Learning-based enhancement of limited-view optoacoustic tomography based on image- and time-domain data

Neda Davoudi; Berkan Lafci; Ali Özbek; Xosé Luís Deán-Ben; Daniel Razansky

doi:10.1117/12.2608717

3 March 2022 Learning-based enhancement of limited-view optoacoustic tomography based on image- and time-domain data

Neda Davoudi, Berkan Lafci, Ali Özbek, Xosé Luís Deán-Ben, Daniel Razansky

Proceedings Volume 11960, Photons Plus Ultrasound: Imaging and Sensing 2022; 1196003 (2022) https://doi.org/10.1117/12.2608717
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Optoacoustic images are often afflicted with distortions and artifacts corresponding to system limitations, including limited-view tomographic data. We developed a convolutional neural network (CNN) approach for optoacoustic image quality enhancement combining training on both time-resolved signals and tomographic reconstructions. Reference human finger data for training the CNN were recorded using a full-ring array system with optimal tomographic coverage. The reconstructions were further refined with a dedicated algorithm that minimizes acoustic reflection artifacts induced by acoustically mismatch structures, such as bones. The combined methodology is shown to outperform other CNN-based methods solely operating on image-domain data.

Conference Presentation

Citation Download Citation

Neda Davoudi, Berkan Lafci, Ali Özbek, Xosé Luís Deán-Ben, and Daniel Razansky "Learning-based enhancement of limited-view optoacoustic tomography based on image- and time-domain data", Proc. SPIE 11960, Photons Plus Ultrasound: Imaging and Sensing 2022, 1196003 (3 March 2022); https://doi.org/10.1117/12.2608717

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