Application of deep learning to improve tangential resolution in photoacoustic tomography

Praveenbalaji Rajendran; Manojit Pramanik

doi:10.1117/12.2577100

5 March 2021 Application of deep learning to improve tangential resolution in photoacoustic tomography

Praveenbalaji Rajendran, Manojit Pramanik

Proceedings Volume 11642, Photons Plus Ultrasound: Imaging and Sensing 2021; 116423E (2021) https://doi.org/10.1117/12.2577100
Event: SPIE BiOS, 2021, Online Only

Conference Poster

Abstract

In circular scanning geometry based Photoacoustic tomographic (PAT) imaging systems, the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially varying and is dependent on the aperture size of the detector. Conventionally, large aperture size detectors are the preferred choice for detection element in circular view PAT imaging systems but it hampers the tangential resolution. Although several techniques have been proposed to improve the tangential resolution, they are also hindered by their inherent limitations. Herein, we propose a deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT.We used a (U-Net) based CNN architecture to improve the tangential resolution of the acquired PAT images. Our results suggest the proposed deep learning architecture improves the tangential resolution of the acquired by five folds, without compromising the image quality.

Conference Presentation

Citation Download Citation

Praveenbalaji Rajendran and Manojit Pramanik "Application of deep learning to improve tangential resolution in photoacoustic tomography", Proc. SPIE 11642, Photons Plus Ultrasound: Imaging and Sensing 2021, 116423E (5 March 2021); https://doi.org/10.1117/12.2577100

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