Deep learning approach to improve tangential resolution in photoacoustic tomography
In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improv...
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sg-ntu-dr.10356-1465452023-12-29T06:50:07Z Deep learning approach to improve tangential resolution in photoacoustic tomography Rajendran, Praveenbalaji Pramanik, Manojit School of Chemical and Biomedical Engineering Engineering::Bioengineering Photoacoustic Tomography (PAT) Tangential Resolution In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improves with the decreasing aperture size. However, using a detector with a smaller aperture reduces the sensitivity of the transducer. Thus, large aperture size detectors are widely preferred in circular scan PAT imaging systems. Although several techniques have been proposed to improve the tangential resolution, they have inherent limitations such as high cost and the need for customized detectors. Herein, we propose a novel deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT imaging systems. We used a fully dense U-Net based convolutional neural network architecture along with 9 residual blocks to improve the tangential resolution of the PAT images. The network was trained on the simulated datasets and its performance was verified by experimental in vivo imaging. Results show that the proposed deep learning network improves the tangential resolution by eight folds, without compromising the structural similarity and quality of image. Ministry of Education (MOE) Published version Ministry of Education - Singapore (RG144/18) 2021-02-25T08:28:57Z 2021-02-25T08:28:57Z 2020 Journal Article Rajendran, P., & Pramanik, M. (2020). Deep learning approach to improve tangential resolution in photoacoustic tomography. Biomedical Optics Express, 11(12), 7311-7323. doi:10.1364/BOE.410145 2156-7085 https://hdl.handle.net/10356/146545 10.1364/BOE.410145 33408998 2-s2.0-85097030446 12 11 7311 7323 en Biomedical Optics Express © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. application/pdf |
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Engineering::Bioengineering Photoacoustic Tomography (PAT) Tangential Resolution Rajendran, Praveenbalaji Pramanik, Manojit Deep learning approach to improve tangential resolution in photoacoustic tomography |
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In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improves with the decreasing aperture size. However, using a detector with a smaller aperture reduces the sensitivity of the transducer. Thus, large aperture size detectors are widely preferred in circular scan PAT imaging systems. Although several techniques have been proposed to improve the tangential resolution, they have inherent limitations such as high cost and the need for customized detectors. Herein, we propose a novel deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT imaging systems. We used a fully dense U-Net based convolutional neural network architecture along with 9 residual blocks to improve the tangential resolution of the PAT images. The network was trained on the simulated datasets and its performance was verified by experimental in vivo imaging. Results show that the proposed deep learning network improves the tangential resolution by eight folds, without compromising the structural similarity and quality of image. |
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School of Chemical and Biomedical Engineering |
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School of Chemical and Biomedical Engineering Rajendran, Praveenbalaji Pramanik, Manojit |
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Article |
author |
Rajendran, Praveenbalaji Pramanik, Manojit |
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Rajendran, Praveenbalaji |
title |
Deep learning approach to improve tangential resolution in photoacoustic tomography |
title_short |
Deep learning approach to improve tangential resolution in photoacoustic tomography |
title_full |
Deep learning approach to improve tangential resolution in photoacoustic tomography |
title_fullStr |
Deep learning approach to improve tangential resolution in photoacoustic tomography |
title_full_unstemmed |
Deep learning approach to improve tangential resolution in photoacoustic tomography |
title_sort |
deep learning approach to improve tangential resolution in photoacoustic tomography |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/146545 |
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1787136644202102784 |