Derek Allman, Austin Reiter, Muyinatu Bell

Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these type of noise artifacts for removal in experimental photoacoustic data.

Dataset Files

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[1] Derek Allman, "Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2ZD39. Accessed: Apr. 22, 2024.
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doi = {10.21227/H2ZD39},
url = {http://dx.doi.org/10.21227/H2ZD39},
author = {Derek Allman },
publisher = {IEEE Dataport},
title = {Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset},
year = {2018} }
TY - DATA
T1 - Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset
AU - Derek Allman
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2ZD39
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Derek Allman. (2018). Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset. IEEE Dataport. http://dx.doi.org/10.21227/H2ZD39
Derek Allman, 2018. Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset. Available at: http://dx.doi.org/10.21227/H2ZD39.
Derek Allman. (2018). "Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset." Web.
1. Derek Allman. Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2ZD39
Derek Allman. "Photoacoustic Source Detection and Reflection Artifact Deep Learning Dataset." doi: 10.21227/H2ZD39