Multi-transducer photoacoustic tomography imaging without radius calibration using deep learning

Praveenbalaji Rajendran; Manojit Pramanik

doi:10.1117/12.2607703

3 March 2022 Multi-transducer photoacoustic tomography imaging without radius calibration using deep learning

Praveenbalaji Rajendran, Manojit Pramanik

Proceedings Volume 11960, Photons Plus Ultrasound: Imaging and Sensing 2022; 1196008 (2022) https://doi.org/10.1117/12.2607703
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

Pulsed laser diodes (PLD) are preferred as excitation sources in photoacoustic tomography due to their low cost, compact size, and high pulse repetition rate. When PLD is used in conjunction with multiple single-element ultrasound transducers (SUT), the imaging speed can be improved. However, during PAT image reconstruction, the exact radius of each SUT is required for accurate reconstruction. Herein, we propose a novel deep learning approach to alleviate the need for radius calibration. We developed a convolutional neural network (fully dense U-Net) with a convolutional long short-term memory (LSTM) block as the bridge to reconstruct the PAT images. In vivo imaging was used to verify the performance of the network. Our results and analysis demonstrate that the proposed network eliminates the need for radius calibration without sacrificing the reconstructed PAT image quality.

Conference Presentation

Citation Download Citation

Praveenbalaji Rajendran and Manojit Pramanik "Multi-transducer photoacoustic tomography imaging without radius calibration using deep learning", Proc. SPIE 11960, Photons Plus Ultrasound: Imaging and Sensing 2022, 1196008 (3 March 2022); https://doi.org/10.1117/12.2607703

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