Deep-learning-enhanced Three-dimensional Photoacoustic Tomography of Human Breast

Huijuan Zhang; Wei Bo; Wenyao Xu; Jun Xia

Deep-learning-enhanced Three-dimensional Photoacoustic Tomography of Human Breast

Huijuan Zhang
, Wei Bo
, Wenyao Xu
, Jun Xia

SUNY Buffalo

Research output: Contribution to journal › Conference article › peer-review

Abstract

We developed a fully-dense neural network to improve the elevation resolution of linear-array-based photoacoustic tomography for better 3D visualization. The algorithm is efficient and fast and is validated in human breast imaging results.

Original language	English
Article number	FTh3B.4
Journal	Optics InfoBase Conference Papers
State	Published - 2022
Event	Frontiers in Optics, FiO 2022 - Rochester, United States Duration: Oct 17 2022 → Oct 20 2022

Cite this

@article{42eb6dfa9d1f4cb68d6b8fb005fc1c74,

title = "Deep-learning-enhanced Three-dimensional Photoacoustic Tomography of Human Breast",

abstract = "We developed a fully-dense neural network to improve the elevation resolution of linear-array-based photoacoustic tomography for better 3D visualization. The algorithm is efficient and fast and is validated in human breast imaging results.",

author = "Huijuan Zhang and Wei Bo and Wenyao Xu and Jun Xia",

note = "Publisher Copyright: {\textcopyright} 2022 The Author (s); Frontiers in Optics, FiO 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

}

TY - JOUR

T1 - Deep-learning-enhanced Three-dimensional Photoacoustic Tomography of Human Breast

AU - Zhang, Huijuan

AU - Bo, Wei

AU - Xu, Wenyao

AU - Xia, Jun

PY - 2022

Y1 - 2022

N2 - We developed a fully-dense neural network to improve the elevation resolution of linear-array-based photoacoustic tomography for better 3D visualization. The algorithm is efficient and fast and is validated in human breast imaging results.

AB - We developed a fully-dense neural network to improve the elevation resolution of linear-array-based photoacoustic tomography for better 3D visualization. The algorithm is efficient and fast and is validated in human breast imaging results.

UR - https://www.scopus.com/pages/publications/85146756933

M3 - Conference article

AN - SCOPUS:85146756933

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - FTh3B.4

T2 - Frontiers in Optics, FiO 2022

Y2 - 17 October 2022 through 20 October 2022

ER -

Deep-learning-enhanced Three-dimensional Photoacoustic Tomography of Human Breast

Abstract

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