Multiple photoacoustic sources localization using deep learning

Ronald de Jong; Hamid Moradi; Vincent Vousten; Robert Rohling; Septimiu Salcudean

doi:10.1117/12.3008451

1 April 2024 Multiple photoacoustic sources localization using deep learning

Ronald de Jong, Hamid Moradi, Vincent Vousten, Robert Rohling, Septimiu Salcudean

Proceedings Volume 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography; 129320W (2024) https://doi.org/10.1117/12.3008451
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Photoacoustic imaging (PAI) has emerged as a promising technique for various image guidance procedures. While convolutional neural networks (CNNs) trained on simulated radiofrequency (RF) data have been employed for point source reconstruction, their performance on real data remains a challenge. This paper addresses this limitation by introducing a novel deep learning-based method that utilizes a limited amount of experimental laser-diode-based data for the reconstruction of multiple point sources. The proposed approach employs a dual generative adversarial network (Dual-GAN) trained on experimental RF data from a combination of point source images. The Dual-GAN exhibits superior performance compared to the conventional delay-and-sum (DAS) method, demonstrating enhanced image contrast and a reduced full width at half maximum (FWHM). Notably, the axial and lateral localization errors of the Dual-GAN predictions surpass previous studies, measuring 0.028±0.018mm and 0.087±0.096mm, respectively. Additionally, the model demonstrates generalization capability by successfully reconstructing multiple point sources imaged using a different Nd:YAG laser system. This innovative method marks a significant advancement, offering improved accuracy and versatility in PAI applications involving multiple point sources.

Conference Presentation

Citation Download Citation

Ronald de Jong, Hamid Moradi, Vincent Vousten, Robert Rohling, and Septimiu Salcudean "Multiple photoacoustic sources localization using deep learning", Proc. SPIE 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography, 129320W (1 April 2024); https://doi.org/10.1117/12.3008451

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