Deep-E: A Fully-Dense Neural Network for Improving the Elevation Resolution in Linear-Array-Based Photoacoustic Tomography

• Published in: IEEE transactions on medical imaging Vol. 41; no. 5; pp. 1279 - 1288
• Main Authors: Zhang, Huijuan, Bo, Wei, Wang, Depeng, DiSpirito, Anthony, Huang, Chuqin, Nyayapathi, Nikhila, Zheng, Emily, Vu, Tri, Gong, Yiyang, Yao, Junjie, Xu, Wenyao, Xia, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Breast imaging; Computer applications; Convolution; convolutional neural network; Deep learning; elevation resolution; Humans; Image enhancement; Image reconstruction; Image resolution; Imaging; Linear arrays; linear transducer array; Medical research; Neural networks; Neural Networks, Computer; Phantoms, Imaging; Photoacoustic effect; Photoacoustic Techniques - methods; photoacoustic tomography; resolution enhancement; Simulation; Three-dimensional displays; Tomography; Tomography, X-Ray Computed; Training; Transducers
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2021.3137060

Linear-array-based photoacoustic tomography has shown broad applications in biomedical research and preclinical imaging. However, the elevational resolution of a linear array is fundamentally limited due to the weak cylindrical focus of the transducer element. While several methods have been propose...