OADAT and Deep Learning for Optoacoustic Imaging

Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging (DLBIRHOUI) was a collaboration between ETH Zurich and the Swiss Data Science Center focused on data-driven methods for hybrid optoacoustic and ultrasound imaging.

The project produced OADAT, a public dataset of experimental and synthetic clinical optoacoustic data, along with reconstruction tools and deep learning methods for limited-view reconstruction, image-to-image translation, and generative modeling of experimental optoacoustic images.

Article: OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing
SDSC Blogpost: Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging
Dataset: OADAT
Demo: Linear-array generator
Demo: Semi-circle generator

Highlights

Public experimental and synthetic optoacoustic datasets.
Deep learning methods for limited-view optoacoustic reconstruction.
Image-to-image translation between acquisition geometries.
Generative models for synthetic experimental optoacoustic images.
Open tools and demos to support reproducible research.

Demos

The apps below allow random sampling from StyleGAN2 models trained on OADAT reconstructions.

(Ozdemir et al., 2023) (Susmelj et al., 2024)

References

2024

Med. Image Anal.

Grid comparing optoacoustic reconstructions from baseline and SDAN methods.

Thumbnail adapted from the corresponding paper.

Signal domain adaptation network for limited-view optoacoustic tomography

Anna Klimovskaia Susmelj, Berkan Lafci, Firat Ozdemir, and 4 more authors

Medical Image Analysis, 2024

Abs DOI Bib HTML Code

@article{susmelj2024signal,
  title = {Signal domain adaptation network for limited-view optoacoustic tomography},
  author = {Susmelj, Anna Klimovskaia and Lafci, Berkan and Ozdemir, Firat and Davoudi, Neda and De{\'a}n-Ben, Xos{\'e} Lu{\'i}s and Perez-Cruz, Fernando and Razansky, Daniel},
  journal = {Medical Image Analysis},
  volume = {91},
  pages = {103012},
  year = {2024},
  publisher = {Elsevier},
  doi = {10.1016/j.media.2023.103012},
  preview_alt = {Grid comparing optoacoustic reconstructions from baseline and SDAN methods.},
  preview_note = {Thumbnail adapted from the corresponding paper.}
}

2023

TMLR

Forearm acquisition setup and semi-circle and multisegment optoacoustic transducer arrays.

Thumbnail adapted from the corresponding paper.

OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing

Firat Ozdemir, Berkan Lafci, Xose Luis Dean-Ben, and 2 more authors

Transactions on Machine Learning Research, 2023

Abs arXiv Bib HTML Code

@article{ozdemir2023oadat,
  title = {OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing},
  author = {Ozdemir, Firat and Lafci, Berkan and Dean-Ben, Xose Luis and Razansky, Daniel and Perez-Cruz, Fernando},
  journal = {Transactions on Machine Learning Research},
  number = {2835-8856},
  year = {2023},
  preview_alt = {Forearm acquisition setup and semi-circle and multisegment optoacoustic transducer arrays.},
  preview_note = {Thumbnail adapted from the corresponding paper.}
}