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Title: Deep-learning-based multi-transducer photoacoustic tomography imaging without radius calibration
Authors: Rajendran, Praveenbalaji
Pramanik, Manojit
Keywords: Engineering::Bioengineering
Issue Date: 2021
Source: Rajendran, P. & Pramanik, M. (2021). Deep-learning-based multi-transducer photoacoustic tomography imaging without radius calibration. Optics Letters, 46(18), 4510-4513.
Project: RG144/18
Journal: Optics Letters
Abstract: Pulsed laser diodes are used in photoacoustic tomography (PAT) as excitation sources because of their low cost, compact size, and high pulse repetition rate. In combination with multiple single-element ultrasound transducers (SUTs) the imaging speed of PAT can be improved.However, during PAT image reconstruction, the exact radius of each SUT is required for accurate reconstruction. Here we developed a novel deep learning approach to alleviate the need for radius calibration. We used a convolutional neural network (fully dense U-Net) aided with a convolutional long short-term memory block to reconstruct the PAT images. Our analysis on the test set demonstrates that the proposed network eliminates the need for radius calibration and improves the peak signal-to-noise ratio by ~73% without compromising the image quality. In vivo imaging was used to verify the performance of the network.
ISSN: 0146-9592
DOI: 10.1364/OL.434513
Rights: © 2021 Optical Society of America. All rights reserved. This paper was published in Optics Letters and is made available with permission of Optical Society of America.
Fulltext Permission: embargo_20220922
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

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