease progression and enable in situ diagnoses. Model-based reconstruction affords state-of-the-art optoacoustic images but cannot be used for real-time imaging. On the other hand, deep learning enables fast reconstruction of optoacoustic images, but the lack of experimental ground-truth training data leads to reduced image quality for in vivo scans. In this work we achieve accurate optoacoustic image reconstruction in 31 ms per image for arbitrary (experimental) input data by expressing model-based reconstruction with a deep neural network. The proposed deep learning framework, DeepMB, generalizes to experimental test data through training on optoacoustic signals synthesized from real-world images and ground truth optoacoustic images generated by model-based reconstruction. Based on qualitative and quantitative evaluation on a diverse dataset of in vivo images, we show that DeepMB reconstructs images approximately 1,000-times faster than the iterative model-based reference method while affording near-identical image qualities. Accurate and real-time image reconstructions with DeepMB can enable full access to the high-resolution and multispectral contrast of handheld optoacoustic tomography, thus adoption into clinical routines.

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Fig. 1: DeepMB pipeline.
Fig. 2: Examples from the in vivo test dataset for different anatomical locations.
Fig. 3: Data residual norms of optoacoustic images from DeepMB, MB and BP reconstructions.
Fig. 4: Unmixing of a representative multispectral breast scan for DeepMB, MB and BP.

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Data availability

In vivo data from two of the six scanned volunteers, the trained DeepMB model used in this work, and a download link for Pascal VOC 2012 dataset38 used to synthesize training data for DeepMB are provided along with the source code on Github (https://github.com/juestellab/deepmb)61. In vivo data from the other four scanned volunteers cannot be shared due to privacy and consent restrictions.

Code availability

The source code for DeepMB is publicly available on GitHub (https://github.com/juestellab/deepmb)61.

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Acknowledgements

We would like to thank A. Longo for her precious contribution during in vivo image acquisition and the conception of Fig. 2, and R. Wilson for his attentive reading and improvements of the manuscript. This project has received funding from the Bavarian Ministry of Economic Affairs, Energy and Technology (StMWi) (DIE-2106-0005// DIE0161/02, DeepOPUS, granted to D.J.) and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 694968 (PREMSOT, granted to V.N.).

Author information

Author notes

  1. These authors contributed equally: Christoph Dehner, Guillaume Zahnd.

  2. These authors jointly supervised this work: Vasilis Ntziachristos, Dominik Jüstel.

Authors and Affiliations

  1. Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany

    Christoph Dehner, Guillaume Zahnd, Vasilis Ntziachristos & Dominik Jüstel

  2. Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany

    Christoph Dehner, Vasilis Ntziachristos & Dominik Jüstel

  3. iThera Medical GmbH, Munich, Germany

    Guillaume Zahnd

  4. Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany

    Vasilis Ntziachristos

  5. Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany

    Dominik Jüstel

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Contributions

C.D. and G.Z. contributed equally to this work. C.D., G.Z., and D.J. conceptualized the initial idea. C.D. and G.Z. implemented the algorithm, conducted the experiments, analysed the results, and wrote the manuscript. D.J. and V.N. supervised the work. All authors provided feedback and approved the manuscript.

Corresponding authors

Correspondence to Vasilis Ntziachristos or Dominik Jüstel.

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Competing interests

V.N. is an equity owner in and consultant for iThera Medical GmbH. G.Z. and C.D. are employees of iThera Medical GmbH. D.J., C.D. and G.Z. are inventors in patent applications related to DeepMB (patent nos. EP22177153.8 and PCT/EP2023/064714).

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Nature Machine Intelligence thanks Andreas Hauptmann, and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary Handling Editor: Jacob Huth, in collaboration with the Nature Machine Intelligence editorial team.

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Extended data

Extended Data Fig. 1 Visual comparison of backprojection images with negative pixel values set to zero after the reconstruction (BP, i-l) and delay-multiply-and-sum with coherence factor (DMAS-CF, m-p) images, against the corresponding deep model-based (DeepMB, a-d) and model-based (MB, e-h) images.

Visual comparison of backprojection (BP) images with negative pixel values set to zero after the reconstruction (third row) and delay-multiply-and-sum with coherence factor (DMAS-CF, fourth row) images, against the corresponding deep model-based (DeepMB) and model-based (MB) images (first two rows). The presented samples are the same as those depicted in Fig. 2. DeepMB and MB images are nearly identical; BP images notably differ from reference model-based reconstructions suffering from lower resolution (see for example structures shown in zoom A of tile i and zoom D of tile j), missing structures in image regions that contained negative pixel values (see for example zoom F of tile j, or the entire region below the skin line (Sk) in tile k and l), and reduced contrast (see for example structures shown in zoom I of tile k and zoom J of tile l). All images show the reconstructed initial pressure in arbitrary units and were slightly cropped to a field of view of 4.16 × 2.80 cm2 to disregard the area occupied by the probe couplant above the skin line.

Extended Data Fig. 2 Examples of deep model-based and model-based images with low and high data residual norms.

Examples from the in vivo test dataset with low and high data residual norms (namely, below the 5th percentile (a-h) and above the 95th percentile (i-p) of all 4814 test samples, respectively), for deep model-based (DeepMB) and model-based (MB). The data residual norm (R) is indicated between round brackets above each image. Panels (a, e) and (l, p) correspond to the samples for which DeepMB afforded the overall lowest and highest data residual norms, respectively. All images show the reconstructed initial pressure in arbitrary units and were slightly cropped to a field of view of 4.16 × 2.80 cm2 to disregard the area occupied by the probe couplant above the skin line (Sk).

Extended Data Fig. 3 Unmixing of a multispectral biceps scan for deep model-based, model-based, backprojection, and delay-multiply-and-sum with coherence factor reconstructions.

Unmixing of a representative multispectral biceps scan for deep model-based (DeepMB; a, e), model-based (MB; b, f), backprojection (BP; c, g), and delay-multiply-and-sum with coherence factor (DMAS-CF; d, h). The unmixed components for fat and water and for oxyheamoglobin and deoxyhaemoglobin are shown in the first two rows, respectively. The third row depicts the reference absorption spectra of the four chromophores used during unmixing (i) and a schematic sketch of the anatomical context for the depicted scan (j). All optoacoustic images show the unmixed components in arbitrary units and were slightly cropped to a field of view of 4.16 × 2.80 cm2 to disregard the area occupied by the probe couplant above the skin line. Mb: probe membrane, Sk: skin, Fa: fascia, Mu: muscle, Ve: blood vessel, Ne: nerve.

Extended Data Fig. 4 Unmixing of a multispectral abdomen scan for deep model-based, model-based, backprojection, and delay-multiply-and-sum with coherence factor reconstructions.

Unmixing of a representative multispectral abdomen scan for deep model-based (DeepMB; a, e), model-based (MB; b, f), backprojection (BP; c, g) and delay-multiply-and-sum with coherence factor (DMAS-CF; d, h). The unmixed components for fat and water and for oxyhaemoglobin and deoxyhaemoglobin are shown in the first two rows, respectively. The third row depicts the reference absorption spectra of the four chromophores used during unmixing (i) and a schematic sketch of the anatomical context for the depicted scan (j). All optoacoustic images show the unmixed components in arbitrary units and were slightly cropped to a field of view of 4.16 × 2.80 cm2 to disregard the area occupied by the probe couplant above the skin line. Mb: probe membrane, Sk: skin, Fa: fascia, Mu: muscle, Ft: fat, Co: colon.

Extended Data Fig. 5 Unmixing of a multispectral carotid scan for deep model-based, model-based, backprojection, and delay-multiply-and-sum with coherence factor reconstructions.

Unmixing of a representative multispectral carotid scan for deep model-based (DeepMB; a, e), model-based (MB; b, f), backprojection (BP; c, g) and delay-multiply-and-sum with coherence factor (DMAS-CF; d, h). The unmixed components for fat and water and for oxyhaemoglobin and deoxyhaemoglobin are shown in the first two rows, respectively. The third row depicts the reference absorption spectra of the four chromophores used during unmixing (i) and a schematic sketch of the anatomical context for the depicted scan (j). All optoacoustic images show the unmixed components in arbitrary units and were slightly cropped to a field of view of 4.16 × 2.80 cm2 to disregard the area occupied by the probe couplant above the skin line. Mb: probe membrane, Sk: skin, Fa: fascia, Mu: muscle, Ca: common carotid artery, Ju: jugular vein, Th: thyroid, Tr: trachea.

Extended Data Fig. 6 Example images from the alternative model DeepMBinitial-images trained using true initial pressure reference images.

Representative examples showing the inaptitude of the alternative model DeepMBinitial-images (that is, trained on true initial pressure images) to reconstruct in vivo images. The three rows depict different anatomies (elbow: a–e, abdomen: f–j, calf: k–o). The three leftmost columns correspond to images reconstructed via model-based (MB), alternative DeepMBinitial-images, and standard DeepMB. The two rightmost columns show the absolute differences between the reference model-based image and the image inferred from DeepMBinitial-images and DeepMB, respectively. The field of view is 4.16 × 4.16 cm2, the enlarged region is 0.61 × 0.61 cm2.

Extended Data Fig. 7 Example images from the alternative model DeepMBin-vivo trained using in vivo data.

Representative examples of reconstruction artefacts (red arrows) from alternative models DeepMBin-vivo (that is, trained on in vivo data instead of synthesized data). The three rows depict different anatomies (biceps: a–e, breast: f–j, thyroid: k–o). The three leftmost columns correspond to images reconstructed via model-based (MB), alternative DeepMB trained on in vivo data (DeepMBin-vivo), and standard DeepMB (DeepMB). The two rightmost columns show the absolute differences between the reference model-based image and the image inferred from DeepMBin-vivo and DeepMB, respectively. The field of view is 4.16 × 4.16 cm2, the enlarged region is 0.61 × 0.61 cm2.

Extended Data Table 1 Quantitative evaluation of deep model-based and model-based reconstructions for different aggregations of the in vivo test dataset
Full size table

Supplementary information

Reporting Summary

Supplementary Video 1

Carotid artery continuously imaged in the transversal view at 800 nm.

Supplementary Video 2

Biceps continuously imaged in the transversal view at 800 nm, while the SOS is gradually adjusted via a series of DeepMB reconstructions.

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Dehner, C., Zahnd, G., Ntziachristos, V. et al. A deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound. Nat Mach Intell 5, 1130–1141 (2023). https://doi.org/10.1038/s42256-023-00724-3

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