An Investigation of Signal Preprocessing for Photoacoustic Tomography

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An Investigation of Signal Preprocessing for Photoacoustic Tomography
Title:
An Investigation of Signal Preprocessing for Photoacoustic Tomography
Journal Title:
Sensors
Publication Date:
03 January 2023
Citation:
Huen, I., Zhang, R., Bi, R., Li, X., Moothanchery, M., & Olivo, M. (2023). An Investigation of Signal Preprocessing for Photoacoustic Tomography. Sensors, 23(1), 510. https://doi.org/10.3390/s23010510
Abstract:
Photoacoustic tomography (PAT) is increasingly being used for high-resolution biological imaging at depth. Signal-to-noise ratios and resolution are the main factors that determine image quality. Various reconstruction algorithms have been proposed and applied to reduce noise and enhance resolution, but the efficacy of signal preprocessing methods which also affect image quality, are seldom discussed. We, therefore, compared common preprocessing techniques, namely bandpass filters, wavelet denoising, empirical mode decomposition, and singular value decomposition. Each was compared with and without accounting for sensor directivity. The denoising performance was evaluated with the contrast-to-noise ratio (CNR), and the resolution was calculated as the full width at half maximum (FWHM) in both the lateral and axial directions. In the phantom experiment, counting in directivity was found to significantly reduce noise, outperforming other methods. Irrespective of directivity, the best performing methods for denoising were bandpass, unfiltered, SVD, wavelet, and EMD, in that order. Only bandpass filtering consistently yielded improvements. Significant improvements in the lateral resolution were observed using directivity in two out of three acquisitions. This study investigated the advantages and disadvantages of different preprocessing methods and may help to determine better practices in PAT reconstruction.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the A*STAR - Horizontal Technology Programme Office Seed Fund, Biomedical Engineering Programme 2021
Grant Reference no. : C211318004

This research / project is supported by the A*STAR - BMRC Central Research Fund (UIBR) 2021
Grant Reference no. : NA
Description:
ISSN:
1424-8220
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