ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography

Awasthi, N and Jain, G and Kalva, SK and Pramanik, M and Yalavarthy, PK (2020) Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography. In: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67 (12). pp. 2660-2673.

IEEE_tra_ult_fer_67-12_2660-2673_2020.pdf - Published Version

Download (20MB) | Preview
Official URL: https://doi.org/10.1109/TUFFC.2020.2977210


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. © 1986-2012 IEEE.

Item Type: Journal Article
Publication: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to The Author(s).
Keywords: Bandwidth; Correlation methods; Deep learning; Deep neural networks; Image enhancement; Image reconstruction; Learning systems; Mean square error; Numerical methods; Optical resolving power; Photoacoustic effect; Signal to noise ratio; Tomography; Transducers, Analytical reconstruction; Bandwidth enhancement; Photoacoustic signals; Photoacoustic tomography; Root mean square errors; Root mean squared errors; Structural similarity indices; Ultrasonic resolution, Neural networks, image processing; imaging phantom; photoacoustics; procedures; tomography; transducer, Deep Learning; Image Processing, Computer-Assisted; Phantoms, Imaging; Photoacoustic Techniques; Tomography; Transducers
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 10 Jan 2023 05:58
Last Modified: 10 Jan 2023 05:58
URI: https://eprints.iisc.ac.in/id/eprint/78985

Actions (login required)

View Item View Item