Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146551
Title: Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography
Authors: Awasthi, Navchetan
Jain, Gaurav
Kalva, Sandeep Kumar
Pramanik, Manojit
Yalavarthy, Phaneendra K.
Keywords: Engineering::Bioengineering
Issue Date: 2020
Source: Awasthi, N., Jain, G., Kalva, S. K., Pramanik, M., & Yalavarthy, P. K. (2020). Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), 2660-2673. doi:10.1109/TUFFC.2020.2977210
Journal: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Abstract: Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical scenarios, data are collected using the limited number of transducers along with data being often corrupted with noise resulting in only qualitative images. Furthermore, the collected boundary data are band-limited due to limited bandwidth (BW) of the transducer, making the PA imaging with limited data being qualitative. In this work, a deep neural network-based model with loss function being scaled root-mean-squared error was proposed for super-resolution, denoising, as well as BW enhancement of the PA signals collected at the boundary of the domain. The proposed network has been compared with traditional as well as other popular deep-learning methods in numerical as well as experimental cases and is shown to improve the collected boundary data, in turn, providing superior quality reconstructed PA image. The improvement obtained in the Pearson correlation, structural similarity index metric, and root-mean-square error was as high as 35.62%, 33.81%, and 41.07%, respectively, for phantom cases and signal-to-noise ratio improvement in the reconstructed PA images was as high as 11.65 dB for in vivo cases compared with reconstructed image obtained using original limited BW data. Code is available at https://sites.google.com/site/sercmig/home/dnnpat.
URI: https://hdl.handle.net/10356/146551
ISSN: 1525-8955
DOI: 10.1109/TUFFC.2020.2977210
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TUFFC.2020.2977210
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

Files in This Item:
File Description SizeFormat 
Awasthi_Jain_IEEETUFFC_2020.pdf11.19 MBAdobe PDFView/Open

SCOPUSTM   
Citations 20

6
Updated on Mar 8, 2021

PublonsTM
Citations 50

1
Updated on Mar 8, 2021

Page view(s)

30
Updated on Jun 14, 2021

Download(s)

1
Updated on Jun 14, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.