Deep learning based high frame rate photoacoustic tomography

Praveenbalaji Rajendran; Manojit Pramanik

doi:10.1117/12.2648035

9 March 2023 Deep learning based high frame rate photoacoustic tomography

Praveenbalaji Rajendran, Manojit Pramanik

Proceedings Volume 12379, Photons Plus Ultrasound: Imaging and Sensing 2023; 123791A (2023) https://doi.org/10.1117/12.2648035
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

Generating an image of acceptable quality will take several minutes in circular scanning geometry-based Photoacoustic tomographic (PAT) imaging systems. Although, the imaging speed can be improved by employing multiple single-element ultrasound transducers (UST) and faster scanning. The low signal-to-noise ratio at higher and the artifacts arising from sparse signal acquisition hamper the imaging speed. Thus, there exists a need to improve the speed of the PAT imaging system without compromising the image quality. To improve the frame rate of the PAT system, we propose a convolutional neural network (CNN) based deep learning architecture for reconstructing the artifact-free PAT images from the fast-scanning data. The proposed model is trained with the simulated dataset and its performance was evaluated using experimental phantom and in-vivo imaging. The efficiency to improve the frame rate was evaluated on both the single-UST and multi-UST PAT systems. Our results suggest that the proposed deep learning architecture improves the frame rate by six-fold in a single UST PAT system and by two-fold in a multi-UST PAT system. The fastest frame rate of ~ 3Hz was achieved without compromising the quality of the PAT image.

Citation Download Citation

Praveenbalaji Rajendran and Manojit Pramanik "Deep learning based high frame rate photoacoustic tomography", Proc. SPIE 12379, Photons Plus Ultrasound: Imaging and Sensing 2023, 123791A (9 March 2023); https://doi.org/10.1117/12.2648035

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